SPEAKER: Prof Jun Zhuang，University at Buffalo (UB)

TIME/DATE: 2026.7.23  15:00

CLASSROOM: A308

ABSTRACT:

Abstract: Strategic decisions often involve uncertainty, incomplete information, and potential deception. This talk examines how intelligent agents reason in such environments, progressing from game-theoretic models to human behavioral experiments and, more recently, large language models (LLMs). Using attacker–defender signaling games, we show how information disclosure shapes adversarial beliefs and target-selection decisions, and how human behavior systematically departs from normative predictions. We then compare Bayesian benchmarks, human decisions, and GPT-4o under identical conditions. The results reveal both alignment and important divergences, including evidence of belief–action decoupling in LLMs. Together, these studies provide insights into strategic reasoning, adversarial decision making, and the capabilities and limitations of AI in uncertain and deceptive environments.

GUEST BIO:

Dr. Jun Zhuang is the Associate Dean for Research in the School of Engineering and Applied Sciences (SEAS), and the Morton C. Frank Professor in the Department of Industrial and Systems Engineering (ISE) at the University at Buffalo (UB), part of the State University of New York (SUNY) system, and also serves as the Editor-in-Chief of Decision Analysis. He received his Ph.D. in Industrial Engineering from the University of Wisconsin-Madison in 2008. He is a Fellow of the American Association for the Advancement of Science (AAAS), the Institute of Industrial and Systems Engineers (IISE), and the Society for Risk Analysis (SRA). Dr. Zhuang’s primary research focuses on integrating operations research, big data analytics, game theory, and decision analysis to enhance mitigation, preparedness, response, and recovery efforts for natural and human-made disasters. His research also extends to applications in healthcare, sports analytics, transportation, supply chain management, sustainability, and architecture.