Scalable, Stochastic and Spatio-temporal Game Theory for
Real-World Human Adversarial Behavior

Principal Investigator

  • Prof. Milind Tambe
    Professor: Helen N. and Emmett H. Jones Professor in Engineering, Professor of Computer Science and Industrial and Systems Engineering
    Phone: (213) 740-6447
    Office: PHE 410 (Powell Hall of Engineering)


For preventing terrorism at home or containing the activities of insurgent networks overseas, it is critical to improve our understanding of adversarial behavior and develop techniques to address it. Most current approaches assume players are isolated “rational agents? when real-world human adversaries are distributed coalitions of socially, culturally and cognitively-biased agents, acting behind a veil of uncertainty. We need fundamentally new gametheoretic concepts and approaches based on more realistic models of player and play. The second generation of operational systems for adversarial reasoning must address scalability, stochasticity and spatiotemporal issues for which progress is inadequate or nonexistent.

Technical Approaches

  • Scalable Behavioral Game Theory

    Human adversaries are rarely rational, observant, computationally-capable entities devoted to a singular objective. We will develop new scalable algorithms that leverage the bounded rationality and observations of humans, and address human cognitive biases, cultural and social values as individuals or groups.
  • Stochastic Coalitional Game Theory

    A real-world adversary is often a collection of distributed agents that must communicate and coordinate to enact a joint action and whose the motivations may not be perfectly aligned. This coordination in particular introduces new issues of stochasticity involving coalition structures, information uncertainty, imperfect execution and robustness.
  • Spatiotemporal Game Theory

    Practical adversarial domains involve acting in a geographical space over time often in the midst of a population with divided loyalties. Incorporating spatiotemporal reasoning can substantially improve our ability to model and predict when and where consequential activities occur and how to address them.

Participating Universities

This research is sponsored by

MURI award W911NF-11-1-0332