Modeling Opponents in Adversarial Risk Analysis.

Area of Science:

• Decision Sciences
• Game Theory
• Artificial Intelligence

Background:

• Adversarial risk analysis addresses risks from intentional adversary actions.
• Forecasting adversary actions requires strategic thinking and modeling opponent behavior.
• Opponents may exhibit diverse rationality paradigms, from random behavior to complex strategic thinking.

Purpose of the Study:

• To provide a framework for adversarial risk analysis supporting decision-makers.
• To model diverse opponent rationality paradigms within a unified approach.
• To enable learning about opponent rationality through observed behavior.

Main Methods:

• Modeling opponent behavior using various rationality paradigms (e.g., Nash equilibrium, level-k thinking, prospect theory).
• Employing Bayesian model averaging to handle uncertainty about the opponent's rationality.
• Calculating posterior probabilities to assess the validity of different rationality models.

Main Results:

• A decision-theoretic solution for adversarial risk analysis incorporating diverse opponent models.
• A method to update beliefs about opponent rationality as their decisions are observed.
• Demonstration of learning opponent rationality through posterior probabilities.

Conclusions:

• The proposed framework offers a robust approach to adversarial risk analysis.
• Bayesian model averaging effectively addresses uncertainty in opponent rationality.
• The ability to learn opponent rationality enhances predictive accuracy and decision support.