In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

We study a distributed computation model for optimizing a sum of convex objective functions corresponding to multiple agents. For solving this (not necessarily smooth) optimization problem, we consider a subgradient method that is distributed among the agents. The method involves every agent minimizing his/her own objective function while exchanging information locally with other agents in the network over a time-varying topology. We provide convergence results and convergence rate estimates for the subgradient method. Our convergence rate results explicitly characterize the tradeoff between a desired accuracy of the generated approximate optimal solutions and the number of iterations needed to achieve the accuracy.

http://www.ifp.illinois.edu/~angelia/distributed_journal_final.pdf

Distributed Subgradient Methods for Multi-Agent Optimization

This paper studies risk attitudes using a large representative survey and a complementary experiment conducted with a representative subject pool in subjects’ homes. Using a question asking people about their willingness to take risks \"in general\", we find that gender, age, height, and parental background have an economically significant impact on willingness to take risks. The experiment confirms the behavioral validity of this measure, using paid lottery choices. Turning to other questions about risk attitudes in specific contexts, we find similar results on the determinants of risk attitudes, and also shed light on the deeper question of stability of risk attitudes across contexts. We conduct a horse race of the ability of different measures to explain risky behaviors such as holdings stocks, occupational choice, and smoking. The question about risk taking in general generates the best all-round predictor of risky behavior.

/pdf/individual-risk-attitudes-measurement-determinants-and-2j4cjvjdkm.pdf

Individual Risk Attitudes: Measurement, Determinants and Behavioral Consequences

This paper studies risk attitudes using a large representative survey and a complementary experiment conducted with a representative subject pool in subjects’ homes. Using a question asking people about their willingness to take risks “in general”, we find that gender, age, height, and parental background have an economically significant impacton willingness to take risks. The experiment confirms the behavioral validity of this measure, using paid lottery choices. Turning to other question about risk attitudes in specific contexts, we find similar results on the determinants of risk attitudes, and also shed light on the deeper question of stability of risk attitudes across contexts. We conduct a horse race of the ability of different measures to explain risky behaviors such as holdings stocks, occupational choice, and smoking. The question about risk taking in general generates the best all-around predictor of risky behavior

First we discuss how extrinsic incentives may come into conflict with other motivations. For example, monetary incentives from principals may change how tasks are perceived by agents, with negative effects on behavior. In other cases, incentives might have the desired effects in the short term, but they still weaken intrinsic motivations. To put it in concrete terms, an incentive for a child to learn to read might achieve that goal in the short term, but then be counterproductive as an incentive for students to enjoy reading and seek it out over their lifetimes. Next we examine the research literature on three important examples in which monetary incentives have been used in a nonemployment context to foster the desired behavior: education; increasing contributions to public goods; and helping people change their lifestyles, particularly with regard to smoking and exercise. The conclusion sums up some lessons on when extrinsic incentives are more or less likely to alter such behaviors in the desired directions.

https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.25.4.191

When and Why Incentives (Don't) Work to Modify Behavior

https://www.stat.berkeley.edu/~aldous/Networks/GK_I.pdf

Bayesian learning in social networks

By using graphical representations of simple portfolio choice problems, we generate a very rich dataset to study behavior under uncertainty at the level of the individual subject. We test the data for consistency with the maximization hypothesis, and we estimate preferences using a two-parameter utility function based on Faruk Gul (1991). This specification provides a good interpretation of the data at the individual level and can account for the highly heterogeneous behaviors observed in the laboratory. The parameter estimates jointly describe attitudes toward risk and allow us to characterize the distribution of risk preferences in the population.

/pdf/consistency-and-heterogeneity-of-individual-behavior-under-3re8x93lpz.pdf

Consistency and heterogeneity of individual behavior under uncertainty

This paper reports an experimental test of how individuals learn from the behavior of others. By using techniques only available in the laboratory, we elicit subjects’ beliefs. This allows us to distinguish informational cascades from herd behavior. By adding a setup with continuous signal and discrete action, we enrich the ball-and-urn observational learning experiments paradigm of Anderson and Holt (1997). We attempt to understand subjects’ behavior by estimating a model that allows for the possibility of errors in earlier decisions. (JEL C92, D8).

/pdf/distinguishing-informational-cascades-from-herd-behavior-in-dwabye7a15.pdf

Distinguishing Informational Cascades from Herd Behavior in the Laboratory

payoff-pairs. We test these data for consistency with utility maximization, and we recover the underlying preferences for giving (trade-offs between own payoffs and the payoffs of others). Two further experiments augment the analysis. An extensive elaboration employs three-person budget sets to distinguish preferences for giving from social preferences (trade-offs between the payoffs of others). And an intensive elaboration employs step-shaped sets to distinguish between behaviors that are compatible with well-behaved preferences and those compatible only with not well-behaved cases. (JEL C72, D64)

/pdf/individual-preferences-for-giving-3dz3icuyl1.pdf

Individual Preferences for Giving

Economic thought has in recent years increasingly departed from the paradigm of narrow self-interestedness to take up other-regarding preferences. We study one class of such preferences - individual preferences for giving. We use graphical representations of modified Dictator Games that vary the price of giving. This generates a very rich data set well suited to studying behavior at the level of the individual subject. We test the data for consistency with preference maximization, and we recover underlying preferences and forecast behavior using both nonparametric and parametric methods. We report two principal results. First, individual preferences for giving, at least in dictator games, are well behaved in the economic sense (and not formally any less rational than preferences over various forms of own-consumption). Second, people's preferences for giving are heterogenous and much more shaded than the standard dictator game literature reports. Fewer people are display the pure or idealized preferences associated with Rawlsian or utilitarian moral theories and more have intermediate or hybrid preferences. Where standard experiments force subjects to make extreme choices and produce results that favor attributions of idealized preferences, our experiment gives subjects a broader range of alternatives and produces data that allows for more subtle extrapolations.

/pdf/individual-preferences-for-giving-ur7kym6kqa.pdf

Shachar Kariv

Papers

Bayesian learning in social networks

Consistency and heterogeneity of individual behavior under uncertainty

Distinguishing Informational Cascades from Herd Behavior in the Laboratory

Individual Preferences for Giving

Individual Preferences for Giving