# The self-organization of selfishness: Reinforcement Learning shows how selfish behavior can emerge from agent-environment interaction dynamics

## TL;DR

This paper demonstrates through reinforcement learning models that selfish behaviors like free-riding can emerge dynamically within a generation via agent-environment interactions, challenging traditional evolutionary perspectives.

## Contribution

It introduces a reinforcement learning framework showing how selfish behaviors and coordination emerge without mutation, emphasizing self-organization over evolutionary stability.

## Key findings

- Selfish behaviors emerge within a generation through learning.
- Two types of free-riding are identified as dynamic interaction patterns.
- Selfishness is shown to be a self-organized, non-stable phenomenon.

## Abstract

When biological communities use signaling structures for complex coordination, 'free-riders' emerge. The free-riding agents do not contribute to the community resources (signals), but exploit them. Most models of such 'selfish' behavior consider free-riding as evolving through mutation and selection. Over generations, the mutation -- which is considered to create a stable trait -- spreads through the population. This can lead to a version of the 'Tragedy of the Commons', where the community's coordination resource gets fully depleted or deteriorated. In contrast to this evolutionary view, we present a reinforcement learning model, which shows that both signaling-based coordination and free-riding behavior can emerge within a generation, through learning based on energy minimisation. Further, we show that there can be two types of free-riding, and both of these are not stable traits, but dynamic 'coagulations' of agent-environment interactions. Our model thus shows how different kinds of selfish behavior can emerge through self-organization, and suggests that the idea of selfishness as a stable trait presumes a model based on mutations. We conclude with a discussion of some social and policy implications of our model.