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Interdependent Network Recovery Games.

Andrew M Smith1, Andrés D González2,3, Leonardo Dueñas-Osorio2

  • 1Department of Computer Science, University of California, Davis, CA, USA.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|October 31, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new game-theoretic model for decentralized infrastructure recovery after disasters. It ensures faster convergence to solutions compared to traditional methods, balancing efficiency and cost optimization.

Keywords:
Game theoryinfrastructure recoveryoptimization

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Area of Science:

  • Resilience and Disaster Recovery
  • Game Theory
  • Sociotechnical Systems

Background:

  • Interdependent infrastructure networks are vital for societal welfare and economic stability.
  • Centralized methods for post-disaster recovery are often suboptimal due to decentralized decision-making by independent utility controllers.
  • Existing decentralized models can be computationally intensive and may not converge.

Purpose of the Study:

  • To develop a computationally efficient decentralized model for infrastructure recovery.
  • To address the urgency and noncooperative nature of utility network controllers in post-disaster scenarios.
  • To guarantee convergence to an equilibrium in recovery processes.

Main Methods:

  • Formulated an ad hoc sequential game-theoretic model as a discrete-time noncooperative game.
  • Applied a best-response heuristic to reduce computation time.
  • Proved bounds on the resulting ε-Nash equilibrium.

Main Results:

  • The proposed ad hoc model guarantees convergence to an equilibrium, unlike best-case decentralized models that may not.
  • The best-response heuristic significantly reduces computation time for finding recovery solutions.
  • Empirical comparisons demonstrate a trade-off between optimality and computational efficiency.

Conclusions:

  • The ad hoc game-theoretic model offers a practical approach to decentralized infrastructure recovery.
  • This model effectively balances the need for rapid decision-making with achieving near-optimal recovery outcomes.
  • Provides a foundation for modeling real-world sociotechnical system restoration processes.