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Repeated parking problems are decision making situations under uncertainty in environments where agents’ scores are affected by the actions of others. The repeated parking problem is considered as a game of N agents who adapt their parking strategies over time based on their individual experiences and preferences. Each agent is assigned a unique profile characterized by a parking arrival time and a parking missing risk, which impacts strategy selection. The agent-based model was implemented in NetLogo to investigate emergent phenomena and adaptive decision-making. The simulation integrates probabilistic elements, including Beta-distributed arrival times. A scoring function evaluates performance by combining parking success rates with arrival times, enabling comparative analysis of strategy effectiveness across agents.

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Agent-Based Simulation of Repeated Parking Problem Inspired by El Farol Bar Model

  • 会议论文

  • 发表日期：

    2026年

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  • Michal Michna,
  • Milan Kořínek,
  • Kamila Štekerová

摘要

Repeated parking problems are decision making situations under uncertainty in environments where agents’ scores are affected by the actions of others. The repeated parking problem is considered as a game of N agents who adapt their parking strategies over time based on their individual experiences and preferences. Each agent is assigned a unique profile characterized by a parking arrival time and a parking missing risk, which impacts strategy selection. The agent-based model was implemented in NetLogo to investigate emergent phenomena and adaptive decision-making. The simulation integrates probabilistic elements, including Beta-distributed arrival times. A scoring function evaluates performance by combining parking success rates with arrival times, enabling comparative analysis of strategy effectiveness across agents.