Search Results (3)

I consider a class of dynamic Bayesian games in which types evolve stochastically according to a first-order Markov process on a continuous type space. Types are privately informed, but they become public together with actions when payoffs are obtained, resulting in a delayed information revelation. In this environment, I show that there exists a stationary Bayesian–Markov equilibrium in which a player’s strategy maps a tuple of the previous type and action profiles and the player’s current type to a mixed action. The existence can be extended to K-periodic revelation. I also offer a computational algorithm to find an equilibrium. Full article

This paper introduces a dynamic mechanism design tailored for uncertain environments where incentive schemes are challenged by the inability to observe players’ actions, known as moral hazard. In these scenarios, the system operates as a Markov game where outcomes depend on both the state of payouts and players’ actions. Moral hazard and adverse selection further complicate decision-making. The proposed mechanism aims to incentivize players to truthfully reveal their states while maximizing their expected payoffs. This is achieved through players’ best-reply strategies, ensuring truthful state revelation despite moral hazard. The revelation principle, a core concept in mechanism design, is applied to models with both moral hazard and adverse selection, facilitating optimal reward structure identification. The research holds significant practical implications, addressing the challenge of designing reward structures for multiplayer Markov games with hidden actions. By utilizing dynamic mechanism design, researchers and practitioners can optimize incentive schemes in complex, uncertain environments affected by moral hazard. To demonstrate the approach, the paper includes a numerical example of solving an oligopoly problem. Oligopolies, with a few dominant market players, exhibit complex dynamics where individual actions impact market outcomes significantly. Using the dynamic mechanism design framework, the paper shows how to construct optimal reward structures that align players’ incentives with desirable market outcomes, mitigating moral hazard and adverse selection effects. This framework is crucial for optimizing incentive schemes in multiplayer Markov games, providing a robust approach to handling the intricacies of moral hazard and adverse selection. By leveraging this design, the research contributes to the literature by offering a method to construct effective reward structures even in complex and uncertain environments. The numerical example of oligopolies illustrates the practical application and effectiveness of this dynamic mechanism design. Full article

A theme that become common knowledge of the literature is the difficulty of developing a mechanism that is compatible with individual incentives that simultaneously result in efficient decisions that maximize the total reward. In this paper, we suggest an analytical method for computing a mechanism design. This problem is explored in the context of a framework, in which the players follow an average utility in a non-cooperative Markov game with incomplete state information. All of the Nash equilibria are approximated in a sequential process. We describe a method for the derivative of the player’s equilibrium that instruments the design of the mechanism. In addition, it showed the convergence and rate of convergence of the proposed method. For computing the mechanism, we consider an extension of the Markov model for which it is introduced a new variable that represents the product of the mechanism design and the joint strategy. We derive formulas to recover the variables of interest: mechanisms, strategy, and distribution vector. The mechanism design and equilibrium strategies computation differ from those in previous literature. A numerical example presents the usefulness and effectiveness of the proposed method. Full article