I will briefly summarize some of the current research on decisions from description and experience in individual interactions with an environment and in interactions with other individuals and agents, in order to frame the motivation to this workshop.

This lecture describes the current state of the art in modeling risk attitudes as the result of interactions between empirically oriented psychologists and theoretically oriented economists. At several stages in history, the next step forward could be made only by empirical inputs and intuitions from psychologists. At several other stages, the next step forward could be made only by theoretical inputs from economists with advanced technical skills. Modern views on the measurement of utility, beliefs, risk, and ambiguity attitudes could only arise from the merger of ideas from all the fields mentioned. New empirical impulses have recently come from psychological studies on decision from experience, criticizing prospect theory for decisions under risk. Theories on decision under ambiguity, rather than risk, may shed new light on decision from experience.

There is a large body of evidence showing that a substantial proportion of people cooperate in social dilemmas, even if the interaction is one-shot and completely anonymous. We consider two major endogenous factors that are known to affect cooperative decisions, and in so doing replicate and extend previous empirical research on public goods problems in several important ways. First, we measure social preferences using a relatively new and well validated method that yields results that are both more highly resolved and reliable than with other measures. Concurrently, we elicit beliefs on the individual level using multiple methods, and repeatedly during the experiment. With this rich set of individual level variables, we can make predictions of people's choices in both one-shot and repeated social dilemma interactions. We show that when heterogeneity in people’s tastes and beliefs is taken into consideration, more than 50% of the variance in individual choices can be accounted for by using a simple statistical model. This approach extends rational choice modeling by accounting for behavioral variation in tastes and expectations, and builds towards understanding under what conditions people are willing to cooperate. Applications to environmental decision making are highlighted and particular mechanisms that can promote cooperation are discussed.

The Allais Paradox is a well-known bias in which people’s preferences result in contradictory choices between two normatively identical gamble pairs. Studies have shown that these preference reversals depend on how information is described and presented. In an experiment, we investigate the Allais gambles in several formats including an experiential paradigm, where participants make selections from two blank buttons and get an outcome as a result of a draw from distributions of outcomes in the selected gamble. Results indicate that a large proportion of Allais reversals are found in the traditional descriptive format, they are reduced when gambles are presented in a descriptive table format, and they disappear when choices are made from experience. Although a majority of participants made consistent choices from experience, the proportion of individual reversals is similar to that of descriptive choices. Detailed analyses of experiential choice suggest interesting behavioral differences between participants classified as consistent and those classified as reversals: consistent participants explore and maximize more than reversal participants. Furthermore, consistent participants demonstrate a different switching behavior after experiencing a rare outcome than do reversal participants. In contrast to the view that people generally underweight rare outcomes in experiential choice, overweighting or underweighting may occur within the course of an individual’s experience depending on the timing of those experiences, the magnitude of the outcomes observed, and the general accumulated value of the gambles.

Causal knowledge plays multiple key roles in our decision-making. In this talk, I will briefly discuss two: (1) our choices are correctly sensitive to causal direction, as acting on causes can influence effects but not vice versa; and (2) our causal knowledge can help solve the problem of action construction/selection. Moreover, as I will attempt to show in the talk, both of these roles can be usefully explained—both theoretically and descriptively—in terms of causal graphical models.

We apply a model of preferences for information to the domain of decision making under uncertainty. An uncertain prospect exposes an individual to an information gap. Gambling makes the missing information more important, attracting more attention to the information gap. To the extent that the uncertainty (or other circumstances) makes the information gap unpleasant to think about, an individual tends to be averse to risk and ambiguity. Yet when an information gap happens to be pleasant, an individual may seek gambles providing exposure to it. The model provides explanations for source preference regarding uncertainty, the comparative ignorance effect under conditions of ambiguity, aversion to compound risk, and more.

We investigate the relationship between individual differences in Social Value Orientation (SVO) and cooperative behavior in iterated Prisoner’s Dilemma (PD) games. SVO is an individual’s preference about how to allocate resources between the self and the other. In an experiment, we tested pairs in four different PD games, some of which being expected to produce similar cooperative behavior according to the well-known Rapoport & Chammah's index of cooperation. A first finding is that this index of cooperation fails to explain our behavioral data across those games. This study also reveals that socially oriented participants, as defined by the SVO, tend to cooperate significantly more often than individualistic participants in some specific PD games, but such social characteristics have no effect in other types of PD games. Furthermore, the SVO as an individual difference measure is robust and not easily affected by the repeated experiences of interaction with another individual. Finally, we find that the more social is the less socially oriented individual within a pair, the more mutual cooperation and the less mutual defection is found within that pair.

In this talk, I will present Decision by Sampling (DbS), a utility-free theory of decision making. DbS is a model of magnitude evaluation based on memory sampling and relative judgment. This model does not rely on stable, underlying value representations to explain valuation and choice, or on choice behavior to derive value functions. Instead, preferences concerning outcomes emerge spontaneously from the distributions of sampled events and the relative nature of the evaluation process. I will illustrate the power and relevance of the model to 3 domains: evaluations of death tolls (e.g., following deadly events), evaluations of life-years (e.g., how much value ascribe to extending their life expectancy), and inter temporal choice.

We introduce a general framework to predict how decision sets used in decision-making experiments impact the quality of parameter estimates. We applied our framework to cumulative prospect theory (CPT) to investigate the expected parameter discrimination achieved by current research practices. Our analysis is the first to accurately predict the relative estimation precision of each parameter of CPT. We additionally applied our framework to analyze the decision sets that were used to produce the empirical evidence for the description–experience gap. Specifically, we found that choices based on few experienced draws from a gamble provided little information for estimating decision weights when compared to equivalent description based choices. Therefore, choices between experienced gambles can be explained by a wider range of decision weights than choices between equivalent described gambles, providing an alternative explanation for the empirical evidence surrounding the description– experience gap. We conclude with implications for future experiments designed to estimate parameters from choice data.

When decision makers repeatedly choose between a safe and a risky asset, the frequency of feedback can influence maximization. Myopic prospect theory proposes that agents evaluate each outcome on its own rather than considering the series of choices as one large game. In combination with loss aversion, this can lower performance in the large game. Revealing outcomes less frequently may discourage such narrow bracketing and improve expected value maximizing behavior when losses are likely to be observed. We explore this prediction in decisions from experience, where participants have to learn about the options from feedback. Our results show that providing feedback about individual outcomes less frequently increases the proportion of choices favoring the risky option when the high outcome occurs only rarely or when the low and high outcome are equiprobable. When the high payoff is common, the effect disappears. We also find that delayed feedback helps overcome recency bias, where the probability of choosing the risky option depends on the last observed outcome.

This paper tests the predictions of expected utility theory and several of its most prominent alternatives and assesses their ability to explain behavior in Allais-type tasks. We investigate whether a transparent presentation reduces violations of expected utility theory and find that it does so dramatically. We also investigate the incremental explanatory power of disappointment aversion, the fanning out hypothesis, and rank-dependent utility theories, including cumulative prospect theory with different probability weighting functions. All these theories can justify the predictions of expected utility as a special case, yet all of their incremental explanatory power is attributable to what we call a zero effect—a change in behavior when a lottery may provide a zero outcome.