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Updated: May 28, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Adversarial risk analysis with incomplete information: a level-k approach.

Casey Rothschild1, Laura McLay, Seth Guikema

  • 1Department of Economics, Wellesley College, 106 College Street, Wellesley, MA 02481, USA. crothsch@wellesley.edu

Risk Analysis : an Official Publication of the Society for Risk Analysis
|November 1, 2011
PubMed
Summary

This study introduces level-k game theory for adversarial risk analysis, offering a practical Bayesian approach. It models bounded rationality in strategic decision-making for improved security assessments.

Related Experiment Videos

Last Updated: May 28, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

  • Decision Sciences
  • Game Theory
  • Risk Analysis

Background:

  • Adversarial risk analysis often assumes perfect rationality.
  • Bounded rationality in strategic interactions is complex to model.
  • Operationalizing Bayesian approaches requires tractable methods.

Purpose of the Study:

  • To propose and develop level-k game theory for adversarial risk analysis.
  • To demonstrate its application in strategic decision-making scenarios.
  • To provide a computationally modest and practically applicable framework.

Main Methods:

  • Applied level-k game theory, a model of bounded rationality.
  • Utilized a Bayesian framework for risk assessment.
  • Illustrated with a defend-attack model with probabilistic information.

Main Results:

  • Level-k game theory effectively operationalizes Bayesian adversarial risk analysis.
  • The approach accommodates asynchronous play and incomplete information.
  • Demonstrated feasibility in a defend-attack scenario with probabilistic countermeasure revelation.

Conclusions:

  • Level-k game theory offers a viable and efficient method for adversarial risk analysis.
  • It provides a practical tool for modeling strategic interactions with bounded rationality.
  • The framework enhances security assessments in complex environments.