  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.
