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Memory loss can suppress chaotic behaviors in congestion games. Increasing learning rates can destabilize systems, but memory loss can lead to predictable outcomes, aiding resource allocation strategies.

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Area of Science:

  • Economic modeling
  • Game theory
  • Complex systems analysis

Background:

  • Congestion games with high learning rates may lead to unpredictable chaotic dynamics.
  • Understanding agent learning behavior is crucial for system stability.

Purpose of the Study:

  • Investigate learning dynamics in two-resource congestion games.
  • Analyze the impact of learning rate, memory loss, and cost asymmetry on system predictability.

Main Methods:

  • Utilized a simplified experience-weighted attraction algorithm for agent learning.
  • Modeled agent behavior using intensity of choice (learning rate) and discount factor (memory loss).
  • Examined the influence of cost function asymmetry on system dynamics.

Main Results:

  • Higher intensity of choice destabilizes the system regardless of the discount factor.
  • System predictability (chaotic vs. predictable) depends on memory loss and cost asymmetry.
  • Increased memory loss can shift dynamics from chaotic to a stable period-2 orbit.

Conclusions:

  • Memory loss plays a critical role in mitigating chaos in congestion games.
  • Findings offer insights for designing control strategies in resource allocation systems.
  • Agent memory and cost structure significantly influence system stability and predictability.