Constructing a type-adjustable mechanism to yield Pareto-optimal outcomes

TL;DR

This paper introduces a novel type-adjustable mechanism in mechanism design that models agents' types as functions of intrinsic and external factors, enabling Pareto-optimal outcomes and improved payoffs for all parties.

Contribution

It proposes a new mechanism where agents' types depend on adjustable external factors, allowing for Pareto efficiency and enhanced payoffs, along with a revised revelation principle.

Findings

Type-adjustable mechanisms can achieve Pareto-optimal outcomes.

External factors can be optimized to improve payoffs for all parties.

Compared to traditional models, the new mechanism offers better outcomes for designers and agents.

Abstract

In mechanism design theory, agents' types are described as their private information, and the designer may reveal some public information to affect agents' types in order to obtain more payoffs. Traditionally, each agent's private type and the public information are represented as a random variable respectively. In this paper, we propose a type-adjustable mechanism where each agent's private type is represented as a function of two parameters, \emph{i.e.}, his intrinsic factor and an external factor. Each agent's intrinsic factor is modeled as a private random variable, and the external factor is modeled as a solution of the designer's optimization problem. If the designer chooses an optimal value of external factor as public information, the type-adjustable mechanism may yield Pareto-optimal outcomes, which let the designer and each agent obtain more expected payoffs than what they would obtain at most in the traditional optimal mechanisms. As a comparison, in an auction with interdependent values, only the seller will benefit from public information which is represented as a random variable. We propose a revised version of revelation principle for type-adjustable Bayesian equilibrium. In the end, we compare the type-adjustable mechanism with other relevant models.