Advancement to Candidacy Presentation: “Simplified Mental Models of Complex Policy; How People Forecast the Effects of Carbon Taxes”, Austin Brooksby, University of California, Santa Barbara
Speaker
Austin Brooksby, University of California, Santa Barbara
Biography
Austin holds a B.S. in Economics from Utah State University, an M.S. in Finance from the University of Utah, and an M.A. in Economics from the University of California, Santa Barbara. His academic and research background spans experimental and behavioral economics, market design, game theory, and algorithmic trading behavior.
As an undergraduate researcher at Utah State University, Austin worked with Dr. James Feigenbaum on behavioral macroeconomic modeling and wealth taxation, contributing to published policy papers on immigration and wealth inequality. Also during his time at Utah State University, he authored work on experimental tests of a novel social preference theory with Dr. Lucas Rentschler, Dr. Vernon Smith, Jacob Meyer, and Robbie Spofford. He also served as an Undergraduate Research Fellow at the Center for Growth and Opportunity, where he applied econometric methods to studies on electricity policy and innovation.
He later joined the University of Utah’s Lab of Experimental Economics and Finance as a Research Assistant working with Dr. Elena Asparouhova, administering market simulations, analyzing data with Python and R, and supporting experimental design. In addition, he has experience teaching graduate finance courses as a Teaching Assistant, reinforcing his dedication to both research and education in economics and finance.
Austin employs behavioral theory and experiments to study how bounded rationality aggregates from individual to group behavior, and the beliefs that individuals hold over the behavior of groups in the presence of institutions such as markets. Another vein of his research uses experiments to study people's underlying models- or causal understandings of the complex world around them- and how these models inform their support for economically important environmental policies. Finally, he also studies human/AI interaction, specifically the determinants of AI-seeking behavior in economics experiments, and what information people seek from AI assistants.
Title
“Simplified Mental Models of Complex Policy; How People Forecast the Effects of Carbon Taxes”
Abstract
Citizens routinely hold strong views about complex economic policies despite having limited capacity to forecast their macroeconomic consequences. This paper studies how individuals form such forecasts by placing them in an incentivized experiment where subjects predict the effect of a carbon tax on GDP using evidence from a real empirical study, (Metcalf and Stock (2023)), accessed only through a tightly constrained chatbot interface. Subjects’ queries to the chatbot reveal the selectively attended mechanisms that constitute their simplified mental models of carbon tax policy. After making an initial forecast, subjects write advice for future participants, allowing the experiment to capture the narratives they communicate and how these narratives shape subsequent information search, belief updating, and forecast accuracy. The study examines (i) how accurate lay forecasts are relative to expert estimates, (ii) which mechanisms- such as cost channels, revenue recycling, or induced innovation- individuals prioritize when simplifying the macroeconomic environment, and (iii) how these
simplifications interact with political orientations and prevailing narratives about carbon taxes. The design provides a bridge between controlled experimental settings and the complexity of real-world policy reasoning, offering new evidence on how citizens construct simplified causal models of the macroeconomy and how these models influence both their own predictions and the guidance they give to others.
JEL Codes: C91, D83, Q58, D91
Event Details
Austin will be presenting his Advancement to Candidacy paper, “Simplified Mental Models of Complex Policy; How People Forecast the Effects of Carbon Taxes”. To access the Advancement paper, you must have an active UCSB NetID and password.
PLEASE DO NOT CIRCULATE, PRELIMINARY WORK
