Foundations of Behavioral and Experimental Economics: Daniel Kahneman and Vernon Smith

Advanced information on the Prize in Economic Sciences 2002. Until recently, economics was widely regarded as a non-experimental science that had to rely on observation of real-world economies rather than controlled laboratory experiments. Many commentators also found restrictive the common assumption of a homo oeconomicus motivated by self-interest and capable of making rational decisions. But research in economics has taken off in new directions. A large and growing body of scientific work is now devoted to the empirical testing and modification of traditional postulates in economics, in particular those of unbounded rationality, pure self-interest, and complete self-control. Moreover, today's research increasingly relies on new data from laboratory experiments rather than on more traditional field data, that is, data obtained from observations of real economies. This recent research has its roots in two distinct, but converging, traditions: theoretical and empirical studies of human decision-making in cognitive psychology, and tests of predictions from economic theory by way of laboratory experiments. Today, behavioral economics and experimental economics are among the most active fields in economics, as measured by publications in major journals, new doctoral dissertations, seminars, workshops and conferences. This year's laureates are pioneers of these two fields of research.behavioral economics; experimental economics

Decision by sampling: the role of the decision environment in risky choice

Decision by sampling (DbS) is a theory about how our environment shapes the decisions that we make. Here, I review the application of DbS to risky decision making. According to classical theories of risky decision making, people make stable transformations between outcomes and probabilities and their subjective counterparts using fixed psychoeconomic functions. DbS offers a quite different account. In DbS, the subjective value of an outcome or probability is derived from a series of binary, ordinal comparisons with a sample of other outcomes or probabilities from the decision environment. In this way, the distribution of attribute values in the environment determines the subjective valuations of outcomes and probabilities. I show how DbS interacts with the real-world distributions of gains, losses, and probabilities to produce the classical psychoeconomic functions. I extend DbS to account for preferences in benchmark data sets. Finally, in a challenge to the classical notion of stable subjective valuations, I review evidence that manipulating the distribution of attribute values in the environment changes our subjective valuations just as DbS predicts

Neurobiological studies of risk assessment: A comparison of expected utility and mean-variance approaches

When modeling valuation under uncertainty, economists generally prefer expected utility because it has an axiomatic foundation, meaning that the resulting choices will satisfy a number of rationality requirements. In expected utility theory, values are computed by multiplying probabilities of each possible state of nature by the payoff in that state and summing the results. The drawback of this approach is that all state probabilities need to be dealt with separately, which becomes extremely cumbersome when it comes to learning. Finance academics and professionals, however, prefer to value risky prospects in terms of a trade-off between expected reward and risk, where the latter is usually measured in terms of reward variance. This mean-variance approach is fast and simple and greatly facilitates learning, but it impedes assigning values to new gambles on the basis of those of known ones. To date, it is unclear whether the human brain computes values in accordance with expected utility theory or with mean-variance analysis. In this article, we discuss the theoretical and empirical arguments that favor one or the other theory. We also propose a new experimental paradigm that could determine whether the human brain follows the expected utility or the mean-variance approach. Behavioral results of implementation of the paradigm are discussed

Parametric Weighting Functions

This paper provides behavioral foundations for parametric weighting functions under rankdependent utility. This is achieved by decomposing the independence axiom of expected utility into separate meaningful properties. These conditions allow us to characterize rank-dependent utility with power and exponential weighting functions. Moreover, by restricting the conditions to subsets of the probability interval, foundations of rank-dependent utility with parametric inverse-S shaped weighting functions are obtained. --Comonotonic independence,probability weighting function,preference foundation,rank-dependent utility

Cumulative prospect theory and gambling

Whilst Cumulative Prospect theory (CPT) provides an explanation of gambling on longshots at actuarially unfair odds, it cannot explain why people might bet on more favoured outcomes. This paper shows that this is explicable if the degree of loss aversion experienced by the agent is reduced for small-stake gambles (as a proportion of wealth), and probability distortions are greater over losses than gains. If the utility or value function is assumed to be bounded, the degree of loss aversion assumed by Kahneman and Tversky leads to absurd predictions, reminiscent of those pointed out by Rabin (2000), of refusal to accept infinite gain bets at low probabilities. Boundedness of the value function in CPT implies that the indifference curve between expected-return and win-probability will typically exhibit both an asymptote (implying rejection of an infinite gain bet) and a minimum at low probabilities, as the shape of the value function dominates the probability weighting function. Also the high probability section of the indifference curve will exhibit a maximum. These implications are consistent with outcomes observed in gambling markets.

Housing and capital in the 21st Century

Thomas Piketty’s Capital in the 21st Century has attracted public, policy and academic attention. Although there is a growing research literature on the formation, distribution, utilization and wider implications of housing wealth there has been little discussion of Piketty’s work in housing studies. This paper outlines and assesses the major contributions of Piketty, including re-emphasizing distribution and political economy perspectives within economics, modelling growth and distribution, establishing detailed long run patterns of wealth change and policy implications. The paper highlights the significance of shifting housing wealth in increasing inequalities in some countries: housing matters in macro-shifts. We also draw out the implications of house price and wealth growth for the balance of rentier vs. entrepreneurial forms of capitalism. If Piketty’s work is important for housing research, we also argue the converse, that housing research findings can strengthen his analysis. The stylized facts of advanced economy metropolitan growth suggest that housing market processes and wealth outcomes will drive higher inequality and lower productivity into the future unless housing and related policies change markedly. Piketty, strong on evidence and conceptualization is weak on policy development and housing studies can drive more effective assessments of change possibilities

Adaptive Probability Theory: Human Biases as an Adaptation

Humans make mistakes in our decision-making and probability judgments. While the heuristics used for decision-making have been explained as adaptations that are both efficient and fast, the reasons why people deal with probabilities using the reported biases have not been clear. We will see that some of these biases can be understood as heuristics developed to explain a complex world when little information is available. That is, they approximate Bayesian inferences for situations more complex than the ones in laboratory experiments and in this sense might have appeared as an adaptation to those situations. When ideas as uncertainty and limited sample sizes are included in the problem, the correct probabilities are changed to values close to the observed behavior. These ideas will be used to explain the observed weight functions, the violations of coalescing and stochastic dominance reported in the literature

Roots and Effects of Investments' Misperception

This work deals with the problem of investors' irrational behavior and financial products' misperception. The theoretical analysis of the mechanisms driving wrong evaluations of investment performances is explored. The study is supported by the application of Monte Carlo simulations to the remarkable case of structured financial products. Some motivations explaining the popularity among retail investors of these complex financial instruments are also provided. Investors are assumed to compare the performances of different projects through stochastic dominance rules and, to pursue our scopes, a new definition of this decision criteria is introduced.