The downside of heterogeneity: How established relations counteract systemic adaptivity in tasks assignments

TL;DR

This paper investigates how heterogeneity in agent fitness can lead to lock-in effects in task assignment networks, reducing systemic adaptability and increasing failure risks, especially as heterogeneity grows.

Contribution

It introduces a model analyzing lock-in phenomena in task assignment networks with heterogeneous agents, highlighting the impact of fitness heterogeneity on systemic resilience.

Findings

Higher fitness heterogeneity increases lock-in probability.

Lock-ins lead to failure cascades in the network.

Entropy measures correlate with systemic vulnerability.

Abstract

We study the lock-in effect in a network of task assignments. Agents have a heterogeneous fitness for solving tasks and can redistribute unfinished tasks to other agents. They learn over time to whom to reassign tasks and preferably choose agents with higher fitness. A lock-in occurs if reassignments can no longer adapt. Agents overwhelmed with tasks then fail, leading to failure cascades. We find that the probability for lock-ins and systemic failures increase with the heterogeneity in fitness values. To study this dependence, we use the Shannon entropy of the network of task assignments. A detailed discussion links our findings to the problem of resilience and observations in social systems.