Reputation-driven Decision-making in Networks of Stochastic Agents

TL;DR

This paper introduces RepNet-MDP, a framework for decision-making in networks of self-interested agents that emphasizes reputation, improves upon previous models, and demonstrates adaptive behavior through experiments, while noting current limitations.

Contribution

It proposes a mathematically consistent, fully observable Markov Decision Process framework for reputation-driven multi-agent systems with an online learning algorithm.

Findings

Agents adapt their behavior based on past interactions.

RepNet-MDP addresses mathematical inconsistencies of prior models.

Framework has limitations in non-primary actor learning scenarios.

Abstract

This paper studies multi-agent systems that involve networks of self-interested agents. We propose a Markov Decision Process-derived framework, called RepNet-MDP, tailored to domains in which agent reputation is a key driver of the interactions between agents. The fundamentals are based on the principles of RepNet-POMDP, a framework developed by Rens et al. in 2018, but addresses its mathematical inconsistencies and alleviates its intractability by only considering fully observable environments. We furthermore use an online learning algorithm for finding approximate solutions to RepNet-MDPs. In a series of experiments, RepNet agents are shown to be able to adapt their own behavior to the past behavior and reliability of the remaining agents of the network. Finally, our work identifies a limitation of the framework in its current formulation that prevents its agents from learning in circumstances in which they are not a primary actor.