Using deep reinforcement learning to promote sustainable human behaviour on a common pool resource problem

TL;DR

This paper demonstrates how deep reinforcement learning can be used to design resource allocation mechanisms that encourage sustainable and equitable contributions in common pool resource dilemmas, outperforming traditional methods.

Contribution

It introduces a novel RL-based approach to develop redistributive policies that promote sustainability and fairness in resource sharing among humans.

Findings

RL-discovered redistributive policy increased overall surplus.

The mechanism fostered more equitable resource distribution.

The AI policy was more popular and explainable than baseline methods.

Abstract

A canonical social dilemma arises when finite resources are allocated to a group of people, who can choose to either reciprocate with interest, or keep the proceeds for themselves. What resource allocation mechanisms will encourage levels of reciprocation that sustain the commons? Here, in an iterated multiplayer trust game, we use deep reinforcement learning (RL) to design an allocation mechanism that endogenously promotes sustainable contributions from human participants to a common pool resource. We first trained neural networks to behave like human players, creating a stimulated economy that allowed us to study how different mechanisms influenced the dynamics of receipt and reciprocation. We then used RL to train a social planner to maximise aggregate return to players. The social planner discovered a redistributive policy that led to a large surplus and an inclusive economy, in which players made roughly equal gains. The RL agent increased human surplus over baseline mechanisms based on unrestricted welfare or conditional cooperation, by conditioning its generosity on available resources and temporarily sanctioning defectors by allocating fewer resources to them. Examining the AI policy allowed us to develop an explainable mechanism that performed similarly and was more popular among players. Deep reinforcement learning can be used to discover mechanisms that promote sustainable human behaviour.