Daniel Martin

Wilcox Family Chair in Entrepreneurial Economics


Curriculum vitae


[email protected]


Department of Economics

University of California, Santa Barbara



Daniel Martin

Wilcox Family Chair in Entrepreneurial Economics


Contact

Daniel Martin

Wilcox Family Chair in Entrepreneurial Economics


Curriculum vitae


[email protected]


Department of Economics

University of California, Santa Barbara




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 impacted by attention, perception, and information disclosure.

Before receiving a PhD in Economics from NYU, I was the co-founder of 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 undergraduate courses on entrepreneurship and behavioral economics and PhD courses on attention and perception and behavioral economics.

Papers on Humans and AI

The ABC's of Who Benefits from Working with AI: Ability, Beliefs, and Calibration
(with Andrew Caplin, David Deming, Shangwen Li, Philip Marx, Ben Weidmann, and Kadachi Jiada Ye) [Appendix]
Summary: We use a controlled experiment to show that ability and belief calibration jointly determine the benefits of working with AI.
      Topics: Humans and AI, Attention and Perception.
      Latest version: October 2024, NBER Working Paper 33021
AI Oversight and Human Mistakes: Evidence from Centre Court
(with David Almog, Romain Gauriot, and Lionel Page)
Summary: We provide the first field evidence AI oversight carries psychological costs that can impact human decision-making by investigating the Hawk-Eye review of umpires in top tennis tournaments.
      Topics: Humans and AI, Attention and Perception.
      Latest version: February 2024
      Coverage: The Economist, Kellogg Insight

Modeling Machine Learning: A Cognitive Economic Approach
(with Andrew Caplin and Philip Marx)
Summary: We show that cognitive economic methods can be applied to machine learning. As a demonstration, we apply our approach to an influential deep learning convolutional neural network that predicts pneumonia from chest X-rays.
      Topics: Humans and AI, Attention and Perception.
      Latest version: October 2024, R&R Journal of Economic Theory
      Previous working paper: NBER Working Paper 30600

Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals
(with Nir Chemaya)
Summary: Little is known about what norms are emerging around AI use in manuscript preparation or how these norms might be enforced. We address both gaps in the literature by conducting a survey of academics about whether it is necessary to report ChatGPT use in manuscript preparation and by running GPT-modified abstracts from published papers through a leading AI detection software.
      Topics: Humans and AI, Information Disclosure.
      Latest version: July 2024, PLoS ONE

Other Working Papers

Rationalizable Learning
(with Andrew Caplin and Philip Marx)
Summary: What can an analyst infer from choice data about what a decision maker has learned? The key constraint we impose, which is shared across models of Bayesian learning, is that any learning must be rationalizable.
      Topics: Attention and Perception.
      Latest version: April 2024