In Trust We Survive: Emergent Trust Learning

TL;DR

Emergent Trust Learning (ETL) is a lightweight trust-based control algorithm that enables AI agents to cooperate in competitive environments by maintaining internal trust states, requiring minimal resources, and demonstrating effectiveness across various scenarios.

Contribution

This paper introduces ETL, a novel trust-based control method that enhances cooperation in AI agents with minimal overhead and broad applicability.

Findings

ETL reduces conflicts and resource depletion in grid worlds.

ETL sustains high survival and cooperation in social dilemmas.

ETL maintains cooperation and avoids exploitation in iterated Prisoner's Dilemma.

Abstract

We introduce Emergent Trust Learning (ETL), a lightweight, trust-based control algorithm that can be plugged into existing AI agents. It enables these to reach cooperation in competitive game environments under shared resources. Each agent maintains a compact internal trust state, which modulates memory, exploration, and action selection. ETL requires only individual rewards and local observations and incurs negligible computational and communication overhead. We evaluate ETL in three environments: In a grid-based resource world, trust-based agents reduce conflicts and prevent long-term resource depletion while achieving competitive individual returns. In a hierarchical Tower environment with strong social dilemmas and randomised floor assignments, ETL sustains high survival rates and recovers cooperation even after extended phases of enforced greed. In the Iterated Prisoner's Dilemma, the algorithm generalises to a strategic meta-game, maintaining cooperation with reciprocal opponents while avoiding long-term exploitation by defectors. Code will be released upon publication.