Colin F. Camerer

The Effects of Financial Incentives in Experiments: A Review and Capital-Labor-Production Framework

We review 74 experiments with no, low, or high performance-based financial incentives. The modal result has no effect on mean performance (though variance is usually reduced by higher payment). Higher incentive does improve performance often, typically judgment tasks that are responsive to better effort. Incentives also reduce “presentation” effects (e.g., generosity and risk-seeking). Incentive effects are comparable to effects of other variables, particularly “cognitive capital” and task “production” demands, and interact with those variables, so a narrow-minded focus on incentives alone is misguided. We also note that no replicated study has made rationality violations disappear purely by raising incentives.

Recent Developments in Modeling Preferences: Uncertainty and Ambiguity

In subjective expected utility (SEU), the decision weights people attach to events are their beliefs about the likelihood of events. Much empirical evidence, inspired by Ellsberg (1961) and others, shows that people prefer to bet on events they know more about, even when their beliefs are held constant. (They are averse to ambiguity, or uncertainty about probability.) We review evidence, recent theoretical explanations, and applications of research on ambiguity and SEU.

A framework for studying the neurobiology of value-based decision making

Neuroeconomics is the study of the neurobiological and computational basis of value-based decision making. Its goal is to provide a biologically based account of human behaviour that can be applied in both the natural and the social sciences. This Review proposes a framework to investigate different aspects of the neurobiology of decision making. The framework allows us to bring together recent findings in the field, highlight some of the most important outstanding problems, define a common lexicon that bridges the different disciplines that inform neuroeconomics, and point the way to future applications.

Experience-weighted attraction learning in normal form games

In ‘experience-weighted attraction’ (EWA) learning, strategies have attractions that reflect initial predispositions, are updated based on payoff experience, and determine choice probabilities according to some rule (e.g., logit). A key feature is a parameter δ that weights the strength of hypothetical reinforcement of strategies that were not chosen according to the payoff they would have yielded, relative to reinforcement of chosen strategies according to received payoffs. The other key features are two discount rates, φ and ρ, which separately discount previous attractions, and an experience weight. EWA includes reinforcement learning and weighted fictitious play (belief learning) as special cases, and hybridizes their key elements. When δ= 0 and ρ= 0, cumulative choice reinforcement results. When δ= 1 and ρ=φ, levels of reinforcement of strategies are exactly the same as expected payoffs given weighted fictitious play beliefs. Using three sets of experimental data, parameter estimates of the model were calibrated on part of the data and used to predict a holdout sample. Estimates of δ are generally around .50, φ around .8 − 1, and ρ varies from 0 to φ. Reinforcement and belief-learning special cases are generally rejected in favor of EWA, though belief models do better in some constant-sum games. EWA is able to combine the best features of previous approaches, allowing attractions to begin and grow flexibly as choice reinforcement does, but reinforcing unchosen strategies substantially as belief-based models implicitly do.

Foundations of Human Sociality: Economic Experiments and Ethnographic Evidence from Fifteen Small-Scale Societies

What motives underlie the ways humans interact socially? Are these the same for all societies? Are these part of our nature, or influenced by our environments? Over the last decade, research in experimental economics has emphatically falsified the textbook representation of Homo economicus. Literally hundreds of experiments suggest that people care not only about their own material payoffs, but also about such things as fairness, equity and reciprocity. However, this research left fundamental questions unanswered: Are such social preferences stable components of human nature; or, are they modulated by economic, social and cultural environments? Until now, experimental research could not address this question because virtually all subjects had been university students, and while there are cultural differences among student populations throughout the world, these differences are small compared to the full range of human social and cultural environments. A vast amount of ethnographic and historical research suggests that peoples motives are influenced by economic, social, and cultural environments, yet such methods can only yield circumstantial evidence about human motives. Combining ethnographic and experimental approaches to fill this gap, this book breaks new ground in reporting the results of a large cross-cultural study aimed at determining the sources of social (non-selfish) preferences that underlie the diversity of human sociality. The same experiments which provided evidence for social preferences among university students were performed in fifteen small-scale societies exhibiting a wide variety of social, economic and cultural conditions by experienced field researchers who had also done long-term ethnographic field work in these societies. The findings of these experiments demonstrated that no society in which experimental behaviour is consistent with the canonical model of self-interest. Indeed, results showed that the variation in behaviour is far greater than previously thought, and that the differences between societies in market integration and the importance of cooperation explain a substantial portion of this variation, which individual-level economic and demographic variables could not. Finally, the extent to which experimental play mirrors patterns of interaction found in everyday life is traced. The book starts with a succinct but substantive introduction to the use of game theory as an analytical tool and its use in the social sciences for the rigorous testing of hypotheses about fundamental aspects of social behaviour outside artificially constructed laboratories. The results of the fifteen case studies are summarized in a suggestive chapter about the scope of the project. Contributors to this volume - Joseph Henrich, Department of Anthropology, Emory University Robert Boyd, Department of Anthropology, UCLA Samuel Bowles, University of Siena and University of Massachusetts, Amherst Colin Camerer, Division of Humanities and Social Sciences, California Institute of Technology Ernst Fehr, Insitute for Empirical Research, University of Zurich Herbert Gintis, Santa Fe Institute, University of Massachusetts, and New York University Richard McElreath, Department of Anthropology, University of California, Davis John Q. Patton, Washington State University Natalie Smith, School of Public Health, Harvard University Frank Marlowe, Department of Anthropology, Harvard University Michael Gurven, Department of Anthropology, University of New Mexico David P. Tracer, Department of Anthropology, University of Colarado at Denver Francisco J. Gil-White, Solomon Asch Center for the Study of Ethnopolitical Conflict, University of Pennsylvania Abigail Barr, Centre for the Study of African Economies, University of Oxford Jean Ensminger, Division of the Humanities and Social Sciences, California Institute of Technology Kim Hill, Department of Anthropology, University of New Mexico Mike Gurven, Department of Anthropology, University of New Mexico Michael S. Alvard, Department of Anthropology, Texas A&M University

Economic man in cross-cultural perspective: Behavioral experiments in 15 small-scale societies

Researchers from across the social sciences have found consistent deviations from the predictions of the canonical model of self-interest in hundreds of experiments from around the world. This research, however, cannot determine whether the uniformity results from universal patterns of human behavior or from the limited cultural variation available among the university students used in virtually all prior experimental work. To address this, we undertook a cross-cultural study of behavior in ultimatum, public goods, and dictator games in a range of small-scale societies exhibiting a wide variety of economic and cultural conditions. We found, first, that the canonical model - based on self-interest - fails in all of the societies studied. Second, our data reveal substantially more behavioral variability across social groups than has been found in previous research. Third, group-level differences in economic organization and the structure of social interactions explain a substantial portion of the behavioral variation across societies: the higher the degree of market integration and the higher the payoffs to cooperation in everyday life, the greater the level of prosociality expressed in experimental games. Fourth, the available individual-level economic and demographic variables do not consistently explain game behavior, either within or across groups. Fifth, in many cases experimental play appears to reflect the common interactional patterns of everyday life.

Neural Systems Responding to Degrees of Uncertainty in Human Decision-Making

Much is known about how people make decisions under varying levels of probability (risk). Less is known about the neural basis of decision-making when probabilities are uncertain because of missing information (ambiguity). In decision theory, ambiguity about probabilities should not affect choices. Using functional brain imaging, we show that the level of ambiguity in choices correlates positively with activation in the amygdala and orbitofrontal cortex, and negatively with a striatal system. Moreover, striatal activity correlates positively with expected reward. Neurological subjects with orbitofrontal lesions were insensitive to the level of ambiguity and risk in behavioral choices. These data suggest a general neural circuit responding to degrees of uncertainty, contrary to decision theory.

Regulation for Conservatives: Behavioral Economics and the Case for 'Asymmetric Paternalism'

Regulation by the state can take a variety of forms. Some regulations are aimed entirely at redistribution, such as when we tax the rich and give to the poor. Other regulations seek to counteract externalities by restricting behavior in a way that imposes harm on an individual basis but yields net societal benefits. A good example is taxation to fund public goods such as roads. In such situations, an individual would be better off if she alone were exempt from the tax; she benefits when everyone (including herself) must pay the tax.

The Predictive Utility of Generalized Expected Utility Theories

Recent experimental choice studies compare expected utility with competing theories of decision-making under risk. Formal tests used to judge the theories usually count the number of consistent responses, ignoring systematic variation in inconsistent responses. A maximum-likelihood estimation method is developed that extracts more information from the data and enables one to judge the predictive utility--fit and parsimony--of utility theories. Analyses of twenty-three data sets suggest a menu of theories that sacrifice the least parsimony for the biggest improvement in fit. The menu is mixed fanning, prospect theory, expected utility, and expected value. Copyright 1994 by The Econometric Society.

The disposition effect in securities trading: an experimental analysis

The ‘disposition effect’ is the tendency to sell assets that have gained value (‘winners’) and keep assets that have lost value (‘losers’). Disposition effects can be explained by the two features of prospect theory: the idea that people value gains and losses relative to a reference point (the initial purchase price of shares), and the tendency to seek risk when faced with possible losses, and avoid risk when a certain gain is possible. Our experiments were designed to see if subjects would exhibit disposition effects. Subjects bought and sold shares in six risky assets. Asset prices fluctuated in each period. Contrary to Bayesian optimization, subjects did tend to sell winners and keep losers. When the shares were automatically sold after each period, the disposition effect was greatly reduced.