ome detail more recent developments in the economic theory of choice, and modifications to this theory that are being forced by experimental evidence from cognitive psychology. I will close with a survey of statistical methods that have developed as part of the research program on economic choice behavior. Science is a cooperative enterprise, and my work on choice behavior reflects not only my own ideas, but the results of exchange and collaboration with many other scholars. 1 First, of course, is my co-laureate James Heckman, who among his many contributions pioneered the important area of dynamic discrete choice analysis. Nine other individuals who played a major role in channeling microeconometrics and choice theory toward their modern forms, and had a particularly important influence on my own work, are Zvi Griliches, L.L. Thurstone, Jacob Marschak, Duncan Luce, Danny Kahneman, Amos Tversky, Moshe Ben-Akiva, Charles Manski, and Kenneth Train. A gallery of their p

Contingent valuation (CV) has become one of the most widely used non-market valuation techniques. CV’s prominence is due to its flexibility and ability to estimate total value, including passive use value. Its use and the inclusion of passive use value in benefit-cost analyses and environmental litigation are the subject of a contentious debate. This paper discusses key areas of the debate over CV and the validity of passive use value. We conclude that many of the alleged problems with CV can be resolved by careful study design and implementation. We further conclude that claims that empirical CV findings are theoretically inconsistent are not generally supported by the literature. The debate over CV, however, has clarified several key issues related to nonmarket valuation and can provide useful guidance both to CV practitioners and the users of CV results.

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Abstract—Previous experimental research has shown that people’s decisions can be influenced by options they have encountered in the past. This paper uses PSID data to study this phenomenon in the field, by observing how long people commute after moving between cities. It is found, as predicted, that (i) people choose longer commutes in a city they have just moved to, the longer the average commute was in the city they came from, and (ii) when they move again within the new city, they revise their commute length, countering the effect their origin city had on their initial decision. I.

Context can influence decisions. This malleability of choice is usually invoked as evidence that people do not maximize stable preference orderings. In a market equilibrium, however, context conveys payoff-relevant information to consumers. Consequently, these consumers rationally violate naïve formulations of standard choice theoretic principles. I identify informational asymmetries under which apparently anomalous behaviors, namely the compromise effect and choice overload, arise as market equilibria. Firms respond to consumers’ contextual inference; in case of the compromise effect, a firm may introduce premium loss leaders (expensive goods of overly high quality that increase the demand for other goods). (JEL D11, D83, M31) Numerous studies demonstrate that seemingly irrelevant factors influence people’s decisions. Perhaps the best known examples of such influence are context effects. A consumer exhibits a context effect if her choice between two alternatives systematically depends on the presence of other options. An extensive literature demonstrates context effects in laboratory settings. One of the most widely studied context effects is the compromise effect (Itamar Simonson 1989), 1 which refers to the finding that people tend to choose the middle option. More precisely, when three alternatives are available, the middle alternative is chosen more often than when it is paired with only one other option. Figure 1 shows the compromise effect obtained by Simonson (1989). This tendency to avoid extreme options has been credited with affecting decisions ranging from the demand for wine (Daniel L. McFadden 1999) to voting (Kaisa Herne 1997) and investing (Shlomo Benartzi and Richard H. Thaler 2002). Even more telling of the importance bestowed on the compromise effect is its didactic use in books such as 101 Ways to Increase Sales (Dirk

conference on economic institutions and behavioral economics. This is a very rough working draft for the conference. The Mark Twain apology applies, we wish we had more time to write less. Comments are genuinely appreciated, especially on what to cut, and on important omissions (self-serving ones are expected). Ideas from the NBER Organizational Economics conference in March, 2004, particularly Bob Gibbons’s presentation, was useful, as were discussions with Chip Heath and Sendhil Mullainathan. 0 This essay is about how behavioral economics can be applied to organizations, and can also be enriched by thinking about how individuals behave in organizations. Behavioral economics modifies economic theory to account for normal limits on rational calculation, willpower and greed, and the natural psychophysical properties of preference and judgment (e.g. Mullainathan and Thaler, 2001; Camerer and Loewenstein, 2004). Thinking about organizations naturally extends this definition to include how socialization and identity shape individual behavior. (While little about these extensions will be discussed in this paper, see Akerlof and Kranton, 2003). From a methodological perspective, behavioral economics is simply a humble

In this note we propose to use the mathematical formalism of Quantum Mechanics to capture the idea that agents ’ preferences, in addition to being typically uncertain, can also be indeterminate. They are determined (realized, and not merely revealed) only when the action takes place. An agent is described by a state which is a superposition of potential types (or preferences or behaviors). This superposed state is projected (or ”collapses”) onto one of the possible behaviors at the time of the interaction. In addition to the main goal of modelling uncertainty of preferences which is not due to lack of information, this formalism, seems to be adequate to describe widely observed phenomena like framing and instances of noncommutativity in patterns of behavior. We propose two experiments to test the theory.

In this paper we discuss Hicksian demand and compensating variation in the context of discrete choice. We first derive Hicksian choice probabilities and the distribution of the (random) expenditure function in the general case when the utilities are nonlinear in income. We subsequently derive exact and simple formulae for the expenditure and choice probabilities under price (policy) changes conditional on the initial utility level. This is of particular interest for welfare measurement because it enables the researcher to compute the distribution of Compensating variation in a simple way. We also derive formulae for the joint distribution of expenditure, the choice before and after a policy change has been introduced.

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(incomplete and preliminary) Abstract: There is overwhelming empirical evidence that cognitive limitations and social interactions lead to biases in responses to survey questions. In addition, there is evidence that some of the underlying processes are moderated by age. The purpose of the Berkeley Internet Virtual Laboratory (IVLab) is to study these phenomena using experimental surveys conducted over the internet. The internet is a cost-effective and flexible way to conduct experiments on survey response bias. There is also a good chance that field surveys will be conducted over the internet in the near future. However, selection problems are a major concern, in particular when the target population consists primarily of older individuals. In this paper, we present a research strategy that allows to address selection problems and the analysis of survey response biases in a consistent framework. For illustration, we report some preliminary results based on data from a pilot study for the Retirement Perspectives Survey (RPS), an experimental survey conducted over the internet with a target population of older Americans.