Economics

We consider a model for decision making based on an adaptive, k-period, learning process where the priors are selected according to Von Neumann-Morgenstern expected utility principle. A preference relation between two prospects is introduced, defined by the condition which prospect is selected more often. We show that the new preferences have similarities with the preferences obtained by Kahneman and Tversky (1979) in the context of the prospect theory. Additionally, we establish that in the limit of large learning period, the new preferences coincide with the expected utility principle.

In random expected utility (Gul and Pesendorfer, 2006), the distribution of preferences is uniquely recoverable from random choice. This paper shows through two examples that such uniqueness fails in general if risk preferences are random but do not conform to expected utility theory. In the first, non-uniqueness obtains even if all preferences are confined to the betweenness class (Dekel, 1986) and are suitably monotone. The second example illustrates random choice behavior consistent with random expected utility that is also consistent with random non-expected utility. On the other hand, we find that if risk preferences conform to weighted utility theory (Chew, 1983) and are monotone in first-order stochastic dominance, random choice again uniquely identifies the distribution of preferences. Finally, we argue that, depending on the domain of risk preferences, uniqueness may be restored if joint distributions of choice across a limited number of feasible sets are available.

This paper uses an axiomatic foundation to create a new measure for the cost of learning that allows for multiple perceptual distances in a single choice environment so that some events can be harder to differentiate between than others. The new measure maintains the tractability of Shannon's classic measure but produces richer choice predictions and identifies a new form of informational bias significant for welfare and counterfactual analysis.

We study preferences estimated from finite choice experiments and provide sufficient conditions for convergence to a unique underlying "true" preference. Our conditions are weak, and therefore valid in a wide range of economic environments. We develop applications to expected utility theory, choice over consumption bundles, menu choice and intertemporal consumption. Our framework unifies the revealed preference tradition with models that allow for errors.

Recently, many matching systems around the world have been reformed. These reforms responded to objections that the matching mechanisms in use were unfair and manipulable. Surprisingly, the mechanisms remained unfair even after the reforms: the new mechanisms may induce an outcome with a blocking student who desires and deserves a school which she did not receive. However, as we show in this paper, the reforms introduced matching mechanisms which are more fair compared to the counterfactuals. First, most of the reforms introduced mechanisms that are more fair by stability: whenever the old mechanism does not have a blocking student, the new mechanism does not have a blocking student either. Second, some reforms introduced mechanisms that are more fair by counting: the old mechanism always has at least as many blocking students as the new mechanism. These findings give a novel rationale to the reforms and complement the recent literature showing that the same reforms have introduced less manipulable matching mechanisms. We further show that the fairness and manipulability of the mechanisms are strongly logically related.

Paul A. Samuelson's (1966) capitulation during the so-called Cambridge controversy on the re-switching of techniques in capital theory had implications not only in pointing at supposed internal contradiction of the marginal theory of production and distribution, but also in preserving vested interests in the academic and political world. Based on a new non-switching theorem, the present paper demonstrates that Samuelson's capitulation was logically groundless from the point of view of the economic theory of production.

Reinforcement learning algorithms describe how an agent can learn an optimal action policy in a sequential decision process, through repeated experience. In a given environment, the agent policy provides him some running and terminal rewards. As in online learning, the agent learns sequentially. As in multi-armed bandit problems, when an agent picks an action, he can not infer ex-post the rewards induced by other action choices. In reinforcement learning, his actions have consequences: they influence not only rewards, but also future states of the world. The goal of reinforcement learning is to find an optimal policy -- a mapping from the states of the world to the set of actions, in order to maximize cumulative reward, which is a long term strategy. Exploring might be sub-optimal on a short-term horizon but could lead to optimal long-term ones. Many problems of optimal control, popular in economics for more than forty years, can be expressed in the reinforcement learning framework, and recent advances in computational science, provided in particular by deep learning algorithms, can be used by economists in order to solve complex behavioral problems. In this article, we propose a state-of-the-art of reinforcement learning techniques, and present applications in economics, game theory, operation research and finance.

We study a communication game between an informed sender and an uninformed receiver with repeated interactions and voluntary transfers. Transfers motivate the receiver's decision-making and signal the sender's information. Although full separation can always be supported in equilibrium, partial or complete pooling is optimal if the receiver's decision-making is highly responsive to information. In this case, the receiver's decision-making is disciplined by pooling extreme states where she is most tempted to defect.

This paper proposes and axiomatizes a new updating rule: Relative Maximum Likelihood (RML) for ambiguous beliefs represented by a set of priors (C). This rule takes the form of applying Bayes' rule to a subset of the set C. This subset is a linear contraction of C towards its subset ascribing a maximal probability to the observed event. The degree of contraction captures the extent of willingness to discard priors based on likelihood when updating. Two well-known updating rules of multiple priors, Full Bayesian (FB) and Maximum Likelihood (ML), are included as special cases of RML. An axiomatic characterization of conditional preferences generated by RML updating is provided when the preferences admit Maxmin Expected Utility representations. The axiomatization relies on weakening the axioms characterizing FB and ML. The axiom characterizing ML is identified for the first time in this paper, addressing a long-standing open question in the literature.

In the era of a growing population, systemic change of the world, and rising risk of crises, humanity has been facing an unprecedented challenge of resource scarcity. Confronting and addressing the issues concerning the scarce resource's conservation, competition, and stimulation by grappling their characters and adopting viable policy instruments calls the decision-makers' attention to a paramount priority. In this paper, we develop the first general decentralized cross-sector supply chain network model that captures the unique features of the scarce resources under fiscal-monetary policies. We formulate the model as a network equilibrium problem with finite-dimensional variational inequality theories. We then characterize the network equilibrium with a set of classic theoretical properties, as well as some novel properties (with λ min ) that are new to the literature of network games application. Lastly, we provide a series of illustrative examples, including a medical glove supply chain, to showcase how our model can be used to investigate the efficacy of the imposed policies in relieving the supply chain distress and stimulating welfare. Our managerial insights encompass the industry profit and social benefit vis-?-vis the resource availability and policy instrument design.