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.)

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).

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.

Reinhard Selten and Thorsten Chmura (2008) recently reported laboratory results for completely mixed 2 X 2 games used to compare Nash equilibrium with four other stationary concepts: quantal response equilibrium, action-sampling equilibrium, payoff-sampling equilibrium, and impulse balance equilibrium. We reanalyze their data, correct some errors, and find that Nash clearly fits worst while the four other concepts perform about equally well. We also report new analysis of other previous experiments that illustrate the importance of the loss aversion hardwired into impulse balance equilibrium: when the other non-Nash concepts are augmented with loss aversion, they outperform impulse balance equilibrium.

We conducted experiments in Vietnamese villages to determine the predictors of risk and time preferences. In villages with higher mean income, people are less loss-averse and more patient. Household income is correlated with patience but not with risk. We expand measurements of risk and time preferences beyond expected utility and exponential discounting, replacing those models with prospect theory and a three-parameter hyperbolic discounting model. Comparable risk parameter estimates have been found for Chinese farmers, using our method. (C83, D12, O12, P38)