David Dillenberger

Ashamed to be selfish

We study a two-stage choice problem, where alternatives are allocations between the decision maker (DM) and a passive recipient. The recipient observes choice behavior in stage two, while stage one choice is unobserved. Choosing selfishly in stage two, in the face of a fairer available alternative, may inflict shame on DM. DM has preferences over sets of alternatives that represent period two choices. We axiomatize a representation that identifies DMs selfish ranking, her norm of fairness and shame. Altruism is the most prominent motive that can explain non-selfish choice. We identify a condition under which shame to be selfish can mimic altruism, when only stage-two choice is observed by the experimenter. An additional condition implies that the norm of fairness can be characterized as the Nash solution of a bargaining game induced by the second-stage choice problem. The representation is generalized to allow for finitely many recipients and applied to explain a social decision makers incentive for obfuscation.

A Theory of Subjective Learning

We study an individual who faces a dynamic decision problem in which the process of information arrival is unobserved by the analyst. We derive two utility representations of preferences over menus of acts that capture the individual’s uncertainty about his future beliefs. The most general representation identifies a unique probability distribution over the set of posteriors that the decision maker might face at the time of choosing from the menu. We use this representation to characterize a notion of “more preference for flexibility” via a subjective analogue of Blackwell’s (1951, 1953) comparisons of experiments. A more specialized representation uniquely identifies information as a partition of the state space. This result allows us to compare individuals who expect to learn differently, even if they do not agree on their prior beliefs. We conclude by extending the basic model to accommodate an individual who expects to learn gradually over time by means of a subjective filtration.

History-dependent risk attitude

We propose a model of history-dependent risk attitude, allowing a decision maker’s risk attitude to be affected by his history of disappointments and elations. The decision maker recursively evaluates compound risks, classifying realizations as disappointing or elating using a threshold rule. We establish equivalence between the model and two cognitive biases: risk attitudes are reinforced by experiences (one is more risk averse after disappointment than after elation) and there is a primacy effect (early outcomes have the greatest impact on risk attitude). In dynamic asset pricing, the model yields volatile, path-dependent prices.

Recursive Ambiguity and Machina’s Examples

Machina (American Economic Review 99 (2009), 385–392; American Economic Review 104 (2014), 3814–40) lists a number of situations where Choquet expected utility, as well as other known models of ambiguity aversion, cannot capture plausible features of ambiguity attitudes. Most of these problems arise in choice over prospects involving three or more outcomes. We show that the recursive nonexpected utility model of Segal (International Economic Review 28 (1987), 175–202) is rich enough to accommodate all these situations and, moreover, that this can be done using the same functional form for all situations.

Dynamic Disappointment Aversion:Don't Tell Me Anything Until You Know for Sure

We show that for a disappointment-averse decision maker, splitting a lottery into several stages reduces its value. To do this, we extend Gul.s (1991) model of disappointment aversion into a dynamic setting while keeping its basic characteristics intact. The result depends solely on the sign of the coefficient of disappointment aversion. It can help explain why people often buy periodic insurance for moderately priced objects, such as electrical appliances and cellular phones, at much more than the actuarially fair rate.

A Theory of Subjective Learning, Second Version

We study an individual who faces a dynamic decision problem in which the process of information arrival is unobserved by the analyst. We elicit subjective information directly from choice behavior by deriving two utility representations of preferences over menus of acts. The most general representation identifies a unique probability distribution over the set of posteriors that the decision maker might face at the time of choosing from the menu. We use this representation to characterize a notion of †more preference for flexibility†via a subjective analogue of Blackwell’s (1951, 1953) comparisons of experiments. A more specialized representation uniquely identifies information as a partition of the state space. This result allows us to compare individuals who expect to learn differently, even if they do not agree on their prior beliefs. On the extended domain of dated-menus, we show how to accommodate an individual who expects to learn gradually over time by means of a subjective filtration.

A Theory of Subjective Learning, Third Version

We study an individual who faces a dynamic decision problem in which the process of information arrival is unobserved by the analyst. We elicit subjective information directly from choice behavior by deriving two utility representations of preferences over menus of acts. One representation uniquely identifies information as a probability measure over posteriors and the other identifies information as a partition of the state space. We compare individuals who expect to learn differently in terms of their preference for flexibility. On the extended domain of dated-menus, we show how to accommodate gradual learning over time by means of a subjective filtration.

Generalized Partition and Subjective Filtration

We study an individual who faces a dynamic decision problem in which the process of information arrival is unobserved by the analyst, and hence should be identified from observed choice data. An information structure is objectively describable if signals correspond to events of the objective state space. We derive a representation of preferences over menus of acts that captures the behavior of a Bayesian decision maker who expects to receive such signals. The class of information structures that can support such a representation generalizes the notion of a partition of the state space. The representation allows us to compare individuals in terms of the preciseness of their information structures without requiring that they share the same prior beliefs. We apply the model to study an individual who anticipates gradual resolution of uncertainty over time. Both the filtration (the timing of information arrival with the sequence of partitions it induces) and prior beliefs are uniquely identified.

Group-Shift and the Consensus Effect

[It is well documented that individuals make different choices in the context of group decisions, such as elections, from choices made in isolation. In particular, individuals tend to conform to the decisions of others -- a property we call the consensus effect -- which in turn implies phenomena such as group polarization and the bandwagon effect. We show that the consensus effect is equivalent to a well-known violation of expected utility, namely strict quasi-convexity of preferences. Our results qualify and extend those of Eliaz, Ray and Razin (2006), who focus on choice-shifts in group when one option is safe (i.e., a degenerate lottery). In contrast to the equilibrium outcome when individuals are expected utility maximizers, the consensus effect implies that group decisions may fail to properly aggregate preferences in strategic contexts and strictly Pareto-dominated equilibria may arise. Moreover, these problems become more severe as the size of the group grows.

Inertial Behavior and Generalized Partition

We call behavior inertial if it does not react to the apparent arrival of relevant information. In a context where the precise information content of signals is subjective, we formulate an axiom that captures inertial behavior, and provide a representation that explains such behavior as that of a rational decision maker who perceives a particular type of information structure, which we call a generalized partition. We characterize the learning processes that can be described by a generalized partition. We proceed to assume that there is a true underlying information structure that may not be a generalized partition, and investigate different channels that may lead the decision maker to nonetheless perceive a generalized partition (and thus to display inertial behavior): A cognitive bias referred to as cognitive inertia and a bound on rationality which we term shortsightedness.