of the process) for helpful comments. 1 Behavioral economics increases the explanatory power of economics by providing it with more realistic psychological foundations. This book consists of representative recent articles in behavioral economics. 1 This chapter is intended to provide an introduction to the approach and methods of behavioral economics, and to some of its major findings, applications, and promising new directions. It also seeks to fill some unavoidable gaps in the chapters ’ coverage of topics. What Behavioral Economics Tries To Do At the core of behavioral economics is the conviction that increasing the realism of the psychological underpinnings of economic analysis will improve economics on its own terms--generating theoretical insights, making better predictions of field phenomena, and suggesting better policy. This conviction does not imply a wholesale rejection of the neoclassical approach to economics based on utility maximization, equilibrium, and efficiency. The neoclassical approach is useful because it provides economists with a theoretical framework that can be applied to almost any form of economic (and even non-economic) behavior, and it makes refutable predictions. Many of these predictions are tested in the chapters of this book, and rejections of those predictions suggest new theories. Most of the papers modify one or two assumptions in standard theory in the direction of greater psychological realism. Often these departures are not radical at all because they relax simplifying assumptions that are not central to the economic approach. For example, there is nothing in core neoclassical theory that specifies that people should not care about fairness, that they should weight risky outcomes in a linear fashion, or that they must discount the future exponentially at a constant rate. 2 Other assumptions simply acknowledge human limits on 1 Since it is a book of advances, many of the seminal articles which influenced those collected here are not included, but are noted below and are widely reprinted elsewhere.

Behavioral economics has demonstrated systematic decision-making biases in both lab and field data. But are these biases learned or innate? We investigate this question using experiments on a novel set of subjects — capuchin monkeys. By introducing a fiat currency and trade to a capuchin colony, we are able to recover their preferences over a wide range of goods and risky choices. We show that standard price theory does a remarkably good job of describing capuchin purchasing behavior; capuchin monkeys react rationally to both price and wealth shocks. However, when capuchins are faced with more complex choices including risky gambles, they display many of the hallmark biases of human behavior, including reference-dependent choices and loss-aversion. Given gambles, these results suggest that certain biases such as loss-aversion are an innate function of how our brains code experiences, rather than learned behavior or the result of misapplied heuristics. We would like to acknowledge a very generous Whitebox Advisors ’ Research Grant, without which this research would not have been possible. Alisia Eckert provided invaluable research assistance throughout this project. Many people gave generously of their time on this and earlier drafts; we would especially like

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This paper tests two explanations for apparent undersaving in lifecycle models: Bounded rationality; and a preference for immediacy. Each was addressed in a separate experimental study. In first study, subjects saved too little initially—providing evidence for bounded rationality—but learned to save optimally within four repeated lifecycles. In the second study, thirsty subjects that consume beverage sips immediately rather than with a delay show greater relative overspending, consistent with quasi-hyperbolic discounting models. The parameter estimates of overspending obtained from the second study—but not the first—are in range of several empirical studies of savings (with an estimated �=0.6-0.7).

”Expert judgments have been worse than those of the simplest statistical models in virtually all domains that have been studied” (Camerer and Johnson, 1991) “In nearly every study of experts carried out within the judgment and decision-making approach, experience has been shown to be unrelated to the empirical accuracy of expert judgments”

The publication of “Advances in Behavioral Economics ” is a testament to the success of behavioral economics. The book contains important second-generation contributions to behavioral economics that build on the seminal work by Kahnemann, Tversky, Thaler, Strotz and others.

We examine the robustness of the fourfold pattern of risk attitudes under two elicitation procedures. We find that individuals are, on average, risk-seeking over low-probability gains and high-probability losses and risk-averse over high-probability gains and low-probability losses when we elicit prices for the gambles. However, a choice-based elicitation procedure, where participants choose between a gamble and its expected value, yields individual decisions that are indistinguishable from random choice. Sensitivity to elicitation procedure holds between and within participants, and remains when participants are allowed to review and change decisions. The price elicitation procedure is more complex; this finding may be further evidence that an increase in cognitive load exacerbates behavioural anomalies. Individual decisions over risky outcomes often deviate from that predicted by expected utility theory, and alternative models have been proposed to explain behaviour better. 1 Perhaps the most accepted alternative is cumulative prospect theory (CPT) by Tversky and Kahneman (1992). 2 Two central assumptions in CPT are that individuals are riskaverse over gains and risk-seeking over losses, and that they tend to overweight lowprobability events while underweighting the likelihood of high-probability ones.

ISSN 1749 ‐ 3293The Centre for Decision Research and Experimental Economics was founded in 2000, and is based in the School of Economics at the University of Nottingham. The focus for the Centre is research into individual and strategic decision‐making using a combination of theoretical and experimental methods. On the theory side, members of the Centre investigate individual choice under uncertainty, cooperative and non‐cooperative game theory, as well as theories of psychology, bounded rationality and evolutionary game theory. Members of the Centre have applied experimental methods in the fields of public economics, individual choice under risk and uncertainty, strategic interaction, and the performance of auctions, markets and other economic institutions. Much of the Centre's research involves collaborative projects with researchers from other departments in the UK and overseas.

The regressivity of lotteries has become an increasingly important issue in the U.S. as the number of state-run lotteries has increased. Despite this, we still know relatively little about the nature of lottery regressivity. I use a new dataset on Powerball lotto sales to analyze how regressivity varies with jackpot size within a single lotto game. I find that these large-stakes games are significantly less regressive at higher jackpot sizes. The results suggest that the lottery becomes progressive at a jackpot around $806 million. This suggests that concerns about regressivity might be allayed (although given that jackpots are generally much lower than $800 million, not entirely eliminated) by concentrating lotto games to produce higher average jackpots. ∗ I am very grateful to the Connecticut State Lottery for providing the data. David Laibson, Markus

Expected utility maximization is not a good predictive model of how people value outcomes. One of the ways in which people systematically deviate from expected utility maximization is loss aversion, where losses (relative to some reference point) are more highly weighted than gains. I investigate whether and how the theoretical guarantees of congestion games change when the agents are assumed to be loss averse. I also introduce and investigate the effects of assuming loss averse agents on a stochastic version of congestion games. 1