# Emergent coordination in temporal partitioning congestion games

## TL;DR

This paper explores how individuals coordinate to optimally use limited resources over time, and finds that learning helps avoid destructive patterns of behavior.

## Contribution

The novel contribution is a model of temporal partitioning congestion games that demonstrates how learning can lead to convergence in optimal coordination.

## Key findings

- Adding learning prevents oscillatory behavior in temporal partitioning congestion games.
- Learning leads to weak convergence without information and strong convergence with information.
- The model was validated using real data on bats' behavior in water-scarce environments.

## Abstract

In this article we study the social dynamic of temporal partitioning congestion games (TPGs), in which participants must coordinate an optimal time-partitioning for using a limited resource. The challenge in TPGs lies in determining whether users can optimally self-organize their usage patterns. Reaching an optimal solution may be undermined, however, by a collectively destructive meta-reasoning pattern, trapping users in a socially vicious oscillatory behavior. TPGs constitute a dilemma for both human and animal communities. We developed a model capturing the dynamics of these games and ran simulations to assess its behavior, based on a 2×2 framework that distinguishes between the players’ knowledge of other players’ choices and whether they use a learning mechanism. We found that the only way in which an oscillatory dynamic can be thwarted is by adding learning, which leads to weak convergence in the no-information condition and to strong convergence in the with-information condition. We corroborated the validity of our model using real data from a study of bats’ behaviour in an environment of water scarcity. We conclude by examining the merits of a complexity-based, agent-based modelling approach over a game-theoretic one, contending that it offers superior insights into the temporal dynamics of TPGs. We also briefly discuss the policy implications of our findings.