Prospect Theory

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9 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Bounded Reasoning: Cognitive Hierarchy in Human-versus-AI Cyber Defense
Oct 4, 2026Zahra Aref, Sheng Wei, Narayan B. MandayamProspect Theory Human-AI Decision Making
Rutgers University
Scaling Multi-Agent Systems with Prospect-State Propagation
Sep 7, 2026Zhimei Chen, Mu Chen, Fakhri KarrayProspect Theory Agent-Based Modeling
1Independent Researcher · 2Mohamed bin Zayed University of Artificial Intelligence · University of Waterloo
Decision-Making under Combinatorial Risk
Jun 8, 2026Yifan Hong, Hongmiao Fan, Chen WangCognitive Modeling Prospect Theory
Department of Industrial Engineering, Tsinghua University
Prospect-Theory Behavior from Bellman Optimality in MDPs with Catastrophic States
May 31, 2026Yujiao ChenMarkov Decision Processes Stochastic Optimal Control
Massachusetts Institute of Technology · Cambridge, MA 02139, USA
Beyond Rational Illusion: Behaviorally Realistic Strategic Classification
May 19, 2026Xinpeng Lv, Yunxin Mao, Renzhe Xu +11Strategic Classification Prospect Theory
College of Computer, National University of Defense Technology, Changsha, China · Institute for Theoretical Computer Science, Shanghai University of Finance and Economics, Shanghai, China · State Key Lab of General AI, School of Intelligence Science and Technology, Peking University, Beijing, China +3
Persona-Conditioned Risk Behavior in Large Language Models: A Simulated Gambling Study with GPT-4.1
Mar 16, 2026Sankalp DubedyProspect Theory LLM Decision-Making
Independent Researcher
Noncooperative Human-AI Agent Dynamics
Mar 10, 2026Dylan Waldner, Vyacheslav Kungurtsev, Mitchelle AshimosiProspect Theory Human-AI Decision Making
Rethinking Prospect Theory for LLMs: Revealing the Instability of Decision-Making under Epistemic Uncertainty
Aug 12, 2025Rui Wang, Qihan Lin, Jiayu Liu +7Prospect Theory LLM Decision-Making
Hong Kong University of Science and Technology · Huazhong University of Science and Technology · University of Illinois Urbana-Champaign
KTO: Model Alignment as Prospect Theoretic Optimization
Feb 2, 2024Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff +2LLM Alignment Human Preference Modeling
Stanford University · Contextual AI

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Bounded Reasoning: Cognitive Hierarchy in Human-versus-AI Cyber Defense

Zahra Aref, Sheng Wei, Narayan B. Mandayam

Rutgers University

Prospect Theory Human-AI Decision Making AI Agent Evaluation Autonomous Cyber Defense Decision-Making under Uncertainty

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Human-agent evaluations often compress interaction into a single performance score, even when human and automated policies adapt differently over time. We study this in a sequential cyber-defense game on an attack graph, where a human or reinforcement-learning defender protects cloud assets against a Deep Q-Network (DQN) attacker. We compare four defender settings: a human reward-only game operationalizing the DQN information structure, a human reward-plus-transition game operationalizing the Cognitive Hierarchy Theory-driven DQN (CHT-DQN) information structure, an automated DQN defender, and an automated CHT-DQN defender. In the reward-only game, participants receive payoff and reward feedback. In the reward-plus-transition game, they also see attacker-aware transition probabilities from the CHT-DQN model. Across 80 Mechanical Turk participants and matched automated simulations, human defenders adapted to outcomes in a way the automated defenders did not: they were more likely to reselect a node after a successful defense than after a failure, in both games, an asymmetry we interpret as consistent with Prospect Theory and Cumulative Prospect Theory. The 40-round average also mixes early rounds, where the DQN attacker acts mostly at random, with late rounds, where it mostly exploits; in the final stage, mean protection ranks the reward-plus-transition human game above the reward-only human game, then the automated CHT-DQN defender, then the automated DQN defender, an ordering the overall mean hides. By contrast, the reward-plus-transition game does not produce a statistically reliable overall gain in weighted data protection over the reward-only game. These results suggest that evaluating human-agent cyber-defense systems only by an averaged task score can miss behaviorally meaningful differences in adaptation, action allocation, and bounded human reasoning.