essay on revolutions

ENCYCLOPEDIC ENTRY

Revolutions are an instrument of change and often an attempt to promote equality and combat oppression.

Geography, Social Studies, World History

Chinese Revolution

Citizens of the Xiangyang commune in Jiangsu Province, China, protest against Lin Biao, who tried to seize power of the Chinese government.

Photograph by Bettman

In the fields of history and political science, a revolution is a radical change in the established order, usually the established government and social institutions. Typically, revolutions take the form of organized movements aimed at effecting change—economic change, technological change, political change, or social change. The people who start revolutions have determined the institutions currently in place in society have failed or no longer serve their intended purpose. Because the objective of revolutions is to upturn established order, the characteristics that define them reflect the circumstances of their birth.

Revolutions are born when the social climate in a country changes and the political system does not react in kind. People become discouraged by existing conditions, which alters their values and beliefs. Over the course of history, philosophers have held different views as to whether revolution is a natural occurrence in a changing society, or whether it indicates social decay . The Greek philosopher Aristotle linked revolution to a number of causes and conditions, but largely to the desire for equality and honor. Plato linked revolution to social decay . He believed that revolutions occur when institutions, such as the Church or the State, fail to instill in society a system of values and a code of ethics that prevent upheaval.

Throughout the Middle Ages, Europeans generally did what they could to prevent revolution and preserve the established order. The Church maintained the authority in medieval times, and it aimed to preserve stability in society at all costs. Sometime during the Renaissance , however, the concept of revolution began to change. People began to believe change was necessary for society to progress.

Between 1450 and 1750, philosophical and political ideas were changing rapidly throughout the world. The Renaissance, the Scientific Revolution, and the Protestant Reformation all took place during this time period, and people expanded their worldviews as they gained knowledge of new concepts and accepted new ideas. At this time in Europe, most countries had absolute monarchies, and people began to question the power of absolute governments. As their discontent grew, their questions turned to protests. A wave of revolutions took place in the 1700s, an era commonly known as the Age Enlightenment—revolutions in France, in Latin America, and in the American colonies. In all these countries, the revolutions not only changed the political systems and replaced them with new ones, but they altered public belief and brought about sweeping changes in society as a whole.

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2 (page 10) p. 10 What causes revolutions?

• Published: December 2013
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‘What causes revolutions?’ shows that revolutions are complex processes that emerge from the social order becoming frayed in many areas at once. There are five elements that create an unstable social equilibrium: economic or fiscal strain, alienation and opposition among the elites, widespread popular anger at injustice, a persuasive shared narrative of resistance, and favorable international relations. Revolutions have both structural and transient causes; structural causes are long-term and large-scale trends that undermine existing social institutions and relationships and transient causes are contingent events, or actions by particular individuals or groups, that reveal the impact of longer term trends and often galvanize revolutionary oppositions to take further action.

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• Published: 07 January 2020

On revolutions

• Armand M. Leroi   ORCID: orcid.org/0000-0002-5603-0351 1 , 2 ,
• Ben Lambert   ORCID: orcid.org/0000-0003-4274-4158 3 , 4 ,
• Matthias Mauch   ORCID: orcid.org/0000-0002-4352-6809 5 ,
• Marina Papadopoulou   ORCID: orcid.org/0000-0002-6478-8365 1 , 6 ,
• Sophia Ananiadou 7 ,
• Staffan I. Lindberg   ORCID: orcid.org/0000-0003-0386-7390 8 , 9 &
• Patrik Lindenfors 9 , 10 , 11  

Palgrave Communications volume  6 , Article number:  4 ( 2020 ) Cite this article

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Sometimes the normal course of events is disrupted by a particularly swift and profound change. Historians have often referred to such changes as “revolutions”, and, though they have identified many of them, they have rarely supported their claims with statistical evidence. Here, we present a method to identify revolutions based on a measure of multivariate rate of change called Foote novelty. We define revolutions as those periods of time when the value of this measure is, by a non-parametric test, shown to significantly exceed the background rate. Our method also identifies conservative periods when the rate of change is unusually low. We apply it to several quantitative data sets that capture long-term political, social and cultural changes and, in some of them, identify revolutions — both well known and not. Our method is general and can be applied to any phenomenon captured by multivariate time series data of sufficient quality.

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Introduction, what is a revolution.

It seems that the word “revolution” was first applied to sublunary events when parliamentarians, aided by the Dutch, deposed James II from the English throne and so brought about the Glorious Revolution. Since then, it has been applied ever more widely (Cohen, 1986 ). Responding to the French Revolution of 1789, Friederich Schlegel called for an Aesthetic Revolution in poetry, and so extended the term beyond politics (Heumakers, 2015 ). In the latter half of the 19th century—an age of revolutions—John Stuart Mill, Karl Marx and Arnold Toynbee, following a French coinage, wrote of the Industrial Revolution (Bezanson, 1922 ). In the 1950s Alexandre Koyré, Herbert Butterfield, A. R. Hall, and Thomas Kuhn, descried the Scientific Revolution (Koyré, 1957 ; Butterfield, 1950 ; Hall, 1954 ; Kuhn, 1957 ; Cohen, 1994 ). In his 1972 book, The Structure of Scientific Revolutions , Kuhn generalized the idea, arguing that science advanced, if it advanced at all, by revolutions (Kuhn, 1972 ). The Darwinian Revolution was swiftly identified (Ruse, 1979 ; Himmelfarb, 1996 ), as were many others. Indeed, Kuhn’s book prompted something of a revolution in scientific discourse, as scientists themselves took to identifying, or calling for, “paradigm shifts”—Kuhn’s term for a revolution—in their fields. A search of all articles indexed by the Web of Science in 2017 reveals more than two thousand that do so, though many of the purported revolutions seem quite modest in scope (e.g., Seward ( 2017 ); Raoult ( 2017 ); Lowenstein and Grantham ( 2017 ); Lonne ( 2017 )).

For all that, revolutions are hard to pin down. Upon close inspection they often seem to shrink. Pick a revolution, even a famous and well-documented one, and you can be sure to find scholars who have sought to cut it down to size or even deny that it happened at all. “The drastic social changes imputed to the Revolution, seem less clear-cut or not apparent at all.”—thus Simon Schama on how his generation of historians viewed the impact of the French Revolution (Schama, 1989 ). “There was no such thing as the Scientific Revolution, and this is a book about it.”—so Steve Shapin, in paradoxical mode, on early modern science (Shapin, 1996 ). Evolutionary biologists may be surprised to learn that the Darwinian Revolution has its skeptics too (Hodge, 2005 ; Bowler, 1988 ).

The difficulty of identifying revolutions has plagued the historical natural sciences as well. In the 1980s archeologists labeled the sudden appearance, fifty thousand years ago, of culture as the Human Revolution (Mellars and Stringer, 1989 ). It was not long before others had dismissed it as the “revolution that wasn’t” (Mcbrearty and Brooks, 2000 ). For much of his life Stephen Jay Gould argued that the Darwinian Revolution had run its course and that evolutionary biology needed another (Gould, 2002 ). (But one not to be confused with the broader Paleobiological Revolution of the 1970s and 80s which he helped shape (Sepkoski, 2012 ; Sepkoski and Ruse, 2009 ).) The coping stone of Gould’s new paradigm, an unstable edifice, was the theory of punctuated equilibrium that he proposed with Niles Eldredge in 1972 (Eldredge and Gould, 1972 ). This theory, shorn of its theoretical structure, postulated that change in fossil lineages is itself best described as stasis interrupted by periods of rapid change rather than gradual evolution. It may seem like a simple matter to decide which, but the ensuing decades-long quarrel among paleontologists about what the fossils show has proved otherwise (Pennel et al., 2013 ).

Unsurprisingly given its fame, the idea of punctuated evolution has spread to other fields. Natural languages (Atkinson et al., 2008 ; Dediu and Levinson, 2012 ; Greenhill, 2017 ), computer languages (Valverde and Solé, 2015 ), technology (De Dreu and van Dijk, 2018 ), and socio-political structures (Spencer, 1990 ; Currie and Mace, 2011 ; Turchin, 2018 ) have all been claimed to to evolve in a punctuated fashion; and theoretical models that explain why they might do so have been developed (Kolodny et al., 2015 , 2016 ). But the idea also appears now in fields as remote from paleontology as management science and policy research (e.g., Flink ( 2017 ); Fowler et al. ( 2017 ); De Ruiter and Schalk ( 2017 )). In them the term has lost its deeper meaning altogether and is just another way to express the existence, or hope, of revolutions.

The problem is clear. Great revolutions may entail change in many dimensions—ideas, wealth, social roles, political structures, the composition of assemblages of artefacts and species or else their features—but to varying degrees, at varying rates, and with varying starts and ends. A revolution’s visibility, then, depends on where you look. Even when considering the same data, some scholars will see discontinuity where others see continuity—it may be merely a matter of temperament—in the absence of an objective method for distinguishing the two, there is no way to know which of their accounts is more true. It seems desirable, then, to give the detecting of revolutions a statistical foundation.

At a minimum, the idea of a revolution supposes a sudden acceleration in the rate of change. The most direct way to estimate a rate, or its increase, is from time series data, and various methods have been developed to just that. Such methods identify “regime shifts”, “phase shifts” and the like in time series data (Andersen et al., 2009 ); some of them have even been used to detect punctuated events in evolving lineages (Hunt, 2010 , 2012 ; Hunt et al., 2015 ). Evolutionary biologists, however, generally lack good time series data: the fossil record is imperfect, and only a few populations of living species have been studied for long periods of time (but see Lambert et al., n.d. ). They do, however, have an abundance of phylogenies. Phylogenies provide a window into the past: they allow models of evolutionary rates to be tested from the distribution of phenotypes across extant species (Pennel et al., 2013 ; Atkinson et al., 2008 ; Pagel et al., 2006 ; Bokma, 2008 ; Harmon, 2010 ; Venditti and Pagel, 2010 ; Uyeda et al., 2011 ; Landis et al., 2012 ; Duchen, 2017 ; Landis and Schraiber, 2017 ). Such studies show that evolutionary rates are not constant, but sometimes accelerate during speciation or else the invasion of new adaptive zones.

These studies all concern the evolution of isolated characteristics such as body size. But revolutions are usually thought of as times when many things change at once. For this reason, here we define revolutions as statistically significant local increases in the multivariate rate of change relative to the background rate. Following this definition, we outline a revolution-detector that identifies such rate changes in time series by means of a non-parametric permutation test. Existing multivariate time series segmentation methods work by dividing series into self-contained windows that assume a time-invariant (typically parametric) relationship (Omranian et al., 2015 ; Preuss et al., 2015 ). Our method, by contrast, uses the local multivariate rate of change of series across neighboring periods to classify time points into “revolutionary” or “conservative” periods. As such, it most resembles some methods that have been used to study spatial change in multivariate ecological data sets (Kent et al., 2006 ). We have previously introduced our method while applying to the evolution of American popular music (Mauch et al., 2015 ). Here we refine its statistical basis, apply it to several large data sets that capture changes in political, social, and cultural systems over time, and identify a variety of revolutions that are well known, as well as some that are not.

Detecting revolutions

A sketch of the method.

We assume that a method for detecting revolutions should consider many characteristics of a population simultaneously, that is, be underpinned by a multivariate metric of change. The idea behind our method is as follows. We begin with a collection of time series of summary statistics that capture the evolving properties of a population. These summary statistics might be frequencies or means or raw observations. For example, such summary statistics might be gene frequencies in an evolving population of fruit flies, topic frequencies in an evolving population of novels, or socio-economic variables in an evolving population of nations. The power of our method to detect revolutions is greatest when series contain ample time steps, and when sampling variances of the summary statistics are small compared to overall variability (i.e., across data pooled over all time periods). These summary statistics will inevitably fluctuate over time. We take a revolution, however, to occur only if many variables change in concert, with exceptional speed.

To locate these revolutionary periods, we calculate a multivariate temporal distance matrix, which computes a univariate measure of discrepancy metering the difference between observations across all possible pairs of time periods. Any distance metric can be used to measure this discrepancy and can be chosen to best suit the data at hand. The temporal distance matrix captures the multivariate rate of change and can be usefully visualized in a heat map displaying pairwise distances between pairs of time periods (rows being the temporally-ordered first time period of a pair and columns being the second). Often this heat map will be without structure and appear as if differences between pairs occur at random. Revolutions, however, induce a quite distinct checkerboard pattern in this matrix. Within the checks, which are blocks of years, the rate of multivariate change is relatively low, but among them it is high. Where the checks meet is where a revolution may have occurred.

To search for the footprints of revolution, we use a quantitative method called Foote novelty. We explain how it works in detail below, but for the moment it is enough to say that Foote novelty mimics our visual perception of checkerboards by applying straightforward mathematical operations to the distance matrix in an attempt to locate checkerboards, should they exist. For a given period, Foote novelty contrasts the variation among diagonal elements (which represent differences between consecutive time periods) of the temporal distance matrix with that among elements from the anti-diagonals (which represent differences between non-consecutive periods) over a given window of time (e.g., five years). Applying Foote novelty to the temporal distance matrix thus returns series representing rates of change over time in which the rate represents a sort of running average, and in which the run length is dictated by the chosen time window. By altering the window width, we can capture the relative rates of change at different temporal scales ranging from one time-step to as many as the data permit. Relatively high Foote novelty scores indicate a potential revolution.

Of course, the Foote novelty scores wobble about over time for any given time series, even when no revolution is present. So we need some way of distinguishing normal, unrevolutionary, variation from extraordinary, revolutionary, variation. We do so by means of a non-parametric statistical test, which compares Foote novelty series calculated on the actual distance matrix with those series obtained from calculation on matrices that have been randomly permuted along their diagonals. These random permutations produce bootstrap samples of the original distance matrices. These, in turn, provide a baseline measurement of Foote Novelty used to define a threshold level, above which a revolution is deemed to have occurred. Like any statistical test, ours is vulnerable to both Type I and II errors. So, using simulations, we have investigated the conditions under which it holds and those under which it might fail, and suggest some procedures to mitigate these errors.

Calculating Foote novelty

Foote novelty is a multivariate metric of change that has its origin in signal processing (Foote, 2000 ). Although particularly suited to estimating change in a multivariate time series, here we explain its workings using a single time series.

Consider the time series (1, 2, 2, 1, 5, 4, 5, 4, 4) and its distance matrix (Fig. 1 ). It has an obvious change point between the fourth and fifth element: the data before (1, 2, 2, 1) and after (5, 4, 5, 4, 4) being both quite homogenous. Calculate a distance matrix, plot them as a heatmap, and these periods appear as distinct blocks of low local variation along the main diagonal (the darker shaded blocks in Fig. 1 ). By contrast, pairwise distances between data points before and after such a change are larger, resulting in two off-diagonal blocks of high-cross variability (the lighter shaded blocks in Fig. 1 ) in the distance matrix. The result is a checkerboard. And where the blocks meet pinpoints a large change.

Top. A univariate time series. Middle. A distance matrix of the time series overlain by the Foote novelty kernel. In the distance matrix, higher values are coloured lighter. The 2 × 2 matrix marked with a blue box delimits the FN kernel and shows the natural checkerboard structure of distances at points of rapid change. Bottom. The Foote novelty values, showing a peak in the middle at the point of maximum change in the series, \({F}_{4}^{2}\) , also colored blue.

In order to capture this checkerboard structure, Foote devised a kernel that itself looks like a checkerboard, being composed of two pairs of blocks of size k —the half-width—with the diagonal blocks equal to −1 and the off-diagonal components equal to +1. For example, the kernel for \(k\,=\,2\) is given by,

This kernel is moved along the main diagonal from one target time step to the next and the elementwise product is taken of the matrix and the values it overlaps (Fig. 1 ). The diagonal components of the Foote kernel (“local variability”) estimates the differences within the series before and after the target time step, while its off-diagonal components (“cross variability”) estimates the differences among them. The Foote novelty score, then, depends on their relative magnitudes. More formally, Foote novelty at target time step t is:

where \({C}^{k}\) is the Foote kernel with half-width k , \({D}_{t}^{k}\) represents the submatrix of the temporally-ordered distance matrix D centered about diagonal element (t, t) and of the same dimensions as the Foote kernel, and \(\odot\) is the Hadamart (i.e. elementwise) product.

Figure 1 shows how Foote novelty can be calculated for our synthetic one-dimensional time series. In practice, we use a kernel with two small modifications. First, we follow Foote in imposing a radially symmetric Gaussian taper with a standard deviation of \(2\,\times\,0.4k\) , to remove edge effects. This amounts to multiplying each grid point by a Gaussian kernel \(\frac{1}{4\pi\,\times\,0.4k}{e}^{-({x}^{2}\,+\,{y}^{2})/2{(2\,\times\,0.4k)}^{2}}\) , where x and y are the horizontal and vertical distances of the grid point from the center of C . This gives distances closer to the target time point more weight than those further away. Second, in order to have a central point of reference, we add a “cross” of zeros between the blocks of the kernel C . As a result, the size of the whole kernel is \(2k\,+\,1\) , and the value \({F}_{t}^{k}\) corresponds precisely to the kernel centered at t . The resulting \(k=2\) kernel is given by (to within numerical error),

Assuming that D is large (e.g., \(100\,\times\,100\) time points) relative to C (e.g., \(4\,\times\,4\) ), \({F}_{t}^{k}\) can be calculated for different points in time. The calculation of the Foote novelty series { \({F}_{1}^{k},\ {F}_{2}^{k},...\ {F}_{T}^{k}\) } hence amounts to sliding C along the central diagonal of distance matrix D , calculating \({F}_{t}^{k}\) as we go.

Statistical inference

In real data, the distance matrix, D , is nearly always based on a multivariate time series. Foote novelty, \({F}_{t}^{k}\) , is generally positive and varies as the underlying variables fluctuate in value. We therefore define revolutions as periods when its value is statistically significantly higher than in the rest of the series.

To determine this we compare the observed \({F}_{t}^{k}\) values to the distribution of \({F}_{t}^{k}\) values obtained from randomly permuting the distance matrix. In our original test, we permuted the distance matrix on its axes (Mauch et al., 2015 ); here, following a suggestion by T. Underwood, H. Long, R. J. So, and Y. Zhu (pers. comm.), we permute on the diagonals.

By this we mean the following. A given distance matrix of dimension \(p\, \times \,p\) has \(p\,-\,1\) diagonals that can be independently permuted (not \(2(p-1)\) because distance matrices must be symmetric). The longest non-degenerate diagonal (since the central diagonal is composed only of zeros) is the vector of elements, \({v}_{1}\,=\,({D}_{1,2},\,{D}_{2,3},\,{D}_{p-1,p})\) and so on. To permute the matrix along its diagonals, we visit each \({v}_{j}\) and randomly sort its elements. The same changes are made on both sides of the central diagonal so that the matrix remains symmetric. Permuting the diagonals of the distance matrix retains more of its structure than permuting its axes does, and so provides a more robust null model. The motivation for this test is based on our empirical results and because it is comparable to carrying out a permutation of the time series in blocks of varying lengths greater than or equal to one time period.

The kernel half-width, k , can be as small as 1 or as large as the data allow, but different k show different aspects of change. Foote novelty acts rather like a microscope. Small k values zoom in on short-term heterogeneities that large k values may obliterate, and large k values may reveal long-term variation invisible at smaller fields of view. A sustained period of rapid change will tend to produce revolutionary signals at many different k , but more complex patterns of rate variation will result in conflicting signals. For example, a large k may well identify a single, long, revolution where a smaller one identifies two or more. The shifting picture of the rate landscape that emerges as we adjust the focus of our Foote Novelty microscope is not a weakness of the method, but a consequence of making the scale of analysis explicit. In practice, we examine all half-widths that the data allow and identify revolutions by their consistency in a given region.

Any significance value is, of course, arbitrary, and we would also like a general picture of fluctuations in the rate of change regardless of whether or not they are statistically significant. To this end we propose an index, \({R}_{t}\) , which captures the relative rate of change at a given time point, t . Assuming a set K of desired kernel half-widths, this index is constructed by first standardizing every \({F}_{t}^{k}\) estimate by the average over all valid time points for its half-width, \({\bar{F}}^{k}\) , and then averaging the standardized values over all \(k\,\in\,K\) estimated for that time point to give a single value:

If \({R}_{t}\,>\,1\) , then the rate of change at a given time point is greater than the average rate of change in the entire series; if \({R}_{t}\ <\ 1\) then it is smaller. Note that statistical inference does not depend directly on the value of \({R}_{t}\) . This means that \({R}_{t}\) may sometimes have a relatively high value even when no revolution has been detected. This is particularly true at the start and finish of time series where the statistical power to detect revolutions diminishes.

Having identified a revolution, we would also like to know which variables contribute to it. One simple way to find out involves removing variables from the data set one at time and re-running the analysis. Variables which, when removed, yield fewer statistically significant \({F}_{t}^{k}\) in a given revolution contribute to it; those which yield more obscure it. Joint effects can be tested by removing combinations of variables.

Figure 2 a shows the method in action on simulated data. We simulated twenty stationary series, each of which represents a measured variable, for 100 time points (Fig. 2 a first row). By a “stationary” series, here we mean one whose mean is constant over time, that is, one that shows stasis before and after (though, obviously, not during) a revolution. Starting at time point 40, we introduced a revolution by allowing the variables to undergo a directional change for ten generations after which the series become stationary again. The rate discontinuity can be clearly seen in the distance matrix (Fig. 2 a second row). To identify the revolution, we estimated \({F}_{t}^{k}\) for all kernel half-widths, k , and time points, t , allowed by the data, in this case \(1\,\le\,k\,\le\,49\) , and calculated the rate index, \({R}_{t}\) , for all time points. A sharp rate discontinuity is visible between time points 37 and 53, where \({R}_{t}\,>\,1\) (Fig. 2 a third row). Finally, we determine the statistical significance for each \({F}_{t}^{k}\) estimate (Fig. 2 a fourth row).

a Evolution of 20 simulated stationary time series with a revolution in the middle. b Evolution of 20 simulated undirected random walk time series with a revolution in the middle. In both sets of simulations, the standard deviation of series perturbations in non-revolutionary periods is set at \(\sigma\,=\,1\) . During the revolutions, which start at time point 40, the size of the change in each time point is increased until time point 50, when the revolution ends. The amount by which each variable, i , changes during during a revolution is drawn from a normal distribution. First row from top : Evolution of the time series. Second row: Distance matrices among time points: dark reds are increasingly dissimilar. Third row: The rate of change index, \({R}_{i}\) , which is the sum of the \({F}_{i}^{k}\) values for any time point i over all k , relative to the sum of the mean \({F}_{i}^{k}\) values over all time points. Fourth row: Identifying revolutions by Foote novelty. Each cell represents the \({F}_{i}^{k}\) estimate for a given half-width, k and time point; the color of the cell gives the relative \({F}_{i}^{k}\) value, light gray being low and dark gray being high. Note that this color scale is only comparable within any given plot. Statistically significant ( \(\alpha\,=\,0.05/2\) ) revolutionary periods are overlain in red; conservative periods are blue. In both cases, we identify a revolution in the correct region, but at larger half-widths, the resolution becomes coarser. Statistically significant time points which are not contiguous with the simulated revolution are false positives. Note that, for the random walk series, the undifferenced data are shown but the distance matrices, \({R}_{i}\,{\mathrm{and}}\,{F}_{i}^{k}\) values are all based on first differences. This means that only revolution boundaries are expected to have high \({F}_{i}^{k}\) values.

On the face of it, considering all k , there is strong evidence for a revolution spanning time points 37–57. But we have carried out 2401 significance tests over all k , of which 60 ( \(\alpha\,=\,0.05/2\) ) are expected to be significant due to chance alone. In fact, we find that 766 are significant, strongly suggesting that the series contains at least one real revolution. This final inferential step is equivalent to a Bonferroni correction. Since the test’s resolution decreases as k increases, the most accurate estimate is given by the smallest k at which the revolution appears: in this case, \(k\,=\,4\) , where it spans time points 42–49—very close to the real values of 40–50. A few statistically significant \({F}_{t}^{k}\) values are seen well outside of the simulated revolution; these are false positives and we discuss their identification below.

Sensitivity and specificity

In the example shown in Fig. 2 a, we simulated stationary variables with a revolution characterized by large changes in a variable’s mean occurring over relatively long periods of time. To see whether our method works in other kinds of series, we applied it to many sets of simulated time series and then counted the revolutions detected. In these simulations, we varied: (1) the persistence of the series, \(\rho\) , (2) the magnitude of change in variable values during a revolution, that is, its strength , s , and (3) the length of the revolution, l and (4) the number of variables measured, n , giving 3300 combinations of parameter values, for which we simulated 20 replicates each, or 66,000 sets of series in all.

The persistence of a series measures the autocorrelation in an autoregressive order-one process. In these simulations, persistence varied between 0 (fully mean stationary) to 1 (random walk); the revolution length between 2 and 18 time units, and variable number between 10 and 190 (See Supplementary Information Materials and Methods for details). For each of these 66,000 sets, we estimated \({F}_{t}^{k}\) for five kernel half-width, k , determined which were statistically significant, and then used these data sets to study the trade-off between sensitivity and specificity by estimating the rate of false positives (Type I errors) and false negatives (Type II errors).

We investigated the rate of false positives in series with no revolutions ( \(s=0\) ). In this subset of the simulations, only three parameters vary: the persistence of the series, \(\rho\) , the number of variables, n , and the kernal half-width, k . Here the overall number of (false) revolutions detected should be equal to, or less than, \(\alpha =0.05\) . For fully stationary series ( \(\rho =0\) ), we found that this was so, however, as the series became more persistent the rate of false positives increased, so that in random walks ( \(\rho =1\) ), revolutions were detected, on average, in 16% of the series (Fig. 3 a). Thus, like many econometric tests, ours requires stationary series.

a Mean Type I error (false positive) rate as function of persistence, \(\rho\) , in series without revolutions ( \(s\,=\,0\) ). When estimated on levels (solid gray line), the observed Type I error rate quickly increases above the significance level, \(\alpha\,=\,0.05\) (solid red line), but when differenced it does not (dotted gray line). The risk of false-positives climbs above the set significance threshold at \(\rho\,=\,0.25\) . b Mean Type II error (false negative) rate as function of persistence in series with revolutions ( \(s\,>\,0\) ). When estimated on levels (solid line), the observed Type II error rate is around 22%, but is higher when estimated on differences, decreasing as persistence increases. c A closer look at Type II error rates in stationary series ( \(\rho\,=\,0\) ) (Top) and first-differenced random walk series ( \(\rho\,=\,1\) ) (Bottom) as as a function of the kernal half-width, k , number of variables in the simulation, n , the strength of the revolution, s , and its length, l . These plots are an expansion of the data in b marked with a circle. In both cases, our method tends to fail to identify short and weak revolution ( \(l\,\le\,6\) , \(s\,\le\,0.5\) ), in data sets based on few variables ( \(n\,\le\,10\) ), particularly when analyzed using very small half-widths ( k  = 1). Although the distribution of false negatives differs somewhat between the two sets of series, the overall mean false negative rates are very similar, 22% and 23%, respectively.

The risk of false-positives climbs above the set significance threshold at \(\rho\,=\,0.25\) (Fig. 3 a). Persistent series can be made stationary by taking their first differences, \(\overline{x}(t)\,-\,\overline{x}(t\,-\,1)\) , where \(\overline{x}\) is the mean at time steps t and \(t\,-\,1\) and, when we do so, we find that the rate of false positives is, once again, equal to or below the set significance threshold regardless of persistence (Fig. 3b ). Figure 2 b illustrates the effect of differencing on one set of random walk time series with a revolution introduced between time points 40–50. Now the revolution appears as spikes in \({F}_{t}^{k}\,{\mathrm{and}}\,{R}_{t}\) marking its start and end and a set of significant \({F}_{t}^{k}\) values between time points 32–41 and 46–57. Smaller k values (e.g., \(k\,=\,8\) ) give the most accurate estimates of the revolution’s boundaries as time points 39–41 and 49–50 (Fig. 2 b third and fourth rows).

We investigated the rate of false negatives in all series which contained revolutions ( \(s\,>\,0\) ). When applied to levels, we found that, regardless of persistence, our test fails to detect about 22% of revolutions (Fig. 3 b). Differencing reduces the power of the test considerably when applied to stationary series, but only slightly in highly persistent series (Fig. 3 b). Focusing on the two extreme cases, stationary series ( \(\rho\,=\,0\) ) and random walk series ( \(\rho\,=\,1\) ) made stationary by differencing, we find that our method often fails to identify short and weak revolutions ( \(l\le 6\) , \(s\le 0.5\) ) in data sets based on few variables ( \(n\,\le\,10\) ), particularly when analyzed using very small half-widths ( \(k\,=\,1\) ) (Fig. 3 c).

In order to balance the risk of Type I and II errors when applying our test to real data, we recommend that investigators first estimate the overall persistence, \(\overline{\rho }\) , of the set of time series. If the series prove to be stationary or weakly persistent ( \(\overline{\rho}\,\le\,0.25\) ), then the test can be safely applied to the original data. But if the series are even moderately persistent, then it should be applied to the first differences.

The prevalence of revolutions

To illustrate our method, we applied it to several real data sets, all of which document changes in the historical record over the course of decades. Some of these use unbounded, continuous, data (Fig. 4 ); others, frequencies (Fig. 5 ). The first concerns a familiar subject: the spread and retreat of democracy across the globe in the course of the 20 \({}^{{\rm{th}}}\) century. In 1991, the political scientist Samuel Huntington identified three great global “waves” of democratization (Huntington, 1991 ). The first wave began around 1820; the second is associated with post-War War II de-colonization, and the third began in 1974 and is associated with the collapse of European and Latin American dictatorships, the fall of the Iron Curtain in 1989, and the spread of democracy in Africa. Huntington evidently based his argument on a simple count of “democracies” without either defining what he meant by the term or presenting any data. Here, using much better data, we ask whether our method can identify the second and third of his waves.

a global democracy; b car models; c crime rates per hundred thousand, UK. Top row of each series. Trends shown as values normalized to the first time point. Middle row. The rate of change index, \({R}_{t}\) . Bottom row. Identifying revolutions by Foote novelty. Each cell represents the \({F}_{t}^{k}\) estimate for a given half-width, k and time point; the color of the cell gives the relative \({F}_{t}^{k}\) value, light gray being low and dark gray being high. Note that this color scale is only comparable within any given plot. Statistically significant ( \(\alpha\,=\,0.05/2\) ) revolutionary periods are overlain in red; conservative periods in blue; when necessary Foote novelty tests were done on differences.

a pop music: Billboard Hot 100, USA; b newborn girls’ names, USA; c BMJ articles; d English, Irish and American novels. Top row of each series. Trends of frequencies shown as stacked area plots. Middle row. The rate of change index, \({R}_{t}\) . Bottom row. Identifying revolutions by Foote novelty. Each cell represents the \({F}_{t}^{k}\) estimate for a given half-width, k and time point; the color of the cell gives the relative \({F}_{t}^{k}\) value, light gray being low and dark gray being high. Note that this color scale is only comparable within any given plot. Statistically significant ( \(\alpha\,=\,0.05/2\) ) revolutionary periods are overlain in red; conservative periods in blue; when necessary Foote novelty tests were done on differences.

To do this we use the V-Dem data set. This data set, the work of many scholars, rates the degree to which the world’s nation states were democratic over the course of the 20 \({}^{{\rm{th}}}\) century by means of a large number of ordinal variables that capture, in fine detail, the political structure of a given state in a given year (Coppedge, 2016 ). V-Dem provides indices where these variables have been aggregated to five higher-level quantitative variables that capture the degree to which a state exhibits: (i) freedom of expression; (ii) freedom of association; (iii) clean elections; (iv) an elected executive and, (v) universal suffrage (see Supplementary Information Materials and Methods for details). Fig. 4 a (top) shows the yearly means of these variables averaged over the states extant in a given year ( \(\le \!\!174\) ). Consistent with previous V-Dem studies (Lindberg et al., 2014 ; Lührmann, 2018 ), it shows that global democracy has increased over the course of the 20 \({}^{{\rm{th}}}\) century but that the rate at which it has done so has not been constant. We first estimated the persistence, \(\overline{\rho }\) , of the series and, finding that it was \(\ge \!\!0.25\) , took the first difference (Supplementary Information Table 1 ). Our index, R , shows that the relative rate of change was elevated in the 1940s, early 1960s and between 1974–1999 (Fig. 4 a). We then carried out 3192 significance tests over all k of which 208 were significant ( \(\alpha\,=\,0.05/2\) ), many more than the 78 expected by chance alone, suggesting that the series contains at least one real revolution. The years in which the rate of change is significantly higher than the background rate fall into four nearly contiguous groups: 1944–1949, 1962, 1975–1985, and 1989–1996 which we then identify as distinct “revolutions” (Supplementary Information Table 2 ).

Even when differenced, the entire series proved to be more persistent than desirable if we wish to avoid a high rate of Type I errors ( \(\overline{\rho }\) = 0.437), but visual examination of the data suggested that, outside of the inferred revolutions, the series was close to stationary. To test this idea we estimated the persistence of periods before, between and after our inferred revolutions, and found that they were indeed acceptably non-persistent ( \(\overline{\rho }\)  = 0.255). We also took the second differences of the entire series, which made it overall stationary ( \(\overline{\rho }\,=\,-0.275\) ), and even so found revolutions in 1947–1948 and 1990–1992, albeit reduced in size. Thus, we are confident that the revolutions we identified are not due to the general persistence of the series.

These revolutions are very consistent with Huntington’s “waves”, if we allow that his “third wave” is composed of two distinct sub-waves (c.f., Kurzman, 1998 ; McFaul, 2002 ; Way, 2005 ). Interestingly, the 1962 revolution—by far the most weakly supported of the four—is an anti-democratic one caused by military coups in Indonesia, Pakistan, Greece, Nigeria, Turkey, and many Latin American countries. Huntington identified this phenomenon too and labeled it a “reverse wave” as have previous V-Dem studies (Mechkova et al., 2017 ). But we can add some detail to this picture. Analysis of the contributions of individual variables shows that, where the revolution of the late 1940s was due to changes in political structures, the 1977–1984 and 1989–1996 revolutions were due to an increase of personal liberty (Supplementary Information Table 3 ). Revolutions, unsurprisingly, differ in their natures and causes. Thus, our method can identify times of rapid political change of the sort that political scientists and historians have spotted using less formal methods.

We now turn to another familiar phenomenon: American pop music (Fig. 5 a). Pop music is also said to undergo revolutionary change as new genres rise and fall, but unlike the spread of democracy there is little consensus as to when those revolutions occurred and what, exactly, changed in them (Frith, 1988 ; Tschmuck, 2006 ). We have previously studied the evolution of the US Billboard Hot 100, 1960–2010 (Mauch et al., 2015 ). In that study, we assayed 17,094 songs for 16 harmonic and timbral features and, using an earlier version of our method, claimed the existence of three revolutions: in the mid-1960s, early 1980s, and late 1980s–early 1990s. We re-analyzed these data using our improved testing procedure and, finding that the series is highly persistent, took the first differences. We find that \({R}_{t}\,>\,1\) during 1967–1969, 1971, 1978, 1982–1983, 1986–1989, 1994–1995, 1998–2000, and 2005. We carried out 552 tests over all k of which 58 show a significantly elevated rate of change, more than the 14 expected by chance alone ( \(\alpha\,=\,0.05/2\) ); these fall into three revolutions: 1968–1969, 1982–1983, 1986–1988. These are very close to the revolutions that we previously identified and that are due, respectively, to the rise of rock-related chords and timbres (aggressive percussion) in the 1960s, the revival of guitar-heavy rock and the arrival of drum–machine percussion in the early 1980s and, in the late 1980s, the rise of hip hop at the expense of rock and pop-related timbres (Supplementary Information Table 3 ). Note that since here we used differenced data, rather than levels, these are the boundaries of revolutions and not, as previously, their entire span. This accounts for the small discrepancy of dates between this analysis and the earlier one.

Besides these data sets we also applied our test to five others: the car models sold in the USA, 1950–2010 (Fig. 4 b); a data set on the crimes committed in England and Wales 1900–2000 (Fig. 4 c); the common names given to newborn girls in the USA, 1945-2010 (Fig. 5 b); the articles published in the British Medical Journal , 1960–2008 (Fig. 5 c), and American, Irish, and English novels published between 1840 and 1890 (Fig. 5 d) (See Supplementary Information Materials and Methods for details). Of these series, two: the girls names and English and Welsh crime rates, showed strong evidence of revolutions.

The girls names showed revolutions particularly in the years 1974–1975 and 1988–1991 (Fig. 5 b; Supplementary Information Table 3 ). These dates mark when a set of names—Jessica, Ashley, Lauren, Amanda, and Amber among others—become swiftly and immensely fashionable and then, about 15 years later, passé and replaced by names such as Emma, Isabella, Olivia and Hannah (Supplementary Information Table 3 , Supplementary Information Fig. 1 ) . Of course, baby names change in frequency all the time (Lieberson, 2000 ): it is the fact that several of them rose and fell in tandem that makes their dynamics revolutionary.

For the crime data set (Fig. 4 c), we carried out 2304 tests, of which 138 show a significantly elevated rate of change, many more than the 58 expected by chance alone ( \(\alpha\,=\,0.05/2\) ). We detected two periods of revolutionary change: general increase in crime between 1965 and 1978 and, then, a general decrease from 1989–1995. The former is the increase of crime rates—and, for decades, their accelerating rate of increase—that occurred in Western democracies after 1960, part of what Francis Fukuyama called “The Great Disruption” (Fukuyama, 1991 ); the latter is the well known sharp decline in crime rates in the 1990s (Pinker, 2011 ). The rise was mostly due to an increase in criminal damage and robbery; the decline mostly due to a decline in burglary.

These examples show that our method can be applied to quite different data sets: some are count data (e.g., baby names), while others are continuous traits (e.g., measure of democracy); some aggregate many individual entities that exist only in a single time interval (e.g., pop songs), while others track the evolution of a collection of entities over time (e.g., the democratic qualities of nations): all it requires is that we can estimate a distance in feature-space between intervals in a time series. Using it we have convincingly identified revolutions—some well known, others not—in several data sets, but not in all of them. This is as expected. After all, revolutions are, by definition, rare.

We began this paper by defining a revolution as a period of time in which the multivariate rate of change is demonstrably higher than at other times. This is most likely to occur when several variables show simultaneous increases in the rate of change. Thus, our definition captures the classical idea of a revolution as a rapid, correlated, change in many properties of a system. The magnitude of change in a revolution—what we have called its strength—may be large or small in absolute terms: what matters is its size relative to the variance of change across the entire series. The period over which it occurs—what we have called its span—may be short or long.

A revolution cannot, however, span an entire time series. This is true even when all variables are changing constantly. To see this consider a collection of variables changing as directed random walks. Since each variable diverges from its original value linearly over time, its rate of change at any time, hence D over any interval, will be, within the limits of stochastic variation, constant as will \({F}_{t}^{k}\) . Thus, viewed retrospectively, although there can be perpetually high rates of change, there are no perpetual revolutions. We can, however, find ourselves perpetually embroiled in revolution. When evolution is super-linear—we are thinking here of patterns such as that expressed in Moore’s law of the evolution of semi-conductor density (Moore, 1965 )—the rate of change, D over any interval, and \({F}_{t}\!{^k}\) , all increase monotonically. In such a series, a revolution will shift as the series grows so that it always defines the cutting edge. Thus, there is a sense in which permanent revolutions can, and probably do, exist.

We have focused on identifying revolutions simply because times of great change capture the imagination and are invariably the subject of scholarly debate. But significance levels are, of course, arbitrary, and the number of revolutions identified will change as they do. They may even be dispensed with. In their absence \({F}_{t}^{k}\) , and its summary index, \({R}_{t}\) , provides a simple way of measuring, and visualizing, local variation in rates of change. We note that evolutionary biologists commonly compare rates of evolution using measures such as the darwin and the haldane. Although both can be applied to any kind of time series data, both are univariate and generally estimated over an entire series (Lambert et al., n.d. ), and so are not well suited to estimating temporal variation in rates of multivariate evolution.

In all our data sets, all variables had non-zero values. However, it is possible to imagine revolutions in which some variables become irrelevant even as others arise. To give a concrete example, consider car design. Over fifty years of car evolution we detected much change, but no revolutions. Now, however, electric cars are upon us. Some of their features are much like those of their fossil-fueled ancestors (e.g., door number), but some (e.g., cylinder number, gear number) are not applicable, others can still be measured but are radically different (e.g., the relationship between maximum torque and RPM), while yet others are altogether new (e.g., power train battery capacity). Such changes in the salience of variables can be handled by our method and, if they have a sufficiently swift and strong effect on the multivariate distribution, will appear as a revolution. However, the revolution they will surely bring about seems to be of a different kind than any involving merely quantitative changes, however rapid, in mean horsepower or chassis length. The fundamental distinction is between revolutions that entail changes in the relationships among variables or, more formally, their variance-covariance structure, and those that do not. We think of the former as “structural” revolutions (c.f., Snodgrass, 1980 ) and the latter “non-structural”— note that they are subsets of the revolutions that our method detects, but leave the problem of telling them apart for future research.

Our method can be applied to identifying dramatic changes in any multivariate time series data of sufficient length and quality. In biology, it might be applied to the study of gene expression profiles, the evolution of gene frequencies or morphology (e.g., Hunt et al., 2015 ; Tu et al., 2005 ; Bergland et al., 2014 ). But the idea of revolution has its origin in historiography and so we have focused on political, social and cultural phenomena. As large data sets capturing their evolution become available (Michel, 2011 ; Hughes et al., 2012 ; Rodriguez Zivica et al., 2013 ; Perc, 2013 ; Klingenstein et al., 2014 ; Rule et al., 2015 ; Bearman, 2015 ), it will be increasingly possible to infer the quantitative patterns of history and so test general explanations for their causes (Kolodny et al., 2015 ).

Data and code availability

The code and data are available at: https://github.com/Armand1/A-revolution-detector .

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Acknowledgements

We thank Simon deDeo, Tim Hitchcock, and Matthew Jockers for supplying data. Arnold Heumakers, Tim Hitchcock, James Rosindell, and Ted Underwood for comments on the paper. B.L. was supported by EPSRC grant code: EP/F500394/1. V-Dem data collection was supported by European Research Council, Grant 724191; Riksbankens Jubileumsfond, Grant M13-0559:1; Knut and Alice Wallenberg Foundation to Wallenberg Academy Fellow, Grant 2013.0166; as well as by internal grants from the Vice-Chancellor’s office, the Dean of the College of Social Sciences, and the Department of Political Science at University of Gothenburg and the Marianne and Marcus Wallenbergs Foundation Grant 2017.0049.

Author contributions

A.M.L., B.L., and M.M. designed the study, contributed new reagents/analytic tools, collected data, carried out analysis, and wrote the paper. M.P. contributed new reagents/analytic tools; P.L., S.L., and S.A. contributed data. All authors gave final approval for publication.

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Leroi, A.M., Lambert, B., Mauch, M. et al. On revolutions. Palgrave Commun 6 , 4 (2020). https://doi.org/10.1057/s41599-019-0371-1

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The Boston Tea Party (1773), The Battles of Lexington and Concord (1775), The Declaration of Independence (1776), The Battle of Saratoga (1777), The Siege of Yorktown (1781)

George Washington: As the commander-in-chief of the Continental Army, George Washington emerged as a central figure in the revolution. His strategic brilliance, perseverance, and moral character helped inspire and lead the troops through challenging times, ultimately leading to victory. Thomas Jefferson: Known for his eloquence and intellect, Thomas Jefferson was the principal author of the Declaration of Independence. His ideas and ideals, including the belief in natural rights and self-governance, greatly influenced the revolutionary cause. Benjamin Franklin: A polymath and influential statesman, Benjamin Franklin played a vital role in rallying support for the revolution. He traveled to Europe as a diplomat, securing crucial aid from France and other countries, and his scientific discoveries further enhanced his reputation. John Adams: A passionate advocate for independence, John Adams was instrumental in driving the revolutionary movement forward. He served as a diplomat, including as a representative to France and as the second President of the United States, and his contributions to shaping the nation were significant. Abigail Adams: Abigail Adams, wife of John Adams, was an influential figure in her own right. Her letters to her husband and other prominent figures provided valuable insights and perspectives on the revolution, and she became an early advocate for women's rights and equality.

In the 18th century, the thirteen American colonies were under British rule. Over time, tensions began to rise as the colonists developed a distinct identity and desired greater autonomy. Several key factors contributed to the buildup of resentment and ultimately led to the revolution. One crucial prerequisite was the concept of colonial self-government. The colonists enjoyed a degree of self-rule, which allowed them to develop their own institutions and local governments. However, as British policies, such as the Stamp Act and Townshend Acts, imposed new taxes and regulations on the colonies, the sense of self-government and individual liberties were threatened. Another significant factor was the Enlightenment era, which spread ideas of natural rights, individual freedoms, and representative government. Influential thinkers like John Locke and Thomas Paine advocated for the rights of the people and challenged the legitimacy of monarchy. The causes of the American Revolution were diverse and multifaceted. The colonists' grievances included taxation without representation, restrictions on trade, and the presence of British troops in the colonies. The Boston Massacre in 1770 and the Boston Tea Party in 1773 further heightened tensions and solidified the resolve for independence. Ultimately, the outbreak of armed conflict in 1775 at Lexington and Concord marked the beginning of the Revolutionary War. The Declaration of Independence, adopted on July 4, 1776, served as a powerful statement of the colonists' grievances and their determination to establish a free and sovereign nation. The historical context of the American Revolution reflects the culmination of colonial aspirations for self-government, Enlightenment ideas of individual rights, and a series of grievances against British rule.

Establishment of the United States as a sovereign nation; the creation of a new form of government based on democratic principles; adoption of the United States Constitution; redefinition of citizenship; abolition of feudalism; expansion of territorial boundaries, etc.

One of the major effects of the American Revolution was the establishment of a new form of government based on the principles of democracy and individual rights. The United States Constitution, born out of the revolution, served as a model for constitutional governments around the world. The idea of a government by the people and for the people spread, inspiring future revolutions and movements for independence. The revolution also challenged the existing colonial powers, particularly the British Empire, and set in motion a wave of decolonization throughout the world. The success of the American colonies in breaking free from British rule demonstrated that colonies could successfully achieve independence, fueling nationalist movements in other parts of the world and ultimately leading to the dissolution of empires. The American Revolution also had significant economic effects. It established the United States as a new economic power and opened up opportunities for trade and commerce. The revolution encouraged the development of industry and innovation, setting the stage for the industrial revolution that would follow. Furthermore, the American Revolution had a profound impact on the institution of slavery. While the revolution did not immediately abolish slavery, it planted the seeds of abolitionism and sparked debates on the issue of human rights and equality. Lastly, the American Revolution inspired and influenced subsequent revolutions and movements for independence, such as the French Revolution, which drew inspiration from the ideals of liberty, equality, and popular sovereignty championed by the American colonists.

Public opinion on the American Revolution varied greatly during the time period and continues to be interpreted differently today. In the 18th century, support for the revolution was not unanimous. Some colonists were loyal to the British Crown and opposed the revolutionary movement, while others actively supported the cause of independence. Public opinion shifted over time as events unfolded and more people became aware of the grievances and aspirations of the revolutionaries. Many colonists, especially those who felt oppressed by British policies, embraced the ideals of liberty, self-determination, and representation. They saw the revolution as a necessary step towards achieving these principles and securing their rights as free individuals. Others were motivated by economic factors, such as trade restrictions and taxation without representation, which fueled their support for independence. However, there were also segments of the population that remained loyal to Britain. Some believed in the benefits of British rule, such as protection and stability, while others feared the potential chaos and uncertainty that could result from a revolution. In modern times, public opinion on the American Revolution tends to be positive, with many viewing it as a pivotal moment in history that laid the foundation for democratic governance and individual freedoms. The ideals and principles that emerged from the revolution continue to shape American identity and influence public discourse on issues of liberty, equality, and self-governance.

1. The American Revolution lasted for eight years, from 1775 to 1783, making it one of the longest and most significant conflicts in American history. 2. The American Revolution had a profound impact on the world stage. It inspired other countries and movements seeking independence and democracy, such as the French Revolution that followed in 1789. 3. While often overlooked, women made significant contributions to the American Revolution. They served as spies, messengers, nurses, and even soldiers. Some notable examples include Deborah Sampson, who disguised herself as a man to join the Continental Army, and Abigail Adams, who advocated for women's rights.

The topic of the American Revolution holds immense importance for academic exploration and essay writing due to its profound impact on the world and the enduring legacy it left behind. Firstly, the American Revolution marked a pivotal moment in history where thirteen colonies fought for their independence from British rule, leading to the formation of the United States of America. It represents a significant event in the development of democracy and self-governance, serving as an inspiration for subsequent revolutions worldwide. Studying the American Revolution allows us to understand the principles and ideals that shaped the nation's foundation, such as liberty, equality, and the pursuit of happiness. It sheds light on the struggles and sacrifices made by individuals who fought for their rights and paved the way for the establishment of a democratic government. Furthermore, exploring this topic provides insights into the complexities of colonial society, the causes of the revolution, the role of key figures, and the social, economic, and political consequences of the conflict.

1. Bailyn, B. (1992). The Ideological Origins of the American Revolution. Belknap Press. 2. Ellis, J. J. (2013). American Creation: Triumphs and Tragedies at the Founding of the Republic. Vintage. 3. Ferling, J. E. (2015). Whirlwind: The American Revolution and the War That Won It. Bloomsbury Publishing. 4. Fischer, D. H. (2006). Washington's Crossing. Oxford University Press. 5. Maier, P. (1997). American Scripture: Making the Declaration of Independence. Vintage. 6. Middlekauff, R. (2005). The Glorious Cause: The American Revolution, 1763-1789. Oxford University Press. 7. Middlekauff, R. (2007). The Glorious Cause: The American Revolution, 1763-1789. Oxford University Press. 8. Nash, G. B. (2006). The Unknown American Revolution: The Unruly Birth of Democracy and the Struggle to Create America. Penguin Books. 9. Tuchman, B. W. (1989). The First Salute: A View of the American Revolution. Random House. 10. Wood, G. S. (1992). The Radicalism of the American Revolution. Vintage.

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Thomas Kuhn

Thomas Samuel Kuhn (1922–1996) is one of the most influential philosophers of science of the twentieth century, perhaps the most influential. His 1962 book The Structure of Scientific Revolutions is one of the most cited academic books of all time. Kuhn’s contribution to the philosophy of science marked not only a break with several key positivist doctrines, but also inaugurated a new style of philosophy of science that brought it closer to the history of science. His account of the development of science held that science enjoys periods of stable growth punctuated by revisionary revolutions. To this thesis, Kuhn added the controversial ‘incommensurability thesis’, that theories from differing periods suffer from certain deep kinds of failure of comparability.

1. Life and Career

2. the development of science, 3. the concept of a paradigm, 4.1 methodological incommensurability, 4.2 perception, observational incommensurability, and world-change, 4.3 kuhn’s early semantic incommensurability thesis, 4.4 kuhn’s later semantic incommensurability thesis, 5. history of science, 6.1 scientific change, 6.2 incommensurability, 6.3 kuhn and social science, 6.4 recent developments, 6.5 assessment, other internet resources, related entries.

Thomas Kuhn’s academic life started in physics. He then switched to history of science, and as his career developed he moved over to philosophy of science, although retaining a strong interest in the history of physics. In 1943, he graduated from Harvard summa cum laude . Thereafter he spent the remainder of the war years in research related to radar at Harvard and then in Europe. He gained his master’s degree in physics in 1946, and his doctorate in 1949, also in physics (concerning an application of quantum mechanics to solid state physics). Kuhn was elected to the prestigious Society of Fellows at Harvard, another of whose members was W. V. Quine. At this time, and until 1956, Kuhn taught a class in science for undergraduates in the humanities, as part of the General Education in Science curriculum, developed by James B. Conant, the President of Harvard. This course was centred around historical case studies, and this was Kuhn’s first opportunity to study historical scientific texts in detail. His initial bewilderment on reading the scientific work of Aristotle was a formative experience, followed as it was by a more or less sudden ability to understand Aristotle properly, undistorted by knowledge of subsequent science.

This led Kuhn to concentrate on history of science and in due course he was appointed to an assistant professorship in general education and the history of science. During this period his work focussed on eighteenth century matter theory and the early history of thermodynamics. Kuhn then turned to the history of astronomy, and in 1957 he published his first book, The Copernican Revolution .

In 1961 Kuhn became a full professor at the University of California at Berkeley, having moved there in 1956 to take up a post in history of science, but in the philosophy department. This enabled him to develop his interest in the philosophy of science. At Berkeley Kuhn’s colleagues included Stanley Cavell, who introduced Kuhn to the works of Wittgenstein, and Paul Feyerabend. With Feyerabend Kuhn discussed a draft of The Structure of Scientific Revolutions which was published in 1962 in the series “International Encyclopedia of Unified Science”, edited by Otto Neurath and Rudolf Carnap. The central idea of this extraordinarily influential—and controversial—book is that the development of science is driven, in normal periods of science, by adherence to what Kuhn called a ‘paradigm’. The functions of a paradigm are to supply puzzles for scientists to solve and to provide the tools for their solution. A crisis in science arises when confidence is lost in the ability of the paradigm to solve particularly worrying puzzles called ‘anomalies’. Crisis is followed by a scientific revolution if the existing paradigm is superseded by a rival. Kuhn claimed that science guided by one paradigm would be ‘incommensurable’ with science developed under a different paradigm, by which is meant that there is no common measure for assessing the different scientific theories. This thesis of incommensurability, developed at the same time by Feyerabend, rules out certain kinds of comparison of the two theories and consequently rejects some traditional views of scientific development, such as the view that later science builds on the knowledge contained within earlier theories, or the view that later theories are closer approximations to the truth than earlier theories. Most of Kuhn’s subsequent work in philosophy was spent in articulating and developing the ideas in The Structure of Scientific Revolutions , although some of these, such as the thesis of incommensurability, underwent transformation in the process.

According to Kuhn himself (2000, 307), The Structure of Scientific Revolutions first aroused interest among social scientists, although it did in due course create the interest among philosophers that Kuhn had intended (and also before long among a much wider academic and general audience). While acknowledging the importance of Kuhn’s ideas, the philosophical reception was nonetheless hostile. For example, Dudley Shapere’s review (1964) emphasized the relativist implications of Kuhn’s ideas, and this set the context for much subsequent philosophical discussion. Since the following of rules (of logic, of scientific method, etc.) was regarded as the sine qua non of rationality, Kuhn’s claim that scientists do not employ rules in reaching their decisions appeared tantamount to the claim that science is irrational. This was highlighted by his rejection of the distinction between discovery and justification (denying that we can distinguish between the psychological process of thinking up an idea and the logical process of justifying its claim to truth) and his emphasis on incommensurability (the claim that certain kinds of comparison between theories are impossible). The negative response among philosophers was exacerbated by an important naturalistic tendency in The Structure of Scientific Revolutions that was then unfamiliar. A particularly significant instance of this was Kuhn’s insistence on the importance of the history of science for philosophy of science. The opening sentence of the book reads: “History, if viewed as a repository for more than anecdote or chronology, could produce a decisive transformation in the image of science by which we are now possessed” (1962/1970, 1). Also significant and unfamiliar was Kuhn’s appeal to psychological literature and examples (such as linking theory-change with the changing appearance of a Gestalt image).

In 1964 Kuhn left Berkeley to take up the position of M. Taylor Pyne Professor of Philosophy and History of Science at Princeton University. In the following year an important event took place which helped promote Kuhn’s profile further among philosophers. An International Colloquium in the Philosophy of Science was held at Bedford College, London. One of the key events of the Colloquium was intended to be a debate between Kuhn and Feyerabend, with Feyerabend promoting the critical rationalism that he shared with Popper. As it was, Feyerabend was ill and unable to attend, and the papers delivered focussed on Kuhn’s work. John Watkins took Feyerabend’s place in a session chaired by Popper. The ensuing discussion, to which Popper and also Margaret Masterman and Stephen Toulmin contributed, compared and contrasted the viewpoints of Kuhn and Popper and thereby helped illuminate the significance of Kuhn’s approach. Papers from these discussants along with contributions from Feyerabend and Lakatos, were published several years later, in Criticism and the Growth of Knowledge , edited by Lakatos and Alan Musgrave (1970) (the fourth volume of proceedings from this Colloquium). In the same year the second edition of The Structure of Scientific Revolutions was published, including an important postscript in which Kuhn clarified his notion of paradigm. This was in part in response to Masterman’s (1970) criticism that Kuhn had used ‘paradigm’ in a wide variety of ways; in addition, Kuhn felt that critics had failed to appreciate the emphasis he placed upon the idea of a paradigm as an exemplar or model of puzzle-solving. Kuhn also, for the first time, explicitly gave his work an anti-realist element by denying the coherence of the idea that theories could be regarded as more or less close to the truth.

A collection of Kuhn’s essays in the philosophy and history of science was published in 1977, with the title The Essential Tension taken from one of Kuhn’s earliest essays in which he emphasizes the importance of tradition in science. The following year saw the publication of his second historical monograph Black-Body Theory and the Quantum Discontinuity , concerning the early history of quantum mechanics. In 1983 he was named Laurence S. Rockefeller Professor of Philosophy at MIT. Kuhn continued throughout the 1980s and 1990s to work on a variety of topics in both history and philosophy of science, including the development of the concept of incommensurability, and at the time of his death in 1996 he was working on a second philosophical monograph dealing with, among other matters, an evolutionary conception of scientific change and concept acquisition in developmental psychology.

In The Structure of Scientific Revolutions Kuhn paints a picture of the development of science quite unlike any that had gone before. Indeed, before Kuhn, there was little by way of a carefully considered, theoretically explained account of scientific change. Instead, there was a conception of how science ought to develop that was a by-product of the prevailing philosophy of science, as well as a popular, heroic view of scientific progress. According to such opinions, science develops by the addition of new truths to the stock of old truths, or the increasing approximation of theories to the truth, and in the odd case, the correction of past errors. Such progress might accelerate in the hands of a particularly great scientist, but progress itself is guaranteed by the scientific method.

In the 1950s, when Kuhn began his historical studies of science, the history of science was a young academic discipline. Even so, it was becoming clear that scientific change was not always as straightforward as the standard, traditional view would have it. Kuhn was the first and most important author to articulate a developed alternative account. Since the standard view dovetailed with the dominant, positivist-influenced philosophy of science, a non-standard view would have important consequences for the philosophy of science. Kuhn had little formal philosophical training but was nonetheless fully conscious of the significance of his innovation for philosophy, and indeed he called his work ‘history for philosophical purposes’ (Kuhn 2000, 276).

According to Kuhn the development of a science is not uniform but has alternating ‘normal’ and ‘revolutionary’ (or ‘extraordinary’) phases. The revolutionary phases are not merely periods of accelerated progress, but differ qualitatively from normal science. Normal science does resemble the standard cumulative picture of scientific progress, on the surface at least. Kuhn describes normal science as ‘puzzle-solving’ (1962/1970a, 35–42). While this term suggests that normal science is not dramatic, its main purpose is to convey the idea that like someone doing a crossword puzzle or a chess problem or a jigsaw, the puzzle-solver expects to have a reasonable chance of solving the puzzle, that his doing so will depend mainly on his own ability, and that the puzzle itself and its methods of solution will have a high degree of familiarity. A puzzle-solver is not entering completely uncharted territory. Because its puzzles and their solutions are familiar and relatively straightforward, normal science can expect to accumulate a growing stock of puzzle-solutions. Revolutionary science, however, is not cumulative in that, according to Kuhn, scientific revolutions involve a revision to existing scientific belief or practice (1962/1970a, 92). Not all the achievements of the preceding period of normal science are preserved in a revolution, and indeed a later period of science may find itself without an explanation for a phenomenon that in an earlier period was held to be successfully explained. This feature of scientific revolutions has become known as ‘Kuhn-loss’ (1962/1970a, 99–100).

If, as in the standard picture, scientific revolutions are like normal science but better, then revolutionary science will at all times be regarded as something positive, to be sought, promoted, and welcomed. Revolutions are to be sought on Popper’s view also, but not because they add to positive knowledge of the truth of theories but because they add to the negative knowledge that the relevant theories are false. Kuhn rejected both the traditional and Popperian views in this regard. He claims that normal science can succeed in making progress only if there is a strong commitment by the relevant scientific community to their shared theoretical beliefs, values, instruments and techniques, and even metaphysics. This constellation of shared commitments Kuhn at one point calls a ‘disciplinary matrix’ (1970a, 182) although elsewhere he often uses the term ‘paradigm’. Because commitment to the disciplinary matrix is a pre-requisite for successful normal science, an inculcation of that commitment is a key element in scientific training and in the formation of the mind-set of a successful scientist. This tension between the desire for innovation and the necessary conservativeness of most scientists was the subject of one of Kuhn’s first essays in the theory of science, “The Essential Tension” (1959). The unusual emphasis on a conservative attitude distinguishes Kuhn not only from the heroic element of the standard picture but also from Popper and his depiction of the scientist forever attempting to refute her most important theories.

This conservative resistance to the attempted refutation of key theories means that revolutions are not sought except under extreme circumstances. Popper’s philosophy requires that a single reproducible, anomalous phenomenon be enough to result in the rejection of a theory (Popper 1959, 86–7). Kuhn’s view is that during normal science scientists neither test nor seek to confirm the guiding theories of their disciplinary matrix. Nor do they regard anomalous results as falsifying those theories. (It is only speculative puzzle-solutions that can be falsified in a Popperian fashion during normal science (1970b, 19).) Rather, anomalies are ignored or explained away if at all possible. It is only the accumulation of particularly troublesome anomalies that poses a serious problem for the existing disciplinary matrix. A particularly troublesome anomaly is one that undermines the practice of normal science. For example, an anomaly might reveal inadequacies in some commonly used piece of equipment, perhaps by casting doubt on the underlying theory. If much of normal science relies upon this piece of equipment, normal science will find it difficult to continue with confidence until this anomaly is addressed. A widespread failure in such confidence Kuhn calls a ‘crisis’ (1962/1970a, 66–76).

The most interesting response to crisis will be the search for a revised disciplinary matrix, a revision that will allow for the elimination of at least the most pressing anomalies and optimally the solution of many outstanding, unsolved puzzles. Such a revision will be a scientific revolution. According to Popper the revolutionary overthrow of a theory is one that is logically required by an anomaly. According to Kuhn however, there are no rules for deciding the significance of a puzzle and for weighing puzzles and their solutions against one another. The decision to opt for a revision of a disciplinary matrix is not one that is rationally compelled; nor is the particular choice of revision rationally compelled. For this reason the revolutionary phase is particularly open to competition among differing ideas and rational disagreement about their relative merits. Kuhn does briefly mention that extra-scientific factors might help decide the outcome of a scientific revolution—the nationalities and personalities of leading protagonists, for example (1962/1970a, 152–3). This suggestion grew in the hands of some sociologists and historians of science into the thesis that the outcome of a scientific revolution, indeed of any step in the development of science, is always determined by socio-political factors. Kuhn himself repudiated such ideas and his work makes it clear that the factors determining the outcome of a scientific dispute, particularly in modern science, are almost always to be found within science, specifically in connexion with the puzzle-solving power of the competing ideas.

Kuhn states that science does progress, even through revolutions (1962/1970a, 160ff). The phenomenon of Kuhn-loss does, in Kuhn’s view, rule out the traditional cumulative picture of progress. The revolutionary search for a replacement paradigm is driven by the failure of the existing paradigm to solve certain important anomalies. Any replacement paradigm had better solve the majority of those puzzles, or it will not be worth adopting in place of the existing paradigm. At the same time, even if there is some Kuhn-loss, a worthy replacement must also retain much of the problem-solving power of its predecessor (1962/1970a, 169). (Kuhn does clarify the point by asserting that the newer theory must retain pretty well all its predecessor’s power to solve quantitative problems. It may however lose some qualitative, explanatory power [1970b, 20].) Hence we can say that revolutions do bring with them an overall increase in puzzle-solving power, the number and significance of the puzzles and anomalies solved by the revised paradigm exceeding the number and significance of the puzzles-solutions that are no longer available as a result of Kuhn-loss. Kuhn is quick to deny that there is any inference from such increases to improved nearness to the truth ((1962/1970a, 170–1). Indeed he later denies that any sense can be made of the notion of nearness to the truth (1970a, 206).

Rejecting a teleological view of science progressing towards the truth, Kuhn favours an evolutionary view of scientific progress (1962/1970a, 170–3), discussed in detail by Wray (2011) (see also Bird 2000 and Renzi 2009). The evolutionary development of an organism might be seen as its response to a challenge set by its environment. But that does not imply that there is some ideal form of the organism that it is evolving towards. Analogously, science improves by allowing its theories to evolve in response to puzzles and progress is measured by its success in solving those puzzles; it is not measured by its progress towards to an ideal true theory. While evolution does not lead towards ideal organisms, it does lead to greater diversity of kinds of organism. As Wray explains, this is the basis of a Kuhnian account of specialization in science, an account that Kuhn was developing particularly in the latter part of his career. According to this account, the revolutionary new theory that succeeds in replacing another that is subject to crisis, may fail to satisfy all the needs of those working with the earlier theory. One response to this might be for the field to develop two theories, with domains restricted relative to the original theory (one might be the old theory or a version of it). This formation of new specialties will also bring with it new taxonomic structures and so leads to incommensurability.

A mature science, according to Kuhn, experiences alternating phases of normal science and revolutions. In normal science the key theories, instruments, values and metaphysical assumptions that comprise the disciplinary matrix are kept fixed, permitting the cumulative generation of puzzle-solutions, whereas in a scientific revolution the disciplinary matrix undergoes revision, in order to permit the solution of the more serious anomalous puzzles that disturbed the preceding period of normal science.

A particularly important part of Kuhn’s thesis in The Structure of Scientific Revolutions focuses upon one specific component of the disciplinary matrix. This is the consensus on exemplary instances of scientific research. These exemplars of good science are what Kuhn refers to when he uses the term ‘paradigm’ in a narrower sense. He cites Aristotle’s analysis of motion, Ptolemy’s computations of plantery positions, Lavoisier’s application of the balance, and Maxwell’s mathematization of the electromagnetic field as paradigms (1962/1970a, 23). Exemplary instances of science are typically to be found in books and papers, and so Kuhn often also describes great texts as paradigms—Ptolemy’s Almagest , Lavoisier’s Traité élémentaire de chimie , and Newton’s Principia Mathematica and Opticks (1962/1970a, 12). Such texts contain not only the key theories and laws, but also—and this is what makes them paradigms—the applications of those theories in the solution of important problems, along with the new experimental or mathematical techniques (such as the chemical balance in Traité élémentaire de chimie and the calculus in Principia Mathematica ) employed in those applications.

In the postscript to the second edition of The Structure of Scientific Revolutions Kuhn says of paradigms in this sense that they are “the most novel and least understood aspect of this book” (1962/1970a, 187). The claim that the consensus of a disciplinary matrix is primarily agreement on paradigms-as-exemplars is intended to explain the nature of normal science and the process of crisis, revolution, and renewal of normal science. It also explains the birth of a mature science. Kuhn describes an immature science, in what he sometimes calls its ‘pre-paradigm’ period, as lacking consensus. Competing schools of thought possess differing procedures, theories, even metaphysical presuppositions. Consequently there is little opportunity for collective progress. Even localized progress by a particular school is made difficult, since much intellectual energy is put into arguing over the fundamentals with other schools instead of developing a research tradition. However, progress is not impossible, and one school may make a breakthrough whereby the shared problems of the competing schools are solved in a particularly impressive fashion. This success draws away adherents from the other schools, and a widespread consensus is formed around the new puzzle-solutions.

This widespread consensus now permits agreement on fundamentals. For a problem-solution will embody particular theories, procedures and instrumentation, scientific language, metaphysics, and so forth. Consensus on the puzzle-solution will thus bring consensus on these other aspects of a disciplinary matrix also. The successful puzzle-solution, now a paradigm puzzle-solution, will not solve all problems. Indeed, it will probably raise new puzzles. For example, the theories it employs may involve a constant whose value is not known with precision; the paradigm puzzle-solution may employ approximations that could be improved; it may suggest other puzzles of the same kind; it may suggest new areas for investigation. Generating new puzzles is one thing that the paradigm puzzle-solution does; helping solve them is another. In the most favourable scenario, the new puzzles raised by the paradigm puzzle-solution can be addressed and answered using precisely the techniques that the paradigm puzzle-solution employs. And since the paradigm puzzle-solution is accepted as a great achievement, these very similar puzzle-solutions will be accepted as successful solutions also. This is why Kuhn uses the terms ‘exemplar’ and ‘paradigm’. For the novel puzzle-solution which crystallizes consensus is regarded and used as a model of exemplary science. In the research tradition it inaugurates, a paradigm-as-exemplar fulfils three functions: (i) it suggests new puzzles; (ii) it suggests approaches to solving those puzzles; (iii) it is the standard by which the quality of a proposed puzzle-solution can be measured (1962/1970a, 38–9). In each case it is similarity to the exemplar that is the scientists’ guide.

That normal science proceeds on the basis of perceived similarity to exemplars is an important and distinctive feature of Kuhn’s new picture of scientific development. The standard view explained the cumulative addition of new knowledge in terms of the application of the scientific method. Allegedly, the scientific method encapsulates the rules of scientific rationality. It may be that those rules could not account for the creative side of science—the generation of new hypotheses. The latter was thus designated ‘the context of discovery’, leaving the rules of rationality to decide in the ‘context of justification’ whether a new hypothesis should, in the light of the evidence, be added to the stock of accepted theories.

Kuhn rejected the distinction between the context of discovery and the context of justification (1962/1970a, 8), and correspondingly rejected the standard account of each. As regards the context of discovery, the standard view held that the philosophy of science had nothing to say on the issue of the functioning of the creative imagination. But Kuhn’s paradigms do provide a partial explanation, since training with exemplars enables scientists to see new puzzle-situations in terms of familiar puzzles and hence enables them to see potential solutions to their new puzzles.

More important for Kuhn was the way his account of the context of justification diverged from the standard picture. The functioning of exemplars is intended explicitly to contrast with the operation of rules. The key determinant in the acceptability of a proposed puzzle-solution is its similarity to the paradigmatic puzzle-solutions. Perception of similarity cannot be reduced to rules, and a fortiori cannot be reduced to rules of rationality. This rejection of rules of rationality was one of the factors that led Kuhn’s critics to accuse him of irrationalism—regarding science as irrational. In this respect at least the accusation is wide of the mark. For to deny that some cognitive process is the outcome of applying rules of rationality is not to imply that it is an irrational process: the perception of similarity in appearance between two members of the same family also cannot be reduced to the application of rules of rationality. Kuhn’s innovation in The Structure of Scientific Revolutions was to suggest that a key element in cognition in science operates in the same fashion.

4. Incommensurability and World-Change

The standard empiricist conception of theory evaluation regards our judgment of the epistemic quality of a theory to be a matter of applying rules of method to the theory and the evidence. Kuhn’s contrasting view is that we judge the quality of a theory (and its treatment of the evidence) by comparing it to a paradigmatic theory. The standards of assessment therefore are not permanent, theory-independent rules. They are not rules, because they involve perceived relations of similarity (of puzzle-solution to a paradigm). They are not theory-independent, since they involve comparison to a (paradigm) theory. They are not permanent, since the paradigm may change in a scientific revolution. For example, to many in the seventeenth century, Newton’s account of gravitation, involving action at a distance with no underlying explanation, seemed a poor account, in that respect at least, when compared, for example, to Ptolemy’s explanation of the motion of the planets in terms of contiguous crystalline spheres or to Descartes’ explanation in terms of vortices. However, later, once Newton’s theory had become accepted and the paradigm by which later theories were judged, the lack of an underlying mechanism for a fundamental force was regarded as no objection, as, for example, in the case of Coulomb’s law of electrostatic attraction. Indeed, in the latter case the very similarity of Coulomb’s equation to Newton’s was taken to be in its favour.

Consequently, comparison between theories will not be as straightforward as the standard empiricist picture would have it, since the standards of evaluation are themselves subject to change. This sort of difficulty in theory comparison is an instance of what Kuhn and Feyerabend called ‘incommensurability’. Theories are incommensurable when they share no common measure. Thus, if paradigms are the measures of attempted puzzle-solutions, then puzzle-solutions developed in different eras of normal science will be judged by comparison to differing paradigms and so lack a common measure. The term ‘incommensurable’ derives from a mathematical use, according to which the side and diagonal of a square are incommensurable in virtue of there being no unit that can be used to measure both exactly. Kuhn stressed that incommensurability did not mean non-comparability (just as the side and diagonal of a square are comparable in many respects). Even so, it is clear that at the very least Kuhn’s incommensurability thesis would make theory comparison rather more difficult than had commonly been supposed, and in some cases impossible.

We can distinguish three types of incommensurability in Kuhn’s remarks: (1) methodological—there is no common measure because the methods of comparison and evaluation change; (2) perceptual/observational—observational evidence cannot provide a common basis for theory comparison, since perceptual experience is theory-dependent; (3) semantic—the fact that the languages of theories from different periods of normal science may not be inter-translatable presents an obstacle to the comparison of those theories. (See Sankey 1993 for a useful discussion of Kuhn’s changing accounts of incommensurability.)

The incommensurability illustrated above whereby puzzle-solutions from different eras of normal science are evaluated by reference to different paradigms, is methodological incommensurability. Another source of methodological incommensurability is the fact that proponents of competing paradigms may not agree on which problems a candidate paradigm should solve (1962/1970a, 148). In general the factors that determine our choices of theory (whether puzzle-solutions or potential paradigm theories) are not fixed and neutral but vary and are dependent in particular on the disciplinary matrix within which the scientist is working. Indeed, since decision making is not rule-governed or algorithmic, there is no guarantee that those working within the same disciplinary matrix must agree on their evaluation of theory (1962/1970a, 200), although in such cases the room for divergence will be less than when the disputants operate within different disciplinary matrices. Despite the possibility of divergence, there is nonetheless widespread agreement on the desirable features of a new puzzle-solution or theory. Kuhn (1977, 321–2) identifies five characteristics that provide the shared basis for a choice of theory: 1. accuracy; 2. consistency (both internal and with other relevant currently accepted theories); 3. scope (its consequences should extend beyond the data it is required to explain); 4. simplicity (organizing otherwise confused and isolated phenomena); 5. fruitfulness (for further research). Even though these are, for Kuhn, constitutive of science (1977c, 331; 1993, 338) they cannot determine scientific choice. First, which features of a theory satisfy these criteria may be disputable (e.g. does simplicity concern the ontological commitments of a theory or its mathematical form?). Secondly, these criteria are imprecise, and so there is room for disagreement about the degree to which they hold. Thirdly, there can be disagreement about how they are to be weighted relative to one another, especially when they conflict.

An important focus of Kuhn’s interest in The Structure of Scientific Revolutions was on the nature of perception and how it may be that what a scientist observes can change as a result of scientific revolution. He developed what has become known as the thesis of the theory-dependence of observation, building on the work of N. R. Hanson (1958) while also referring to psychological studies carried out by his Harvard colleagues, Leo Postman and Jerome Bruner (Bruner and Postman 1949). The standard positivist view was that observation provides the neutral arbiter between competing theories. The thesis that Kuhn and Hanson promoted denied this, holding that the nature of observation may be influenced by prior beliefs and experiences. Consequently it cannot be expected that two scientists when observing the same scene will make the same theory-neutral observations. Kuhn asserts that Galileo and an Aristotelian when both looking at a pendulum will see different things (see quoted passage below).

The theory-dependence of observation, by rejecting the role of observation as a theory-neutral arbiter among theories, provides another source of incommensurability. Methodological incommensurability (§4.1 above) denies that there are universal methods for making inferences from the data. The theory-dependence of observation means that even if there were agreed methods of inference and interpretation, incommensurability could still arise since scientists might disagree on the nature of the observational data themselves.

Kuhn expresses or builds on the idea that participants in different disciplinary matrices will see the world differently by claiming that their worlds are different:

In a sense I am unable to explicate further, the proponents of competing paradigms practice their trades in different worlds. One contains constrained bodies that fall slowly, the other pendulums that repeat their motions again and again. In one, solutions are compounds, in the other mixtures. One is embedded in a flat, the other in a curved, matrix of space. Practicing in different worlds, the two groups of scientists see different things when they look from the same point in the same direction (1962/1970a, 150).

Remarks such as these gave some commentators the impression that Kuhn was a strong kind of constructivist, holding that the way the world literally is depends on which scientific theory is currently accepted. Kuhn, however, denied any constructivist import to his remarks on world-change. (The closest Kuhn came to constructivism was to acknowledge a parallel with Kantian idealism, which is discussed below in Section 6.4.)

Kuhn likened the change in the phenomenal world to the Gestalt-switch that occurs when one sees the duck-rabbit diagram first as (representing) a duck then as (representing) a rabbit, although he himself acknowledged that he was not sure whether the Gestalt case was just an analogy or whether it illustrated some more general truth about the way the mind works that encompasses the scientific case too.

Although the theory-dependence of observation plays a significant role in The Structure of Scientific Revolutions , neither it nor methodological incommensurability could account for all the phenomena that Kuhn wanted to capture with the notion of incommensurability. Some of his own examples are rather stretched—for instance he says Lavoisier saw oxygen where Priestley saw dephlogisticated air, describing this as a ‘transformation of vision’ (1962/1970a, 118). Moreover observation—if conceived of as a form of perception—does not play a significant part in every science. Kuhn wanted to explain his own experience of reading Aristotle, which first left him with the impression that Aristotle was an inexplicably poor scientist (Kuhn 1987). But careful study led to a change in his understanding that allowed him to see that Aristotle was indeed an excellent scientist. This could not simply be a matter of literally perceiving things differently. Kuhn took the incommensurability that prevented him from properly understanding Aristotle to be at least partly a linguistic, semantic matter. Indeed, Kuhn spent much of his career after The Structure of Scientific Revolutions attempting to articulate a semantic conception of incommensurability.

In The Structure of Scientific Revolutions Kuhn asserts that there are important shifts in the meanings of key terms as a consequence of a scientific revolution. For example, Kuhn says:

… the physical referents of these Einsteinian concepts are by no means identical with those of the Newtonian concepts that bear the same name. (Newtonian mass is conserved; Einsteinian is convertible with energy. Only at low relative velocities may the two be measured in the same way, and even then they must not be conceived to be the same.) (1962/1970a, 102)

This is important, because a standard conception of the transition from classical to relativistic physics is that although Einstein’s theory of relativity supersedes Newton’s theory, what we have is an improvement or generalization whereby Newton’s theory is a special case of Einstein’s (to a close approximation). We can therefore say that the later theory is closer to the truth than the older theory. Kuhn’s view that ‘mass’ as used by Newton cannot be translated by ‘mass’ as used by Einstein allegedly renders this kind of comparison impossible. Hence incommensurability is supposed to rule out convergent realism, the view that science shows ever improving approximation to the truth. (Kuhn also thinks, for independent reasons, that the very ideas of matching the truth and similarity to the truth are incoherent (1970a, 206).)

Kuhn’s view as expressed in the passage quoted above depends upon meaning holism—the claim that the meanings of terms are interrelated in such a way that changing the meaning of one term results in changes in the meanings of related terms: “To make the transition to Einstein’s universe, the whole conceptual web whose strands are space, time, matter, force, and so on, had to be shifted and laid down again on nature whole.” (1962/1970a, 149). The assumption of meaning holism is a long standing one in Kuhn’s work. One source for this is the later philosophy of Wittgenstein. Another not unrelated source is the assumption of holism in the philosophy of science that is consequent upon the positivist conception of theoretical meaning. According to the latter, it is not the function of the theoretical part of scientific language to refer to and describe unobserved entities. Only observational sentences directly describe the world, and this accounts for them having the meaning that they do. Theories permit the deduction of observational sentences. This is what gives theoretical expressions their meaning. Theoretical statements cannot, however, be reduced to observational ones. This is because, first, theoretical propositions are collectively involved in the deduction of observational statements, rather than singly. Secondly, theories generate dispositional statements (e.g. about the solubility of a substance, about how they would appear if observed under certain circumstances, etc.), and dispositional statements, being modal, are not equivalent to any truth-function of (non-modal) observation statements. Consequently, the meaning of a theoretical sentence is not equivalent to the meaning of any observational sentence or combination of observational sentences. The meaning of a theoretical term is a product of two factors: the relationship of the theory or theories of which it is a part to its observational consequences and the role that particular term plays within those theories. This is the double-language model of the language of science and was the standard picture of the relationship of a scientific theory to the world when Kuhn wrote The Structure of Scientific Revolutions . Kuhn’s challenge to it lay not in rejecting the anti-realism implicit in the view that theories do not refer to the world but rather in undermining the assumption that the relationship of observation sentence to the world is unproblematic. By insisting on the theory-dependence of observation, Kuhn in effect argued that the holism of theoretical meaning is shared by apparently observational terms also, and for this reason the problem of incommensurability cannot be solved by recourse to theory-neutral observation sentences.

(Although it is true that Kuhn uses the expression ‘physical referent’ in the passage quoted above, this should not be taken to mean an independently existing worldly entity. If that were the case, Kuhn would be committed to the worldly existence of both Newtonian mass and Einsteinian mass (which are nonetheless not the same). It is implausible that Kuhn intended to endorse such a view. A better interpretation is to understand Kuhn as taking reference, in this context, to be a relation between a term and a hypothetical rather than worldly entity. Reference of anything like the Fregean, worldly kind plays no part in Kuhn’s thinking. Again this may be seen as a reflection of the influence of one or other or both of the (later) Wittgensteinian downplaying of reference and of the positivist view that theories are not descriptions of the world but are in one way or another tools for the organization or prediction of observations.)

Although Kuhn asserted a semantic incommensurability thesis in The Structure of Scientific Revolutions he did not there articulate or argue for the thesis in detail. This he attempted in subsequent work, with the result that the nature of the thesis changed over time. The heart of the incommensurability thesis after The Structure of Scientific Revolutions is the idea that certain kinds of translation are impossible. Early on Kuhn drew a parallel with Quine’s thesis of the indeterminacy of translation (1970a, 202; 1970c, 268). According to the latter, if we are translating one language into another, there are inevitably a multitude of ways of providing a translation that is adequate to the behaviour of the speakers. None of the translations is the uniquely correct one, and in Quine’s view there is no such thing as the meaning of the words to be translated. It was nonetheless clear that Quine’s thesis was rather far from Kuhn’s thesis, indeed that they are incompatible. First, Kuhn thought that incommensurability was a matter of there being no fully adequate translation whereas Quine’s thesis involved the availability of multiple translations. Secondly, Kuhn does believe that the translated expressions do have a meaning, whereas Quine denies this. Thirdly, Kuhn later went on to say that unlike Quine he does not think that reference is inscrutable—it is just very difficult to recover (1976, 191).

Subsequently, Kuhn developed the view that incommensurability arises from differences in classificatory schemes. This is taxonomic incommensurability. A field of science is governed by a taxonomy, which divides its subject matter into kinds. Associated with a taxonomy is a lexical network—a network of related terms. A significant scientific change will bring with it an alteration in the lexical network which in turn will lead to a re-alignment of the taxonomy of the field. The terms of the new and old taxonomies will not be inter-translatable.

The problematic nature of translation arises from two assumptions. First, as we have seen, Kuhn assumes that meaning is (locally) holistic. A change in the meaning of one part of the lexical structure will result in a change to all its parts. This would rule out preservation of the translatability of taxonomies by redefining the changed part in terms of the unchanged part. Secondly, Kuhn adopts the ‘no-overlap’ principle which states that categories in a taxonomy must be hierarchically organised: if two categories have members in common then one must be fully included within the other; otherwise they are disjoint—they cannot simply overlap. This rules out the possibility of an all-encompassing taxonomy that incorporates both the original and the changed taxonomies. (Ian Hacking (1993) relates this to the world-change thesis: after a revolution the world of individuals remains as it was, but scientists now work in a world of new kinds .)

Kuhn continued to develop his conceptual approach to incommensurability. At the time of his death he had made considerable progress on a book in which he related incommensurability to issues in developmental psychology and concept acquisition.

Kuhn’s historical work covered several topics in the history of physics and astronomy. During the 1950s his focus was primarily on the early theory of heat and the work of Sadi Carnot. However, his first book concerned the Copernican revolution in planetary astronomy (1957). This book grew out of the teaching he had done on James Conant’s General Education in Science curriculum at Harvard but also presaged some of the ideas of The Structure of Scientific Revolutions . In detailing the problems with the Ptolemaic system and Copernicus’ solution to them, Kuhn showed two things. First, he demonstrated that Aristotelian science was genuine science and that those working within that tradition, in particular those working on Ptolemaic astronomy, were engaged in an entirely reasonable and recognizably scientific project. Secondly, Kuhn showed that Copernicus was himself far more indebted to that tradition than had typically been recognized. Thus the popular view that Copernicus was a modern scientist who overthrew an unscientific and long-outmoded viewpoint is mistaken both by exaggerating the difference between Copernicus and the Ptolemaic astronomers and in underestimating the scientific credentials of work carried out before Copernicus. This mistaken view—a product of the distortion caused by our current state of knowledge—can be rectified only by seeing the activities of Copernicus and his predecessors in the light of the puzzles presented to them by tradition that they inevitably had to work with. While Kuhn does acknowledge the influence of causes outside science (such as a resurgence in Sun worship (1962/70a, 152–3)), he nonetheless emphasizes the fact that astronomers were responding primarily to problems raised within science. What appealed to them in Copernicus’ model was its ability to do away with ad hoc devices in Ptolemy’s system (such as the equant), to explain key phenomena in a pleasing fashion (the observed retrograde motion of the planets), and to explain away otherwise inexplicable coincidences in Ptolemy’s system (such as the alignment of the Sun and the centres of the epicycles of the inferior planets).

In the 1960s Kuhn’s historical work turned toward the early history of quantum theory, culminating in his book Black-Body Theory and the Quantum Discontinuity . According to classical physics a particle could possess any energy in a continuous range and if it changes energy it does so in a continuous fashion, possessing at some point in time every energy between the initial and final energy states. Modern quantum theory denies both these classical principles. Energy is quantised—a particle may possess only one of a set of discrete energies. Consequently if it changes in energy from one value to the next permitted value it does so discontinuously, jumping straight from one energy to the other without taking any of the intermediate (‘forbidden’) values. In order to explain the distribution of energy within a cavity (black-body radiation), Planck used the device of dividing up the energy states into multiples of the unit or ‘quantum’ h ν (where ν is the frequency of radiation and h is what subsequently became known as Planck’s constant). Planck did this in order to employ a statistical technique of Boltzmann’s whereby the range of possible continuous energies is divided into ‘cells’ of similar energies that could be treated together for mathematical purposes. Kuhn notes that Planck was puzzled that in carrying out his derivation, only by fixing the cell size at h ν could he get the result he wanted—the technique should have worked for any way of dividing the cells, so long as they were small enough but not too small. This work of Planck’s was carried out in the period 1900–1, which is the date tradition has accorded to the invention of the quantum concept. However, argued Kuhn, Planck did not have in mind a genuine physical discontinuity of energies until 1908, which is after Albert Einstein and Paul Ehrenfest had themselves emphasized it in 1905–6.

Many readers were surprised not to find mention of paradigms or incommensurability. Kuhn later added an Afterword, “Revisiting Planck”, explaining that he had not repudiated or ignored those ideas but that they were implicit in the argument he gave. Indeed the whole essay may be seen as a demonstration of an incommensurability between the mature quantum theory and the early quantum theory of Planck which was still rooted in classical statistical physics. In particular the very term ‘quantum’ changed its meaning between its introduction by Planck and its later use. Kuhn argues that the modern quantum concept was introduced first not by Planck but by Einstein. Furthermore, this fact is hidden both by the continued use of the same term and by the same distortion of history that has affected our conception of Ptolemy and Copernicus. As in Copernicus’ case, Planck has been seen as more revolutionary than in fact he was. In Planck’s case, however, this misconception was also shared by Planck himself later in life.

6. Criticism and Influence

Kuhn’s work met with a largely critical reception among philosophers. Some of this criticism became muted as Kuhn’s work became better understood and as his own thinking underwent transformation. At the same time other developments in philosophy opened up new avenues for criticism. That criticism has largely focussed on two areas. First, it has been argued that Kuhn’s account of the development of science is not entirely accurate. Secondly, critics have attacked Kuhn’s notion of incommensurability, arguing that either it does not exist or, if it does exist, it is not a significant problem. Despite this criticism, Kuhn’s work has been hugely influential, both within philosophy and outside it. The Structure of Scientific Revolutions was an important stimulus to what has since become known as ‘Science Studies’, in particular the Sociology of Scientific Knowledge (SSK).

In The Structure of Scientific Revolutions periods of normal science and revolutionary science are clearly distinguished. In particular paradigms and their theories are not questioned and not changed in normal science whereas they are questioned and are changed in revolutionary science. Thus a revolution is, by definition revisionary, and normal science is not (as regards paradigms). Furthermore, normal science does not suffer from the conceptual discontinuities that lead to incommensurability whereas revolutions do. This gives the impression, confirmed by Kuhn’s examples, that revolutions are particularly significant and reasonably rare episodes in the history of science.

This picture has been questioned for its accuracy. Stephen Toulmin (1970) argues that a more realistic picture shows that revisionary changes in science are far more common and correspondingly less dramatic than Kuhn supposes, and that perfectly ‘normal’ science experiences these changes also. Kuhn could reply that such revisions are not revisions to the paradigm but to the non-paradigm puzzle-solutions provided by normal science. But that in turn requires a clear distinction between paradigmatic and non-paradigmatic components of science, a distinction that, arguably, Kuhn has not supplied in any detail.

At the same time, by making revisionary change a necessary condition of revolutionary science, Kuhn ignores important discoveries and developments that are widely regarded as revolutionary, such as the discovery of the structure of DNA and the revolution in molecular biology. Kuhn’s view is that discoveries and revolutions come about only as a consequence of the appearance of anomalies. Yet it is also clear that a discovery might come about in the course of normal science and initiate a ‘revolution’ (in a non-Kuhnian sense) in a field because of the unexpected insight it provides and the way it opens up opportunities for new avenues of research. The double-helical structure of DNA was not expected but immediately suggested a mechanism for the duplication of genetic information (e.g. in mitosis), which had enormous consequences for subsequent biological research.

Kuhn’s incommensurability thesis presented a challenge not only to positivist conceptions of scientific change but also to realist ones. For a realist conception of scientific progress also wishes to assert that, by and large, later science improves on earlier science, in particular by approaching closer to the truth. A standard realist response from the late 1960s was to reject the anti-realism and anti-referentialism shared by both Kuhn’s picture and the preceding double-language model. If we do take theories to be potential descriptions of the world, involving reference to worldly entities, kind, and properties, then the problems raised by incommensurability largely evaporate. As we have seen, Kuhn thinks that we cannot properly say that Einstein’s theory is an improvement on Newton’s in the sense that the latter as deals reasonably accurately (only) with a special case of the former. Whether or not the key terms (such as ‘mass’) in the two theories differ in meaning, a realist and referentialist approach to theories permits one to say that Einstein’s theory is closer to the truth than Newton’s. For truth and nearness to the truth depend only on reference and not on sense. Two terms can differ in sense yet share the same reference, and correspondingly two sentences may relate to one another as regards truth without their sharing terms with the same sense. And so even if we retain a holism about the sense of theoretical terms and allow that revolutions lead to shifts in sense, there is no direct inference from this to a shift in reference. Consequently, there is no inference to the inadmissibility of the comparison of theories with respect to their truth-nearness.

While this referentialist response to the incommensurability thesis was initially framed in Fregean terms (Scheffler 1967), it received further impetus from the work of Kripke (1980) and Putnam (1975b), which argued that reference could be achieved without anything akin to Fregean sense and that the natural kind terms of science exemplified this sense-free reference. In particular, causal theories of reference permit continuity of reference even through fairly radical theoretical change. (They do not guarantee continuity in reference, and changes in reference can occur on some causal theories, e.g. Gareth Evans’s (1973). Arguing that they do occur would require more, however, than merely pointing to a change in theory. Rather, it seems, cases of reference change must be identified and argued for on a case by case basis.) Therefore, if taken to encompass terms for quantities and properties (such as ‘mass’), the changes that Kuhn identified as changes in meaning (e.g. those involved in the shift from Newtonian to relativistic physics) would not necessarily be changes that bear on reference, nor, consequently, on comparison for nearness to the truth. The simple causal theory of reference does have its problems, such as explaining the referential mechanism of empty theoretical terms (e.g.caloric and phlogiston) (c.f. Enç 1976, Nola 1980). Causal-descriptive theories (which allow for a descriptive component) tackle such problems while retaining the key idea that referential continuity is possible despite radical theory change (Kroon 1985, Sankey 1994).

Of course, the referentialist response shows only that reference can be retained, not that it must be. Consequently it is only a partial defence of realism against semantic incommensurability. A further component of the defence of realism against incommensurability must be an epistemic one. For referentialism shows that a term can retain reference and hence that the relevant theories may be such that the later constitutes a better approximation to the truth than the earlier. Nonetheless it may not be possible for philosophers or others to know that there has been such progress. Methodological incommensurability in particular seems to threaten the possibility of this knowledge. Kuhn thinks that in order to be in a position to compare theories from older and more recent periods of normal science one needs a perspective external to each and indeed any era of science–what he calls an ‘Archimedean platform’ (1992, 14). However, we never are able to escape from our current perspective. A realist response to this kind of incommensurability may appeal to externalist or naturalized epistemology. These (related) approaches reject the idea that for a method to yield knowledge it must be independent of any particular theory, perspective, or historical/cognitive circumstance. So long as the method has an appropriate kind of reliability it can generate knowledge. Contrary to the internalist view characteristic of the positivists (and, it appears, shared by Kuhn) the reliability of a method does not need to be one that must be evaluable independently of any particular scientific perspective. It is not the case, for example, that the reliability of a method used in science must be justifiable by a priori means. Thus the methods developed in one era may indeed generate knowledge, including knowledge that some previous era got certain matters wrong, or right but only to a certain degree. A naturalized epistemology may add that science itself is in the business of investigating and developing methods. As science develops we would expect its methods to change and develop also.

Kuhn’s influence outside of professional philosophy of science may have been even greater than it was within it. The social sciences in particular took up Kuhn with enthusiasm. There are primarily two reasons for this. First, Kuhn’s picture of science appeared to permit a more liberal conception of what science is than hitherto, one that could be taken to include disciplines such as sociology and psychoanalysis. Secondly, Kuhn’s rejection of rules as determining scientific outcomes appeared to permit appeal to other factors, external to science, in explaining why a scientific revolution took the course that it did.

The status as genuine sciences of what we now call the social and human sciences has widely been held in doubt. Such disciplines lack the remarkable track record of established natural sciences and seem to differ also in the methods they employ. More specifically they fail by pre-Kuhnian philosophical criteria of sciencehood. On the one hand, positivists required of a science that it should be verifiable by reference to its predictive successes. On the other, Popper’s criterion was that a science should be potentially falsifiable by a prediction of the theory. Yet psychoanalysis, sociology and even economics have difficulty in making precise predictions at all, let alone ones that provide for clear confirmation or unambiguous refutation. Kuhn’s picture of a mature science as being dominated by a paradigm that generated sui generis puzzles and criteria for assessing solutions to them could much more easily accommodate these disciplines. For example, Popper famously complained that psychoanalysis could not be scientific because it resists falsification. Kuhn’s account argues that resisting falsification is precisely what every disciplinary matrix in science does. Even disciplines that could not claim to be dominated by a settled paradigm but were beset by competing schools with different fundamental ideas could appeal to Kuhn’s description of the pre-paradigm state of a science in its infancy. Consequently Kuhn’s analysis was popular among those seeking legitimacy as science (and consequently kudos and funding) for their new disciplines. Kuhn himself did not especially promote such extensions of his views, and indeed cast doubt upon them. He denied that psychoanalysis is a science and argued that there are reasons why some fields within the social sciences could not sustain extended periods of puzzle-solving normal science (1991b). Although, he says, the natural sciences involve interpretation just as human and social sciences do, one difference is that hermeneutic re-interpretation, the search for new and deeper intepretations, is the essence of many social scientific enterprises. This contrasts with the natural sciences where an established and unchanging interpretation (e.g. of the heavens) is a pre-condition of normal science. Re-intepretation is the result of a scientific revolution and is typically resisted rather than actively sought. Another reason why regular reinterpretation is part of the human sciences and not the natural sciences is that social and political systems are themselves changing in ways that call for new interpretations, whereas the subject matter of the natural sciences is constant in the relevant respects, permitting a puzzle-solving tradition as well as a standing source of revolution-generating anomalies.

A rather different influence on social science was Kuhn’s influence on the development of social studies of science itself, in particular the ‘Sociology of Scientific Knowledge’. A central claim of Kuhn’s work is that scientists do not make their judgments as the result of consciously or unconsciously following rules. Their judgments are nonetheless tightly constrained during normal science by the example of the guiding paradigm. During a revolution they are released from these constraints (though not completely). Consequently there is a gap left for other factors to explain scientific judgments. Kuhn himself suggests in The Structure of Scientific Revolutions that Sun worship may have made Kepler a Copernican and that in other cases, facts about an individual’s life history, personality or even nationality and reputation may play a role (1962/70a, 152–3). Later Kuhn repeated the point, with the additional examples of German Romanticism, which disposed certain scientists to recognize and accept energy conservation, and British social thought which enabled acceptance of Darwinism (1977c, 325). Such suggestions were taken up as providing an opportunity for a new kind of study of science, showing how social and political factors external to science influence the outcome of scientific debates. In what has become known as social constructivism/constructionism (e.g. Pickering 1984) this influence is taken to be central, not marginal, and to extend to the very content of accepted theories. Kuhn’s claim and its exploitation can be seen as analogous to or even an instance of the exploitation of the (alleged) underdetermination of theory by evidence (c.f. Kuhn 1992, 7). Feminists and social theorists (e.g. Nelson 1993) have argued that the fact that the evidence, or, in Kuhn’s case, the shared values of science, do not fix a single choice of theory, allows external factors to determine the final outcome (see Martin 1991 and Schiebinger 1999 for feminist social constructivism). Furthermore, the fact that Kuhn identified values as what guide judgment opens up the possibility that scientists ought to employ different values, as has been argued by feminist and post-colonial writers (e.g. Longino 1994).

Kuhn himself, however, showed only limited sympathy for such developments. In his “The Trouble with the Historical Philosophy of Science” (1992) Kuhn derides those who take the view that in the ‘negotiations’ that determine the accepted outcome of an experiment or its theoretical significance, all that counts are the interests and power relations among the participants. Kuhn targeted the proponents of the Strong Programme in the Sociology of Scientific Knowledge with such comments; and even if this is not entirely fair to the Strong Programme, it reflects Kuhn’s own view that the primary determinants of the outcome of a scientific episode are to be found within science. External history of science seeks causes of scientific change in social, political, religious and other developments of science. Kuhn sees his work as “pretty straight internalist” (2000: 287). First, the five values Kuhn ascribes to all science are in his view constitutive of science. An enterprise could have different values but it would not be science (1977c, 331; 1993, 338). Secondly, when a scientist is influenced by individual or other factors in applying these values or in coming to a judgment when these values are not decisive, those influencing factors will typically themselves come from within science (especially in modern, professionalized science). Personality may play a role in the acceptance of a theory, because, for example, one scientist is more risk-averse than another (1977c, 325)—but that is still a relationship to the scientific evidence. Even when reputation plays a part, it is typically scientific reputation that encourages the community to back the opinion of an eminent scientist. Thirdly, in a large community such variable factors will tend to cancel out. Kuhn supposes that individual differences are normally distributed and that a judgment corresponding to the mean of the distribution will also correspond to the judgment that would, hypothetically, be demanded by the rules of scientific method, as traditionally conceived (1977c, 333). Moreover, the existence of differences of response within the leeway provided by shared values is crucial to science, since it permits “rational men to disagree” (1977c, 332) and thus to commit themselves to rival theories. Thus the looseness of values and the differences they permit “may . . . appear an indispensable means of spreading the risk which the introduction or support of novelty always entails” (Ibid.).

Even if Kuhn’s work has not remained at the centre of the philosophy of science, a number of philosophers have continued to find it fruitful and have sought to develop it in a number of directions. Paul Hoyningen-Huene (1989/1993), as a result of working with Kuhn, developed an important neo-Kantian interpretation of his discussion of perception and world-change. We may distinguish between the world-in-itself and the ‘world’ of our perceptual and related experiences (the phenomenal world). This corresponds to the Kantian distinction between noumena and phenomena. The important difference between Kant and Kuhn is that Kuhn takes the general form of phenomena not to be fixed but changeable. A shift in paradigm can lead, via the theory-dependence of observation, to a difference in one’s experiences of things and thus to a change in one’s phenomenal world. This change in phenomenal world articulates the sense in which the world changes as a result of a scientific revolution while also capturing Kuhn’s claims about the theory-dependence of observation and consequent incommensurability (Hoyningen-Huene 1990).

A rather different direction in which Kuhn’s thought has been developed proposes that his ideas might be illuminated by advances in cognitive psychology. One the one hand work on conceptual structures can help understand what might be correct in the incommensurability thesis (Nersessian 1987, 2003). Several authors have sought in different ways to emphasize what they take to be the Wittgensteinian element in Kuhn’s thought (for example Kindi 1995, Sharrock and Read 2002). Andersen, Barker, and Chen (1996, 1998, 2006) draw in particular on Kuhn’s version of Wittgenstein’s notion of family resemblance. Kuhn articulates a view according to which the extension of a concept is determined by similarity to a set of exemplary cases rather than by an intension. Andersen, Barker, and Chen argue that Kuhn’s view is supported by the work of Rosch (1972; Rosch and Mervis 1975) on prototypes; furthermore, this approach can be developed in the context of dynamic frames (Barsalou 1992), which can then explain the phenomenon of (semantic) incommensurability.

On the other hand, the psychology of analogical thinking and cognitive habits may also inform our understanding of the concept of a paradigm. Kuhn himself tells us that “The paradigm as shared example is the central element of what I now take to be the most novel and least understood aspect of [ The Structure of Scientific Revolutions ]” (1970a, 187). Kuhn, however, failed to develop the paradigm concept in his later work beyond an early application of its semantic aspects to the explanation of incommensurability. Nonetheless, other philosophers, principally Howard Margolis (1987, 1993) have developed the idea that habits of mind formed by training with paradigms-as-exemplars are an important component in understanding the nature of scientific development. As explained by Nickles (2003b) and Bird (2005), this is borne out by recent work by psychologists on model-based and analogical thinking.

Assessing Kuhn’s significance presents a conundrum. Unquestionably he was one of the most influential philosophers and historians of science of the twentieth century. His most obvious achievement was to have been a major force in bringing about the final demise of logical positivism. Nonetheless, there is no characteristically Kuhnian school that carries on his positive work. It is as if he himself brought about a revolution but did not supply the replacement paradigm. For a period in the 1960s and 1970s it looked as if there was a Kuhnian paradigm ‘historical philosophy of science’, flourishing especially in newly formed departments of history and philosophy of science. But as far as the history of science and science studies more generally are concerned, Kuhn repudiated at least the more radical developments made in his name. Indeed part of Kuhn’s fame must be due to the fact that both his supporters and his detractors took his work to be more revolutionary (anti-rationalist, relativist) than it really was.

Turning to the philosophy of science, it was clear by the end of the 1980s that the centreground was now occupied by a new realism, one that took on board lessons from general philosophy of language and epistemology, in particular referentialist semantics and a belief in the possibility of objective knowledge and justification. There is some irony therefore in the fact that it was the demise of logical positivism/empiricism that led to the rebirth of scientific realism along with causal and externalist semantics and epistemology, positions that Kuhn rejected.

One way of understanding this outcome is to see that Kuhn’s relationship on the one hand to positivism and on the other hand to realism places him in an interesting position. Kuhn’s thesis of the theory-dependence of observation parallels related claims by realists. In the hands of realists the thesis is taken to undermine the theory-observation dichotomy that permitted positivists to take an anti-realist attitude to theories. In the hands of Kuhn however, the thesis is taken, in effect, to extend anti-realism from theories to observation also. This in turn fuels the thesis of incommensurability. The fact that incommensurability is founded upon a response to positivism diametrically opposed to the realist response explains why much of Kuhn’s later philosophical work, which developed the incommensurability thesis, has had little impact on the majority of philosophers of science.

The explanation of scientific development in terms of paradigms was not only novel but radical too, insofar as it gives a naturalistic explanation of belief-change. Naturalism was not in the early 1960s the familiar part of philosophical landscape that it has subsequently become. Kuhn’s explanation contrasted with explanations in terms of rules of method (or confirmation, falsification etc.) that most philosophers of science took to be constitutive of rationality. Furthermore, the relevant disciplines (psychology, cognitive science, artificial intelligence) were not then advanced enough to to support Kuhn’s contentions concerning paradigms, or those disciplines were antithetical to Kuhn’s views (in the case of classical AI). Now that naturalism has become an accepted component of philosophy, there has recently been interest in reassessing Kuhn’s work in the light of developments in the relevant sciences, many of which provide corroboration for Kuhn’s claim that science is driven by relations of perceived similarity and analogy. It may yet be that a characteristically Kuhnian thesis will play a prominent part in our understanding of science.

Books by Thomas Kuhn

• 1957, The Copernican Revolution: Planetary Astronomy in the Development of Western Thought , Cambridge Mass: Harvard University Press.
• 1962/1970a, The Structure of Scientific Revolutions , Chicago: University of Chicago Press (1970, 2nd edition, with postscript).
• 1977a, The Essential Tension. Selected Studies in Scientific Tradition and Change , Chicago: University of Chicago Press.
• 1978, Black-Body Theory and the Quantum Discontinuity , Oxford: Clarendon Press (2nd edition, Chicago: University of Chicago Press).
• 2000, The Road Since Structure , edited by James Conant and John Haugeland, Chicago: University of Chicago Press.

Selected papers of Thomas Kuhn

• 1959, “The Essential Tension: Tradition and Innovation in Scientific Research”, in The Third (1959) University of Utah Research Conference on the Identification of Scientific Talent C. Taylor, Salt Lake City: University of Utah Press: 162–74.
• 1963, “The Function of Dogma in Scientific Research”, in Scientific Change , A. Crombie (ed.), London: Heinemann: 347–69.
• 1970b, “Logic of Discovery or Psychology of Research?”, in Criticism and the Growth of Knowledge , edited by I. Lakatos and A. Musgrave, London: Cambridge University Press: 1–23.
• 1970c, “Reflections on my Critics”, in Criticism and the Growth of Knowledge , I. Lakatos and A. Musgrave (eds.), London: Cambridge University Press: 231–78.
• 1974, “Second Thoughts on Paradigms”, in The Structure of Scientific Theories F. Suppe (ed.), Urbana IL: University of Illinois Press: 459–82.
• 1976, “Theory-Change as Structure-Change: Comments on the Sneed Formalism” Erkenntnis 10: 179–99.
• 1977b, “The Relations between the History and the Philosophy of Science”, in his The Essential Tension , Chicago: University of Chicago Press: 3–20.
• 1977c, “Objectivity, Value Judgment, and Theory Choice”, in his The Essential Tension , Chicago: University of Chicago Press: 320–39.
• 1979, “Metaphor in Science”, in Metaphor and Thought , edited by A. Ortony Cambridge: Cambridge University Press: 409–19.
• 1980, “The Halt and the Blind: Philosophy and History of Science”, (review of Howson Method and Appraisal in the Physical Sciences , Cambridge: Cambridge University Press) British Journal for the Philosophy of Science 31: 181–92.
• 1983a, “Commensurability, Comparability, Communicability”, PSA 198: Proceedings of the 1982 Biennial Meeting of the Philosophy of Science Association , edited by P. Asquith. and T. Nickles, East Lansing MI: Philosophy of Science Association: 669–88.
• 1983b, “Rationality and Theory Choice”, Journal of Philosophy 80: 563–70.
• 1987, “What are Scientific Revolutions?”, in The Probabilistic Revolution edited by L. Krüger, L. Daston, and M. Heidelberger, Cambridge: Cambridge University Press: 7–22. Reprinted in Kuhn 2000: 13–32.
• 1990, “Dubbing and Redubbing: The Vulnerability of Rigid Designation”, in Scientific Theories edited by C. Savage, Minnesota Studies in Philosophy of Science 14, Minneapolis MN: University of Minnesota Press: 298–318.
• 1991a, “The Road Since Structure”, PSA 1990. Proceedings of the 1990 Biennial Meeting of the Philosophy of Science Association vol.2 , edited by A. Fine, M. Forbes, and L. Wessels., East Lansing MI: Philosophy of Science Association: 3–13.
• 1991b, “The Natural and the Human Sciences”, in The Interpretative Turn: Philosophy, Science, Culture , edited by D. Hiley, J. Bohman, and R. Shusterman, Ithaca NY: Cornell University Press: 17–24.
• 1992, “The Trouble with the Historical Philosophy of Science”, Robert and Maurine Rothschild Distinguished Lecture, 19 November 1991, An Occasional Publication of the Department of the History of Science, Cambridge MA: Harvard University Press.
• 1993, “Afterwords” in World Changes. Thomas Kuhn and the Nature of Science , edited by P. Horwich, Cambridge MA: MIT Press: 311–41.

Other references and secondary literature

• Andersen, H., 2001, On Kuhn , Belmont CA: Wadsworth.
• Andersen, H., P. Barker, and X. Chen, 1996, “Kuhn’s mature philosophy of science and cognitive psychology”, Philosophical Psychology , 9: 347–63.
• Andersen, H., P. Barker, and X. Chen, 1998, “Kuhn’s theory of scientific revolutions and cognitive psychology”, Philosophical Psychology , 11: 5–28.
• Andersen, H., P. Barker, and X. Chen, 2006, The Cognitive Structure of Scientific Revolutions , Cambridge: Cambridge University Press.
• Barnes, B., 1982, T.S.Kuhn and Social Science , London: Macmillan.
• Barsalou, L. W.. 1992, “Frames, concepts, and conceptual fields”, in A. Lehrer and E. F. Kittay, (eds.) Frames, Fields, and Contrasts: New Essays in Semantic and Lexical Organization , Hillsdale NJ: Lawrence Erlbaum Associates, 21–74
• Bird, A., 2000, Thomas Kuhn , Chesham: Acumen and Princeton, NJ: Princeton University Press.
• Bird, A., 2005, “Naturalizing Kuhn”, Proceedings of the Aristotelian Society , 105: 109–27.
• Bird, A., 2007, “Incommensurability naturalized”, in L. Soler, H. Sankey, and P. Hoyningen-Huene (eds.), Rethinking Scientific Change and Theory Comparison (Boston Studies in the Philosophy of Science 255), Dordrecht: Springer, 21–39.
• Bruner, J. and Postman, L., 1949, “On the Perception of incongruity: A paradigm”, Journal of Personality , 18: 206–23.
• Cohen, I. B., 1985, Revolution in Science , Cambridge MA: Harvard University Press.
• Devitt, M., 1979, “Against incommensurability”, Australasian Journal of Philosophy , 57: 29–50.
• Doppelt, G., 1978, “Kuhn’s epistemological relativism: An interpretation and defense”, Inquiry , 21: 33–86;
• Enç, B. 1976, “Reference and theoretical terms”, Noûs , 10: 261–82.
• Evans, G. 1973 “The causal theory of names”, Proceedings of the Aristotelian Society (Supplementary Volume), 47: 187–208.
• Fuller, S. 2000, Thomas Kuhn: A Philosophical History for our Times , Chicago: University of Chicago Press.
• Gutting, G., 1980, Paradigms and Revolutions , Notre Dame: University of Notre Dame Press.
• Hacking, I. (ed.), 1981, Scientific Revolutions , Oxford: Oxford University Press.
• Hacking, I. (ed.), 1993, “Working in a new world: The taxonomic solution”, in Horwich 1993, 275–310.
• Hanson, N. R., 1958, Patterns of Discovery , Cambridge: Cambridge University Press.
• Horwich, P. (ed.), 1993, World Changes. Thomas Kuhn and the Nature of Science , Cambridge MA: MIT Press.
• Hoyningen-Huene, P., 1989, Die Wissenschaftsphilosophie Thomas S. Kuhns: Rekonstruktion und Grundlagenprobleme , translated as Hoyningen-Huene, P., 1993, Reconstructing Scientific Revolutions: Thomas S. Kuhn’s Philosophy of Science , Chicago: University of Chicago Press.
• Hoyningen-Huene, P., 1990, “Kuhn’s conception of incommensurability” Studies in History and Philosophy of Science Part A , 21: 481–92.
• Hung, E. H.-C., 2006, Beyond Kuhn. Scientific Explanation, Theory Structure, Incommensurability and Physical Necessity , Aldershot: Ashgate.
• Kindi, V., 1995, Kuhn and Wittgenstein: Philosophical Investigation of the Structure of Scientific Revolutions , Athens: Smili editions.
• Kripke, S., 1980, Naming and Necessity , Cambridge MA: Harvard University Press.
• Kroon, F. 1985, “Theoretical terms and the causal view of reference”, Australasian Journal of Philosophy , 63: 143–66.
• Lakatos, I. and Musgrave, A. (eds.), 1970, Criticism and the Growth of Knowledge , London: Cambridge University Press.
• Longino, H., 1994, “In search of feminist epistemology”, Monist , 77: 472–85.
• Margolis, H., 1987, Patterns, Thinking, and Cognition: A Theory of Judgment , Chicago: University of Chicago Press.
• Margolis, H., 1993, Paradigms and Barriers: How Habits of Mind Govern Scientific Beliefs , Chicago: University of Chicago Press.
• Martin, E., 1991, “The egg and the sperm: How science has constructed a romance based on stereotypical male-female sex roles”, Signs , 16: 485–501. Reprinted in E. Keller and H. Longino (eds.), 1996, Feminism and Science , Oxford: Oxford University Press.
• Masterman, M., 1970. “The nature of a paradigm”, in Lakatos and Musgrave 1970, 59–89.
• Mizrahi, M. (ed.), 2018, The Kuhnian Image of Science , London: Rowman and Littlefield.
• Musgrave, A., 1971, “Kuhn’s second thoughts”, British Journal of the Philosophy of Science , 22: 287–97.
• Nagel, E. 1961, The Structure of Science , London: Routledge and Kegan Paul.
• Nelson, L. H., 1993, “Epistemological communities”, in L. Alcoff and E. Potter (eds.), Feminist Epistemologies , New York: Routledge.
• Nersessian, N., 1987, “A cognitive-historical approach to meaning in scientific theories”, in N. Nersessian (ed.) The Process of Science , Dordrecht: Kluwer, 161–77.
• Nersessian, N., 2003, “Kuhn, conceptual change, and cognitive science”, in Nickles 2003a, 178–211.
• Newton-Smith, W., 1981, The Rationality of Science , London: Routledge.
• Nickles, T., 2003a (ed.), Thomas Kuhn , Cambridge: University of Cambridge Press.
• Nickles, T., 2003b, “Normal science: From logic to case-based and model-based reasoning”, in Nickles 2003a, 142–77.
• Nola, R., 1980, “Fixing the Reference of Theoretical Terms”, Philosophy of Science , 47: 505–31.
• Pickering, A., 1984, Contructing Quarks: A Sociological History of Particle Physics , Chicago: University of Chicago Press.
• Popper, K., 1959, The Logic of Scientific Discovery , London: Hutchinson.
• Putnam, H., 1975a, Mind, Language, and Reality: Philosophical Papers Vol. 2 , Cambridge: Cambridge University Press.
• Putnam, H., 1975b, “The meaning of ‘meaning’” in Putnam 1975a.
• Renzi, B. G., 2009, “Kuhn’s evolutionary epistemology and its being undermined by inadequate biological concepts”, Philosophy of Science , 58: 143–59.
• Rosch, E., 1973, “On the internal structure of perceptual and semantic categories”, in T. E. Moore (ed.) Cognitive Development and the Acquisition of Language , New York NY: Academic, 111–44.
• Rosch, E. and Mervis C. B., 1975, “Family resemblances: Studies in the internal structures of categories”, Cognitive Psychology , 7: 573–605.
• Sankey, H., 1993, “Kuhn’s changing concept of incommensurability”, British Journal of the Philosophy of Science , 44: 759–74.
• Sankey, H., 1994, The Incommensurability Thesis , Aldershot: Avebury.
• Scheffler, I., 1967, Science and Subjectivity , Indianapolis: Bobbs-Merrill.
• Schiebinger, L., 1999, Has Feminism Changed Science? , Cambridge MA: Harvard University Press.
• Shapere, D., 1964, “The Structure of Scientific Revolutions”, Philosophical Review , 73: 383–94.
• Sharrock, W. and Read, R., 2002, Kuhn: Philosopher of Scientific Revolution , Cambridge: Polity.
• Siegel, H., 1980 “Objectivity, rationality, incommensurability and more”, British Journal of the Philosophy of Science , 31: 359–84.
• Toulmin, S., 1970 “Does the distinction between normal and revolutionary science hold water?”, in Lakatos and Musgrave 1970, 39–5.
• Wray, K. B., 2011, Kuhn’s Evolutionary Social Epistemology , Cambridge: Cambridge University Press.

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• Thomas Kuhn—A Snapshot by Frank Pajares
• The Structure of Scientific Revolutions—An Outline and Study Guide by Frank Pajares
• Guide to Thomas Kuhn’s The Structure of Scientific Revolutions by Malcolm R. Forster
• Thomas Kuhn (Wikipedia)
• The Structure of Scientific Revolutions (Wikipedia)
• Obituary in The New York Times by Lawrence Van Gelder

epistemology: evolutionary | epistemology: social | feminist philosophy, interventions: epistemology and philosophy of science | Feyerabend, Paul | incommensurability: of scientific theories | Lakatos, Imre | Popper, Karl | Quine, Willard Van Orman | rationality: historicist theories of | reference | relativism | scientific knowledge: social dimensions of | scientific realism | Wittgenstein, Ludwig

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PUNK! The Revolution of Everyday Life • Kounosuke Kawakami Laboratory at Kurashiki University of Science and the Arts

Revolutions in reverse: essays on politics, violence, art, and imagination.

This collection of essays takes its title from an astute observation that modern revolutions appear to be happening in precisely the reverse order compared to historical imagination.

T hey proceed by reinventing the dynamics and norms of social life, bringing these new life forms out in the public to celebrate, only to culminate in often violent clashes with the state. The interplay between violence and imagination was central to David’s work especially as they related to the possibility of pursuing forms of value other than money. It reiterates a call David made throughout his works to create a new language and a new common sense which he perceived as the ultimate kind of revolution.

by Andris Suvajevs

This book is translated to: Czech, English, German, Greek, Italian, Turkish

If you’re a publisher, please contact [email protected]

If a translation’s missing from this list, fill the feedback form and let us know!

How Jazz Became the Voice of Revolution

Martin Luther King Jr. knew better than anyone what it would take to pull off a revolution capable of reshaping the soul of America and at long last leveling the playing field for American Blacks.

First you had to have followers and allies who were prepared to challenge not just racist leaders, but the cultural bedrock of a racist nation. Nobody’s outcry was more heartfelt and unexpected than that of trumpeter and vocalist Louis “Satchmo” Armstrong , who shortly after the 1957 Little Rock school crisis offered this knife-edge rebuke that made headlines from Boston to Budapest: “The way they are treating my people in the South, the Government can go to hell.” Satchmo mocked segregationist Governor Orval Faubus of Arkansas as a “motherfucker” (to make it fit for print, he and the reporter toned it down to “uneducated plow boy”), and derided war-hero President Dwight Eisenhower as “two-faced” and having “no guts” for failing early on to protect the brave Black kids desegregating Little Rock’s Central High.

A successful revolution also needed inspirational anthems and symbols. Edward Kennedy “Duke” Ellington more than rose to the occasion by composing transformative tunes like “Black, Brown, and Beige” a musical homage to the history of African-America, and writing Jump for Joy, a play that banished Uncle Tom from the stage and American life and that insisted it was time to stop turning the other cheek.

A mass movement also required money for everything from bringing people to rallies to bailing them out of jail. William James “Count” Basie wrote checks, while his wife Catherine Basie not only raised bagfuls more, but played pivotal roles in civil rights groups in New York and beyond.

Most of all, with Black people constituting just 10% of the population at the time, you needed support in white America. No trio did as much as Ellington, Armstrong, and Basie to set the table for the insurrection by opening white America’s ears and souls to the grace of their music and their personalities, demonstrating the virtues of Black artistry and Black humanity. They toppled color barriers on radio and TV; in jukeboxes, films, newspapers, and newsmagazines; and in the White House, concert halls, and living rooms from the Midwest and both coasts to the Heart of Dixie. But they did it carefully, knowing that to do otherwise in their Jim Crow era would have been suicidal. If James Brown, Chuck Berry, and Little Richard are rightfully credited with opening the door to the acceptance of Black music, it was Louis Armstrong, Count Basie, and Duke Ellington who inserted the key in the lock.

Read More: How the U.S. Used Jazz as a Cold War Secret Weapon

Whether or not young activists who dismissed the aging musicians as Uncle Toms understood that, Rev. King did. That’s why he went to Chicago to see Ellington and Jump for Joy , embraced Catherine Basie and Lucille Armstrong along with their husbands, and appreciated how the dancehall’s “joyful rhythms” and “language of soul” provided the countermelody for his movement. “Jazz speaks for life,” King wrote to the organizers of the Berlin Jazz Festival in 1964. Three years later he told the Negro National Association of Radio Announcers, “You have paved the way for social and political change by creating a powerful cultural bridge between Black and white. School integration is much easier now that they share a common music, a common language, and enjoy the same dances.”

Other leaders and luminaries joined King in recognizing the revolutionary power of jazz and its practitioners. Malcolm X unabashedly adored the Count and Ellington. Ralph Ellison preached the gospel of Satchmo. Jackie Robinson tapped his love of jazz and jazzmen to raise money for King’s Southern Christian Leadership Conference. Even Frank Sinatra, a surprising anti-racism activist, got the message.“Maybe the political scientists will never find the cure for intolerance,” said The Sultan of Swoon." Until they do, I challenge anyone to come up with a more effective prescription than Duke Ellington’s music, and Duke Ellington’s performance as a human being.”

Armstrong’s activism was the most counterintuitive, since he was the one most disparaged as an Oreo and a sellout. That hurt because he’d worked so hard to dig out of a life of Louisiana-style racism. He’d hoped fellow Black people would acknowledge and appreciate how his becoming world-famous helped them along with him.

Armstrong and his mixed-race sidemen traveled the South long before the freedom riders did, and before it was safe to do so. No Black person had ever starred on commercially-sponsored network radio before he took over for Rudy Vallee on NBC’s Fleischmann’s Yeast Show in 1937, back when Jackie Robinson was eighteen and the only Reverend King was eight-year-old Martin’s daddy. Still in his 30s, Louis became the first of his race to be featured in mainstream American films. No jazz musician of any color had made the cover of TIME magazine (1949)or of Life (1966) until Armstrong , none had published a memoir until his Swing that Music (1936), and none had dazzled British royalty (King George V in 1932). “As time went on and I made a reputation,” he said, “I had it put in my contracts that I wouldn’t play no place I couldn’t stay. I was the first Negro in the business to crack them big white hotels–Oh, yeah! I pioneered, Pops!”

The public could see all of that at the time. We now know even more about his attitudes and activism, thanks to the release of hundreds of hours of his private tape recordings. They include not just racy jokes and random musings, but tortured reactions to what he labeled the “shame” of racism. He blasted famous Black leaders he thought were false prophets, insisting that Marcus Garvey and Josephine Baker were exploiters, not healers. But he adored Martin Luther King, making audio tapes of hour after hour of the TV coverage of his assassination in 1968. Sometimes he counseled friends to endure racial blows, others he boasted about doing just the opposite. When a white workman disrespected him, he shouted into the recorder the insults he’d shot back at the laborer, explaining, “You try to be a gentleman, they won’t let you, that’s all. I’m just showing you what we have to go through.”

Daily battles like that were exhausting, but he used his wit to mask his despair. Before a performance at New York’s Basin Street East nightclub in the 1950s, pianist Errol Garner stuck his head into the trumpeter’s dressing room to ask, “Hey, Pops, how’s everything?” Louis: “White folks still in the lead.”

Ellington’s version of subversion, while comparably non-confrontational, was more straightforward.

While the up-tempo and airy “Take the ‘A’ Train” was Ellington’s signature tune in his early years, a very different number characterized the maestro later on. The song, “King Fit the Battle of Alabam,” marked a rare occasion when he employed Satchmo-like verbal idiom and an even rarer one of him using his music to sound off on the racial violence engulfing America. Few remember the song because it played only during the six-week run of My People, a show staged in Chicago in 1963 during a centenary celebration of the Emancipation Proclamation. His libretto railed against Bull Connor, the racist police chief in Birmingham, Alabama, for violently assaulting youthful Black demonstrators with a barrage of fire hoses, club-wielding officers, and snarling German Shepherds: "King fit the battle of Alabam’ – Birmingham . . ./And the bull got nasty – ghastly – nasty/Bull turned the hoses on the church people/And the water came splashing – dashing – crashing/Freedom rider–ride/ Freedom rider–go to town/Y’all and us–gonna get on the bus/Y'all aboard – sit down, sit tight, you’re right"

While the theater world didn’t see any money-making possibilities, My People resonated with the cultural moment and with Ellington’s life journey. It was staged mere months after the violence in Birmingham, and just two weeks before the March on Washington that demanded jobs and justice for Black Americans. More significantly, it prompted the first meeting between Ellington and King Jr., who came to Chicago to catch the show.

Ellington, who had just woken up, “came down in his cashmere coat and wrap and his little pork-pie hat,” recalled Marion Logan, a mutual friend. “Martin saw us and he jumped out of the limousine, and he and Ellington embraced . . . It was a very warm embrace.” The three proceeded to the theater and watched from the director’s booth a rehearsal of the song written in King’s honor. “It was the first time Martin had ever heard that, and he was very impressed – very proud. It was quite a moment.”

“King Fit the Battle” surprised audiences because it seemed so out of character for the avowedly apolitical and dispassionate Ellington, but it wasn’t entirely unforeseen. A generation earlier, in 1941, he had scripted Jump for Joy, a musical he said was meant to “take Uncle Tom out the theater.” He left unsaid his intention that the show would end any discussion of his being an Uncle Tom.

Read More: Still Loving Him Madly

The musical’s title tune offered this optimistic liftoff: "Fare thee well, land of cotton/ Cotton lisle is out of style/Honey chile, Jump for Joy/When you stomp up to heaven/And you meet old St. Pete/Tell that boy, “Jump for joy!”/ Step right in/ Give Pete some skin/ And Jump for Joy."

A year and a half later, Ellington debuted “Black, Brown and Beige , ”his loftiest extended composition and, at forty-four minutes, the longest. It was his first concert at New York’s grand Carnegie Hall. While Jump for Joy was Duke’s effort to slay Jim Crow with satire and mockery, “Black, Brown, and Beige” was deadly earnest. He’d planned to write an opera, but when he couldn’t find backers he turned to the familiar form of symphony to tell a story unfamiliar to most Americans. It spanned the gamut of Black experiences—from slavery through emancipation, segregation, and increasing integration—incorporating music evocative of those times and places. And it drew on all the emotions—from gloom to joy, purposefulness to the patriotism that fit a country at war. “Just as always before, the Black, Brown, and Beige were soon right in there for the Red, White, and Blue,” said Ellington, who meant to educate – not alienate – the white audience he’d spent decades nurturing.

Nobody was better at infiltration and circumvention than Bill Basie. He believed in the cause of African-American rights as passionately as his brethren bandleaders, but unlike Ellington and Armstrong, Basie waged his campaign so discreetly that the FBI never considered him worthy of a file, the NAACP and the Black press didn’t give him awards or much coverage, and young activists neither attacked nor applauded. Which was just how the Count liked it.

Everyone knew that Basie could make listeners bob their feet and rush to the dance floor, but few noticed that his was the first Black band to play at Pittsburgh’s stately William Penn Hotel back in 1937. Or that just after, at Philadelphia’s Nixon Grand Theater, he solicited signatures for an anti-lynching petition. In 1939, he toured with a revue called Meet the People that took swipes at segregation much like those in Ellington’s My People 24 years later. In 1945, the Count told the managers of Kansas City’s whites-only Tower Theatre, “If you don’t want my people, then you don’t want my music.” Basie followed through on the ultimatum and turned down the gig. The Amsterdam News was so surprised and delighted that it wrote, “Associate this statement with Count Basie and automatically one would think this is the theme of a new Basie hit tune.” The Count, the paper added, “is the first of the boogie-woogie band leaders to make the drastic step. If others follow his lead, both the entertainers and the laity feel the situation can be remedied.” All of which led Basie trumpeter Sweets Edison to boast, “We started integration.”

Basie confronted the same Jim Crow outrages as Armstrong and Ellington, perhaps even more egregious ones, since his audience was blacker and poorer, with plumbers, maids, and chauffeurs instead of doctors, lawyers, and teachers. “I can’t remember when I had not experienced discrimination. That’s how the world was ever since I started performing back in the ‘20s,” the Count said. “But you don’t let that stop you if you know what you want to be.” In 1937, he appeared with Billie Holiday at Detroit’s Fox Theatre. “Detroit was between race riots then, and after three performances the first day, the theater management went crazy. They claimed they had so many complaints about all those Negro men up there on the stage with those bare-legged white girls, all hell cut loose backstage,” Holiday remembered.

Ten years later the Count was discussing business with his press agent on the corner of Broadway and 51 st Street in Manhattan. “Basie took his notebook out of his pocket to make notations of several appointments,” a reporter wrote. “No sooner had he started writing than one of New York’s finest grabbed his arm, demanded to see the notebook and informed the Count that he was under arrest. ‘You bookies are getting nervier every day,’ declared the bluecoat to the flabbergasted Basie. . . John Law examined the notebook and got the shock of his life when he saw that the man he was about to arrest as a bookie was none other than the famous Count Basie.”

The low-pressure Count reached his boiling point one afternoon in the late 1950s, when his bandmates were refused service in a small tavern in Gettysburg, Pennsylvania. “Basie walked up to the manager, looked at him coldly, and said, ‘You want us to go out in the street and drink it? You want us to get arrested for breaking the law?’” reported jazz writer Nat Hentoff. “The manager was shaken but stubborn, and the musicians decided to at least leave a memory behind. The biggest members of the band – The Killers’ – Eddie Jones, Billy Mitchell, Henry Coker – began to roam around the tavern in the manner of lions deciding just which part of their prey they’d savor first. Basie watch the scene, made no move to stop it, and in fact quite evidently enjoyed the morality play. The band wasn’t served, but at least it hadn’t slunk away.”

Like Duke and Satchmo, the Count generally let his music speak for him. But he made an exception to his say-as-little-as-possible rule in 1960, in the thick of the sit-in movement. Basie didn’t join the demonstrators, but he called their activism “beautiful” and said, “They’re starting a real move and I am 100 per cent for it.” Intimidating Black leaders into backing down was not just wrong but wouldn’t work, he argued: “They’re trying to knock us down but we get right up again.” As for Martin Luther King, “Like the cats would put it, he’s saying something.”

Adapted from the book THE JAZZMEN by Larry Tye. Copyright Ó 2024 by Larry Tye. Reprinted by permission of HarperCollins Publishers.

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Revolutions of the 1848 Essay

Introduction, the hypothetical debate, works cited.

The wave of revolutions that swept across Western Europe in 1848 brought what can be described as lasting reforms to the regimes that governed the affected territories; and even those that watched their neighbors’ revolts changed for fear of the same in their backyards. The genesis of these revolutions is attributed to a number of factors such as political, technological, and economic among others (Dowe 459).

New ideas and values were crafted and people clamored for liberty, nationalism, and socialism. Nobility was considered as the huddle to achieving these values. The working class that was the fuel for most revolutions had undergone radical transformation courtesy of technological changes.

Economic downturns had led to unprecedented hunger due to crop failure and the economic system, capitalism, made the urban poor poorer and the peasants, beggars at the feet of their masters. This is what made France to start the revolution in February 1848. In this paper, the writer discusses what would transpire in an imaginary debate where Karl Marx, Jean-Jacques Rousseau, and Jonathan Swift converse about these revolutions of 1848.

The Hegelian dialectic triad of thesis-antithesis-synthesis heavily influenced Karl Marx. Though Hegel used his theory to elucidate the influence of world history to the Spirit (Geist), Marx contextualized it and traced the origin of socio-economic order starting with serfs to proletariat and finally envisioning a classless society (Magstadt 65). In such a discussion, therefore, Marx would attribute capitalism as the cause of the revolutions.

Two years before the revolution, there had been an economic crisis in France caused by the bourgeoisies in Louis-Philippe’s regime. The opposition in the parliamentary session of 1847 representing the workers formed parties (banquets) to shield the poor workers against the occurrence of similar economic disasters.

It was the refusal of King Luis-Philippe to grant parties the permission for meeting that sparked off the revolution, which ended his monarchy and instituted the Second Republic (Horstman 72). Marx would argue that the exploitation of the proletariat by agents of capitalism, bourgeoisies, brought the antithesis – revolutions/class struggles – with a view of bringing a new social order, communism, for the benefit of all.

Jean-Jacques Rousseau would interject by bringing to the attention of his interlocutors the aspect of natural right that man had in the state of nature. He would argue that the revolutions experienced in most part of Western Europe were due to the degeneration of human society through the formation of a secular society. The division of labor and ownership of private property as distinctive features of the latter brought greater injustice and inequality to people (Scott 135).

Man, therefore, became susceptible to competition in almost all aspects of his life with fellow men while depending on them, at the same time, for his survival. Rousseau would say of the upheavals of 1848 in Europe because of competition that punctuated the life of man in a society having abandoned his primitive state of nature where survival was presupposed by compassion and cooperation.

Marx would quickly agree with Rousseau by substantiating his initial claims with the construct of social alienation. He would explain this by inviting his fellow debaters to look at how various states such as Germany, France, Britain, Denmark, Prussia, et cetera had laws that favored the rich (nobles) but harsh on the rest of the populace. Given that the political systems in most regions were led by monarchies, it was obvious that such regimes had to place the interests of their fellow aristocrats above those of commoners.

Therefore, when the economic meltdown swept the region due to crop failure and the infamous potato blight that affected northern Europe, the aristocratic regimes cushioned their own at the expense of other citizens. Marx would then link this situation to a case of blatant social alienation whose climax was the rapturous revolutions aimed at changing the oppressive political systems (Magstadt 68).

At this point, Jonathan Swift would take the floor to point out the reasons for the revolutions, without his characteristic satirical stance. Perhaps quoting from his renowned essay A Modest Proposal, he would attribute the situation to the culminating spirit of people’s will to power after having stomached all the social ills directed to them by those occupying the political offices.

He however, would term as illogical, the means used by the masses to wrest power from the malevolent leaders. The most important thing for him would be the necessity to solve the social and economic injustices that political systems were administering (Real 152).

Consequently, he would absolve the revolutionists of any wrongdoing given that they attempted to right the socio-economic structural wrongs for a better society. He would easily relate the oppressive socio-economic structures of 1848 in most countries to his time and ridicule the mockery projects by those in authority to establish equal superstructures.

In fact, he would openly express his loathe for such schemes and urge the revolutionists to continue pressing on for a change in both political and social systems that would begin by deposing political leaders of the affected countries. In the debate therefore, the three interlocutors would have a common ground as far as support for the revolutionists is concerned.

Reading through the arguments for each debater, one can clearly tell that they are in agreement with the cause of revolution as economic and political. Other factors such as social and technological are bred by the latter two. That is, they are subsets of both economic and political factors. Karl Marx brings in the concept of classless society as the greatest impetus for the revolution.

The drive was due to alienation in which the workers were given paltry pay despite the huge profit margins that they made to the capitalist who only lazed around. It is also political in the sense that the regimes that were affected by the revolution perpetrated capitalism and abandoned the masses at the mercy of bourgeoisies (Magstadt 66).

Rousseau’s argument is also leaning heavily toward economic and political reasons. The reason was economical in as far as the revolution was caused by frequent competition that pitted men against one another in the society after exiting the state of nature.

By leaving the later state and joining a society in which law and morality were codified to guide the division of labour and ownership of private property, it was clear that one had to struggle to get material wealth. To the extent that the sovereign made these laws, they tended to favor their interests but not that of the common person hence a political anomaly and the need to restructure them.

Swift also finds common ground in the discussion by underscoring the economic and political causes of the revolution. His observation that governments’ unfair economic structures that only favored a given clique of people is congruous with the economic reasons espoused by the previous debaters. Similarly, to the extent that political systems conjured up such structures, he forms a common ground with his colleagues. The skewed political systems the revolutionists; dared to restructure European nations.

The 1848 revolutions across European territories was the culmination of economic and socio-political injustices that the affected regimes administered on their people. The reason behind revolution was to restructure the political systems to cater for the needs of all people under their respective jurisdictions.

In the hypothetical discussion above, the debaters, Karl Marx, Jean-Jacques Rousseau, and Jonathan Swift have a common argument regarding the fundamental causes of the revolution and the aim of the revolutionists. These causes have been mentioned as economic and political, though the substantiation of each debater is unique. Therefore, it can be concluded that they collaborate on a common manifesto.

Dowe, Dieter. Europe in 1848: revolution and reform. New York: Berghahn Books, 2001.

Horstman, Allen. The Essentials of European History: 1789 to 1848, revolution and the new European order. Thousand Oaks, FL: Research & Education Association, 1996.

Magstadt, Thomas. Understanding Politics: Ideas, Institutions, and Issues. Chicago, ILL: Cengage, 2010.

Real, Hermann. Reading Swift: papers from the third Munster Symposium on Jonathan Swift. Bonn, German: Wilhelm Fink Verlag, 1998.

Scott, John. Jean-Jacques Rousseau: Human nature and history. New York, NY: Taylor & Francis, 2006.

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IvyPanda. (2018, September 13). Revolutions of the 1848. https://ivypanda.com/essays/revolutions-of-the-1848/

"Revolutions of the 1848." IvyPanda , 13 Sept. 2018, ivypanda.com/essays/revolutions-of-the-1848/.

IvyPanda . (2018) 'Revolutions of the 1848'. 13 September.

IvyPanda . 2018. "Revolutions of the 1848." September 13, 2018. https://ivypanda.com/essays/revolutions-of-the-1848/.

1. IvyPanda . "Revolutions of the 1848." September 13, 2018. https://ivypanda.com/essays/revolutions-of-the-1848/.

Bibliography

IvyPanda . "Revolutions of the 1848." September 13, 2018. https://ivypanda.com/essays/revolutions-of-the-1848/.

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Key Inventions that Transformed the Industrial Revolution

This essay about the Industrial Revolution explores how innovations in manufacturing, transportation, and communication revolutionized societies in the late 18th to early 19th centuries. Inventions like the steam engine, spinning jenny, cotton gin, Bessemer process, steam locomotive, and telegraph transformed production, transportation, and communication, shaping modern society. While these advancements brought economic growth and accessibility, they also posed challenges like harsh working conditions and environmental degradation, leading to labor reforms and social movements. Overall, the Industrial Revolution’s impact on production, transportation, and communication remains profound and continues to shape our world today.

How it works

The Industrial Revolution denoted a seismic transition in societal modes of production and consumption, heralding a paradigmatic shift in economies, urban landscapes, and daily existence. Spanning from the latter part of the 18th to the early 19th centuries, it witnessed an unparalleled surge of innovations that redefined manufacturing, transit, and correspondence. These groundbreaking developments laid the foundation for the contemporary industrialized globe, exerting enduring influence on contemporary living and labor dynamics.

Among the epoch’s most pivotal inventions was the steam engine, perfected by James Watt in the latter portion of the 18th century.

Watt’s enhancements to preceding designs, notably those of Thomas Newcomen, endowed the steam engine with unprecedented efficiency, ameliorating fuel consumption and augmenting power yield. This innovation facilitated the utilization of reliable mechanical energy to power factories, liberating production from conventional sources like watermills. Moreover, steam-powered locomotion via trains and ships revolutionized transit, compressing spatial distances and facilitating swift movement of goods and individuals.

In the realm of textiles, machines such as the spinning jenny, water frame, and power loom catalyzed cloth production expansion. James Hargreaves’ spinning jenny, for instance, enabled a singular operative to concurrently spin multiple thread spools, vastly enhancing output. Richard Arkwright’s water frame leveraged hydrokinetic energy to automate spinning, while Edmund Cartwright’s power loom mechanized weaving, accelerating the process manifold. These innovations metamorphosed textile manufacturing from a cottage industry into a mechanized, factory-centered operation, rendering textiles more affordable and ubiquitous.

Eli Whitney’s 1794-patented cotton gin also wielded profound influence. By expeditiously segregating cotton fibers from seeds, the cotton gin streamlined cotton processing, fueling southern United States’ cotton industry ascension to global textile market primacy. However, it concurrently entrenched slavery in the American South by augmenting demand for inexpensive labor to cultivate lucrative cotton plantations.

In metallurgy, Henry Bessemer devised a transformative method for steel mass production, dubbed the Bessemer process. By aerating molten iron to expel impurities, this technique facilitated large-scale, cost-effective high-quality steel production. This breakthrough proved indispensable for infrastructure construction, including bridges, railways, and skyscrapers, further propelling industrial expansion.

Transportation underwent radical transformation via inventions like George Stephenson’s steam locomotive, which inaugurated Britain’s initial railway network, and Robert Fulton’s steamboat, which streamlined river travel and commerce. These advancements not only expedited goods and raw materials conveyance but also democratized travel, fostering market expansion, economic opportunity proliferation, and cultural exchange.

In tandem with manufacturing and transit, the Industrial Revolution fostered substantial communication progress. Samuel Morse’s 1844-patented telegraph facilitated rapid long-distance message transmission via electrical signals and Morse code. This innovation revolutionized communication, enabling near-instantaneous information dissemination across continents, particularly advantageous for commerce, governance, and journalism.

The Industrial Revolution, with its panoply of inventions, irrevocably altered human existence and labor modalities. It precipitated agrarian to industrial economies transition, urbanization promotion, and emergence of an industrial labor class. Nonetheless, it posed challenges such as onerous labor conditions, juvenile labor exploitation, and environmental degradation, precipitating labor reforms and social activism.

In summation, the Industrial Revolution’s seminal inventions reshaped production, transit, and communication, perpetuating their influence on contemporary global societal frameworks. The steam engine, textile machinery, cotton gin, Bessemer process, steam locomotive, and telegraph emerge as trailblazing innovations instrumental in erecting modern society’s bedrock, attesting to the era’s enduring technological progress legacy.

Cite this page

Key Inventions That Transformed the Industrial Revolution. (2024, May 12). Retrieved from https://papersowl.com/examples/key-inventions-that-transformed-the-industrial-revolution/

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PapersOwl.com. (2024). Key Inventions That Transformed the Industrial Revolution . [Online]. Available at: https://papersowl.com/examples/key-inventions-that-transformed-the-industrial-revolution/ [Accessed: 15-May-2024]

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Home Blog BI Students Win Competition with Essay on AI Revolution

BI Students Win Competition with Essay on AI Revolution

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MSc students Philipp Athanasiadis and Johannes Javorsky came out on top with their essay discussing who really stands to gain from the rapid rise of generative AI. 

The two are this year’s winners of BI’s Opinion Essay Competition, which is organised as part of the master course Ethics and Sustainability in Organizations each semester. 

“We feel very honored to have won this year’s opinion essay competition and are happy to see that critical thinking is valued and encouraged,” say Philipp and Johannes. 

In their essay, the two students question who really will benefit from the generative AI revolution and the possible extreme boost in productivity it is expected to bring. 

Addressing AI responsibly

“We deeply believe that generative AI is an increasingly disruptive technology that will certainly change our future. How we deal with it will define whether the changes will be for the better or the worse. In our opinion, addressing generative AI responsibly is one of the most important challenges of today.”

The winners of the competition receive a prize of NOK 10,000 in addition to having their essay  “Generative AI: A bright future ahead of us – but for whom?”  published as a featured article on BI Business Review. 

Leaders of tomorrow

Professor Caroline Dale Ditlev-Simonsen is responsible for the competition. She describes the essay competition as a great alternative to just tasking students with writing a regular assignment. 

“This competition demands students to apply what they have learned, critically reflect, and take a personal stand on how the world of business approaches sustainability. Initiatives like this aim to support and enhance students’ skills and engagement when it comes to sustainable development and corporate sustainability. These are all highly important things to learn for a group of people that represent the leaders of tomorrow,” says Ditlev-Simonsen.

This year’s jury consisted of Karen Spens (President of BI), Abhimanyu Manimaran (Director Strategy and Partnerships, UN Global Compact Norway), Linn Dybdahl (Senior Adviser, NMBU) and Pål Nygaard (Associate Professor, BI). 

Learning from Ricardo and Thompson: Machinery and Labor in the Early Industrial Revolution, and in the Age of AI

David Ricardo initially believed machinery would help workers but revised his opinion, likely based on the impact of automation in the textile industry. Despite cotton textiles becoming one of the largest sectors in the British economy, real wages for cotton weavers did not rise for decades. As E.P. Thompson emphasized, automation forced workers into unhealthy factories with close surveillance and little autonomy. Automation can increase wages, but only when accompanied by new tasks that raise the marginal productivity of labor and/or when there is sufficient additional hiring in complementary sectors. Wages are unlikely to rise when workers cannot push for their share of productivity growth. Today, artificial intelligence may boost average productivity, but it also may replace many workers while degrading job quality for those who remain employed. As in Ricardo’s time, the impact of automation on workers today is more complex than an automatic linkage from higher productivity to better wages.

The authors are co-directors of the MIT Shaping the Future of Work Initiative, which was established through a generous gift from the Hewlett Foundation. Relevant disclosures are available at shapingwork.mit.edu/power-and-progress, under “Policy Summary.” For their outstanding work, we thank Gavin Alcott (research and drafting), Julia Regier (editing), and Hilary McClellen (fact-checking). We also thank Joel Mokyr for his helpful comments. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

We are grateful to David Autor for useful comments. We gratefully acknowledge financial support from Toulouse Network on Information Technology, Google, Microsoft, IBM, the Sloan Foundation and the Smith Richardson Foundation.

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Google helped make an exquisitely detailed map of a tiny piece of the human brain

A small brain sample was sliced into 5,000 pieces, and machine learning helped stitch it back together.

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A team led by scientists from Harvard and Google has created a 3D, nanoscale-resolution map of a single cubic millimeter of the human brain. Although the map covers just a fraction of the organ—a whole brain is a million times larger—that piece contains roughly 57,000 cells, about 230 millimeters of blood vessels, and nearly 150 million synapses. It is currently the highest-resolution picture of the human brain ever created.

To make a map this finely detailed, the team had to cut the tissue sample into 5,000 slices and scan them with a high-speed electron microscope. Then they used a machine-learning model to help electronically stitch the slices back together and label the features. The raw data set alone took up 1.4 petabytes. “It’s probably the most computer-intensive work in all of neuroscience,” says Michael Hawrylycz, a computational neuroscientist at the Allen Institute for Brain Science, who was not involved in the research. “There is a Herculean amount of work involved.”

Many other brain atlases exist, but most provide much lower-resolution data. At the nanoscale, researchers can trace the brain’s wiring one neuron at a time to the synapses, the places where they connect. “To really understand how the human brain works, how it processes information, how it stores memories, we will ultimately need a map that’s at that resolution,” says Viren Jain, a senior research scientist at Google and coauthor on the paper, published in Science on May 9 . The data set itself and a preprint version of this paper were released in 2021 .

Brain atlases come in many forms. Some reveal how the cells are organized. Others cover gene expression. This one focuses on connections between cells, a field called “connectomics.” The outermost layer of the brain contains roughly 16 billion neurons that link up with each other to form trillions of connections. A single neuron might receive information from hundreds or even thousands of other neurons and send information to a similar number. That makes tracing these connections an exceedingly complex task, even in just a small piece of the brain..  

To create this map, the team faced a number of hurdles. The first problem was finding a sample of brain tissue. The brain deteriorates quickly after death, so cadaver tissue doesn’t work. Instead, the team used a piece of tissue removed from a woman with epilepsy during brain surgery that was meant to help control her seizures.

Once the researchers had the sample, they had to carefully preserve it in resin so that it could be cut into slices, each about a thousandth the thickness of a human hair. Then they imaged the sections using a high-speed electron microscope designed specifically for this project. 

Next came the computational challenge. “You have all of these wires traversing everywhere in three dimensions, making all kinds of different connections,” Jain says. The team at Google used a machine-learning model to stitch the slices back together, align each one with the next, color-code the wiring, and find the connections. This is harder than it might seem. “If you make a single mistake, then all of the connections attached to that wire are now incorrect,” Jain says. 

“The ability to get this deep a reconstruction of any human brain sample is an important advance,” says Seth Ament, a neuroscientist at the University of Maryland. The map is “the closest to the  ground truth that we can get right now.” But he also cautions that it’s a single brain specimen taken from a single individual. 

The map, which is freely available at a web platform called Neuroglancer , is meant to be a resource other researchers can use to make their own discoveries. “Now anybody who’s interested in studying the human cortex in this level of detail can go into the data themselves. They can proofread certain structures to make sure everything is correct, and then publish their own findings,” Jain says. (The preprint has already been cited at least 136 times .) 

The team has already identified some surprises. For example, some of the long tendrils that carry signals from one neuron to the next formed “whorls,” spots where they twirled around themselves. Axons typically form a single synapse to transmit information to the next cell. The team identified single axons that formed repeated connections—in some cases, 50 separate synapses. Why that might be isn’t yet clear, but the strong bonds could help facilitate very quick or strong reactions to certain stimuli, Jain says. “It’s a very simple finding about the organization of the human cortex,” he says. But “we didn’t know this before because we didn’t have maps at this resolution.”

The data set was full of surprises, says Jeff Lichtman, a neuroscientist at Harvard University who helped lead the research. “There were just so many things in it that were incompatible with what you would read in a textbook.” The researchers may not have explanations for what they’re seeing, but they have plenty of new questions: “That’s the way science moves forward.” 

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