Manipulative auction design

This paper considers an auction design framework in which bidders get partial feedback about the distribution of bids submitted in earlier auctions: either bidders are asymmetric but past bids are disclosed in an anonymous way or several auction formats are being used and the distribution of bids but not the associated formats are disclosed. I employ the analogy-based expectation equilibrium (Jehiel, 2005) to model such situations. First-price auction in which past bids are disclosed in an anonymous way generates more revenues than the second-price auction while achieving an efficient outcome in the asymmetric private values two-bidder case with independent distributions. Besides, by using several auction formats with coarse feedback a designer can always extract more revenues than in Myerson's optimal auction, and yet less revenues than in the full information case whenever bidders enjoy ex-post quitting rights and the assignment and payment rules are monotonic in bids. These results suggest an important role of feedback disclosure as a novel instrument in mechanism design.Auction design, feedback equilibrium, manipulation

Allocative and Informational Externalities in Auctions and Related Mechanisms

We study the effects of allocative and informational externalities in (multi-object) auctions and related mechanisms. Such externalities naturally arise in models that embed auctions in larger economic contexts. In particular, they appear when there is downstream interaction among bidders after the auction has closed. The endogeneity of valuations is the main driving force behind many new, specific phenomena with allocative externalities: even in complete information settings, traditional auction formats need not be efficient, and they may give rise to multiple equilibria and strategic non-participation. But, in the absence of informational externalities, welfare maximization can be achieved by Vickrey-Clarke- Groves mechanisms. Welfare-maximizing Bayes-Nash implementation is, however, impossible in multi-object settings with informational externalities, unless the allocation problem is separable across objects (e.g. there are no allocative externalities nor complementarities) or signals are one-dimensional. Moreover, implementation of any choice function via ex-post equilibrium is generically impossible with informational externalities and multidimensional types. A theory of information constraints with multidimensional signals is rather complex, but indispensable for our study

Learning to Play Games in Extensive Form by Valuation

A valuation for a player in a game in extensive form is an assignment of numeric values to the players moves. The valuation reflects the desirability moves. We assume a myopic player, who chooses a move with the highest valuation. Valuations can also be revised, and hopefully improved, after each play of the game. Here, a very simple valuation revision is considered, in which the moves made in a play are assigned the payoff obtained in the play. We show that by adopting such a learning process a player who has a winning strategy in a win-lose game can almost surely guarantee a win in a repeated game. When a player has more than two payoffs, a more elaborate learning procedure is required. We consider one that associates with each move the average payoff in the rounds in which this move was made. When all players adopt this learning procedure, with some perturbations, then, with probability 1, strategies that are close to subgame perfect equilibrium are played after some time. A single player who adopts this procedure can guarantee only her individually rational payoff

Social Leanring with Course Inference

We study social learning by boundedly rational agents. Agents take a decision in sequence, after observing their predecessors and a private signal. They are unable to understand their predecessors’ decisions in their finest details: they only understand the relation between the aggregate distribution of actions and the state of nature. We show that, in a continuous action space, compared to the rational case, agents put more weight on early signals. Despite this behavioral bias, beliefs converge to the truth. In a discrete action space, instead, convergence to the truth does not occur even if agents receive signals of unbounded precisions.

Bubbles and crashes with partially sophisticated investors

We consider a purely speculative market with finite horizon and complete information. We introduce partially sophisticated investors, who know the average buy and sell strategies of other traders, but lack a precise understanding of how these strategies depend on the history of trade. In this setting, it is common knowledge that the market is overvalued and bound to crash, but agents hold different expectations about the date of the crash. We define conditions for the existence of equilibrium bubbles and crashes, characterize their structure, and show how bubbles may last longer when the amount of fully rational traders increases.speculative bubbles ; crashes ; bounded rationality

A theory of deception.

This paper proposes an equilibrium approach to belief manipulation and deception in which agents only have coarse knowledge of their opponent's strategy. Equilibrium requires the coarse knowledge available to agents to be correct, and the inferences and optimizations to be made on the basis of the simplest theories compatible with the available knowledge. The approach can be viewed as formalizing into a game theoretic setting a well documented bias in social psychology, the fundamental attribution error. It is applied to a bargaining problem, thereby revealing a deceptive tactic that is hard to explain in the full rationality paradigm.belief manipulation; deception; Bargaining Theory;