Daniel Martin

Wilcox Family Chair in Entrepreneurial Economics, UCSB Economics

I am a behavioral, cognitive, and experimental economist who studies attention and perception (how information is processed) and information disclosure (how information is communicated). My current research explores how human and AI interactions are shaped by attention, perception, and information disclosure.

Current Research Focus

Humans and AI

Working Papers

April 2026

Learning from an Unknown DGP: Experimental Evidence on Belief Updating with AI Recommendations

with Matthew Kovach and Gerelt Tserenjigmid

We experimentally study belief updating when a decision maker does not know the DGP, but learns from qualitative statements such as AI recommendations.

Humans and AI / Attention and Perception / Information Disclosure

April 2026 - Submitted

Human Responses to AI Oversight: Evidence from Centre Court

with David Almog, Romain Gauriot, and Lionel Page

We provide field evidence that AI oversight can affect human decision-making by investigating Hawk-Eye review of umpires in top tennis tournaments.

Humans and AI / Attention and Perception

February 2026 - R&R, Behavior Research Methods

Improving Crowdsourcing for AI through Cognitive-Inspired Data Engineering

with Gunnar Epping, Andrew Caplin, Erik Duhaime, William Holmes, and Jennifer Trueblood

We investigate whether ideas from cognitive science can mitigate cognitive constraints and biases in crowdsourced datasets and improve models trained on these data.

Humans and AI / Attention and Perception

March 2026 - Submitted

Managing Cognitive Bias in Human Labeling Operations for Rare-Event AI: Evidence from a Field Experiment

with Gunnar Epping, Andrew Caplin, Erik Duhaime, William Holmes, and Jennifer Trueblood

We run a field experiment on DiagnosUs to address the prevalence effect in the AI lifecycle.

Humans and AI / Attention and Perception

Published Papers

Journal of Economic Theory, 2025

Modeling Machine Learning: A Cognitive Economic Approach

with Andrew Caplin and Philip Marx

We investigate whether the predictions of modern machine learning algorithms are consistent with economic models of human cognition.

Humans and AI / Attention and Perception

Decision Analysis, 2026

Harnessing Human Uncertainty to Train More Accurate and Aligned AI Systems

with Gunnar Epping, Andrew Caplin, Erik Duhaime, William Holmes, and Jennifer Trueblood

We propose an approach to AI-augmented decision-making systems that uses human labeler uncertainty to improve accuracy and alignment with expert uncertainty.

Humans and AI / Attention and Perception

Selected Publications

A selected set of papers on attention, perception, and information disclosure.

  1. Search and Satisficing

    with Andrew Caplin and Mark Dean

    American Economic Review 101 (7), 2899-2922, 2011

  2. Is No News (Perceived As) Bad News? An Experimental Investigation of Information Disclosure

    with Ginger Jin and Michael Luca

    American Economic Journal: Microeconomics 13 (2), 141-73, 2021

  3. Comparison of Decisions under Unknown Experiments

    with Andrew Caplin

    Journal of Political Economy 129 (11), 3185-3205, 2021

  4. A Testable Theory of Imperfect Perception

    with Andrew Caplin

    The Economic Journal 125 (582), 184-202, 2014

  5. Strategic Pricing with Rational Inattention to Quality

    Games and Economic Behavior 104, 131-145, 2017

  6. Complex Disclosure

    with Ginger Jin and Michael Luca

    Management Science 68 (5), 3236-3261, 2022

Teaching and Background

Before receiving a PhD in Economics from NYU, I co-founded a small business that is now one of the leading providers of IT services to small and medium-sized businesses in the Carolinas.

At UCSB I teach a seminar course on entrepreneurship and a lecture class on behavioral economics. I also teach PhD classes on behavioral economics and attention and perception.

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