# Reinforcement learning account of network reciprocity

## TL;DR

This paper demonstrates that reinforcement learning, specifically the Bush-Mosteller model, can explain when network structures promote cooperation in social dilemmas, aligning with experimental observations and extending prior numerical findings.

## Contribution

It introduces a reinforcement learning framework to explain network reciprocity in social dilemmas, bridging the gap between theory and experimental results.

## Key findings

- Reinforcement learning accounts for observed network reciprocity effects.
- The Bush-Mosteller model explains the lack of reciprocity in certain network conditions.
- The model extends previous numerical results to a broader parameter space.

## Abstract

Evolutionary game theory predicts that cooperation in social dilemma games is promoted when agents are connected as a network. However, when networks are fixed over time, humans do not necessarily show enhanced mutual cooperation. Here we show that reinforcement learning (specifically, the so-called Bush-Mosteller model) approximately explains the experimentally observed network reciprocity and the lack thereof in a parameter region spanned by the benefit-to-cost ratio and the node's degree. Thus, we significantly extend previously obtained numerical results.