Modeling the quantum and classical dynamics of decision making using open system theory

In many important settings, including national security analysis, emergency response, health care, transportation, and human-machine teaming, people must make decisions before all uncertainty is resolved, while managing streams of social information and other competing uncontrolled demands from the environment. This project asks how decision processes maintain coherence under these uncertain and uncontrolled environments, when they become unstable, and what factors help uncertain judgments settle into stable decisions. The project develops and tests a general open-system model of how preferences form over time, meaning a model in which decision states can be shaped by interaction with the surrounding environment. The model draws on tools from quantum theory, not to claim that the brain is a quantum computer, but to mathematically describe decision states that are initially unresolved and later become more definite. This approach is useful for studying preferences that begin in uncertainty, fluctuate as information is encountered, and eventually stabilize into a choice. A central goal is to measure decoherence, the process by which an initially uncertain or unstable decision state becomes more definite through interaction with its environment. The research team collects preference ratings over time and uses computational models to estimate the timing and strength of this shift from unstable to stable preferences. The research then tests how different forms of social, cognitive, and motivational factors inject outside disturbances to this stabilization process. Experiments examine social information, including advice that varies in reliability or conflicts across sources; cognitive load, including multitasking demands; and accountability demands, including the need to justify one’s decisions. These studies identify factors that accelerate, delay, or alter the transition from unstable to stable decision states. The findings advance basic understanding of human decision making under uncertain and uncontrolled environments and contribute to national priorities in quantum information science. By studying how environmental disturbances change the stability of decision states, the project provides a behavioral testbed for modeling decoherence-like processes relevant to quantum computing, adaptive artificial intelligence, and decision-support systems. The project trains undergraduate and graduate students in behavioral science, computational modeling, and quantum-inspired approaches to decision science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2547658 | Program: 01002526DB NSF RESEARCH & RELATED ACTIVIT,01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Timothy Pleskac | Institution: Indiana University, BLOOMINGTON, IN | Award Amount: $729,617 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2547658 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2547658.html