Economic Theory最新文献

We apply an algorithm from the wisdom-of-crowds literature to optimally combine behavioral game theory models to more accurately predict strategic choice in one-shot, simultaneous-move games. We find that the optimal weighted average of seven behavioral game theory models predicts out-of-sample choice behavior significantly better than any of the individual models. The crowd of behavioral game theory models is wiser than any single one of them. Different strategic choice models complement each other by capturing distinct patterns of behavior. The field of behavioral game theory is enriched by having this diversity of models.

The notion of stationary equilibrium is one of the most crucial solution concepts in stochastic games. However, a stochastic game can have multiple stationary equilibria, some of which may be unstable or counterintuitive. As a refinement of stationary equilibrium, we extend the concept of perfect equilibrium in strategic games to stochastic games and formulate the notion of perfect stationary equilibrium (PeSE). To further promote its applications, we develop a differentiable homotopy method to compute such an equilibrium. We incorporate vanishing logarithmic barrier terms into the payoff functions, thereby constituting a logarithmic-barrier stochastic game. As a result of this barrier game, we attain a continuously differentiable homotopy system. To reduce the number of variables in the homotopy system, we eliminate the Bellman equations through a replacement of variables and derive an equivalent system. We use the equivalent system to establish the existence of a smooth path, which starts from an arbitrary total mixed strategy profile and ends at a PeSE. Extensive numerical experiments, including relevant applications like dynamic oligopoly models and dynamic legislative voting, further affirm the effectiveness and efficiency of the method.

In sequential social dilemmas with stranger matching, initiating cooperation is inherently risky for the first mover. The disclosure of the second mover’s past actions may be necessary to instigate cooperation. We experimentally compare the effect of mandatory and voluntary disclosure with non-disclosure in a sequential prisoner’s dilemma situation. Our results confirm the positive effects of disclosure on cooperation. We also find that voluntary disclosure is as effective as mandatory disclosure, which runs counter to the results of existing literature on this topic. With voluntary disclosure, second movers who have a good track record chose to disclose, suggesting that they anticipate non-disclosure would signal non-cooperativeness. First movers interpret non-disclosure correctly as a signal of non-cooperativeness. Therefore, they cooperate less than half as often when the second mover decides not to disclose.

Abstract

We follow the future trajectory of more targeted wage formation in labor matching platforms, such as freelancing, crowd-sourcing, home-delivery, and ride-hailing, where local job search is coordinated by improving prediction algorithms. A labor matching platform is modelled as a directed search and matching market. We observe that targeted wage setting promotes efficient matching and longer employment spells. However, because a higher employment rate accentuates any disparities between available workers and vacancies, the effects of targeted wage setting on firm competition depend on prevailing market tightness. The impact of targeted wage formation on workers is positive when the vacancy-to-worker ratio is intermediate but turns negative at both extremes. Our results suggest that targeted wage setting may benefit occasional workers while potentially posing drawbacks for full-time platform workers.

Abstract

In today’s world, social networks have a significant impact on information processes, shaping individuals’ beliefs and influencing their decisions. This paper proposes a model to understand how boundedly rational (DeGroot) individuals behave when seeking information to make decisions in situations where both social communication and private learning take place. The model assumes that information is a local public good, and individuals must decide how much effort to invest in costly information sources to improve their knowledge of the state of the world. Depending on the network structure and agents’ positions, some individuals will invest in private learning, while others will free-ride on the social supply of information. The model shows that multiple equilibria can arise, and uniqueness is controlled by the lowest eigenvalue of a matrix determined by the network. The lowest eigenvalue roughly captures how two-sided a network is. Two-sided networks feature multiple equilibria. Under a utilitarian perspective, agents would be more informed than they are in equilibrium. Social welfare would be improved if influential agents increased their information acquisition levels.

A global (cooperative) game describes the utility that the whole set of players generates depending on the coalition structure they form. These games were introduced by Gilboa and Lehrer (Int J Game Theory 20:129–147, 1991) who proposed and characterized a generalization of the Shapley value. We introduce two families of point valued solutions that contain the Gilboa–Lehrer value. We characterize each family by means of reasonable properties, which are related to the ones used by Gilboa and Lehrer.

We study a method for calculating the utility function from a candidate of a demand function that is not differentiable, but is locally Lipschitz. Using this method, we obtain two new necessary and sufficient conditions for a candidate of a demand function to be a demand function. The first concerns the Slutsky matrix, and the second is the existence of a concave solution to a partial differential equation. Moreover, we show that the upper semi-continuous weak order that corresponds to the demand function is unique, and that this weak order is represented by our calculated utility function. We provide applications of these results to econometric theory. First, we show that, under several requirements, if a sequence of demand functions converges to some function with respect to the metric of compact convergence, then the limit is also a demand function. Second, the space of demand functions that have uniform Lipschitz constants on any compact set is compact under the above metric. Third, the mapping from a demand function to the calculated utility function becomes continuous. We also show a similar result on the topology of pointwise convergence.

Cooperation through repetition is an important theme in game theory. In this regard, various celebrated “folk theorems” have been proposed for repeated games in increasingly more complex environments. There has, however, been insufficient attention paid to the robustness of a large set of equilibria that is needed for such folk theorems. Starting with perfect public equilibrium as our starting point, we study uniformly strict equilibria in repeated games with private monitoring and direct communication (cheap talk). We characterize the limit equilibrium payoff set and identify the conditions for the folk theorem to hold with uniformly strict equilibrium.

We develop a model of contests on networks. Each player is connected to a set of contests and exerts a single effort to increase the probability of winning each contest to which she is connected. We explore how behavior is shaped by the pattern of interactions and characterize the networks that tend to induce greater effort; in particular, we show that the complete bipartite network is the unique structure that maximizes aggregate player effort. We also obtain a new exclusion result—akin to the Exclusion Principle of Baye et al. (Am Econ Rev 83(1):289-294, 1993)—which holds under the lottery CSF, and contrasts prior work in contests. Finally, new insight into uniqueness of equilibrium for network contest games is provided. Our framework has a broad range of applications, including research and development, advertising, and research funding.

This paper studies implications of the consistency conditions among prior, posteriors, and information sets on introspective properties of qualitative belief induced from information sets. The main result reformulates the consistency conditions as: (i) the information sets, without any assumption, almost surely form a partition; and (ii) the posterior at a state is equal to the Bayes conditional probability given the corresponding information set. The main implication of this result is to provide a tractable epistemic model which dispenses with the technical assumptions inherent in the standard epistemic model such as the countable number of information sets. Applications are agreement theorem, no-trade theorem, and the epistemic characterization of correlated equilibria. Implications are as follows. First, since qualitative belief reduces to fully introspective knowledge in the standard environment, a care must be taken when one studies non-veridical belief or non-introspective knowledge. Second, an information partition compatible with the consistency conditions is uniquely determined by the posteriors. Third, qualitative and probability-one beliefs satisfy truth axiom almost surely. The paper also sheds light on how the additivity of the posteriors yields negative introspective properties of beliefs.