Human-Machine Interfacing

Human decision-making models have long been studied in behavioral economics and psychology, with classical approaches grounded in Expected Utility Theory. However, experimental results—particularly those by Kahneman and Tversky—have revealed systematic violations of its axioms, prompting the development of alternative models like Prospect Theory. Still, these models fall short of fully capturing the complexity of human behavior.

Quantum Decision Theory (QDT) offers a novel framework by modeling mental states as quantum states in Hilbert space, naturally accommodating phenomena such as order effects and violations of the Sure Thing Principle—analogous to non-commutativity and interference in quantum systems. Importantly, QDT does not imply the brain is a quantum system, but rather provides a compact, empirically grounded black-box model of cognition.

Recent advances in QDT use quantum dynamical systems to model evolving preferences. In contrast to classical Markov models, formulations based on Schrödinger’s equation (e.g., Busemeyer et al., 2009) explain violations of the law of total probability and choice-induced preference shifts. Extensions using open quantum systems (Asano et al., 2012; Martinez et al., 2016) generalize preference dynamics while retaining these explanatory benefits. Empirical work (Busemeyer et al., 2021) supports this approach as the most comprehensive model of decision evolution.

Building on this foundation, we implement the open-quantum model of Martinez et al. (2016) in two human-machine systems: (1) a stochastic control interface where a machine adaptively guides human decisions toward optimality, and (2) a sequential change detection framework where an analyst infers system changes through behavior-driven observations. Our contributions in these domains are outlined below.

Relevant Publications