Encyclopedia of Evolutionary Psychological Science

Living Edition
| Editors: Todd K. Shackelford, Viviana A. Weekes-Shackelford

Behavioral Economics

  • Vincent BarnettEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-16999-6_2806-1

Definition

Behavioral economics is conventionally defined as economic theory that incorporates insights from contemporary psychology, cognitive science, cultural theory, human physiology/emotional states, and neuroscience. However, the term “behavioral economics” can be seen as potentially subversive because it can be taken to suggest that the remainder of economics is nonbehavioral and – by implication – excessively abstract and therefore partially inaccurate.

Introduction

This entry will first provide an account of the nature and scope of behavioral economics as it was outlined by one of its founding fathers, George Katona. This entry will then go on to discuss the work of four very important more recent behavioral economists – Daniel Kahneman, Amos Tversky, Richard Thaler, and Vernon Smith – in more detail. Following this, in the latter part of the entry, it will consider the consequences of some ideas developed by evolutionary psychologists and biologists for the nature of behavioral understanding in economics more widely conceived. It will accomplish this task by means of case-studies of how two individual concepts from economics, the invisible hand metaphor and the maximization conception of economic rationality, fit (or do not fit) within the framework of analysis provided by evolutionary psychology. Finally, a relatively new subfield of behavioral economics – neuroeconomics – will briefly be considered in relation to its significance for evolutionary psychology.

George Katona

George Katona (1901–1981) was a pioneer in the field of behavioral economics with an early background in Gestalt psychology who was born in Budapest, but who later emigrated to the USA and worked at the Cowles Commission and then the University of Michigan (Katona 1960). He was the author of Psychological Analysis of Economic Behavior (Katona 1951) and the inventor in the mid-1940s of the US Index of Consumer Sentiment (ICS), an innovative empirical measure of consumer expectations.

Katona defined behavioral economics as containing three fields of analysis: empirical studies of the behavior of businesspeople/consumers; analysis of decision-making processes in relation to behaviors like spending, saving, and investing; and the study of economic motives, attitudes, and expectations at a deeper level. He also identified subfields of behavioral economics as follows: the study of consumer/savings/business behavior; how income is spent and augmented; the nature of different market systems and associate behaviors; politico-economic attitudes (e.g., attitudes to taxation, government spending and so on); and workplace behavior/broader organizational behavior (Katona 1980, 3–6).

To examine one topic in more detail, the mainstream economist’s conception of rational behavior, on this topic Katona explained that:

In contrast to the emphasis of [mainstream] economic theory on rationality, defined as weighing available alternatives and choosing the alternative that maximizes utility or profits, behavioral economists found a common occurrence of habitual behavior – doing what has been done in the past, following established routines and following leaders – occasionally interrupted by genuine decision making. Only in those relatively few instances when the decision really matters do strong motivational forces arise that lead businessmen and consumers to proceed in a circumspect manner. Even then the alternatives considered are usually restricted according to their attitudes and expectations, and a satisfying course of action is chosen rather than one that will optimize the outcome. (Katona 1980, 13–14)

This account articulates the significant difference between what were classified by Katona as the unrealistic assumptions of much of mainstream economics, and the more realistic assumptions of his own brand of behavioral economics. Elsewhere, he presented a more detailed account of how he believed human actions and decision-making processes operated, highlighting five ways in which real-world behavior differed significantly from that proposed by utility-maximization-based economics.

Real human behavior reflected the influence of multiple and sometimes competing motives, for instance with respect to evaluating the sometimes-opposing multiple qualities of goods; real behavior involved a significant degree of group decision-making in addition to individual decision-making, for example, family-based or firm-based decision-making; real behavior involved the prevalence of uncertainty about the future, for example, in relation to spending capacity; real behavior involved ignorance of at least some of the relevant facts, for example, in relation to the scope of a given product’s market availability; and finally, real behavior involved a degree of confusion/uncertainty about how decision-making processes should best be undertaken, even if all the relevant facts were available (Katona 1980, 27).

Following on directly from J.M. Keynes’s work in the 1930s on the importance of psychological propensities for an understanding of economic behavior (Katona 1942, 17; Barnett 2017), Katona and his brand of behavioral economics was virtually the sole efforts of a lone wolf in the first two decades after World War Two. However, by the end of the twentieth-century, behavioral economics had developed significantly and blossomed into a very important subfield of economics in its own right, partly through the very influential efforts of two key individuals, Daniel Kahneman and Amos Tversky.

Daniel Kahneman and Amos Tversky

Daniel Kahneman (1934–) is well-known for his work on heuristics and biases, loss aversion, prospect theory, framing effects, and choice under uncertainty. He is the co-author of Judgment under Uncertainty: Heuristics and Biases (Kahneman and Tversky 1982) and was awarded the Nobel Prize in Economics in 2002 for his work on prospect theory. Amos Tversky (1937–1996) was Kahneman’s co-author of the work on prospect theory, and his important contribution to this theory was acknowledged posthumously in the Nobel Prize citation about Kahneman from 2002. Some of Tversky’s work on heuristic choice mechanisms has been usefully employed by contemporary evolutionary psychologists (Todd and Gigerenzer 2007, 206).

In their work on heuristics and biases, Kahneman and Tversky argued that individual decisions are often made using a limited number of heuristic principles that simplify the decision-making process, but which can lead to either moderate or severe errors in certain situations. They have also identified various specific biases that people can be subject to, such as the representativeness heuristic, the conjunction effect, base-rate neglect, and the law of small numbers. In the latter case, Kahneman and Tversky’s research suggested that individuals tended to exaggerate how often a small sample accurately resembled the wider population from which it was drawn.

In their work on prospect theory, Kahneman and Tversky maintained that the real carriers of value to human individuals were relative changes in wealth and welfare, rather than final or absolute states. For example, the same degree of wealth could connote extreme poverty for one person and great wealth for another, depending on the particular context of their lives. The billionaire whose total wealth was suddenly reduced to only $10,000 would feel like they had been greatly impoverished, whereas if a rich benefactor gave a homeless individual with no possessions at all $10,000 as a gift, then they would feel almost like a millionaire.

Following on from this, Kahneman and Tversky maintained that people are generally loss-averse, i.e., they value an equivalent loss higher than an equivalent gain. People also exhibit diminishing sensitivity to ongoing gains made of the same extent as they accumulate and are subject to the framing effect or to precisely how a particular issue is formulated. Differently framed formulations of the same question can sometimes result in very different answers. For example, patients can respond differently when it is recommended that they have surgery with a 70% survival rate, compared to the same surgery when it is recommended with a 30% chance of death.

Kahneman and Tversky not only frequently collaborated with themselves in their behavioral economics innovations but sometimes with other individuals as well. One very significant such individual was Richard Thaler with respect to collaborative work on the endowment effect (Kahneman et al. 1991). The endowment effect is the hypothesis that people ascribe greater value to an item merely because they currently own it, as compared to what their valuation of the same item would be if they did not own it or if someone else owned it.

Richard Thaler

Richard Thaler (1945–) is well-known for his work on the consequences of the limitations of rationality, the nature of social preferences, and a lack of self-control for how economic decisions are made, and how they affect market outcomes. He is the co-author of Nudge: Improving Decisions about Health, Wealth, and Happiness (Thaler and Sunstein 2008) and the author of Quasi Rational Economics (Thaler 1994). He was awarded the Nobel Prize in Economics in 2017 for his work on bridging the gap between the economic and the psychological analyses of decision-making.

Thaler’s co-authored book Nudge analyses how groups and organizations can assist individuals in making superior choices in their daily lives from a perspective of libertarian paternalism. Thaler and Sunstein divided human thinking/thought processes into two types: automatic/instinctive versus reflective/self-conscious. According to Thaler and Sunstein, in part because of differences/conflicts between the two types of thinking, individuals can be subject to various biases, fallacies, and heuristic frameworks, which may in certain situations lead to them making bad decisions and/or mistaken judgments.

The various heuristic frameworks and/or biases outlined by Thaler and Sunstein include: anchoring, or relying too much on one trait or one piece of information when making judgments, rather than using a wide range of information; availability, or using an example very easily brought to mind rather than a more relevant example that is more difficult to locate; status quo bias, or blindly continuing an existing behavior without further contemplation; and the herd mentality, or being too influenced by other people and/or existing trends.

From the perspective of evolutionary psychology, it could reasonably be hypothesized that some of these biases and heuristic frameworks are psychological mechanisms/instincts that have evolved by natural selection to provide human beings with a “fast and frugal” means of making decisions in situations in which speed of decision-making is a crucial factor at play (Todd and Gigerenzer 2007, 197). However in other situations, when speed is not so crucial, using these same instincts/biases, without additional reflective/self-conscious thought, might result in mistaken judgments and decisions being made.

Vernon Smith

Vernon Smith (1927–) is an experimental economist who was awarded the Nobel Prize in Economics in 2002, and who is known also for his work on understanding the nature of behavioral rationality. As Smith himself explained, “experimental market economics and behavioral economics are complementary” (Smith 2008a, 155).

Rationality is usually defined by mainstream economists as some type of maximization behavior in pursuit of self-interest. Methodological individualism underlies this approach, where each human being is regarded as a separate, self-contained unit of decision-making and action. However, a fundamental principle of evolutionary biology – inclusive fitness – challenges this approach, as genetic relatedness means that individuals are embedded in kinship groups (Barnett 2015, 653). In fact from a gene’s-eye perspective, there are no such things as individuals, as genetic code is shared among family members.

Smith’s definition of constructivist rationality widens the framework of operation of maximization-type rationality by applying it to individuals and to organizations. Its function is explicitly limited to the conscious analysis of superior alternative feasible pathways and it refers to optimal design in the incentive structure (Smith 2008b, 138). However, Smith is aware that this constructivist rationality is not comprehensive and requires augmentation by a different type of rationality that takes account of the non-conscious aspects of behavior.

He defined his own version of ecological rationality in expansive terms as follows. An ecologically rational order was:

an undesigned ecological system that emerges out of cultural and biological evolutionary processes: homegrown principles of action, norms, traditions and “morality.” Ecological rationality uses reason – rational reconstruction – to examine the behavior of individuals based on their experience and folk knowledge, who are “naïve” in their ability to apply constructivist tools to the decisions they make; to understand the emergent order in human cultures; to discover the possible intelligence embodied in the rules, norms, and institutions of our cultural and biological heritage that are created from human interactions but not by deliberate human design. People follow rules without being able to articulate them, but they can be discovered. (Smith 2003, 469–70)

Smith was careful to explain the relation between his two types of rationality. Constructivist rationality accepted current economic structures (e.g., particular market auction systems) as given and attempted to formally model these structures using explicit rules of behavior and operation. Ecological rationality in contrast asked: how did these structures come into being (Smith 2003, 471)?
For evolutionary psychologists, however, ecological rationality has facilitated adaptive problem-solving in relation to the contexts that the human mind encountered during its long-period of evolution. As has been explained about environmentally specific statistical regularities:

… heuristics that use just a little of the patterned [environmental] information and process it is simple ways can make decisions that are fast, accurate, and adaptive … Adaptive behavior emerges just when the mechanisms of the mind are properly matched to the (information) structure of the environment – producing what we call ecological rationality. (Todd and Gigerenzer 2007, 199)

As all ecologically rational cognitive processes relate to specific, concrete problems, they are context-dependent/path-dependent rather than general in nature (Egidi and Rizzello 2006, 675).

While Smith was confident that both of his types of rationality had relevance to understanding how markets operated, evolutionary psychologists see their conception of ecological rationality as being in conflict with the standard account of rationality provided by most economists. Thus, it has been suggested by some evolutionary psychologists that “we should expect humans and other animals to show ‘ecological rationality’, rather than the economists’ perfect rationality” (Dunbar and Barrett 2007, 128). Contrast this with how economists usually interpret Smith’s work in experimental economics: “People are more rational than the agents in our models” (Eckel 2008, 570: emphasis added).

Smith concluded one article with a list of “working hypotheses” that originated from his work on experimental economics. Listed as number five was the idea that rules governing markets as institutions emerge as spontaneous order, through evolutionary processes that have adapted them to requirements:

What emerges is a form of “social mind” that solves complex organizational problems without conscious cognition. This “social mind” is born of the interactions among all individuals through the rules of institutions that have to date survived cultural selection processes. (Smith 2003, 500)

In the final section of this entry, it will be suggested that the idea of the emergence of a “social mind” is potentially equivalent to the hidden hand metaphor common in economics, and also to a well-known and long-standing fallacy in evolutionary biology.

The Invisible Hand

The invisible hand is perhaps the most famous metaphor found in all of economics: individuals pursuing their own self-interested behavior invariably end up serving the good of society as a whole. As a metaphor, it is used primarily to lend support to a laissez faire approach to economic policy or providing an instinctive argument as to why less governmental or state control and intervention is generally better than more.

The idea of the invisible hand also has what can be called a technical meaning, i.e., if it were possible to calculate the effects of an aggregate of all individuals pursuing unbridled self-interest, versus this aggregate with additional controls on self-interest that were designed “for the social good,” then the result would necessarily and always be superior in the former case than the latter. It will be demonstrated here that this technical meaning of the invisible hand is false, as it is the equivalent of an argument that was previously made in support of the idea of group selection in evolutionary biology, an argument which is now accepted as erroneous.

This basic parallel has recently been made by Robert Trivers, a leading evolutionary biologist. Trivers wrote that mainstream economists:

… argue that individuals acting for personal utility will tend to benefit the group; they are (thus) often blind to situations where unrestrained pursuit of personal utility leads to disastrous effects on group benefits … This is a well-known fallacy in biology, with hundreds of examples. Nowhere do we assume in advance that the two kinds of utility are positively aligned … the result is the equivalent of the group selection fallacy – yes, selection acts on individual utility but magically enough, general utility is thereby generated? (Trivers 2013, 311–312)

As economists are not always well-versed in evolutionary biology, this entry will outline the consequences of Trivers’ parallel between the invisible hand and group selection in more detail, in order to illustrate how behavioral economics needs to take account of the principles of evolutionary biology.

One argument previously advanced in support of group selection is now acknowledged as mistaken, what will be called the “higher good” argument. The “higher good” argument is that evolution by natural selection can sometimes act for the good of the group or the species as a whole, when in fact evolution only ever acts to promote the interests of individual genetic lines. The consequences of natural selection acting on genes for higher-level units such as families, tribes, nations, species, and ecosystems cannot be known a priori, instead the particular circumstance have to be examined before the effects of evolution on higher-level units are known. The meaning of this mistake will be elaborated in what follows.

Consider a group of birds that are divided into two types of foragers, prudent and imprudent foragers, each subgroup initially being of equal size. The former birds restrain their feeding and reproduction in order to manage their environment for the long-term, while the latter birds eat and reproduce as much as is physically possible (Wilson 2007, 50). If the invisible hand were to operate on this hypothetical group, then it would balance the behaviors of the two subgroups to produce a long-run equilibrium that maintained the environment in a healthy state for both types of foragers to enjoy, by selecting to some degree against the imprudent foragers who naturally reproduce more quickly, so as to maintain the initial position of ecological balance.

However what would happen in the real world would be that, because the imprudent foragers have the higher relative fitness and reproduce more, they would increase their percentage frequency in the group over time. The fact that this would eventually produce a population collapse after the growing number of imprudent birds exhausted their food source is in no way precluded by natural selection, as it never acts “for the good of the group” or the good of the ecosystem.

Another example was provided by Trivers himself. In some types of monkeys, male infanticide of dependent offspring of current mates, who were fathered by other males, was in the past interpreted by biologists as a population-control mechanism designed to serve the good of the group, by ensuring that the total population level was kept in line with the local ecology (Trivers 1985, 74). In fact, this murder simply serves the individual interest of the current male partner by bringing the female into reproductive readiness more quickly and preventing her attention being taken by offspring from previous mates (Trivers 2013, 308). Since a “horrific” example of monkey behavior was being considered (the murder of infants), there was a human desire to believe that a “higher good” would be the outcome (population management), but in fact, nature has no such desire to guarantee the good of the group.

The fact that the “higher good” argument for group selection is erroneous has a twofold parallel with the invisible hand in economics, what can be translated into the rightwing invisible hand fallacy and the leftwing anti-invisible hand fallacy. If the rightwing invisible hand fallacy is that the “good” invisible hand magically guarantees that individuals pursuing their self-interest will always result in the social good being enhanced, then the leftwing anti-invisible hand fallacy is the opposite that the absence of the invisible hand (or a wicked invisible hand) guarantees that individuals pursuing their self-interest will never result in the social good being enhanced.

The scientific position based on the analogy with natural selection is that sometimes individuals pursuing their self-interest will translate into the social good being enhanced and sometimes it will not do so, it all depends on the specific details of the particular example that is being considered. Although producing a “higher good” above the individual level is never an aim of natural selection, it is sometimes an accidental byproduct. The examples considered above of natural selection acting to produce negative results for the group, need, therefore, to be supplemented by other examples in which group benefits do result, but only by accident. In order for behavioral economics to fully recognize the implications of evolutionary biology for understanding behavior, it has to incorporate this type of scientific reasoning into its foundations.

Neuroeconomics

One relatively new subfield of behavioral economics is, therefore, particularly promising from an evolutionary perspective: neuroeconomics. Neuroeconomics combines analysis from neuroscience with aspects of behavioral and experimental economics and also cognitive psychology. It uses some of the methods developed by neuroscientists to study brain activity in order to investigate the relationship between economic behavior and the neural matter, the neural mechanisms, and the neural structures of the brain (Lohrenz and Montague 2008). By discovering the brain regions linked to particular thought/feeling processes, the neurological foundations of economic decision-making can potentially be better understood.

For example, in relation to neural representation of temporal discounting, or whether an individual prefers $X today or $X + 1 in 1 week, it has been discovered that there are two specific neural systems involved in deciding the final outcome: a more impulsive system located in the limbic system, and a more rational system located in the prefrontal cortex (McClure et al. 2004). Another study has highlighted the neurological foundations of asset investment decisions, suggesting that insula activation was linked to higher degrees of investment risk with respect to particular stock/bond choices, and that striatum activity was linked to more aggressive investment choices (Kuhnen and Knutson 2005). It has been suggested in consequence that the function of the insula is to model the hypothesized outcomes of particular risk-taking behaviors.

From the perspective of evolutionary psychology, neuroeconomics provides one potential avenue for the possibility of locating individual psychological mechanisms in specific regions of the brain, and thus the possibility of beginning to identify the concrete neurological bases of these mechanisms. Or, in other words, of creating models of the evolved computational architecture of the mind “that can be cashed out genetically” (Tooby and Cosmides 2005, 63), in order to contribute towards mapping an evolutionary psychological equivalent of Grey’s Anatomy (Goetz and Shakelford 2007, 15).

Conclusion

This entry, like another related entry on economists and evolutionary psychology (Barnett 2018), has attempted to present both a basic account of some of the main contributions of some well-known behavioral economists and to evaluate these contributions to some extent using concepts imported from evolutionary psychology and evolutionary biology. It has also evaluated a few basic principles of mainstream economics, like the invisible hand, in similar fashion.

From the perspective of evolutionary psychology, behavioral economics has certainly made a sincere effort to counter some of the unrealistic assumptions and principles of neoclassical economics by presenting numerous examples of suboptimal, irrational, biased, distorted, mistaken, and incomplete behaviors, and by providing some more realistic assumptions and principles to replace the unrealistic abstractions of much of mainstream economics. Some of these more realistic assumptions and principles overlap to a degree with those of evolutionary psychology, for example, Smith’s use of the ecological rationality concept.

However, in some other areas, costly signaling theory and the handicap principle, for example, behavioral economics, has yet to fully recognize the importance of modern evolutionary concepts (Miller 2009, 91; Spence 1973), even though institutionalist economists have been analyzing conspicuous consumption since the end of the nineteenth century (Veblen (1925) [1899]). This neglect is epitomized by the work of George Akerlof and Robert Shiller, both of whom have fully acknowledged the importance of psychology for economics (Akerlof and Shiller 2009): unfortunately, this acknowledgment is only of the importance of psychology [sic], not of evolutionary psychology.

Many evolutionary psychologists would argue that what is really required in order to make behavioral economics fully realistic is for economists to embrace wholeheartedly an evolutionary approach to their subject, and place all of their theories on more solid Darwinian footings. One potentially positive breach in this regard has recently been provided by neuroeconomics. However if all of economics as a subject embraced evolutionary psychology as its underlying foundations, then it would become entirely behavioral, truly realistic, and take its rightful place as a new branch of the natural sciences (Cosmides and Tooby 2005, 585).

Cross-References

References

  1. Akerlof, G., & Shiller, R. (2009). Animal spirits: How human psychology drives the economy, and why it matters for global capitalism. Princeton: PUP.Google Scholar
  2. Barnett, V. (2015). Evolutionary psychology and economics. In F. Wherry & J. Schor (Eds.), The SAGE encyclopedia of economics and society (Vol. 2, pp. 652–656). Thousand Oaks: Sage.Google Scholar
  3. Barnett, V. (2017). Keynes, animal spirits and instinct: Reason plus intuition is better than rational. Journal of the History of Economic Thought, 39(3), 381–399.CrossRefGoogle Scholar
  4. Barnett, V. (2018). Economists and evolutionary psychology. In T. K. Shackelford & V. A. Weekes-Shackelford (Eds.), Encyclopedia of evolutionary psychological science. Switzerland: Springer.  https://doi.org/10.1007/978-3-319-16999-6_3836-1.CrossRefGoogle Scholar
  5. Cosmides, L., & Tooby, J. (2005). Neurocognitive adaptations designed for social exchange. In D. M. Buss (Ed.), The handbook of evolutionary psychology (pp. 584–627). New York: Wiley.Google Scholar
  6. Dunbar, R. I. M., & Barrett, L. (2007). Introduction: Evolutionary neurobiology and cognition. In R. I. Dunbar & L. Barrett (Eds.), The Oxford handbook of evolutionary psychology (pp. 127–128). Oxford: OUP.Google Scholar
  7. Eckel, C. C. (2008). Vernon Smith. In S. N. Durlauf & L. E. Blume (Eds.), The new Palgrave dictionary of economics (Vol. 7, pp. 568–571). London: Macmillan.Google Scholar
  8. Egidi, M., & Rizzello, S. (2006). Cognitive economics. In T. Raffaelli, G. Becattini, & M. Dardi (Eds.), The Elgar companion to Alfred Marshall (pp. 672–678). Cheltenham: Elgar.Google Scholar
  9. Goetz, A. T., & Shakelford, T. K. (2007). Introduction to evolutionary theory and its modern application to human behavior and cognition. In S. M. Platek, J. P. Keenan, & T. K. Shackelford (Eds.), Evolutionary cognitive neuroscience (pp. 5–19). Cambridge, MA: MIT Press.Google Scholar
  10. Kahneman, R., & Tversky, A. (1982). Judgment under uncertainty: Heuristics and biases. New York: Cambridge University Press.CrossRefGoogle Scholar
  11. Kahneman, R., Knetch, J., & Thaler, R. (1991). The endowment effect, loss aversion, and status quo bias. Journal of Economic Perspectives, 5(1), 193–206.CrossRefGoogle Scholar
  12. Katona, G. (1942). War without inflation: The psychological approach to problems of war economy. New York: Columbia University Press.Google Scholar
  13. Katona, G. (1951). Psychological analysis of economic behavior. New York: McGraw-Hill.Google Scholar
  14. Katona, G. (1960). The powerful consumer: Psychological studies of the American economy. New York: McGraw-Hill.Google Scholar
  15. Katona, G. (1980). Essays on behavioral economics. Ann Arbor: University of Michigan.Google Scholar
  16. Kuhnen, C. M., & Knutson, B. (2005). The neural basis of financial risk taking. Neuron, 47, 763–770.CrossRefGoogle Scholar
  17. Lohrenz, T., & Montague, P. R. (2008). Neuroeconomics. In A. Lewis (Ed.), The Cambridge handbook of psychology and economic behaviour (pp. 457–491). Cambridge: CUP.CrossRefGoogle Scholar
  18. McClure, S. M., Laibson, D., Lowestein, G., & Cohen, J. D. (2004). Separate neural systems value immediate and delayed monetary rewards. Science, 306, 503–507.CrossRefGoogle Scholar
  19. Miller, G. (2009). Spent: Sex, evolution, and consumer behavior. New York: Viking.Google Scholar
  20. Smith, V. (2003). Constructivist and ecological rationality in economics. The American Economic Review, 93(3), 465–508.CrossRefGoogle Scholar
  21. Smith, V. (2008a). Rationality in economics. Cambridge: CUP.Google Scholar
  22. Smith, V. (2008b). Experimental Economics. In S. N. Durlauf & L. E. Blume (Eds.), The new Palgrave dictionary of economics (Vol. 3, pp. 138–152). London: Macmillan.Google Scholar
  23. Spence, M. (1973). Job market signaling. Quarterly Journal of Economics, 87(3), 355–374.CrossRefGoogle Scholar
  24. Thaler, R. (1994). Quasi rational economics. New York: Russell Sage.Google Scholar
  25. Thaler, R., & Sunstein, C. (2008). Nudge: Improving decisions about health, wealth, and happiness. New Haven: Yale University Press.Google Scholar
  26. Todd, P. M., & Gigerenzer, G. (2007). Mechanisms of ecological rationality: Heuristics and environments that make us smart. In R. I. Dunbar & L. Barrett (Eds.), The Oxford handbook of evolutionary psychology (pp. 197–210). Oxford: OUP.Google Scholar
  27. Tooby, J., & Cosmides, L. (2005). Conceptual foundations of evolutionary psychology. In D. M. Buss (Ed.), The handbook of evolutionary psychology (pp. 5–66). Hoboken: Wiley.Google Scholar
  28. Trivers, R. (1985). Social evolution. Menlo Park: Cummings.Google Scholar
  29. Trivers, R. (2013). Deceit and self-deception. London: Penguin.Google Scholar
  30. Veblen, T. (1925 [1899]). The theory of the leisure class. London: Allen and Unwin.Google Scholar
  31. Wilson, D. S. (2007). Group-level evolutionary processes. In R. I. Dunbar & L. Barrett (Eds.), The Oxford handbook of evolutionary psychology (pp. 49–55). Oxford: OUP.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Independent ScholarLondonUK

Section editors and affiliations

  • Guilherme S. Lopes
    • 1
  1. 1.Department of PsychologyOakland UniversityRochesterUSA