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The Downside of Heterogeneity: How Established Relations Counteract Systemic Adaptivity in Tasks Assignments.

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Increased fitness heterogeneity in task assignment networks amplifies lock-in effects and systemic failures. Agents learn to reassign tasks, but adaptation limits lead to cascading failures when overloaded.

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Area of Science:

  • Complex Systems
  • Network Science
  • Agent-Based Modeling

Background:

  • Task assignment networks involve agents with varying capabilities.
  • Agents can reassign unfinished tasks, learning preferred recipients.
  • Adaptation limits can lead to system-wide failures.

Purpose of the Study:

  • Investigate the lock-in effect in task assignment networks.
  • Analyze the impact of fitness heterogeneity on system resilience.
  • Explore the relationship between network entropy and failure probability.

Main Methods:

  • Agent-based modeling of task reassignments.
  • Analysis of network dynamics and agent learning.
  • Utilizing Shannon entropy to quantify fitness heterogeneity.

Main Results:

  • Lock-in effects and cascading failures increase with fitness heterogeneity.
  • Agents preferentially reassign tasks to higher-fitness individuals.
  • Shannon entropy effectively measures the dependence of failures on heterogeneity.

Conclusions:

  • Heterogeneity in agent fitness is a critical factor in network resilience.
  • Understanding task reassignment dynamics is key to preventing systemic failures.
  • Findings offer insights into social systems and resilience challenges.