Overview

My research sits at the intersection of neuroeconomics, behavioral economics, and experimental methods, and returns again and again to one broad question: how people actually make economic decisions, why their choices so often depart from what standard theory predicts, and how beliefs, emotions, attention, and neural processes shape those departures. Rooted in laboratory neuroeconomics, my work has broadened over the years to take in financial decision-making and artificial intelligence, and today it centers on three connected themes.

The theme most central to my work concerns beliefs, emotions, and reciprocity in strategic interaction. Using psychological game theory, I study how expectations about others' intentions give rise to feelings such as guilt and anger, and how those feelings in turn shape promises, punishment, credible threats, and reciprocal behavior, running from work on how promises are believed and enforced to my current project tracing how a gap between what people expect and what they receive can escalate from anger into retaliation.

A second theme asks how rational economic decisions really are, and how much that rationality varies across people and settings. Rather than treating rationality as a single trait, I examine whether someone who is consistent under risk is equally consistent under ambiguity or in social choice, how ambiguity attitudes separate into dispositional and informational components, and how stable individual differences and personality types account for the heterogeneity we observe. My dissertation approached these questions through behavioral experiments and transcranial direct current stimulation, showing, for instance, that stimulating the temporoparietal junction can improve rationality in prosocial decisions.

A third theme turns to attention, computation, and financial decision-making. Using eye tracking, I study how visual attention shapes choices under risk and in asset valuation, including how payoff-irrelevant price histories sway investors and how markets overreact, and I increasingly pursue these questions computationally, using gaze sequences to predict decisions and drawing on brain-inspired and neuromorphic computing to model financial reasoning. A related strand asks how artificial intelligence reshapes human choice, from AI-generated advice that shifts investors' risk-taking to people's reliance on algorithms.

Across these threads runs a single aim: to understand the mechanisms behind economic decisions well enough to explain individual differences, predict behavior, and design better interventions.