essay on rational choice theory

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Rational Choice Theory Essay

Rational choice theory postulates that individuals make decisions that they think will best advance their self interest, even though this is not usually the case. It is based on the premise that human being is a rational being, and freely chooses their behavior, both conforming and deviant, rationally. That is, to make a decision, it involves a cost benefit analysis. It is an approach that is widely employed by social scientists to understand the human behavior, based on the effect of incentives and constraints on the human behavior. This approach was widely originally a reserve of economics but it has found general acceptance in other disciplines. This paper examines critically the rational choice theory, and its relationship to the situational crime prevention. It traces the history of the theory, and applies the theory to a contemporary problem, the cultivation of marijuana, to explain how cultivation sites are chosen. The paper finally makes recommendations how the theory can be employed to reduce outdoor marijuana cultivation.

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In criminology, it is employed to explain the criminal behavior. It assumes that the state is responsible for the maintenance of order and for preserving the common good through legislation. The laws controls human behavior through swiftness, severity and certainty of punishments (Phillips, 2011,7).The theory consists of 3 core elements: a reasoning criminal, crime specific focus and separate analysis of criminal involvement and criminal event (Phillips, 2011, 4). The reasoning criminal element postulates that criminals commit crimes in order to benefit themselves. The element proposes that criminals have specific goals and alternative ways to achieve these goals. In addition, they hold information that assists them in choosing the best alternative to implement their goals.

The element on crime specific focus, assumes that decision making differs with the nature of crime, that is, decision making is different for each crime. For instance, the decision making to commit a robbery differs with the decision making to commit burglaries, while the decision making by a burglar to target wealthy neighborhood, differs with the one to target middle class and public housing.

The last element addresses three issues: deciding to get involved in a crime, continuing to get involved once one has decided to get involved, and the decision to withdraw from the commission of the crime. On the other hand, criminal event implies the decision to get involved with a specific crime.

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Crime prevention is critical, since it is too expensive to wait until crimes have been committed in order to act. Traditionally, criminologists approach crime prevention through identification of the social and psychological and social causes of crime and treating the offenders as a remedy to the deficiencies, examples through correctional measures. An alternative to the above is situational crime prevention that is based on the rational choice theory. It based on the assumption that criminals will proceed to commit crimes where the benefits outweigh the risks and costs involved and whereby the opportunity to commit a crime exists. Therefore, situational crime prevention aims to make the costs of a crime outweigh the benefits derived, and eliminate the opportunity to c omit that crime.

This theory has deep roots in economic, and has made important inroads in other spheres. Rational choice theory first emerged in the mid-eighteenth century, and was first referred to as classical theory. It was developed by the classical school of criminology, through the works of Cesare Beccaria and Jeremy Bentham, in their response to primitive and cruel justice system that existed prior to the advent of the French revolution.

The modern theory stems from the age of reason. It is classical origins is captured in Leviathan (1651) by Thomas Hobbes, where he tried to explain the role of individuals choices in functioning of political institutions. His efforts were developed further by other scholars such as Adam Smith, David Hume and utilitarians such as Jeremy BenthamThe rational choice theory was developed by Derek Cornish and Ronald Clarke.

In relation to outdoor marijuana cultivation, rational choice theory is conspicuous. The predicaments that face outdoor cannabis growers are the same one that faces other criminals. Therefore, they employ the rational choice theory like other criminals in examining whether to grow cannabis and if yes, where. The growers are rational beings, driven by rational theory’s key tenets. For instance, the growers choose locations that have the greatest potential for a greater reward, pursuant to the rational choice theory of maximum greatest benefit.

To begin with, the choice of outdoor cultivation is informed by the fact that it has a lower cost of production. Consequently, they choose locations whereby they maximize their rewards, while minimizing their efforts. While doing this, they consider the risk that the site will be detected by the law enforcement officers. Therefore, they have to choose a site that will not only give the maximum yield, but also one that cannot be easily detected.

To add, the rational choice theory applies where the grower decides the number of plants to grow in the selected site. Here also, the grower has to weigh the benefit derived by an extra plant grown versus the increased risk of detection by the law enforcement officers. Growers gamble with the number, but only to a certain extent, and the location of the site. Therefore, the numbers are likely to be high in prime sites where the risk of detection is low. To add, the finding of a research by Bouchard et al (2011,16) concludes that even in areas where aerial detection is less common, even if the risk of detection is perceived as nontrivial, offenders are willing to take a chance in the event of a successful outcome.

Moreover, growers have several sites among which to choose from. Like other rational beings, they also possess information, which helps them with choosing among the alternatives. For instance, they know areas where the yields are likely to be high, that is, areas whose topography and climate, suits the growth of marijuana, and areas that are prone to detection by law enforcement agencies. On the other hand, law enforcers also employ the theory to increase the detection of outdoor growers and, therefore, curb growth of cannabis while at the same time reducing the benefits and increasing the costs in order to deter growers.

Rational choice theory explains to a certain extent why growers choose certain sites to grow cannabis. The site must be weighed against other competing interests in order to arrive at the site that gives the maximum yield at the least cost. To begin with, a rational grower gathers information about possible sites and chooses the best alternative based on the information that is available.

According to Bouchard et al (2011,23), such information involves the distance from the road, distance from sources of water, nature of the terrain and security of the region generally. For instance, with regards to proximity to the nearest road, the longer the site is from the road, the greater the effort required to set up the site and to take care of the plants, while the closer the sites to the road, the greater the risks of detection, by both the law enforcers as well as thieves. To add, with regards to the accessibility of the areas, the growers have to consider the easiness of access and exit from the area.

Moreover, the growers have to rationally decide the number of cannabis plants to grow in a certain site. While it would be more profitable to grow the maximum yield, rational choice theory requires that they should balance the number of plants, with the desire to hide the site from the law enforcement officers.

Situational crime prevention techniques can be employed in a number of ways to curb outdoor growth of marijuana, through increasing the risk, reducing the reward, increasing the effort of the growers, and reducing the provocation that is associated with specific criminal events. Risk can be increased through increasing and coordinating the anti-marijuana growth operations efforts across police agencies. This would increase the level of detection, and, therefore, the growers will materially decrease production of marijuana, for example by growing lesser plants to reduce the risk of detection.

With regards to decreasing the rewards, all international law enforcement stakeholders should work hand in hand to reduce the opportunities for production, trafficking and sale of marijuana. This shall affect the market of marijuana, while making it more difficult to produce and traffic marijuana, and, therefore, reduce the rewards while increasing the costs. Finally, the penalties for production and possession of marijuana should, to a large extent, be made severe in order to deter the users, producers and traffickers.

In conclusion, therefore, rational choice theory has wide applicability in criminology, especially in situational crime prevention. It explains why the criminals act the way they do, and consequently, the law enforcers can employ the theory to detect and prevent the crime before it happens.

Bouchard, M. Beauregard, E. and Kalacska, M. (2011). Journey to grow: Linking process to

outcome in target site selection for cannabis cultivation. Journal of Research in Crime and Delinquency.

Clarke, R. V. (2010). Situational crime prevention. In Bradley R. E. Wright and Ralph B.

McNeal Jr. (eds.), Boundaries: Readings in deviance, crime and criminal justice. Boston:Pearson Custom Publishing.

Sacco, V. F. and Kennedy, L. W. (2011). The criminal event: An introduction to criminology in Canada 5th ed. Toronto: Nelson Education, pp. 128-131, 195-198, and 370-375.

Phillips, C.(2011). Situational crime Prevention and crime Displacement: Myths and miracles? Internet Journal of Criminology.

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Rational Choice Theory: Paul Bernardo Essay

Introduction, rational choice theory, media coverage.

There are multiple theories in criminology that aim to explain the unlawful actions of criminals. The rational choice theory is based on the premise or rationalization of benefits versus cost, which ultimately influences the decision-making process. Thus, the outcomes are direct results of rational choices in which the criminals pick the option that is most rewarding and less harmful as perceived by them. Based on the case of the Canadian serial killer Paul Bernardo, this paper will argue that the rational choice theory can explain why the crimes were committed. Moreover, media coverage will be discussed in regards to the portrayal of the case and the offenders.

In order for the rationality of the crimes to be identified and examined, it is important to provide an overview of the violent offenses that made Paul Bernardo one of the most infamous serial killers in Canada. Bernardo, alongside his wife, Karla Homolka, sexually assaulted 18 women and killed 3 (Wickenheiser, 2021). The assaults, rapes, and murders are often attributed to Bernardo’s psychopathy, yet a certain pattern can be determined, which suggests that the crimes were rationalized and correlate with the rational choice theory.

The rational choice theory in criminology illustrates that individuals commit offenses based on the weighing in of means and ends. Thus, the action that is being taken is one that is perceived as having the highest utility for the perpetrator (Paternoster et al., 2017). In order for the implications of the framework in Paul Bernardo’s case to be identified, it is essential to examine what factors the individual views as rewarding and which ones are perceived as punishable, hence, negative. In terms of the costs, a prison sentence is the main factor that can stop a person from committing a crime. However, since Bernardo sexually assaulted and murdered several women, it is certain that the benefits were perceived as more crucial than the potential arrest. In this case, the explanation suggests that Bernardo’s aim was to receive satisfaction from the crimes that were committed while rationalizing the options for the minimization of possible costs. As a result, the serial killer managed to rationalize the murders and rapes by ensuring low danger and negative consequences.

One example of how rational choice theory can be applied is the lack of involvement of the police. According to researchers, Bernardo’s case is an illustration of uncoordinated and ineffective police work, especially since the killer’s DNA sample was collected but unchecked years before he was finally captured (McKenna, 2018). Thus, Bernardo’s rational idea of avoiding police became more relevant when even after the law enforcement had his DNA, the criminal was not punished or caught. As a result, rationality suggested the danger to be unsubstantiated due to the lack of involvement from the police officers. As mentioned prior, the serial killer operated based on the rational choice theory, and in this case, the punishment of being incarcerated became redundant since no follow-up procedures were performed to match the sample to the criminal. As a result, a logical chain of events that followed was a continuation of crimes, including rapes and murder, since no negative outcome was associated with the deeds.

Another explanation of the rational choice theory being applied to the case of Paul Bernardo is the pattern of the crimes. First, it is important to mention that the killing of his wife’s sister, Tammy Homolka, was framed as an accident (McAleese, 2019). Thus, both Bernardo and Karla Homolka were aware of the consequences and tried to frame the murder in a way that they would not be affected. This illustrates how the serial killer was rational in terms of understanding the repercussions and actively trying to avoid them. This, indeed, worked since both Homolka and Bernardo were captured years later. The killers were making sure the victims would be blindfolded and unable to identify them, which also portrays the picture of rational thinking and an effort to avoid prison. These actions fit into the paradigm of rationality in decision-making processes since all the actions performed with the intent to hide evidence suggests a sense of rationality was present.

It is also essential to mention that Bernardo was a serial rapist, and the murders were less frequent. This may highlight that the intent of the criminal was sexual gratification, and the murders were illustrations of the intent to remain unidentified or a solution to the situations that escalated from the standard behavior of the killer. According to researchers, individuals are more likely to commit crimes that are intrinsically rewarding but less risky (Thomas et al., 2020). This correlates with the criminal’s pattern of sexual assault and violence, which prevailed compared to the number of victims who were murdered.

Firstly, such crimes are punished less harshly or remain unaddressed. Due to the fact that victims are often apprehensive in regards to going to the police after being raped, the criminal may have rationalized that such behavior is less likely to lead to Bernardo receiving a prison sentence. Secondly, rape was present in most of the couple’s crimes, while killing was less frequent. This highlights that sexual gratification was the main driving factor that allowed Bernardo to rationalize that giving in to his desires is more rewarding than avoiding the violence altogether. As a result, rational choice, in this case, does not imply that the criminal does not view incarceration as a severe repercussion. Instead, the sexual assault, which gives the serial killer fulfillment and satisfaction, is worth being carried out despite the punishment that can follow.

Based on the premises of the rational choice theory, the person committing the crimes is mindful of the actions, yet in their value systems, the satisfaction or benefits outweigh the negative implications. In the case of Paul Bernardo and his wife, Karla Homolka, the deviances created the circumstances in which the sexual assaults and murders were justifiable despite understanding the repercussions of being caught. The theory does not minimize urges and emotions altogether since it is clear that Bernardo was driven by harmful desire. However, the actions themselves are results of rational thinking and are elevated by the understanding that the police are not particularly effective. Moreover, as mentioned prior, the fact that not all assaults have resulted in murders and some victims were blindfolded highlights premeditation. This is directly linked to rationality and the complete understanding that a victim identifying the criminal will lead to a prison sentence.

Needless to say, there were multiple Canadian serial killers who were widely discussed in the media and became infamous for the crimes they have committed. However, Bernardo and Homolka were the murderers that have been in international news for the atrocities discovered during the court case and the circumstances of the murders and rapes. First, only US media was allowed to cover the cases, yet eventually, both Bernardo and Homolka became figures of evil as portrayed in Canadian media. There were several reasons why the two serial killers became so infamous and often mentioned in the news, TV shows, and newspapers. First, as mentioned prior, Homolka’s sister was one of the victims who were murdered, which emphasized the atrocities performed by the couple. While initially, such actions may not appear to correlate with the rational choice theory since the victim and the killers were in close relationships, the murder was framed to look like an accident, which the media also published. Thus, the sensational news that a serial killer and his wife killed her sister after raping her became a story that was widely discussed in the press.

Another aspect that has drawn even more media attention was the presence of explicit videos in which the couple assaulted, raped, and killed girls. Videos were shown in court, which the press found out about and highlighted in reports (Regehr et al., 2022). The existence of video evidence not only proves the guaranteed guilt of the criminals but also creates more publicity for the situation due to the public’s interest. Performing the crime is perceived as evil in itself, yet the pragmatic and rational idea of documenting it on film allowed media to create an even more negative image of both Bernardo and Homolka.

Last but not least, the public was interested in the story due to the incompetence of the police and the understanding that the criminals would have been caught much earlier. It is certain that most people want to feel safe and protected by law enforcement. However, the way police officers handled the situation and did not prevent future atrocities showed the public the flaws within the system. The media was not only successful in making the crimes public but also focused on showcasing the reasons why the atrocities have not been addressed. In this case, the police would be able to detain Bernardo and his wife after thoughtfully investigating the death of Homolka’s minor sister, checking the DNA sample provided by the serial killer, or paying close attention to the victims who described the rapist. Instead, Bernardo managed to avoid incarceration, resulting in multiple more rapes and murders to take place before, finally, the atrocities were linked to him. Thus, the general opinion formed after the media’s extensive portrayal of the case was focused both on the crimes and the lack of involvement of the police officers.

The infamous Canadian serial killer Paul Bernardo has committed multiple violent crimes, yet rational choice can explain the course of events that have led to such outcomes. Based on the premises of this particular theory, the criminal was aware of his actions and decided to commit the atrocities because Bernardo’s value system allowed him to justify the satisfaction. The killer’s actions, while seemingly passion-driven, have been rationalized, which can be explained by the prevalence of rapes over murders and the efforts to avoid police encounters when covering tracks and framing the murders to look like accidents. The media portrayal of the case has been extensive, and there are also several reasons that explain such coverage. Since one of the killings involved Bernardo’s wife’s sister, there were videotapes, and police did not respond accordingly; the public was interested in the case. Thus, rational choice theory explains the crimes committed by Bernardo, and the public was drawn to the publicity aspect of the court hearings due to the multiple circumstances portraying Bernardo’s situation as a particularly atrocious one.

McAleese, S. (2019). Suspension, not expungement: Rationalizing misguided policy decisions around cannabis amnesty in Canada. Canadian Public Administration , 62 (4), 612–633. Web.

McKenna, P. F. (2018). Evidence-based policing in Canada. Canadian Public Administration , 61 (1). Web.

Paternoster, R., Jaynes, C. M., & Wilson, T. (2017). Rational choice theory and interest in the “Fortune of others.” Journal of Research in Crime and Delinquency , 54 (6), 847–868. Web.

Regehr, C., Regehr, K., & Birze, A. (2022). Traumatic residue, mediated remembering and video evidence of sexual violence: A case study. International Journal of Law and Psychiatry , 81 , 101778. Web.

Thomas, K. J., Loughran, T. A., & Hamilton, B. C. (2020). Perceived arrest risk, psychic rewards, and offense specialization: A partial test of rational choice theory. Criminology , 58 (3), 485–509. Web.

Wickenheiser, R. A. (2021). Reimagining forensic science – the mission of the forensic laboratory. Forensic Science International: Synergy , 3 , 100153. Web.

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Rational Choice Theory in Sociology (Examples & Criticism)

Rational choice theory is a theory that is used to explain and understand the reasoning behind human behavior. The underlying assumption is that human beings are rational creatures, which means they rely on reason and logic to make decisions.

The theory can be used to understand both individual and social groups ’ behavior in various disciplines including economics, sociology and politics.

The commonly cited definition of the rational choice theory is that by Elster (1989), who submitted that:

“…when faced with several courses of action, people usually do what they believe is likely to have the best overall outcome.”

In other words, human beings are rational actors who act in their own self-interest. An individual tends to act as if balancing costs against benefits to arrive at action that maximizes personal advantage.

This definition points to the widely accepted assumption in psychology that human beings are naturally egocentric. The assumption here is that self-preservation and wellbeing are the driving forces behind every individual’s actions.

The Four Assumptions of Rational Choice Theory

The theory makes several assumptions about human behavior that are often disputed. These include:

Decisions are a simple matter of cost vs reward
People take action when benefits outweigh costs
People will take no action, stop taking action, or take opposite action, when costs outweigh benefits
People utilize the resources they have at hand to maximize opportunity and tip the scales of a decision in their favor

Strengths of Rational Choice Theory

Some of the key strengths of the rational choice theory include:

1. It effectively explains individual behavior

Becker (1976) argued that the rational choice theory model is a unified framework for understanding all human behavior.

The rational choice theory can be used to explain why people behave the way they do. It gives an insight into the motivating factors or psychology behind human decision making. Self-interests are the central factor in explaining human behavior

2. It predicts human behavior

Once the knowledge of why individuals behave in a certain way is available, predictions can then be made.

Because almost every decision-making process is a cost-benefit analysis , actions that result in the most utility maximization can be predicted or anticipated.

In economics, for instance, the rational choice theory can be used to predict customer purchasing patterns and trends.

3. It explains and predicts social group behavior

Rational choice theory can be used to explain the behavior of a social group of like-minded individuals.

Individuals with similar preferences are likely to form a group. The shared interests or common values influence their behavior as a group and can be used to predict future behavior.

Examples of groups that share common interests and values include religious groups, political groups and other social groups.

Criticisms of Rational Choice Theory

Rational choice theory has been criticized for the following reasons.

1. It does not account for altruistic or selfless actions

Rational choice theory is based on the assumption that there is a direct or indirect relationship between an individual action and a benefit.

The theory assumes that all human decisions are driven by self-interest but this is not always the case.

There are actions which are selfless or altruistic in nature and the rational choice theory does not convincingly take these into account.

2. It does not explain impulsive actions

Another weakness of the rational choice theory is that it assumes a careful thought process or cost-benefit analysis whenever an individual has to make a decision.

However, this is not always the case. There are some decisions which are made “impulsively” or without consideration of the consequences.

Not all decisions are rational hence the difference between a good decision and a bad decision.

Intuitive decisions can also be made without much time to weigh the cost and benefits. The theory assumes that there is always enough time to carefully consider the costs and benefits of a decision thereby disregarding impromptu and emotional decisions.

3. When team goals supersede individual goals

Due to its strong emphasis on individual decisions, the rational choice theory ignores the influence of collective groups.

It assumes that all decisions begin and end at individual level. Studies have shown that not all decisions are self-serving as individuals may, at times, succumb to social pressure even at the expense of their own benefit.

Therefore, the rational choice theory can be criticized for failing to take into account societal factors like cultural values and norms which may have a significant influence on the decisions of an individual.

Rational Choice Theory Examples

1. political scenario: which political party or candidate to vote for.

When choosing who to vote for, voters will prefer the party that will be of most benefit to them as an individual.

For instance, a wealthy person is more likely to vote for a party that wants to lower taxes on the rich. This person is in turn less likely to vote for socialist parties who tend to favor higher taxes.

Similarly, poorer people are more likely to endorse higher taxes on the rich because it doesn’t affect them, but may lead to better social services.

We can see, in terms of social trends, that wealthier people vote in higher numbers for right-of-center parties and poorer people vote in higher numbers for left-of-center parties.

Nevertheless, this rule does not hold up for all individuals. Some people may vote against their immediate personal interests if they place more value on social cohesion , freedom, or another value. In these situations, a rational choice theorist would still say that the person is making the rational choice – it’s just that they are weighing up costs and benefits in a different way to what we might have expected.

2. Personal scenario: Healthy diet or instant gratification?

Most people have become conscious of the importance of a healthy lifestyle and diet. This is why the fitness industry is fast becoming a lucrative one.

Those who can afford to choose when and what they eat often have to decide between maintaining a healthy diet or instantly gratify themselves albeit with excessive unhealthy foods.

In most cases, individuals prefer the former as it tends to have long term health benefits for individuals. The potential personal benefits of healthy eating influence the rational option to maintain a healthy diet.

3. Social scenario: Visit family or enjoy vacation alone?

Another example is that of an individual deciding between spending time with their family or enjoy a vacation alone.

Human beings are naturally social beings which means the need to belong to a group is one that cannot be ignored easily. This might mean overriding individualistic desires to satisfy the social need to belong to a group.

The benefits of enjoying a vacation alone may be outweighed by the benefits of belonging to and participating in a social group hence spending time with family might be the rational choice.

4. Economic scenario: Save for retirement or donate all income?

Consider an individual choosing between saving some money for retirement or donating all of his income without any consideration of his future wellbeing.

Conventionally, saving money would be a favorable choice as it guarantees future financial security for the person.

While donating might give a degree of moral fulfillment as a potential benefit, that benefit would not necessarily outweigh the economic benefit of self-preservation. The rational choice, therefore, would be to save some money to guarantee oneself’s future wellbeing.

Theoretical Applications

The theory can be applied in a range of fields. Some examples include:

Marketing: Companies put in place limited-time offers to tip the scales of cost vs benefit and encourage consumers to pull out their money.
Economics and political science: Governments instate policies designed to tip the scales of cost vs benefit on a national scale. For example, subsidies for electric cars can help encourage consumers to buy electric vehicles and stimulate the green economy.
Sociology and criminology: The theory helps to describe deviant behavior and why people choose to engage in antisocial behaviors.
International relations: The theory underpins approaches to international relations, including Germany’s (now failed) approach to integrating Russian and German economies to make a Russian war in Europe a bad choice for Russians whose economy would tank. It also underpins the concept of mutually assured destruction in nuclear policy.

Rational choice theory is a theory that explains human decisions. This theory tries to describe the self-serving nature of individual decisions. It borrows from the general principle that human beings are naturally selfish and will therefore always choose an option that costs less and benefits them more.

Rational choice theory can be utilized to explain and predict human behavior in various disciplines including economics, sociology and politics as illustrated in the examples section.

The main strength of the theory lies in its ability to explain and predict both individual and social groups’ behavior. However, it often fails in cases where individuals do not have adequate information or time and opportunity to weigh the cost and benefits before making decisions.

Abell, P. (2000). Putting Social Theory Right? Sociological Theory, 18(3), 518–523.

Becker, G. (1976); The Economic Approach to Human Behavior , Chicago and London: The University of Chicago Press, pp. 3-14.

Elster, J. (1989); Social Norms and Economic Theory , Journal of Economic Perspectives, American Economic Association, vol. 3(4), pages 99-117.

Friedman, M. (1953); Essays in Positive Economics , Chicago: University of Chicago Press. pp. 15, 22, 31.

Hechter, M. (2019). The future of rational choice theory and its relationships to quantitative macro-sociological research. In Rational Choice Theory and Large-Scale Data Analysis (pp. 281-290). Routledge.

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Introduction to Rational Choice Theory in Social Work

Rational choice theory in social work is an important concept because it helps explain how individuals make decisions. According to the definition of rational choice theory , every choice that is made is completed by first considering the costs, risks and benefits of making that decision. Choices that seem irrational to one person may make perfect sense to another based on the individual’s desires.

Those who are studying for a social work degree will learn a variety of evidence-based theories to help them inform their work. Learning and understanding the meaning behind rational choice theory and seeing rational choice theory examples help future social workers characterize, explain and anticipate social outcomes. That can improve the treatment and services they provide their clients.

What is Rational Choice Theory?

Rational choice theory can apply to a variety of areas, including economics, psychology and philosophy. This theory states that individuals use their self-interests to make choices that will provide them with the greatest benefit. People weigh their options and make the choice they think will serve them best.

How individuals decide what will serve them best is dependent on personal preferences. For example, one individual may decide that abstaining from smoking is best for them because they want to protect their health. Another individual will decide they want to smoke because it relieves their stress. Although the choices are opposite, both individuals make these choices to get the best result for themselves.

Rational choice theory conflicts with some other theories in social work . For example, psychodynamic theory states that humans seek gratification due to unconscious processes. Conversely, rational choice theory states that there is always a rational justification for behaviors. Individuals try to maximize their rewards because they’re worth the cost.

History of rational choice theory

Rational choice theory origins date back centuries. Philosopher Adam Smith is considered the originator of rational choice theory . His essay “An Inquiry into the Nature and Causes of the Wealth of Nations,” from 1776, proposed human nature’s tendency toward self-interest resulted in prosperity. Smith’s term “the invisible hand” referred to unseen forces driving the free market.

Smith used the work of philosopher Thomas Hobbes’ “Leviathan” (1651) to influence his own work. In “Leviathan,” Hobbes explained that political institution functioning was a result of individual choices. Philosopher Niccolò Machiavelli, who wrote “The Prince” in 1513, also introduced ideas related to rational choice theory in his treatise.

Moving from economics to the social sciences, in the 1950s and 1960s, sociologists George C. Homans, Peter Blau and James Coleman promoted rational choice theory in relation to social exchange. These social theorists stated that a rational calculation of an exchange of costs and rewards drives social behavior.

Rational choice theory in social interactions explains why people enter into or end individual and group relationships.

Assumptions of rational choice theory

In order to fit the criteria for rational choice theory, the following assumptions are made.

All actions are rational and are made due to considering costs and rewards.
The reward of a relationship or action must outweigh the cost for the action to be completed.
When the value of the reward diminishes below the value of the costs incurred, the person will stop the action or end the relationship.
Individuals will use the resources at their disposal to optimize their rewards.

Rational choice theory expresses that individuals are in control of their decisions. They don’t make choices because of unconscious drives, tradition or environmental influences. They use rational considerations to weigh consequences and potential benefits.

Applications of rational choice theory

Rational choice theory has a wide variety of applications in all types of spheres affecting human populations.

Economics and business: Rational choice theory can explain individual purchasing behaviors.
Politics: Rational choice theory can be used to explain voting behaviors, the actions of politicians and how political issues are handled.
Sociology: Rational choice theory can explain social phenomena. This is because all social change and institutions occur because of individual actions.
Addiction treatment: Rational choice theory can be used to identify addiction motivations and provide substance alternatives that are equally beneficial to patients.

When there’s a need to describe, predict and explain human behavior, rational choice theory can be applied.

Strengths and weaknesses of rational choice theory

Rational choice theory can be helpful in understanding individual and collective behaviors. It helps to pinpoint why people, groups and society as a whole move toward certain choices, based on specific costs and rewards.

Rational choice theory also helps to explain seemingly “irrational” behavior. Because rational choice theory states that all behavior is rational, any type of action can be examined for underlying rational motivations. Rational choice theory can promote inquiry and understanding, helping differing parties, like a client and a therapist, to recognize the other’s rationale.

A limitation of rational choice theory is that it focuses on individual action. While one could say that individual action drives large social structures, some rational choice theory critics argue the theory is too limited in its explanation.

Another weakness of rational choice theory is that it doesn’t account for intuitive reasoning or instinct. For decisions that must be made in an instant, such as decisions that influence survival, there may not be time to weigh the costs and benefits.

How Does Rational Choice Theory Apply to Social Work?

In social work , rational choice theory helps social workers understand the motivations of those they work with. Using rational choice theory, social workers can uncover why their clients do certain things and have gotten into certain situations, even when they seem unfavorable.

Rational choice theory can also help social workers when they’re designing interventions and treatments. Knowing that their clients will make decisions based on what benefits them, social workers can use that understanding to guide their interactions with and recommendations for their clients.

Social workers can use rational choice theory to:

Investigate the meaning behind their clients’ relationships, including with friend groups and romantic partners, including when those relationships are abusive or seem toxic.
Examine why their clients behave in certain ways, including engaging in self-destructive behaviors and addictions.
Understand how family dynamics and social interactions affect their clients.
Create a better relationship between themselves and their clients, by positioning their work in a way that benefits the client.
Promote interventions and create treatments that their clients will want to engage in because they see the benefits.
Position resources so that clients understand how those resources will benefit them.

To optimize the use of rational choice theory in social work, social workers will need to create a thorough assessment that takes into consideration the details of the motivations behind their clients’ behavior.

Criticism of Rational Choice Theory

One potential issue with rational choice theory (PDF, 287 KB) is that it doesn’t account for non-self-serving behavior, such as philanthropy or helping others when there’s a cost but no reward to the individual. Rational choice theory also doesn’t take into consideration how ethics and values might influence decisions.

Another criticism is that rational choice theory doesn’t comment on the influence of social norms. An argument against rational choice theory is that most people follow social norms, even when they’re not benefitting from adhering to them.

Also, some critics say that rational choice theory doesn’t account for choices that are made due to situational factors or that are context-dependent. Factors like emotional state, social context, environmental factors and the way choices are posed to the individual may result in decisions that don’t align with rational choice theory assumptions.

Some critics also state that rational choice theory doesn’t account for individuals who make decisions based on fixed learning rules, in that they do things because that’s the way they’ve learned to do them—even when the decision has higher costs and fewer benefits.

Summary and Resources for Further Learning

Rational choice theory can be used in conjunction with other social work theories, like social learning theory and psychosocial development theory. Rational choice theory provides a framework for social worker intervention. It’s a jumping-off point for understanding clients and for analyzing cases using research and evidence to create more effective treatment.

To learn more about rational choice theory, check out these resources.

“Choice, Preferences and Procedures: A Rational Choice Theoretic Approach”: In this book, author Kotaro Suzumura uses essays to examine collective decision making and the nature of individual and social choice.
Exchange and Rational Choice Theories (PDF, 1.2 MB) : This in-depth publication covers key rational choice concepts promoted by sociologists Homans, Blau and Coleman.
“Modern Social Work Theory”: This book by Malcolm Payne examines the major theories informing social work practice, so social workers and students can compare rational choice theory to other theories and see how various theories can influence client work.
“Predictably Irrational: The Hidden Forces That Shape Our Decisions”: This book by Dan Ariely challenges rational choice theory and questions the role rational thought plays in decision-making.
“Rational Choice”: This book by Itzhak Gilboa explains how rational choice theory relates to fields ranging from philosophy to economics and draws on ideas developed in psychology and sociology.
“Rational Choice Theory and Organizational Theory: A Critique”: This book by economic sociologist Mary Zey examines how rational choice theory affects organizations and the relationships between individuals and the organizations they work for.
“Routine Activity and Rational Choice”: This book edited by Ronald V. Clarke and Marcus Felson examines how rational choice theory applies to criminal behavior such as drunk driving and gun use.

Did you know that you can study for a degree in social work online? We have made it easier for you to find and compare online social work degrees at all levels.

Last Updated: February 2022

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Published: 01 October 2020

Deviations of rational choice: an integrative explanation of the endowment and several context effects

Joost Kruis ORCID: orcid.org/0000-0001-8700-0326 1 ,
Gunter Maris 2 ,
Maarten Marsman ORCID: orcid.org/0000-0001-5309-7502 1 ,
Maria Bolsinova 3 &
Han L. J. van der Maas ORCID: orcid.org/0000-0001-8278-319X 1

Scientific Reports volume 10 , Article number: 16226 ( 2020 ) Cite this article

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Human behaviour
Statistical physics

People’s choices are often found to be inconsistent with the assumptions of rational choice theory. Over time, several probabilistic models have been proposed that account for such deviations from rationality. However, these models have become increasingly complex and are often limited to particular choice phenomena. Here we introduce a network approach that explains a broad set of choice phenomena. We demonstrate that this approach can be used to compare different choice theories and integrates several choice mechanisms from established models. A basic setup implements bounded rationality, loss aversion, and inhibition in a natural fashion, which allows us to predict the occurrence of well-known choice phenomena, such as the endowment effect and the similarity, attraction, compromise, and phantom context effects. Our results show that this network approach provides a simple representation of complex choice behaviour, and can be used to gain a better understanding of how the many choice phenomena and key theoretical principles from different types of decision-making are connected.

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Introduction

The response behaviour of humans on (discrete) choice problems has been extensively studied in many fields of science, such as economics 1 , 2 , 3 , 4 , psychology 5 , 6 , 7 , 8 , psychometrics 9 , 10 , cognitive science 11 , 12 , 13 , 14 , neuroscience 15 , 16 , and engineering 17 , 18 . Traditional theories of choice assume the decision-maker as a homo economicus 19 , 20 , i.e., rational 1 , 5 , 21 . For choices to be rational all choice alternatives must be comparable and have transitive preference relations, so they can be ordered by the decision-maker. A second feature, and a central principle of rational choice theory, is that a rational decision-maker consistently chooses the outcome that maximises utility, or expected utility for risky or uncertain choices 5 , 22 , 23 , 24 . These assumptions clearly fail the scrutiny of everyday experience. To account for the observed inconsistencies, most models nowadays characterise choice as a probabilistic process 6 , 9 , 21 , 24 , 25 , 26 , 27 , 28 , 29 .

A prominent group of probabilistic choice models, such as Luce’s strict utility model 6 , 24 and the multinomial logit model 21 for preference, and Bock’s nominal categories model 30 for aptitude, are characterised by the following distribution for the choices:

in which $p_S(x) \in [0,1]$ represents the probably of choosing alternative x from the set of possible alternatives S as a function of the utility of alternative x , $\exp (\pi _x)$ , where $\pi _x \in \mathbb {R}$ . This distribution is also known as the Boltzmann distribution 31 , 32 from statistical mechanics. For binary choice problems $(S = \{x,y\})$ Eq. ( 1 ) takes a form known as the Bradley–Terry–Luce model in the decision-making literature 33 , 34 , or as the Rasch model 9 in psychometrics:

Models with this form have the property of simple scalability, which implies that the probability of choosing option x over option y is strictly increasing in the utility of x and strictly decreasing in the utility of y . Several properties with respect to independence from irrelevant alternatives (IIA) and transitivity follow from simple scalability. The degree to which choices are considered rational from a probabilistic perspective, is often described by the extent to which the choice probabilities for a set of choice alternatives possess these properties 6 , 24 , 35 , 36 , 37 .

For example, the weakest form of IIA is regularity, which implies that the probability of choosing an alternative can never increase by adding more alternatives. A set of preference probabilities is regular if $x \in A \subseteq S$ and $p_{\scriptscriptstyle {A}}(x) \ge p_{\scriptscriptstyle {S}}(x)$ . The strongest form of IIA is the choice axiom that is satisfied when $x \in A \subseteq S$ and $p_{\scriptscriptstyle {S}}(x) = p_{\scriptscriptstyle {A}}(x) \sum _{y \in {\scriptscriptstyle {A}}}p_{\scriptscriptstyle {S}}(y)$ . Meaning that the probability of choosing an alternative from a particular set is equal to the probability of choosing the alternative from a subset times the probability of selecting any alternative in this subset from the original set. Models are characterised as either strict, strong, or weak binary utility models depending on the expression that can be used to obtain the binary choice probabilities. If the positive real number $v_i$ denotes the utility of alternative i , then a model is strict if $p_{x,y}(x) = v_x/(v_x + v_y)$ , strong if $\phi$ is a cumulative distribution function with $p_{x,y}(x) = \phi [v_x - v_y]$ and $\phi [v_x - v_x] = {}^{1}\!/_{2}$ , and weak if $p_{x,y}(x) \ge {}^{1}\!/_{2}$ when $v_x \ge v_y$ . Rationality is also assessed by considering different observable properties of pairwise probabilities, such as the product rule, quadruple condition, strong, moderate, and weak stochastic transitivity, and the multiplicative and triangle conditions, each describing a different degree of strictness in the ordering of the choice probabilities. We refer the reader to Luce and Suppes 24 for a comprehensive treatment of these properties.

Although models with simple scalability have statistically desirable properties, their assumptions are often violated in reality. In the preferential choice literature for example, violations of IIA known as the similarity, attraction, compromise, and phantom context effects describe different situations in which the preference relation between two choice alternatives changes when a third alternative is introduced 7 , 24 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 . Another example is the endowment effect that describes the tendency of people to perceive an alternative as having increased in value after they have chosen it 3 . We will discuss these violations and phenomena in more detail later.

Over time, theories and models have been adapted or extended to account for these deviations. Bounded rationality 53 , for example, is the theory that instead of searching for the alternative with maximum utility, we search until we find the first alternative with satisfactory utility. Loss aversion, which postulates that the perceived utility of not losing something is greater than the perceived utility of gaining that exact same thing, was offered as an explanation for the endowment effect 3 , 54 , 55 . Whereas the elimination by aspects model 42 provided a first account for the similarity effect only, multi-attribute multi-alternative sequential sampling models, such as multi-alternative decision field theory 14 , 56 , 57 , 58 , 59 , 60 , the leaky competing accumulator model 61 , 62 , 63 , 64 , the multi-attribute linear ballistic accumulator model 65 , 66 , 67 , the $2N-$ ary choice tree model 68 , 69 , and the associations and accumulation model 70 , also account for the attraction and compromise context effect using a range of different mechanisms. The interested reader is referred to three recent papers that offer a comprehensive comparison between the different models 37 , 71 , 72 .

Although these approaches are capable of modelling context effects, one drawback is that they are fairly complex. With increasing complexity generalisability often takes a hit, and models that are tuned to account for one type of choice effect fail to account for other choice effects, hence drawing inferences beyond the task-setting becomes challenging. Developing a simple choice model that is capable of connecting a broader spectrum of choice phenomena would thus be a worthwhile effort. For one, unifying distinct phenomena in a collective framework puts them on equal footing and hence can stimulate the development of formal theories that can account for all of them. Also, formalising our theories requires us to be precise and concrete, this in contrast to verbally formulated theories which are easily misinterpreted, often hold hidden assumptions, claim predictions that are not clearly derived from the theory, as well as hide consequences from the model that are not desired. Moreover, formal theories may lead to interesting predictions and new insights, and hence new possibilities to falsify the theory, that might not have been discovered if the phenomena are investigated independently. In this paper we propose such a probabilistic model for choices that conceptualises choice problems as a combination of a choice structure, an alternative evaluation process, and a choice trigger condition.

In the remainder of this paper, we start by introducing the choice structure, represented by a network in which the nodes are the cues and alternatives, and the edges describe the relationships between them. We demonstrate a basic setup of our choice model, in which the binary node states follow a distribution known as the quadratic exponential binary distribution, or Ising model, and alternatives are evaluated with single spin-flip dynamics. Sampling choices from the invariant distribution of the configurations in which only one alternative is active, the choice conditions, gives us choice probabilities with the same form as the Boltzmann distribution from Eq. ( 1 ). Triggering a choice as soon as the condition holds for the first time, implements bounded rationality and predicts the occurrence of context effects. We then discuss how our approach compares to multi-attribute multi-alternative models and implements, or might be extended with, the mechanics used by those models. Finally, we discuss some challenges and future directions for the model.

Here we introduce the different components of the choice model and derive predictions for choice probabilities and response times.

Choice model

The choice model consists of a structure, a process, and a trigger. The choice structure describes the alternatives available for a choice and the origin of their utilities. The choice process describes how the alternatives are evaluated. The choice trigger describes the condition that stops the evaluation process and prompts a decision.

The specific form of these three components allows for some variation depending on the specific setting. For example, in this paper we let the state of cues and alternatives in the choice structure be either active or inactive. While this is reasonable in the case of preferential choice, in the case of modelling an opinion we might want to use three possible states, namely pro, neutral, or against. These types of variations are also possible in the case of the process and trigger elements of the choice model, and we discuss several of them throughout the paper.

In their simplest form choices can be structured as a combination of cues and alternatives and the relationships between them. Cues represent the conditions of the choice, e.g., ‘buy a book’, ‘select a present’, or ‘solve for x ’, and alternatives describe the possible choices. An appropriate representation of such a structure is a network in which the nodes correspond to the alternatives and the cues, and the edge between two nodes describes their relation. Figure 1 shows how the structure of a particular choice problem can be seen as a subset from a larger collection of related concepts.

Graph of (related) concepts and subsets of concepts as possible choice problems. Nodes represent concepts and edges represent a relationship between concepts. Nodes surrounded by a dashed box, represent concepts that can form a potential choice problem. For example, ( a ) If you have to choose between taxes ( TAX ), migration ( MIG ), or universal health care ( UHC ), which policy ( POL ) is most important for you? ( b ) Do you prefer Candidate 1 $(C_1)$ , Candidate 2 $(C_2)$ or Candidate 3 $(C_3)$ , as the Presidential Nominee ( PN )? ( c ) Is Washington DC ( WDC ) or Paris ( PAR ) the capital ( CAP ) of France ( FRA )? ( d ) Do you want a sandwich ( SDW ), a baguette ( BAG ), or a croissant ( CRO ) for breakfast ( BRF )? Concepts present in the graph but not part of a subset are respectively, age ( AGE ), United States of America ( USA ), Congress ( CON ), and Eiffel Tower ( EIF ).

To arrive at predictions about choice behaviour we assume that both the type and strength of a relationship between two nodes can vary, and that nodes outside of the choice subset can also influence a decision through their relationship with nodes that are in the choice subset. In Fig. 2 possible relationships between a cue and the alternatives are illustrated for the choice structure from Fig. 1 b.

Choice structure with a single cue ( PN ) and three alternatives $(C_1, C_2, C_3)$ . Cues are represented as dark grey nodes with white text and alternatives are represented as light grey nodes with black text. Edges represent a positive (solid) or negative (dashed) relationship between nodes, and a ring around a node represents whether the nodes is generally appealing (solid) or unappealing (dashed). The thickness of both the edges and rings around the nodes corresponds to intensity of the relationship/appeal.

We refer to the experienced magnitude and direction of an alternative’s utility in terms of an alternative’s appeal. Figure 2 shows that an alternative’s appeal is a function of its general appeal and relationship with the cue and the other alternatives. The general appeal of an alternative captures the relation between the alternative and nodes that are not in the choice structure. For example, in Fig. 1 we see that the general appeal of a candidate is a function of policy and age. The relation with a cue can positively or negatively affect the appeal of an alternative. For example, asking Do you want a nice and fresh croissant, yesterdays leftover sandwich, or a somewhat dry baguette, for breakfast? enhances the appeal of the croissant through the suggestive phrasing of the cue. A relation between two alternatives signals that the appeal of one is related to that of the other alternative. The next step is formalising the choice structure as a probability distribution.

For a choice structure with n nodes, let $\mathbf {x} = [x_1, x_2, \dots , x_n]$ be a vector representing the configuration of the node states in which $x_i \in \{0,1\}$ denotes whether node i is active $(x_i = 1)$ or inactive $(x_i = 0)$ . Let $\mathbf {A}$ be a symmetric $n \times n$ matrix in which $a_{ij} \in \mathbb {R}$ describes the relation between the node i and node j in the choice structure. Let $\mathbf {b} = [b_1, b_2, \dots , b_n]$ be a vector of length n in which $b_i \in \mathbb {R}$ describes the general appeal of node i . A valid probability distribution over the states is obtained by endowing them with the following distribution:

in which $\beta$ , a non-negative real number, and $\mu \in \mathbb {R}$ are scaling constants, $\sum _{\langle i,j \rangle }$ denotes the summation over all distinct pairs of i and j , and Z is the normalising constant that sums over all the $2^n$ possible configurations of $\mathbf {x}$ such that the probabilities of the possible states sum to one. We can multiply $\beta$ by some constant and divide $\mathbf {A}$ and $\mathbf {b}$ by the same constant without affecting the probabilities of the states, the same holds for $\mu$ and $\mathbf {b}$ . As such, we set both $\beta$ and $\mu$ to one for now, making them drop out of the equation, and discuss later how they might be used to model choice setting variations, such as time-pressure and/or individual differences.

The distribution in Eq. ( 3 ) can be recognised as the Ising model 73 , 74 , a highly popular and one of the most studied models in modern statistical physics 75 , or as the quadratic exponential binary distribution as it is known in the statistics literature 76 , 77 . Capable of capturing complex phenomena by modelling the joint distribution of binary variables as a function of main effects and pairwise interactions 78 , it has been used in fields such as genetics 79 , educational measurement 80 , and psychology 78 , 81 , 82 , 83 . In the context of choice it has been applied in sociology in Galam’s work on group decisions in binary choice problems 84 , 85 . In this application each node represents the choice of one person on a specific problem, and the pairwise interactions describe the influence of all people in the group on the individuals choice. Another application is the Ising Decision Maker from Verdonck and Tuerlinckx 86 , a sequential sampling model for speeded two-choice decision-making. In this model each of the two alternatives is represented by a pool of nodes, inside a pool nodes excite each other, between pools nodes inhibit each other. A stimulus is represented by a change in the external field, after which the node states are sequentially updated. The response process monitors the mean activity per pool, and chooses the first alternative for which this activity crosses a threshold. Both these models use this distribution in a substantially different way compared to the current application, and have not been applied to explain deviations from rationality. As such we will not discuss them in more detail for this paper.

A connection between Eq. ( 3 ) and probabilistic choice models is found by realising that the distribution of $\mathbf {x}$ is a function of the Hamiltonian:

and that the probability of each configuration is given by plugging $H_{\mathbf {x}}$ in the Boltzmann distribution from Eq. ( 1 ). That is, if S is the set of all configurations that a particular system can take and $\mathbf {x}$ is one possible configuration of this system, then the probability of the system being in this state is given by:

We assume that until a person is faced with a choice, the internal state of the decision-maker (the resting configuration) is distributed according to Eq. ( 3 ). An advantage of this assumption is that well defined stochastic processes for these systems exist and can be used in the next component of the choice model that describes how alternatives are evaluated until a choice is triggered. When a person is confronted with a choice all cue nodes are activated and remain so during the choice process. The alternatives will, in most cases, be distributed according to the resting state distribution. Exceptions to this are discussed later on.

Although many configurations for the choice process are possible, to illustrate our approach we use a simple stochastic process for interacting particle systems to model the process of alternative evaluation. Specifically, a Metropolis algorithm with single spin-flip dynamics 87 in which a proposal configuration is generated at each iteration by sampling one alternative and flipping its state:

Let $\mathbf {x}$ denote the current configuration of the system with $H_{\mathbf {x}}$

Select one node i at random and flip its value $x^*_i = 1 - x_i$

Calculate $H_{\mathbf {x}^*}$ for the configuration with the flipped node.

If $H_{\mathbf {x}^*} < H_{\mathbf {x}}$ , keep the configuration with the flipped node.

If $H_{\mathbf {x}^*} \ge H_{\mathbf {x}}$ , keep the configuration with flipped node with probability $\exp {\left( H_{\mathbf {x}} - H_{\mathbf {x}^*}\right) }$ .

For a choice with m alternatives the evaluation process will thus transition between $2^m$ possible configurations of the alternative states.

From Eq. ( 4 ) it can be derived that in a choice structure in which both the general appeal and the relationships are positive, the most likely configuration is the one with all alternatives active. This is reasonable as it implies that the most preferred state for a decision-maker is to posses all alternatives. In most applications a person is forced to choose only one of the alternatives, however. We impose this by defining potential choice conditions as configurations in which only a single alternative is active and discuss two possibilities for making decisions.

The first is that the alternative evaluation process is terminated when the single-spin flip algorithm has converged and a choice is sampled from the invariant distribution of the potential choice configurations:

in which $M = [x_1, x_2, \dots , x_m]$ denotes the subset of m alternative nodes and $K = [x_{m + 1}, x_{m + 2}, \dots , x_{m+k}]$ denotes the subset of k cue nodes. If we let the Markov chain run until convergence, the effect of any interactions between choice alternatives will have worn out and the property of simple scalability will hold for the choice probabilities, guaranteeing that choices are in accordance with the choice axiom. The choice axiom is known to be violated in particular choice problems, however, which leads us to the second choice trigger possibility.

At some moment during the process a potential choice condition is met for the first time. One could say that a choice has effectively been made and there is no need for a decision-maker to continue. This choice trigger implements the idea of bounded rationality and explains various types of irrational choices as we explain after we discuss the consequences of our model setup for rational choices.

Rational choice

Although our setup implements bounded rationality, it does not preclude rational choices. However, while choice structures can be made for which even the strongest gradation of rationality holds, finding clear cut rules for when a structure adheres to which gradations of rationality is a different kettle of fish. In the methods section we show that a very simple expression exists for the expected choice probabilities in the single spin-flip algorithm as a function of the transition matrix for the possible configurations of the alternatives. Deriving general rules for the adherence to different types of rationality requires one to express these probabilities as a function of the parameters $\mathbf {A}$ and $\mathbf {b}$ . As this expression is already of a gargantuan size for $n=3$ , and there is no reasonable way to derive general algebraic properties from it, we only work out the binary case in the methods section and show that even then determining when choices are guaranteed to be at least weakly rational is not necessarily straightforward.

For $n>2$ the expectation of rational behaviour for a particular choice structure has to be derived on a case by case basis. As for n alternatives there are $2^n - n - 1$ possible subsets of at least two variables, investigating the assumption of independence of irrelevant alternatives will be more time consuming compared to determining properties of the pairwise probabilities of a choice set. A statistical program such as R 88 can calculate these expected pairwise choice probabilities in reasonable time for choice situations with up to 15 alternatives using the expression from the methods section. For larger numbers of alternatives numerical solutions can be obtained with a simulation approach. Additionally, assumptions that simplify the analytical expression for the expected choice probabilities can also be used to derive rational choice properties.

Irrational choice

We define irrational decision-making as those choice situations in which the odds of choosing one alternative over the other, as established by their pairwise choice probabilities, changes as a function of adding other alternatives to the set. We realise that for readers well versed within the choice literature this definition may seem both rather vague, because our definition creates a dividing line somewhere between the choice axiom and regularity, as well as strict, as violating the choice axiom means that the strictest rules and conditions for rationality can still hold for the binary choice probabilities. However, although we touched upon the different gradations of rationality in the previous paragraphs, we think that a more conceptual approach is more appropriate here. We will discuss examples in which it is immediately clear that the choice probabilities as predicted by rational choice theory are conceptually counter intuitive.

Context effects are perhaps the most well known and studied violations of IIA and are often described by a situation in which a preference relation between two alternatives, a target and a rival, is established. Then a third alternative is introduced, the decoy, and it is demonstrated that adding the decoy changes the choice probabilities in favour of the target. These effects can range from only increasing the probability for the target while keeping the original order of the preference relations between the alternatives intact, to a full reversal of the preference relation. In our model these effects can be explained by the presence of a relationship between two choice alternatives and its influence on the resting state distribution and the alternative evaluation process.

For several types of context effects we provide an example and show how it can be explained in our model. As our explanation of the context effect does not require bias in the presentation of the choice, we assume the relationship between all pairs of a cue and an alternative to be the same across the board $(a_{mk} = 1)$ . In the Supplementary Materials we work out the specific steps to calculate the choice probabilities for our example of the attraction effect, as well as provide the parameter values for the other examples.

The similarity effect 38 , 39 describes the situation in which adding a decoy that is highly similar to the rival results in an increased preference for a dissimilar target alternative. The classic example for this effect was given as a thought experiment that provides the choice probabilities, expected under rational choice theory for a choice between three recordings:

“Let the set U have the following three elements: $D_C$ , a recording of the Debussy quartet by the C quartet. $B_F$ , a recording of the eighth symphony of Beethoven by the B orchestra conducted by F . $B_K$ , a recording of the eighth symphony of Beethoven by the B orchestra conducted by K . The subject will be presented with a subset of U , will be asked to choose an element in that subset, and will listen to the recording he has chosen. When presented with $\{D_C, B_F\}$ he chooses $D_C$ with probability ${}^{3}\!/_{5}$ . When presented with $\{B_F, B_K\}$ he chooses $B_F$ with probability ${}^{1}\!/_{2}$ . When presented with $\{D_C, B_K\}$ he chooses $D_C$ with probability ${}^{3}\!/_{5}$ . What happens if he is presented with $\{D_C, B_F, B_K\}$ ? ...He must choose $D_C$ with probability ${}^{3}\!/_{7}$ . Thus if he can choose between $D_C$ and $B_F$ , he would rather have Debussy. However, if he can choose between $D_C$ , $B_F$ , and $B_K$ , while being indifferent between $B_F$ and $B_K$ , he would rather have Beethoven.” Debreu, 1960 38 .

It is clear that these expected choice probabilities are highly implausible. Specifically, in this case one would expect that when presented with $\{D_C, B_F, B_K\}$ , $D_C$ would be chosen with probability ${}^{3}\!/_{5}$ and the remaining ${}^{2}\!/_{5}$ would be split evenly among $B_F$ and $B_K$ . Such intuition has been proven correct in studies with a similar format as the thought experiment 7 , 41 , 42 .

One choice structure that explains the similarity effect does this by introducing a negative association between the two Beethoven recordings, as in shown in Fig. 3 . The negative relation between $B_F$ and $B_K$ has no influence on choice probabilities for any of the possible two-element subsets, as such the slightly larger base appeal of $D_C$ will result in choosing $D_C$ with probability ${}^{3}\!/_{5}$ when presented with $\{D_C, B_F\}$ or $\{D_C, B_K\}$ . When presented with $\{B_F, B_K\}$ , the negative relation works in both ways and $B_F$ and $B_K$ are chosen with equal probability. While the conditional distribution of the model from Eq. ( 6 ) predicts that $D_C$ will be chosen with probability ${}^{3}\!/_{7}$ when a choice has to be made from all three alternatives together, the rule that one stops as soon as the choice conditions hold for the first time will actually predict that when presented with $\{D_C, B_F, B_K\}$ , $D_C$ is chosen with probability ${}^{3}\!/_{5}$ and $B_F$ and $B_K$ are both chosen with probability ${}^{1}\!/_{5}$ . Our explanation of the ‘irrational’ (yet intuitive) choice behaviour in this example of the similarity effect rests on the presence of a negative relation between the Beethoven recordings.

Choice structure for Debreu’s example of the similarity effect. With cue ‘choose a recording’ ( R ), and alternatives, ‘Beethoven conducted by F ’ $(B_F)$ , ‘Beethoven conducted by K ’ $(B_K)$ , and ‘Debussy by the C quartet’ $(D_C)$ .

One could argue that the ability to reverse engineer a network structure until the desired choice probabilities are obtained is a weakness of our approach. We believe that this is actually an advantage as, for one, it is possible to check if adaptations of the choice structure will still result in plausible choice behaviour. For example, imagine that you chose $B_K$ from the set $\{D_C, B_F, B_K\}$ and are asked to choose once more from the remaining recordings $\{D_C, B_F\}$ . Taking into account that you already have $B_K$ $(x_{B_K} = 1)$ , the negative relation between $B_K$ and $B_F$ in our choice structure results in a prediction that you will choose $D_C$ with near certainty. This demonstrates that the choice structure does not only explain observed behaviour, but also predicts new, and in this case plausible, behaviour for adaptations of the choice problem. Furthermore, as we will discuss in the next example, it also allows one to come up with theoretically distinct choice structures for a single choice phenomenon and compare them. While the initially expected choice probabilities might be the same, manipulations that result in distinct predictions for each choice structure can be tested.

The attraction, or asymmetric dominance, effect 44 , 45 describes the situation in which the addition of a decoy alternative that is a substandard version of the target increases the preference for the target. Simonson and Tversky 46 investigated this effect by offering two groups a choice between (a subset of) 6 dollar $({\$})$ , a nice pen $(P_+)$ , and a (less attractive) plain pen $(P_-)$ . In the first group, choosing from the subset $\{{\$}, P_+\}$ , more people chose the money (64%) compared to the nice pen (36%). In the second group, choosing from the set $\{{\$}, P_+, P_-\}$ , as expected, almost no one chose the plain pen (2%), however, the money was now only chosen 52% of the time, while the proportion of people choosing the nice pen rose to 46%.

Figure 4 shows two possible choice structures that predict expected choice frequencies similar to those found in the experiment, however, each of these explain the attraction effect in a different way. In Fig. 4 a the explanation of the attraction effect rests on the presence of a negative association between the money and the plain pen, while in Fig. 4 b the effect is explained by a positive association between both of the pens. Our model thus provides two theoretically distinct choice structures that both explain how the mere addition of a less appealing decoy can boost the choice probabilities for the otherwise less frequently chosen target alternative.

Choice structure for Simonson & Tversky’s example of the attraction effect. With cue ‘choose an Reward’ ( R ), and alternatives, ‘money’ $({\$})$ , ‘nice pen’ $(P_+)$ , and ‘plain pen’ $(P_-)$ . The attraction effect can be explained with a negative relationship between the money and the plain pen ( a ), or a positive relationship between the two pens ( b ).

Obtaining the same results from different structures allows us to compare the different theories and predictions that characterise each structure. For example, someone who chose $P_+$ as reward from the set $\{{\$}, P_+, P_-\}$ is asked to choose once more from the remaining rewards $\{{\$}, P_-\}$ . Taking into account that this person already possesses $P_+$ $(x_{P_+} = 1)$ , the negative relation between ${\$}$ and $P_-$ , in choice structure a , results in a prediction of choosing ${\$}$ in more than 90% of the cases, whereas the positive relation between $P_+$ and $P_-$ , in choice structure b , results in a prediction of still choosing $P_-$ in 33% of the cases. While the initially expected probability distribution for choice structures a and b are thus the same, from the different prediction they make about a new situation, we can clearly distinguish which structure seems more plausible. The fact that our approach allows for these kinds of comparisons, and provides testable predictions, makes the theories captured in the model falsifiable.

In some cases the addition of a substandard version of the target alternative actually decreases the probability of selecting the target 89 , 90 , 91 , 92 . This reversed attraction effect, called the negative attraction or repulsion effect, although not consistently demonstrated, is mostly observed when choices are framed such that the decoy highlights the shortcomings of the more similar target alternative. For example, adding a smaller clementine to the choice between a fruit flavoured candy bar and an orange, might boost the probability of choosing the orange, as the clementine highlights the freshness and health aspects of citrus fruits. However, if the clementine shows some signs of a reduced freshness, e.g. crumpled skin or beginning to mould, it highlights the fleeting freshness of citrus fruit, and might instead boost the probability for the sugar filled candy bars and their long shelf life.

Just as the repulsion effect is the opposite of the attraction effect, so is its explanation, i.e., a positive relation between the rival and decoy alternatives. In the pen example from Fig. 4 , switching the sign of the relation between the money $({\$})$ and the plain pen $(P_-)$ so it becomes positive, while keeping all other parameters the same, predicts a boost in the probability of choosing of the money $({\$})$ with respect to the nice pen $(P_+)$ . Interestingly, whereas the negative relation in the attraction effect can result in a relatively large gain in choice probability for the target $(+ 10\%)$ , the same structure but with a positive relation results in only a modest gain in the predicted choice probability for the rival $(+ 2\%)$ . To increase the magnitude of the repulsion effect one has to decrease the general appeal of the added decoy. Finally, adding both an attracting and a repulsing decoy results in the context effects cancelling each other out when choosing between all four options.

The compromise effect 45 describes the situation in which a decoy is added for which the distance to the target mirrors that of the distance between the rival and the target, but in the opposite direction. This boosts the preference for the target alternative by making it seem like the compromise. Distance should in this context be interpreted as the relative position of the alternatives on particular attributes, such as prize and quality in the next example.

Tversky and Simonson 47 investigated the compromise by offering two groups a choice between (a subset of) cameras of either low ( L ), medium ( M ), or high ( H ), prize and quality. While in the first group, choosing from the subset $\{L, M\}$ , people chose both cameras with approximately equal probability, in the second group, choosing from the set $\{L, M, H\}$ , people now chose both cameras L and H each with a probability of approximately ${}^{1}\!/_{4}$ , while camera M was still chosen with a probability of approximately ${}^{1}\!/_{2}$ . Based on the seemingly equal appeal of the L and M camera in group one, one would expect that both would be also be chosen with an approximately equal probability in group two. Or conversely based on the lower appeal of camera L in group 2, one would expect that in group one camera L would be chosen with a probability of approximately ${}^{1}\!/_{3}$ and hence camera M with a probability of approximately ${}^{2}\!/_{3}$ .

A possible explanation for why this is not the case might be that the (dis)advantages between the cameras H and L camera are much more evident than those between the cameras M and L or M and H . Therefore, the weakness of camera L gets highlighted when camera H is part of the choice set, this in turn frames the camera M as the compromise that is of higher quality compared to camera L , but not as expensive as camera H . Once again, as is shown in Fig. 5 , our explanation of the compromise effect can be captured by introducing a negative relation between the rival camera L and the decoy camera H .

Choice structure for Tversky & Simonson’s example of the compromise effect. With cue ‘buy a Camera’ ( C ), and alternatives with respective quality and prize levels, ‘Low’ ( L ), ‘Medium’ ( M ), and ‘High’ ( H ).

So far the similarity, attraction, and compromise effect are each explained in our model by a negative interaction between the decoy and the rival. Whereas in the similarity effect, this relation is assumed to exist because of the large similarities between the rival and decoy alternatives, in the attraction and compromise effects, however, this relation is a function of the large dissimilarities between the two.

One explanation for this could be that only when (dis)similarities go into the extreme they are highlighted and start influencing the choice process. Another explanation comes from observed correlations between context effects, i.e., one study found that people who show the attraction effect also show the compromise effect, but not the similarity effect 60 . This could suggest that people either focus on similarities or dissimilarities, and hence the choice structure of a person only contains negative relations for one of those types. Whereas the attraction and compromise effect occur when a choice structure contains only negative relations as a function of dissimilarity, a choice structure in which negative relations are the result of similarity will only elicit the similarity effect. Not all context effects can be explained by a (negative) relation between the rival and decoy alternatives alone. In some cases it also manifests itself through the influence of the choice structure on the initial alternative configuration.

The phantom decoy effect 52 describes the situation in which the added decoy alternative is superior to both the target and rival alternatives, yet more similar to the target compared to the rival, but most importantly, unavailable. When it is communicated that the decoy cannot be chosen it subsequently boosts the preference for the target alternative.

Pratkanis and Farquhar 52 studied the phantom decoy effect by offering two groups a choice between (a subset of) paperclips each with varying degrees of friction and flexibility. The target paperclip ( T ) and the rival paperclip ( R ), although different in these properties, were of comparable quality. The decoy paperclip ( D ) had a quality superior to both T and R but was in terms of friction and flexibility more alike to paperclip T . In the first group, choosing from the subset $\{T, R\}$ , people chose each paperclip with approximately equal probability. People in the second group, however, who thought they where choosing from the set $\{T, R, D\}$ , chose the paperclip of type T with a probability of approximately ${}^{4}\!/_{5}$ , after the decoy D was revealed to be unavailable and hence the choice had to made again from the subset $\{T, R\}$ .

As is shown in Fig. 6 , our explanation of the phantom decoy effect, at this point perhaps unsurprisingly, partially rests on the presence of a negative relation between the rival and the decoy. It depends however, on when the unavailability of the decoy is communicated how the phantom effect is elicited. If this is communicated before the choice is offered the first time, the choice process is still updated to still sample and flip, but not terminate at, paperclip D . As shown in Fig. 6 a, the combination of a negative relation between the D and R paperclips, together with the larger general appeal of paperclip D , reduces the probability for choosing paperclip R . If the unavailability of paperclip D is not communicated before the first choice and all three paperclips appear to be available, the choice structure from Fig. 6 a without the previously introduced constrained will be evaluated and paperclip D is most likely to be chosen. At this point, the configuration of the choice structure is known, as only the cue and the node for paperclip D will be active. If at this point one is informed that paperclip D is unavailable, the choice process starts again from the known configuration. Given that node D is active, we can from this moment regard it as an additional cue, as is shown in Fig. 6 b. Consequently, due to the negative interaction between paperclip D and paperclip R , flipping the R node and hence choosing it become less likely compared to paperclip T .

Choice structure for Pratkanis & Farquhar’s example of the phantom decoy effect. With cue ‘choose a Paper Clip’ ( PC ), and the decoy ( D ), rival ( R ), and target ( T ) paperclip alternatives. Depending on when the unavailability of the decoy is communicated, the phantom decoy effect is explained by a constrained version of the regular choice process ( a ), or an additional choice process in which the decoy is an extra cue ( b ).

As shown in the Supplementary Materials , eliciting the phantom effect requires a much stronger negative relation between the decoy and rival when the unavailability of the decoy is known upfront, compared to when the unavailability is communicated after a choice is made for the first time. While it is easily argued that this is a rather intuitive hypothesis, it once again shows that our approach allows for making diverging predictions based on variations in the model setup.

The endowment effect 3 describes the situation in which people value an object higher if they possess it compared to when they do not. To illustrate this effect we consider a variation on the Debreu example in which you are given a Beethoven recording ( B ) and are immediately asked if you want to exchange it for an equally appealing Debussy recording ( D ). While the choice axiom predicts that you would exchange Beethoven for Debussy about half the time, the endowment effect says that people are unlikely to switch, a prediction that has been experimentally verified 93 . The endowment effect has been explained with choice-supportive bias 94 and loss aversion 54 .

In our model both explanations would translate to an increase in base appeal of an alternative as soon as it has been chosen. With our setup we obtain a new explanation that does not depend on changes in the values of the choice problem but ties into the choice process itself. Having been given the Beethoven makes the choice conditions satisfied, and hence the initial configuration of the alternatives is known when offered to exchange it for the Debussy. Exchanging them requires a sequence of events in the choice process that, due to the equal appeal of both alternatives, has a lower probability compared to keeping the Beethoven. Specifically, the only way that switching becomes an option is when the initial state, the choice condition for the Beethoven, is left in the first iteration by sampling and accepting the flip of either node B or node D . From the resulting configurations both choices are then equally likely. Let $u_R = a_{RB} + b_B = a_{RD} + b_D$ denote the appeal for both the Beethoven and Debussy recordings. The probability of exchanging B for D is then given by:

Equation ( 7 ) shows that only when someone is indifferent about both alternatives $(u_R = 0)$ , i.e., they are neither appealing nor unappealing, the probability of exchanging is a half. In all other cases the endowment effect rears its head and the probability of exchanging will be less a half. Having demonstrated how several choice phenomena are explained in this setup, we turn to another property of our model, response times.

Response times

Response time predictions can be very informative when comparing different choice structures, evaluation processes and trigger conditions. As shown in the methods section, the single spin-flip algorithm provides the expected number of iterations until a choice condition is reached as a proxy for time. This can be used to investigate expected ordering of response times for a particular choice structure. For example, in a simple structure with no relationship existing between alternatives the expected number of iterations before a choice is triggered increases in the number and appeal of the alternatives. Or, assuming that longer response times are indicative of more deliberate decision-making, i.e., requiring more visits to a choice condition before a choice is triggered, we expect that context effects diminish and choices get increasingly rational. With increasing the required number of visits to a choice condition, choice probabilities go to Eq. ( 6 ) if a choice is sampled proportional to the number of visits of each condition. If the first alternative for which the choice condition has been visited the required number times is chosen, choice probabilities go to one for the alternative with the highest general appeal.

The model also allows incorporating response time phenomena such as the speed-accuracy trade-off 95 , which predicts that under time-pressure choices are faster but less accurate, through $\beta$ . In an application of the Ising model to attitudes 96 , 97 , the attention to an attitude object is represented by $\beta$ . This interpretation fits well within the choice model, as such an inverse relation can also be assumed between time-pressure and attention. As $\beta$ scales the magnitude of the entire choice structure, lower values will not only reduce the expected number of iterations before a choice is made, but also the effect of $\mathbf {A}$ and $\mathbf {b}$ , and with that the magnitude of the context effects. This is also in line with research that showed that context effects tend to be smaller under time-pressure 66 , 98 . Choice expectations under time-pressure can be even more fine-tuned by using $\mu$ . For example, the assumption that people under time-pressure only focus on the general appeal of the alternative can be modelled by letting $\mu = {}^{1}\!/_{\beta }$ . In the methods section we show how different forms of time-pressure, modelled as variations in the relation between $\beta$ and $\mu$ , influence the expected choice probabilities for the attraction effect.

In this article we proposed a model for choices in which the choice structure is represented by a network, for which the node states have a distribution known as the quadratic exponential binary distribution or Ising model. Single spin-flip dynamics describe the alternative evaluation process in our basic setup, and potential choice conditions are states in which only one alternative is active. The invariant distribution of this choice process is the same as that of several classic choice models with the property of simple scalability, which guarantees choices to be rational. Stopping when the choice conditions hold for the first time predicts a series of well known violations of rationality known as context effects and several other choice phenomena. This approach allows one to represent choice situations in an accessible way, and can be used to compare different choice structures, alternative evaluation process assumptions, and trigger variations with respect to the choice behaviour they predict. Furthermore, as we show next, it implements or can be extended with features and mechanics used in more complex choice models. We first review the relation between our model and the elimination by aspects (EBA) model, multi-alternative decision field theory (MDFT), the leaky competing accumulator model (LCA), and (simple) 2N-ary Choice Tree (2NCTs), and end with discussing some limitations and prospects of our approach.

One of the first models to offer an explanation for the similarity effect was Tversky’s EBA model 42 . In the EBA model an alternative is characterised by a collection of attributes. At each step in the choice process one attribute is selected proportional to an attention weight, and alternatives without this attribute are eliminated until only one alternative remains. Although the utility of an alternative in the EBA model is a function of unique attributes and those shared between pairs of alternatives, choice probabilities can be calculated independently of the specific attributes. The EBA model can only explain the similarity effect, which occurs when a subset of the alternatives share some attributes that are not shared with the other alternatives. For example, the two Beethoven recordings share attributes that are not shared by the Debussy recording. As such the probability of selecting an attribute that is unique to a Beethoven is smaller when both recordings are in the choice set with the Debussy, compared to when only one is.

MDFT, the LCA, and 2NCTs, are capable of explaining more context effects. They are sequential sampling models, which entails that (noisy) information about the alternatives is integrated in an accumulator for each alternative throughout the choice process. Whereas MDFT and the LCA each assume one accumulator per alternative, in the 2NCTs each alternative has two accumulators, one for positive information and one for negative information. For all models the process stops when either a time limit is reached or one of the (positive) accumulators crosses a threshold, triggering in both cases a choice for the alternative for which the most (positive) information is accumulated. In the 2NCTs the process can also terminate when for all but one of the alternatives the threshold of the negative accumulator is crossed and these alternatives are eliminated.

As in the EBA model, an alternative’s appeal is a function of its attributes and attention switches between these attributes over time. The explanation of the context effects in MDFT, the LCA, and 2NCTs rest primarily on some form of asymmetry between, or particular positioning of, the alternatives on attributes and must therefore be specified for all alternative-attribute combinations. In our model we do not need to specify different attributes or assume switching between attributes to predict context effects, as the influence of attributes is captured in the general appeal of the alternative. The influence of different attributes can be made explicit by adding an additional layer of attribute nodes , in which the position of the alternative on the attribute is encoded in the edge between them. During the process one can assume that attributes are always active and function as cues, or let their activity vary over time to incorporate the assumption that attention stochastically switches between attributes.

The models require several other mechanisms to explain context effects. Lateral inhibition 99 , a neural concept in which an exited neuron inhibits its neighbours, is applied with the same magnitude for all alternative pairs in the LCA, and decreasing with the distance between alternatives in MDFT. The LCA and 2NCTs (also) rely on an implementation of loss aversion. In the LCA accumulators can only take non-negative values and the influence of negative differences between attribute values relative to positive differences is reduced. In the 2NCTs the evaluation process decreases the probability of updating a negative accumulator relative to that of a positive accumulator.

Both inhibition and loss aversion are part of our model. Loss aversion is implemented within the process of single spin-flip dynamics, i.e., the probability of activating an appealing alternative is always one, whereas the probability of eliminating an appealing alternative is decreasing in the appeal. Inhibition comes in the form of the negative interactions between the alternatives and plays a vital role in the prediction of context effects. Global inhibition as found in the LCA can be implemented by lowering the interaction between each pair of alternatives with a constant. Otherwise independent alternatives then become negatively related and the evaluation process will move faster to a potential choice condition 100 .

Although our approach has several advantages with respect to the more complex multi-attribute multi-alternative models, it does not provide the same insight in choices times distributions. That being said, our predictions with respect to the ordering of response times are often the same as these models, and the model even provides novel explanations for some response time phenomena. For example, the prediction that context effects strengthen with longer decision times can, in addition to time-pressure, also be explained by the format of the experiment. The study manipulated time-pressure by letting a participants evaluate the characteristics of novel stimuli for 2, 4, 6, or 8 s, after which a choice had to be made immediately. Participants who could look to the stimuli for less than 8 s made choices less consistent with the context effects compared to participants that could look for 8 s 98 .

Whenever a choice is presented the available alternatives must be encoded to determine their appeal, a process that takes time. For daily choices alternatives are recurring and embedded within a stable choice structure such that decoding takes almost no time. New alternatives must be placed within the global structure and connected to the relevant concepts before their perceived appeal stabilises. As multi-attribute multi-alternative models must specify all alternatives-attribute combinations, studies often use fictional products defined on small numbers of attributes only. Fictional alternatives are new to the participant and time is required to derive the general appeal of, and establish the appropriate relationships between, alternatives. This explanation is consistent with a transition from $\beta = 0$ , a choice structure with no relationships and no general appeal, to $\beta = 1$ , a fully formed structure, during this transition the magnitude of the context effects increases. In contrast to the other models, however, our model predicts that context effects diminish when time-pressure is reduced even further and more deliberation can take place before a choice is triggered.

We discussed our model assuming that all alternatives are known and everyone has the same choice structure. It is clear that choice situations exist in which the alternatives are not necessarily provided, or in such large numbers that evaluating them all might be infeasible. Furthermore, while it is a common assumption that the behaviour of individuals can be described by a set of parameters for the group, it is often rather unrealistic. Future research should focus on extending the model for these situations. For example by introducing initial selection probabilities for alternatives to be included in the choice structure, or interpreting $\beta$ and $\mu$ as parameters that are different for each person, or extend the model for individual choice structures 101 .

We also recognise that data and findings in psychology from a few decades ago are sometimes questionable with respect to the current standards of research. For example, papers that used alternative methods to analyse data from a study by Tversky on within-person transitivity 102 , find that for several (but not all) persons for whom Tversky asserted that they showed intransitive behaviour, the results are no longer significant 103 , 104 , 105 . Although we believe that there is general consensus, based on a large body of research, that humans do not always make rational decisions, some experiments about irrational choice behaviour remain disputed. We therefore want to stress the importance of replicating these studies, or setting up new experiments that investigate the same phenomenon.

Another point, discussed in the paper by Regenwetter et al. 104 , is that often behaviour that may seem intransitive is actually rational when previously unobserved variables are taken into account. As an example they take a student that assumes that their supervisor’s perceived utility of meeting locations is stable over time, but that this is not the case as the varying teaching location of the supervisor actually determines this utility. As such, while the student judges the choices for meeting locations of the supervisor to be intransitive, this is in reality not the case. This example shows that, particularly for within-person choice effects, it is important to take context variables into account. In our model this could be accounted for by introducing nodes for these context variables, in case of the example a node for each of the teaching locations, that has a positive relation with the closest meeting locations, and which is active if the day of the meeting the supervisor has to teach in that location.

Even though very precise quantitative predictions are generally out of reach with behavioural data, there is merit in the prediction of qualitative phenomena, such as the ordering of probabilities, interaction effects, shapes of distributions, and even phenomena such as phase transitions. Formalising our theories about behaviour allows us to obtain these predictions. While we already propose multiple extensions to our model, ideally, it will be formal theories that dictate the assumptions, mechanisms, and structural characteristics to be used in a particular setup. By taking the opportunities to extend, refine, and improve the elements of the choice model, we can hopefully create a broad understanding of how the many phenomena and key theoretical principles from different types of decision-making are connected.

We derive the expression for the expected choice probabilities for the single spin-flip algorithm, demonstrate these steps for the attraction effect, and provide the parameter values for all examples used in the main text. We then visualise how variations of $\beta$ and $\mu$ influence the choice probabilities for the attraction effect. We end by showing the parameter based expression for the binary case and discuss some properties with respect to rational choice.

Single spin-flip dynamics

For a choice structure with k cues and m alternatives there are $2^m$ possible configurations of $\mathbf {x}$ . We use $\mathbf {x}_i$ to denote the i th of these $2^m$ possible states. Let $\mathbf {P}$ be a square matrix of order $2^m$ in which element $P_{ij}$ contains the probability of transitioning from $\mathbf {x}_i$ to $\mathbf {x}_j$ in one step of the single spin-flip algorithm, and $P_{ii}$ contains the probability staying in the current state. As the algorithm changes at most one alternative at each iteration $\mathbf {P}$ will be highly sparse with at most m non-zero elements in each row. From $\mathbf {P}$ we obtain the expected rational choice probabilities, contained in the stationary distribution, as they are proportional to the elements of the first eigenvector of $\mathbf {P}$ that correspond to states in which the choice conditions are met (i.e., $\sum _{i=1}^n x_i = k + 1)$ . We will not go into this approach at length as these probabilities can simply be obtained from the conditional distribution presented in Eq. ( 6 ).

The expected choice probabilities for stopping as soon as the choice conditions are met for the first time are obtained by reformulating the Markov chain with transition matrix $\mathbf {P}$ as an absorbing chain. To that end we make a distinction between the m absorbing states, i.e., those states in which only one alternative is active, and $2^m - m$ transient states, i.e., those states in which more than one alternative or no alternatives are active. The transition matrix for the absorbing chain $\mathbf {P}^{*}$ has the canonical form:

in which $\mathbf {Q}$ contains the transition probabilities between transient states, $\mathbf {R}$ contains the transition probabilities from transient states to absorbing states, $\mathbf {1}$ contains the transition probabilities between absorbing states, i.e., an identity matrix of order n , and $\mathbf {0}$ contains the transition probabilities from absorbing states to transient states, i.e., a matrix with zeros. Rearranging $\mathbf {P}$ in its canonical form allows us to derive the expected progression of the Markov chain more easily 106 .

Let $y \in \{1,2,\dots , m\}$ denote the chosen alternative, let $\mathbf {z} = [z_1, z_2, \dots , z_{{\scriptscriptstyle {2}}^m}]$ denote the resting state probabilities in which $z_i \in [0,1]$ denotes the probability for the choice process to start in alternative configuration $\mathbf {x}_i$ . We divide $\mathbf {z}$ into the probabilities for starting in an absorbing state $(\mathbf {z}_a)$ , and for starting in a transient state $(\mathbf {z}_t)$ . Lastly, let t denote the number of iterations of the Metropolis algorithm.

The (marginal) probability that alternative y is chosen from the set of alternatives S is:

in which $\mathbf {1}_y$ and $\mathbf {R}_y$ represent the y th column of the matrices $\mathbf {1}$ and $\mathbf {R}$ respectively. Using the property of geometric series to rewrite the infinite sum over $\mathbf {Q}^t$ this expression simplifies to:

The expected number of Metropolis iterations before alternative y is chosen is:

Once again we can rewrite the infinite sum over $t \, \mathbf {Q}^{t-1}$ and simplify the expression to:

$\varvec{\beta }$ and $\varvec{\mu }$

Influence of $\beta$ and $\mu$ on the choice probabilities ( a ) and response times ( b ) for the attraction effect example. The axes for both plots show $\beta$ and $\mu$ on a log-scale. ( a ) 6 different response phases are identified as a function of changes in $\beta$ and $\mu$ . ( b ) Response times are approximated with the log of the expected number of iterations before termination, averaged over all three choices.

Figure 7 visualises the effect of $\beta$ and $\mu$ on the expected choice probabilities and response times for the attraction effect example from Fig. 4 a. In Fig. 7 a we have identified 6 different response phases for this example.

Random phase (RND) The choice probabilities for choosing the money $({\$})$ , the nice pen $(P_+)$ , or the not so nice pen pen $(P_+)$ , differ at most $10\%$ from one another.

Strong attraction effect phase (AE+) The probability of choosing $P_+$ is greater than the probability of choosing ${\$}$

Normal attraction effect phase (AE) The probability of choosing ${\$}$ is smaller than $62\%$

Choice axiom phase (CA) The choice probabilities for all options differs only a marginally from those expected under Eq. ( 6 ) if $\beta$ and $\mu$ would both be one.

Increasingly rational phase (RA) The probability of choosing ${\$}$ is larger than $66\%$

Rational phase (RA+) The probability of choosing ${\$}$ is larger than $90\%$

The distribution of the different phases as a function of $\log (\beta )$ and $\log (\mu )$ in Fig. 7 a shows that the random phase primarily takes place when both $\beta$ and $\mu$ are small. When $\mu$ is small and $\beta$ goes up the attraction effect is at its strongest, which makes sense as the influence of relationships between alternatives becomes stronger, while keeping the influence of the general appeal small. When both $\beta$ and $\mu$ increase we find that choices become increasingly rational, i.e., eventually the money is chosen almost with near certainty. At this point we run into the limit of our computational precision, as is shown by the white area in the upper right corner of both plots. Specifically, as the initial condition will go to one for all alternatives active, and the probability of transitioning out of this state becomes increasingly close to zero, calculating the inverse of the transitions matrix can no longer be done accurately.

This is also what we see when looking to the distribution of the log mean response times as a function of $\log (\beta )$ and $\log (\mu )$ in Fig. 7 b, which shows clearly that these are increasing in both $\beta$ and $\mu$ . While in most cases the expected number of iterations is thus small, the median of the plot range is approximately 38 iterations before a response is selected, at some point the mean expected number of iterations before a choice is made goes up to 54419290677, or fifty-four billion four hundred nineteen million two hundred ninety thousand six hundred seventy-seven. This tells us that for those corresponding values of $\beta$ and $\mu$ , the probability of getting out of a transitive state is extremely small. Of course, the number of iterations is only a proxy for response times and therefore does not tell us how long the choice process will actually take. For example, if the log number of iterations would be the number of seconds a choice process takes, 54419290677 iterations would amount to less than 25 s.

Looking to both Fig. 7 a,b we find that our model predicts random behaviour for very short response times, and with increasing these the context effects become visible. When response times go up even further, eventually the context effects diminish again and choices become increasingly rational. In the limit, choice probabilities go to one for the alternative with the largest general appeal and the expected number of iterations goes to infinite.

Rational choices

Expressing the expected choice probabilities as a function of the parameters $\mathbf {A}$ and $\mathbf {b}$ for a choice with n alternatives, requires one to write out all the possible paths to the n choice conditions from all $2^n - n - 1$ configurations in which at least two alternatives are active. As this expression becomes already incomprehensible for $n=3$ , we will limit ourselves to the simplest case of a binary choice problem. If we define $u_i = \sum _{k \in {\scriptscriptstyle {K}}} a_{ik} + b_i$ , the probability of choosing alternative x over y in a binary choice problem is given by:

in which $p_x$ denotes the probability to start in the configuration in which $x=1$ and $y=0$ , as such meeting the choice conditions and directly triggering a choice for alternative x . $p_{\scriptscriptstyle {0}}$ and $p_{\scriptscriptstyle {1}}$ denote the probabilities to start in a configuration in which all alternatives are inactive $(p_{\scriptscriptstyle {0}})$ or active $(p_{\scriptscriptstyle {1}})$ . Although this seems like a straightforward expression, even when assuming that there is no relation between the alternatives $(a_{xy} = 0)$ , we already obtain the four possible formulations depending on the values for $u_x$ and $u_y$ :

From Eqs. ( 13 ) and ( 14 ) it becomes clear that if no relationships exist between alternatives, the probability of choosing alternative x is purely a function of the difference between $u_x$ and $u_y$ if we started in either $p_{\scriptscriptstyle {0}}$ or $p_{\scriptscriptstyle {1}}$ . While it might seem a plausible assumption that choices will be at least weakly rational, i.e., $p_{x,y}(x) \ge {}^{1}\!/_{2} \iff u_x \ge u_y$ , if there is no interaction between the alternatives, this is not necessarily the case.

For example, if the relation between cue k and alternative x is positive ( $a_{kx} = 10$ ), but the general appeal of x is negative ( $b_x = -5$ ), and the relation between cue k and alternative y is negative ( $a_{ky} = 10$ ), but the general appeal of y is positive ( $b_y = 5$ ). We find that $u_x = 5$ and $u_y = -5$ , and as $u_x > u_y$ we would expect $p_{x,y}(x) \ge {}^{1}\!/_{2}$ . Using Eq. ( 14 ) we can calculate that when starting in a non-absorbing configuration both transition probabilities are almost one, such that $p_{x,y}(x) \approx p_x + p_{\scriptscriptstyle {0}} + p_{\scriptscriptstyle {1}}$ and the probability of choosing x over y is the sum of all starting configurations except $p_y$ , i.e., $p_{x,y}(x) \approx 1 - p_y$ . However, whereas the transition probabilities are only a function of the difference between $u_x$ and $u_y$ , the starting probabilities are not. In the resting state distribution the cue would almost always be inactive if $b_k<< 0$ , and the probabilities $p_{\scriptscriptstyle {0}}, p_x, p_y$ and $p_{\scriptscriptstyle {1}}$ become primarily a function of $b_x$ and $b_y$ . As $b_y>> b_x$ , we find that $p_y \approx .99$ , which implies that 99% of the time we will start in the configuration that will immediately trigger the choice for alternative y . Although this situation might not necessarily be encountered in real life, it shows that determining when choices are guaranteed to be even weakly rational is not straightforward.

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Acknowledgements

This research was supported by NWO (The Dutch organisation for scientific research); No. 022.005.0 (J.K.), No. CI1-12-S037 (G.M.), No. 451-17-017 (M.M.), No. 314-99-107 (H.M.). During the preparation of the manuscript M.B. was working at ACTNext by ACT (Iowa City, USA). We thank Andrew Cantine (ACT) for proof-reading the manuscript.

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Kruis, J., Maris, G., Marsman, M. et al. Deviations of rational choice: an integrative explanation of the endowment and several context effects. Sci Rep 10 , 16226 (2020). https://doi.org/10.1038/s41598-020-73181-2

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Rational Choice Theory of Criminology

Ayesh Perera

B.A, MTS, Harvard University

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Rational choice theory of criminology views offenders as rational actors who weigh the costs and benefits of committing a crime. It assumes individuals decide to offend based on a cost-benefit analysis of both personal factors and situational factors, choosing to commit crimes when the perceived benefits outweigh potential costs.

Key Takeaways

Rational choice theory in criminology states that individuals partake in criminal activity following a logical thought process that consciously analyzes and weighs the benefits and costs of committing crimes. If the perceived cost of committing the crime is outweighed by the benefit, people will be more likely to offend.
Rational choice theory is a school of thought which holds that individuals decide upon the course of action most in line with their subjective preferences.
The theory has been employed to model decision-making in contexts ranging from economics to sociology.
Rational choice theory in criminology states that individuals partake in criminal activity following a logical thought process that consciously analyzes and weighs the benefits and costs of committing crimes.
If the perceived cost of committing the crime is outweighed by the benefit, people will be more likely to offend.

Theoretical Origins

The emphasis on the relationship between human rationality and criminal conduct dates to the 17th century which saw increasingly naturalistic views on human beings gain predominance in intellectual discourse.

Hobbes, for instance, contended that humans pursue self-interest with little regard to such a pursuit’s impact on others (Hobbes, 1651). Thus, the ensuing chaos would necessitate a social contract to ensure safety and order.

The 18th century saw Beccaria and Bentham arguing that a conscious endeavor to minimize pain and maximize pleasure governs human behavior (Beccaria, 1764, 1789). Beccaria additionally held that certain, severe and swift punishment, proportionate to the crime perpetrated, constitutes the finest deterrent against future maleficence.

More recently, Clarke and Cornish (1987) have appealed to rational choice theory to better understand crime control policies. Using the theory as a framework, they have introduced ‘choice structures’ to classify crimes, and identify factors individuals must consider before engaging in transgressions.

Clarke and Cornish reason that the rational choice model can unveil lines of inquiry accounting for criminal conduct, and that attempts to fight crime should ideally increase the barriers to perpetrating it.

What are the key components of rational choice theory criminology?

In criminology, rational choice theory assumes that a decision to offend is taken by a reasoning individual, weighing up the costs and benefits of their action, in order to make a rational choice.

The following assumptions underpin rational choice theory in criminology (Beaudry-Cyr, 2015; Turner, 1997):

Humans possess the power to freely choose their conduct.
Humans are goal-oriented and purposive.
Humans have hierarchically ordered utilities or preferences.
Humans act based on rational judgments pertaining to:
The utility of alternatives based on their hierarchically ordered preferences.
The cost of each alternative.
The best opportunity to maximize utility.

How Rational Choice Theory Explains Crime

Rational choice theory employs two subsidiary theories to explain crime (Beaudry-Cyr, 2015):

Routine Activity Theory

Developed by Lawrence Cohen and Marcus Felson, routine activity theory holds that the occurrence of a crime is dependent upon the presence of three elements (Cohen & Felson, 1979)(Felson & Cohen, 1980):

A suitable target
A motivated offender
The absence of guardianship

Instead of attributing crime to what some sociologists identify as root causes (such as poverty and inequality), routine activity theory argues that the interplay between opportunity, motivation and vulnerable targets primarily accounts for criminal conduct.

The rise of crime following WWII, which coincided with the boom of Western economies and the expansion of welfare states, has been cited as an example of how the opportunity to steal more (because of the society’s increased prosperity), better accounts for crime (rather than problems such as inequality and poverty).

Cohen and Felson have further noted that significant structural phenomena, rather than random and trivial factors, characterize the conditions under which predatory violations transpire.

Though routine activity theory remains controversial among many, its potency to explain crimes such as the following seems seldom disputed:

Ponzi schemes
Copyright infringement
Insider trading
Corporate crime

Situational Choice Theory (Situational Crime Prevention)

Advanced by R.V. Clarke, this subsidiary theory holds that, in addition to the crime itself, situational factors inspire people to commit crime (Clarke, 1997).

As such, it seeks to reduce criminogenic opportunities by manipulating the environment and portraying criminal conduct as riskier, harder and less rewarding.

Instead of merely responding to a crime following its perpetration, situation choice theory calls for the systematic design and permanent management of the physical and social atmosphere.

Examples of such measures have included better streetscaping, the installation of alarms, improved lighting over public spaces, surveillance of neighborhood activities, the employment of security guards and the incorporation of property marking (Homel, 1996).

Strengths and Weaknesses of the Theory

Empirical support.

Research by Clarke and Harris points out that auto thieves selectively choose their targets as well as varying vehicle types based on the objective of their theft (Clarke & Harris, 1992) (Government of Ontario, Rational Choice and Routine Activities Theory).

This implies that rational decision-making governs identifying opportunities and targets. The same seemingly holds true for sex-trade workers. Research shows that women rationally decide whom to solicit and engage with, and what risks to take in fulfilling an interaction (Maher, 1996).

Moreover, substance offenders too, appear to rationally make their decisions to use drugs based upon the apparent benefits thereof in light of potential related costs (Petraitis, Flay and Miller, 1995).

Furthermore, research on drug dealers point out that a cost-benefit analysis on the economics of the drug trade likely precedes involvement in illicit drug distribution (MacCoun and Reuter, 1992).

Additionally, theft and acts of violence too, seem to conform to the rational choice theory model (Matsueda, Kreager and Huizinga, 2006). They appear to be a function of perceived opportunities, risk of arrest and psychic rewards—especially involving being viewed as ‘cool’ within groups.

Perpetrators of violence seem highly selective in choosing their targets, often picking vulnerable people incapable of protecting themselves.

Despite its wide appeal, the rational choice model in criminology has garnered notable criticism. O’Grady, for instance, has argued that the theory falsely assumes that all persons are capable of making rational choices (O”Grady, 2011).

He has pointed out that the theory fails to explain why young offenders, unlike their adult counterparts, would have the burden of responsibility excused from them. O’Grady further reasons that the theory seem to disregard persons considered NCRMD (Not Criminally Responsible on account of Mental Disorder).

Additionally, research suggests that rational choice considerations can be overridden by emotional arousal (Carmichael and Piquero, 2004). The role of anger in the perpetration of assault is one such example.

Moreover, individuals who, without regard for possible alternatives or long-term consequences, engage in impulsive robberies to procure the basic necessities of life for immediate gratification provide another case to consider (Wright, Brookman and Bennett, 2006).

Further Information

Cornish, D. B., & Clarke, R. V. (1987). Understanding crime displacement: An application of rational choice theory. Criminology, 25 (4), 933-948.

De Haan, W., & Vos, J. (2003). A crying shame: The over-rationalized conception of man in the rational choice perspective. Theoretical Criminology, 7 (1), 29-54.

How Offenders Make Decisions: Evidence of Rationality

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Social Disorganization and Rational Choice Theory

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Rational Choice Theory Essay Sample, 1556 Words, 4 Pages ...
Rational Choice Theory Essay. Rational choice theory postulates that individuals make decisions that they think will best advance their self interest, even though this is not usually the case. It is based on the premise that human being is a rational being, and freely chooses their behavior, both conforming and deviant, rationally.
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Rational Choice Theory in Sociology (Examples & Criticism)

The Four Assumptions of Rational Choice Theory

Strengths of Rational Choice Theory

1. It effectively explains individual behavior

2. It predicts human behavior

3. It explains and predicts social group behavior

Criticisms of Rational Choice Theory

1. It does not account for altruistic or selfless actions

2. It does not explain impulsive actions

3. When team goals supersede individual goals

Rational Choice Theory Examples

2. Personal scenario: Healthy diet or instant gratification?

3. Social scenario: Visit family or enjoy vacation alone?

4. Economic scenario: Save for retirement or donate all income?

Theoretical Applications

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Introduction to Rational Choice Theory in Social Work

What is Rational Choice Theory?

History of rational choice theory

Assumptions of rational choice theory

Applications of rational choice theory

Strengths and weaknesses of rational choice theory

How Does Rational Choice Theory Apply to Social Work?

Criticism of Rational Choice Theory

Summary and Resources for Further Learning

Deviations of rational choice: an integrative explanation of the endowment and several context effects

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Rational Choice Theory of Criminology

Key Takeaways

Theoretical Origins

What are the key components of rational choice theory criminology?

How Rational Choice Theory Explains Crime

Routine Activity Theory

Situational Choice Theory (Situational Crime Prevention)

Strengths and Weaknesses of the Theory

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Social Disorganization and Rational Choice Theory

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