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We study the lock-in effect in a network of task assignments. Agents have aheterogeneous fitness for solving tasks and can redistribute unfinished tasksto other agents. They learn over time to whom to reassign tasks and preferablychoose agents with higher fitness. A lock-in occurs if reassignments can nolonger adapt. Agents overwhelmed with tasks then fail, leading to failurecascades. We find that the probability for lock-ins and systemic failuresincrease with the heterogeneity in fitness values. To study this dependence, weuse the Shannon entropy of the network of task assignments. A detaileddiscussion links our findings to the problem of resilience and observations insocial systems.

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