Neuroeconomics aims to discover mechanisms of economic decision, and express them mathematically, to predict observed choice. While the contents of neuroeconomic models and evidence are obviously different than in traditional economics, (some of the) goals are identical: to explain and predict choice, the effects of comparative statics, and perhaps make interesting new welfare judgments that are defensible. To this end, Paul Glimcher's important book carefully describes how economics, psychological, and neural levels of explanation can be linked (a structure which has been successful in visual neuroscience). As Glimcher shows, the neural evidence is quite strong for a process of learning valuations through prediction error, and a simple model of neural valuation and comparison that corresponds to random utility (though subject to normalization, which produces menu effects). There is also rapidly growing evidence for more complicated constructs in behavioral economics, including prospect theory's account of risky choice, hyperbolic time discounting, level-k models of games, and social preferences corresponding to internal reward based on what happens to other agents.

Microeconomic theory typically concerns exchange between individuals or firms in a market setting. To make predictions precise, individuals are usually assumed to use the laws of probability in structuring and revising beliefs about uncertainties. Recent evidence, mostly gathered by psychologists, suggests probability theories might be inadequate descriptive models of individual choice. (See the books edited by Daniel Kahneman et al., 1982a, and by Hal Arkes and Kenneth Hammond, 1986.)

We test whether a model of reputation formation in an incomplete information game, using sequential equilibrium, predicts behavior of players in an experiment. Subjects play an abstracted lending game: a B player lends or does not lend; then if B lends, an E player can pay back or renege. The game is played 8 times, and there is a small controlled probability that the E player's induced preferences make him prefer to pay back (but usually he prefers to renege). In sequential equilibrium, even E players who prefer to renege should pay back in early periods of the game, and renege with increasing frequency in later periods, to establish reputations for preferring to pay back. After many repetitions of the 8-period game, actual play is roughly like the sequential equilibrium, except that E players pay back later in the game and more often than they should. This behavior is rational if B players have a "homemade" prior probability of .17 (in addition to the controlled probability) that E players will prefer to pay back. We conclude that sequential equilibrium with homemade incomplete information describes actual behavior well enough that it is plausible to apply it to theoretical settings where individuals make choices (e.g., product markets, labor markets, bargaining).

To test whether naturally occurring markets can be strategically manipulated, $500 and $1,000 bets were made, then canceled, at horse racing tracks. The net effects of these costless temporary bets give clues about how market participants react to information large bets might contain. The bets moved odds on horses visibly (compared to matched‐pair control horses with similar prebet odds) and had a slight tendency to draw money toward the horse that was temporarily bet, but the net effect was close to zero and statistically insignificant. the results suggest that some bettors inferred information from bets and others did not, and their reactions roughly canceled out.

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.

Bottom-up stimulus-driven visual salience is largely automatic, effortless, and independent of a person’s “top-down” perceptual goals; it depends only on features of a visual stimulus. Algorithms have been carefully trained to predict stimulus-driven salience values for each pixel in any image. The economic question we address is whether these salience values help explain economic decisions. Our first experimental analysis shows that when people pick between sets of fruits that have artificially induced value, predicted salience (which is uncorrelated with value by design) leads to mistakes. Our second analysis uses evidence from games in which choices are locations in images. When players are trying to cooperatively match locations, predicted salience is highly correlated with the success of matching (r = .57). In competitive hider-seeker location games, players choose salient locations more often than predicted by the unique Nash equilibrium. This tendency creates a disequilibrium “seeker’s advantage” (seekers win more often than predicted in equilibrium). The result can be explained by level-k models in which predicted stimulus-driven salience influences level-0 choices and thereby influences overall perceptions, beliefs, and choices of higher-level players. The third analysis shows that there is an effect of visual salience in matrix games, but it is small and statistically weak. Applications to behavioral IO, price and tax salience, nudges and design, and visually influenced beliefs are suggested.

Psychological studies show that most people are overconfident about their own relative abilities, and unreasonably optimistic about their futures (e.g. Shelly E. Taylor and J.D. Brown, 1988; Neil D. Weinstein, 1980). When assessing their position in a distribution of peers on almost any positive trait-- like driving ability (Ola Svenson, 1981 ), income prospects, or longevity-- a vast majority of people say they are above the average, although of course, only half can be (if the trait is symmetrically distributed). This paper explores whether optimistic biases could plausibly and predictably influence economic behavior in one particular setting-- entry into competitive games or markets. Many empirical studies show that most new businesses fail within a few years. For example, using plant level data from the U.S. Census of Manufacturers spanning 1963-1982, Timothy Dunne et al. (1988) estimated that 61.5 percent of all entrants exited within five years and 79.6 percent exited within 10 years. Most of these exits are failures (see also Dunne et al., 1989a, 1989b; D. Shapiro and R.S. Khemani, 1987).

Many alternative theories have been proposed to explain violations of expected utility (EU) theory observed in experiments. Several recent studies test some of these alternative theories against each other. Formal tests used to judge the theories usually count the number of responses consistent with the theory, ignoring systematic variation in responses that are inconsistent. We develop a maximum-likelihood estimation method which uses all the information in the data, creates test statistics that can be aggregated across studies, and enables one to judge the predictive utility-the fit and parsimony-of utility theories. Analyses of 23 data sets, using several thousand choices, suggest a menu of theories which sacrifice the least parsimony for the biggest improvement in fit. The menu is: mixed fanning, prospect theory, EU, and expected value. Which theories are best is highly sensitive to whether gambles in a pair have the same support (EU fits better) or not (EU fits poorly). Our method may have application to other domains in which various theories predict different subsets of choices (e.g., refinements of Nash equilibrium in noncooperative games).

Noncooperative game theory combines strategic thinking, best-response, and mutual consistency of beliefs and choices (equilibrium). Hundreds of experiments show that in actual behavior these three forces are limited, even when subjects are highly motivated and analytically skilled (Camerer, 2003). The challenge is to create models that are as general, precise, and parsimonious as equilibrium, but which also use cognitive details to explain experimental evidence more accurately and to predict new regularities. This paper describes three exemplar models of behavior in one-shot games (thinking), learning over time, and how repeated “partner” matching affects behavior (teaching) (see Camerer et al., 2002b).

This paper considers the measurement of consumer loss aversion in product markets. We introduce a test based on a “substitution effect,” focusing on how the end of a sale affects sales not of the good itself, but a substitute good. Such an effect cannot be easily confounded with consumer stockpiling. Using a unique dataset from an online hardware retailer, we find evidence consistent with consumer loss aversion. Moreover, we find that less experienced consumers suffer a more prominent loss aversion bias compared to more experienced consumers.