gecon.ai

University of San Francisco

Abstract

The AI Economics Research Lab (gecon.ai) at the University of San Francisco is dedicated to advancing the intersection of artificial intelligence and economics. Our research focuses on developing innovative AI-driven methodologies to understand economic systems, model complex market behaviors, and address pressing economic challenges through cutting-edge technology.

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Goals

The AI Economics Research Lab at the University of San Francisco pursues three primary goals that guide our research agenda and shape our contributions to the field. These goals reflect our commitment to understanding the complex relationship between artificial intelligence and economic systems, both in theory and practice.

Exploring Effects of Generative AI on Economics. We investigate how generative AI technologies transform economic theory, policy, and practice. This includes examining how AI-driven tools reshape market dynamics, alter economic modeling approaches, and create new opportunities for understanding complex economic phenomena. Our research explores both the positive impacts, such as enhanced predictive capabilities and more sophisticated economic simulations, as well as potential challenges including algorithmic bias, market manipulation, and the need for new regulatory frameworks.

Effects of AI on Human Behaviour. We study how artificial intelligence systems influence human decision-making, preferences, and behavioral patterns in economic contexts. This research examines whether exposure to AI recommendations changes how individuals make economic choices, how AI-mediated interactions affect trust and cooperation in economic settings, and how the presence of AI systems might alter traditional economic behaviors. Understanding these effects is crucial for predicting the long-term impact of AI integration into economic systems.

Exploring Ways to Evaluate LLMs as Subjects of Behavioural Research. We develop methodological frameworks for treating Large Language Models (LLMs) as research subjects in behavioral economics experiments. This involves creating standardized protocols for testing economic preferences, decision-making patterns, and behavioral responses in LLMs. Our goal is to establish rigorous evaluation methods that allow researchers to systematically study AI systems' economic behaviors, compare them across different models, and understand how these behaviors emerge from the underlying architectures and training processes.

Mission

The mission of the AI Economics Research Lab (gecon.ai) is to bring Generative AI into the field of economics as both a powerful tool and a compelling subject of research. We recognize that artificial intelligence, particularly large language models and generative systems, represents a dual opportunity: it can serve as an innovative instrument for economic analysis and modeling, while simultaneously functioning as a novel research subject that challenges our understanding of economic behavior and decision-making.

As a tool, Generative AI enables us to process vast amounts of economic data, generate sophisticated economic models, simulate complex market scenarios, and provide insights that were previously difficult to obtain. We leverage these capabilities to advance economic research, improve policy analysis, and develop new methodologies for understanding economic systems.

As a subject of research, Generative AI systems themselves exhibit behaviors, preferences, and decision-making patterns that warrant scientific investigation. By treating AI systems as research subjects, we can explore fundamental questions about economic rationality, preference formation, and behavioral consistency. This dual approach allows us to not only use AI to study economics but also to study economics through the lens of AI behavior, creating a rich and multifaceted research program that pushes the boundaries of both fields.

Research Projects

Large Language Models exhibit consistent economic preference structures that can be systematically evaluated through experimental economics methodologies.

We investigate whether LLMs demonstrate consistent preference structures, risk attitudes, and decision-making patterns that mirror or diverge from human economic behavior across various economic choice scenarios.See full proof.

Large Language Models exhibit values and behaviors characteristic of WEIRD (Western, Educated, Industrialized, Rich, and Democratic) populations in economic decision-making contexts.

We examine the cultural and ethical biases embedded in LLM responses and decision-making processes, evaluating the extent to which these systems reflect the cultural biases of their training data in economic contexts.See full proof.

AI-generated media is indistinguishable from human-generated media under controlled experimental conditions.

Through blind evaluation protocols, we investigate whether human evaluators can reliably distinguish between AI-generated and human-generated media, examining the implications for media authenticity and the validity of using AI systems in content generation tasks.See full proof.

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Members

Nikita Tkachenko Nikita Tkachenko is the CEO of Evalyn and a co-founder of the AI Economics Research Lab. His work focuses on the intersection of artificial intelligence and economics, with particular interest in how generative AI systems can be used as both tools and subjects in economic research. He brings expertise in AI technology and its applications to economic analysis and decision-making.

Jesse Antilla-Hughes Jesse Antilla-Hughes is the Economics Department Chair at the University of San Francisco and a co-founder of the AI Economics Research Lab. His research interests include experimental economics, behavioral economics, and the application of AI methodologies to economic research. He leads the department's efforts in integrating cutting-edge technology with traditional economic analysis.

Alessandsa Cassar Alessandsa Cassar is a Professor at the University of San Francisco and a co-founder of the AI Economics Research Lab. Her research focuses on behavioral economics, experimental economics, and the study of economic preferences and decision-making. She is particularly interested in how AI systems can be evaluated as research subjects in behavioral economics experiments.

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Collaborate

We welcome collaborations with researchers, institutions, and organizations interested in the intersection of artificial intelligence and economics. Whether you are working on related research, have data or resources to share, or are interested in joint projects, we would be delighted to hear from you. ⊕ We welcome collaboration opportunities even outside the generative AI space.

Our lab is particularly interested in collaborations involving behavioral economics, experimental economics, computational economics, and AI ethics. We are open to partnerships that can advance our understanding of how AI systems can be used as both tools and subjects in economic research.

If you would like to collaborate with us, please contact us at natkachenko@usfca.edu.