# On the Equilibrium Elicitation of Markov Games Through Information Design

arXiv: https://arxiv.org/abs/2102.07152 · HTML: https://mlsanity.com/paper/2102.07152/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 51 references that are in the map.
- First posted on arXiv 14 Feb 2021.

## Builds on (most cited first, 7 of 7)

- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Inverse Reward Design](https://mlsanity.com/paper/1711.02827/) (2017)
- [An Optimal Bidimensional Multi-Armed Bandit Auction for Multi-unit Procurement](https://mlsanity.com/paper/1502.06934/) (2015)
- [On Incentive Compatibility in Dynamic Mechanism Design With Exit Option in a Markovian Environment](https://mlsanity.com/paper/1909.13720/) (2019)
- [Optimal Two-Sided Market Mechanism Design for Large-Scale Data Sharing and Trading in Massive IoT Networks](https://mlsanity.com/paper/1912.06229/) (2019)
- [Reward Engineering for Object Pick and Place Training](https://mlsanity.com/paper/2001.03792/) (2020)
- [On the Differential Private Data Market: Endogenous Evolution, Dynamic Pricing, and Incentive Compatibility](https://mlsanity.com/paper/2101.04357/) (2021)

## Built on by (most cited first, 1 of 1)

- [Symbiotic Game and Foundation Models for Cyber Deception Operations in Strategic Cyber Warfare](https://mlsanity.com/paper/2403.10570/) (2024)

## Abstract (arXiv)

This work considers a novel information design problem and studies how the craft of payoff-relevant environmental signals solely can influence the behaviors of intelligent agents. The agents' strategic interactions are captured by an incomplete-information Markov game, in which each agent first selects one environmental signal from multiple signal sources as additional payoff-relevant information and then takes an action. There is a rational information designer (designer) who possesses one signal source and aims to control the equilibrium behaviors of the agents by designing the information structure of her signals sent to the agents. An obedient principle is established which states that it is without loss of generality to focus on the direct information design when the information design incentivizes each agent to select the signal sent by the designer, such that the design process avoids the predictions of the agents' strategic selection behaviors. We then introduce the design protocol given a goal of the designer referred to as obedient implementability (OIL) and characterize the OIL in a class of obedient perfect Bayesian Markov Nash equilibria (O-PBME). A new framework for information design is proposed based on an approach of maximizing the optimal slack variables. Finally, we formulate the designer's goal selection problem and characterize it in terms of information design by establishing a relationship between the O-PBME and the Bayesian Markov correlated equilibria, in which we build upon the revelation principle in classic information design in economics. The proposed approach can be applied to elicit desired behaviors of multi-agent systems in competing as well as cooperating settings and be extended to heterogeneous stochastic games in the complete- and the incomplete-information environments.