Casiraghi Giona, Zingg Christian, Schweitzer Frank
Department of Management, Technology, and Economics, ETH Zürich, Weinbergstrasse 56/58, 8092 Zürich, Switzerland.
Entropy (Basel). 2021 Dec 14;23(12):1677. doi: 10.3390/e23121677.
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.
我们研究任务分配网络中的锁定效应。主体在解决任务方面具有异质性适应度,并且可以将未完成的任务重新分配给其他主体。随着时间的推移,它们会学习该将任务重新分配给谁,并优先选择适应度较高的主体。如果重新分配无法再进行调整,就会出现锁定。不堪重负的主体随后会失败,从而导致失败级联。我们发现,锁定和系统性故障的概率会随着适应度值的异质性而增加。为了研究这种依赖性,我们使用任务分配网络的香农熵。详细讨论将我们的发现与社会系统中的恢复力问题及观察结果联系起来。