Games and Economic Behavior最新文献

We consider an auction design problem with private values, where the seller and bidders may enjoy heterogeneous priors about their (possibly correlated) valuations. Each bidder forms an (interim) belief about the others based on his own prior updated by observing his own value. If the seller faces uncertainty about the bidders' priors, even if he knows that the bidders' priors are within any given distance from his, he may find it worst-case optimal to propose a dominant-strategy auction mechanism.

Condorcet domains are sets of preference orders such that the majority relation corresponding to any profile of preferences from the domain is acyclic. The best known examples in economics are the single-peaked, the single-crossing, and the group separable domains. We survey the latest developments in the area since Monjardet's magisterial overview (2009), provide some new results and offer two conjectures concerning unsolved problems. The main goal of the presentation is to illuminate the rich internal structure of the class of maximal Condorcet domains. In an appendix, we present the complete classification of all maximal Condorcet domains on four alternatives obtained by Dittrich (2018).

We examine behavior in a two-stage group contest where intra-group contests are followed by an inter-group contest. Rewards accrue to the winning group, with winners of the intra-group contest within that group receiving a greater reward. The model generates a discouragement effect, where losers from the first stage exert less effort in the second stage than winners. In contrast to the related literature, we show that a prior win may be disadvantageous, generating lower profits for first stage winners as compared to losers. We consider exogenous asymmetry between groups arising from a biased group contest success function. Although the asymmetry occurs in the second stage, its effect plays out in the first stage, with higher intra-group conflict in the advantaged group. Experimental results support the qualitative predictions of the model. However, losers from the first stage bear a higher burden of the group contribution than the theoretical prediction.

The standard extensive-form partitional representation of information in sequential games fails to distinguish the description of the rules of interaction from the description of players' personal traits. Indeed, this representation does not model how the information given to players as per the rules of the game blends with players' cognitive abilities. We propose a representation of sequential games that explicitly describes the flow of information accruing to players rather than the stock of information retained by players encoded in information partitions. Then, we add a game-independent description of players' mnemonic abilities. If players have perfect memory, our flow representation gives rise to information partitions satisfying the perfect recall property, but different combinations of information flows and players' mnemonic abilities may induce the same information partitions. We show how to use our framework to explicitly model a wide array of cognitive limitations and embed them in the representation of game situations.

Observed individual behavior in the presence of ambiguity shows insufficient responsiveness to changes in subjective likelihoods. Despite being integral to theoretical models and relevant in many domains, evidence on the causes and determining factors of such likelihood insensitive behavior is scarce. This paper investigates the role of beliefs in the form of ambiguity perception – the extent to which a decision-maker has difficulties assigning a single probability to each possible event – as a potential determinant. Using an experiment, I elicit measures of ambiguity perception and likelihood insensitivity and exogenously vary the level of perceived ambiguity. The results provide strong support for a perception-based explanation of likelihood insensitivity. The two measures are highly correlated at the individual level, and exogenously increasing ambiguity perception increases insensitivity, suggesting a causal relationship. In contrast, ambiguity perception is unrelated to ambiguity aversion – the extent to which a decision-maker dislikes the presence of ambiguity.

The paper examines information structures that can guarantee full surplus extraction via collusion-proof mechanisms. Our collusion-proofness notion requires that there does not exist any coalition whose manipulation can affect the mechanism designer's payoff. When the mechanism designer is restricted to using standard Bayesian mechanisms, we show that under almost every prior distribution of agents' types, there exist payoff structures under which there is no collusion-proof full surplus extracting mechanism. However, when ambiguous mechanisms are allowed, we provide a weak necessary and sufficient condition on the prior such that collusion-proof full surplus extraction can be guaranteed. Thus, the paper sheds light on how the collusion-proofness requirement resolves the full surplus extraction paradox of Crémer and McLean, 1985, Crémer and McLean, 1988 and how engineering ambiguity in mechanism rules restores the paradox.

This paper examines capacity-constrained oligopoly pricing with sellers who seek myopic improvements. We employ the Myopic Stable Set solution concept and establish the existence of a unique pure-strategy price solution for any given level of capacity. This solution is shown to coincide with the set of pure-strategy Nash equilibria when capacities are large or small. For an intermediate range of capacities, it predicts a price interval that includes the mixed-strategy support. This stability concept thus encompasses all Nash equilibria and offers a pure-strategy solution when there is none in Nash terms. It particularly provides a behavioral rationale for different pricing patterns, including Edgeworth price cycles and states of hyper-competition with supply shortages. We also analyze the impact of a change in firm size distribution. A merger among the biggest firms may lead to more price dispersion as it increases the maximum and decreases the minimum myopically stable price.

We study strong core and Pareto-optimal solutions for multiple partners matching problem under lexicographic preference domains from a computational point of view. The restriction to the two-sided case is called stable many-to-many matching problem and the general one-sided case is called stable fixtures problem. We provide an example to show that the strong core can be empty even for many-to-many problems, and that deciding the non-emptiness of the strong core is NP-hard. On the positive side, we give efficient algorithms for finding a near feasible strong core solution and for finding a fractional matching in the strong core of fractional matchings. In contrast with the NP-hardness result for the stable fixtures problem, we show that finding a maximum size matching that is Pareto-optimal can be done efficiently for many-to-many problems. Finally, we show that for reverse-lexicographic preferences the strong core is always non-empty in the many-to-many case.

I explain puzzles in the school assignment literature using a many-to-one matching model in which participants on one side of the market, the students, are endowed with ego-utilities à la Köszegi (2006). Ego concerns generate a form of information avoidance that results in non-truthful participation in DA matching mechanisms. In particular, students' best replies may be non-monotonic in school ranks. I show that truthful reporting can be restored by imposing a limit on the measure of students that a school can deem acceptable. Furthermore, students may be sensitive to signal garbling, in the sense of Blackwell (1953). In terms of policy, the results imply that admission committees' reliance on application dimensions that are seemingly weak proxies of academic performance may be beneficial. Other implications suggest that affirmative action policies might be beneficial. However, when students' best replies exhibit non-monotonicity in schools' selectivity, such policies might backfire.

In a multi-agent contracting problem, agents are linked in performance through two channels, effort spillover, governed by spillover network, and risk correlation, governed by risk structure. Assigning compensation weights on peers' performances can not only filter out common risks but also alter agent's incentives. We study how the network and risk structure jointly determine the optimal linear contract. First, the relative compensation sensitivity is determined by ratio of the dot product, between spillover vector and pure hedge portfolio, to unhedgeable risk. We then propose an index named informativeness along the spillover direction and argue that this index measures how precisely the principal can infer the agent's effort. By showing that both the implemented effort and induced welfare are increasing in the informativeness index, we argue that this index captures how central each agent is in this economy. Finally, results regarding relative sensitivities still hold under optimal contract with bounded compensation.