# Correlated Equilibria and Fairness in Concurrent Stochastic Games

arXiv: https://arxiv.org/abs/2201.09702 · HTML: https://mlsanity.com/paper/2201.09702/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2022.

## Built on by (most cited first, 4 of 4)

- [Multi-Agent Verification and Control with Probabilistic Model Checking](https://mlsanity.com/paper/2308.02829/) (2023)
- [Robust Verification of Concurrent Stochastic Games](https://mlsanity.com/paper/2601.12003/) (2026)
- [Expectation vs. Reality: Towards Verification of Psychological Games](https://mlsanity.com/paper/2411.05599/) (2024)
- [Robust PAC Learning of Concurrent Stochastic Games](https://mlsanity.com/paper/2609.04189/) (2026)

## Abstract (arXiv)

Game-theoretic techniques and equilibria analysis facilitate the design and verification of competitive systems. While algorithmic complexity of equilibria computation has been extensively studied, practical implementation and application of game-theoretic methods is more recent. Tools such as PRISM-games support automated verification and synthesis of zero-sum and (epsilon-optimal subgame-perfect) social welfare Nash equilibria properties for concurrent stochastic games. However, these methods become inefficient as the number of agents grows and may also generate equilibria that yield significant variations in the outcomes for individual agents. Instead, we consider correlated equilibria, in which players can coordinate through public signals, and introduce an alternative optimality criterion of social fairness, which can be applied to both Nash and correlated equilibria. We show that correlated equilibria are easier to compute, are more equitable, and can also improve joint outcomes. We implement algorithms for both normal form games and the more complex case of multi-player concurrent stochastic games with temporal logic specifications.