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Often the most difficult obstacle to game-theoretic analysis of complex scenarios is developing a model of the game situation in the first place. In the empirical game-theoretic analysis (EGTA) approach, expert modeling is augmented by empirical sources of knowledge: data obtained through real-world observations or (as emphasized here) outcomes of high-fidelity simulation.
A well-functioning financial system is critical for the operation of a complex global economy, and finance itself represents a major sector of economic activity. The financial system is in many respects a computational device: allocating capital resources based on beliefs about current and future productivity, processing payments through a distributed account network, and making credit decisions based on trust and expectations of future payment capacity.
A computational (AI) agent is autonomous to the extent it makes decisions in its encountered circumstances without direct intervention by human designers. By this definition, autonomous agents are here today: trading in financial markets, controlling vehicles, and performing a variety of other specific functions.
In the security domain, we are concerned with protecting resources from known or potential adversaries, who we assume may themselves possess sophisticated reasoning capacity. Viewed this way, the problem is to develop strategies for security games, choosing policies based on assessments of the capabilities, knowledge, and objectives of adversaries, recognizing that they may also be considering our own objectives, capabilities, and knowledge.