# MEDIATE: Mutually Endorsed Distributed Incentive Acknowledgment Token Exchange

arXiv: https://arxiv.org/abs/2404.03431 · HTML: https://mlsanity.com/paper/2404.03431/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2024.

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

- [Emergent Cooperation in Quantum Multi-Agent Reinforcement Learning Using Communication](https://mlsanity.com/paper/2601.18419/) (2026)

## Abstract (arXiv)

Recent advances in multi-agent systems (MAS) have shown that incorporating peer incentivization (PI) mechanisms vastly improves cooperation. Especially in social dilemmas, communication between the agents helps to overcome sub-optimal Nash equilibria. However, incentivization tokens need to be carefully selected. Furthermore, real-world applications might yield increased privacy requirements and limited exchange. Therefore, we extend the PI protocol for mutual acknowledgment token exchange (MATE) and provide additional analysis on the impact of the chosen tokens. Building upon those insights, we propose mutually endorsed distributed incentive acknowledgment token exchange (MEDIATE), an extended PI architecture employing automatic token derivation via decentralized consensus. Empirical results show the stable agreement on appropriate tokens yielding superior performance compared to static tokens and state-of-the-art approaches in different social dilemma environments with various reward distributions.