Human and Artificial Agents in Social Decision-Making: Experimental, Computational, and Neurobiological Evidence

Author: Henning, Thomas L.

Year: 2027

Degree: Dissertation (Ph.D.)

Advisors: Camerer, Colin F.; Rangel, Antonio

Committee Members: O'Doherty, John P.; Rangel, Antonio; Alvarez, R. Michael; Camerer, Colin F.

Option: Social and Decision Neuroscience

DOI: 10.7907/vahk-8h11

Abstract

Artificial intelligence is changing both the environments in which humans make decisions and the methods scientists use to study decision-making. This dissertation investigates decision-making in complex, uncertain, and socially embedded settings by combining experimental economics, psychophysiology, machine learning, large language models, online text analysis, and neuroimaging. Across five chapters, I study how humans form beliefs, respond emotionally, communicate socially, and distinguish real from synthetic information, while also examining how artificial intelligence can serve as both a scientific tool and a comparison point for human behavior.

Chapter 2 introduces a research agenda for using AI agents in experimental social science. Large language model agents can act as interactive partners, synthetic participants, and scientific assistants, enabling new forms of piloting, hypothesis generation, cross-cultural simulation, and large-scale data analysis. Rather than replacing human subjects or scientists, the chapter argues that AI systems are most valuable when used as complements that expand the range, speed, and scale of social scientific inquiry.

Chapter 3 uses AI and machine learning techniques to study the psychobiological correlates of experimental asset bubbles. In experimental markets with a known, constant fundamental value, participants traded a risky asset while providing forecasts, completing risk-elicitation tasks, and, for in-lab participants, wearing biometric sensors. Traditional market variables, especially lagged returns and participant forecasts, substantially predict future returns. Period-by-period tonic electrodermal activity does not add incremental predictive power for one-period-ahead returns. However, we find evidence that smoothed physiological arousal, more akin to “mood,” is associated with longer-horizon returns and crash dynamics. Additional exploratory results indicate that lower arousal variability is associated with higher trading earnings and that elevated arousal predicts de-risking behavior. The chapter also shows that neither higher monetary stakes nor prior market experience eliminates bubbles, and that participant forecasts systematically violate rational-expectations benchmarks.

Chapter 4 compares the performance of out-of-the-box large language model agents in agent-only markets with that of human traders in human-only markets, using the same experimental finance paradigm described in Chapter 3. Unlike humans, LLM agents generally price assets near fundamental value and display only a muted propensity for bubble formation. This pattern persists across homogeneous single-model markets, heterogeneous “battle royale” markets, dividend shocks, and repeated-exposure treatments. Analyses of LLM-generated strategy text indicate lower variance, reduced bias, and stronger reliance on fundamentals relative to humans. These results suggest that out-of-the-box LLM agents may fail to reproduce core features of human-driven market behavior, especially large emergent bubbles.

Chapter 5 uses LLM-based text analysis to study social incentives in online financial communities. We analyze posts from r/wallstreetbets in which users disclose realized trading gains or losses. Loss disclosures receive disproportionate engagement, especially when written in a self-deprecating tone. The magnitude of self-reported losses also increases over time, consistent with the possibility that platform-level engagement mechanisms reward and amplify negative financial outcomes. These results suggest that investors may derive nonpecuniary utility from publicly sharing losses, and that social rewards can shape financial communication and potentially influence risk-taking.

Chapter 6 examines whether the human brain encodes real and AI-generated faces differently. The results provide exploratory evidence that real and AI-generated faces evoke distinguishable neural response patterns in several visual, parietal, and frontal regions.

In the chapter appendix, we use these representational differences to test whether neural responses contain information about image authenticity beyond what is captured by overt behavioral judgments. We do so using multivariate fMRI decoding methods. In some configurations, neural features modestly improve classification relative to behavioral measures alone. However, these effects are sensitive to analytic choices, and generalization across subjects remains limited. The chapter therefore provides proof of concept that neural activity contains information relevant to synthetic-media perception, while emphasizing the methodological challenges that remain before neuroimaging could contribute to practical deepfake detection.

Together, these studies demonstrate that human decision-making is shaped by interactions among cognition, emotion, social incentives, and perceptual sensitivity to authenticity. They also show how artificial intelligence can be useful not only as a tool for measurement and analysis, but also as a subject of study in its own right. By integrating behavioral science, machine learning, and neuroscience, this dissertation advances a framework for studying decision-making in an increasingly AI-mediated world.

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