Daniel Martin
Wilcox Family Chair in Entrepreneurial Economics
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Daniel Martin
Wilcox Family Chair in Entrepreneurial 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 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.
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
Human Responses to AI Oversight: Evidence from Centre Court
(with David Almog, Romain Gauriot, and Lionel Page)
Summary: We provide the first field evidence AI oversight 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: October 2024
Coverage: The Economist, Kellogg Insight
Modeling Machine Learning: A Cognitive Economic Approach
(with Andrew Caplin and Philip Marx)
Summary: We investigate whether the predictions of modern machine learning algorithms are consistent with economic models of human cognition.
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: We conduct a survey of academics about whether it is necessary to report ChatGPT use in manuscript preparation and run GPT-modified abstracts through AI detection software.
Topics: Humans and AI, Information Disclosure.
Latest version: July 2024, PLoS ONE
(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
Human Responses to AI Oversight: Evidence from Centre Court
(with David Almog, Romain Gauriot, and Lionel Page)
Summary: We provide the first field evidence AI oversight 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: October 2024
Coverage: The Economist, Kellogg Insight
Modeling Machine Learning: A Cognitive Economic Approach
(with Andrew Caplin and Philip Marx)
Summary: We investigate whether the predictions of modern machine learning algorithms are consistent with economic models of human cognition.
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: We conduct a survey of academics about whether it is necessary to report ChatGPT use in manuscript preparation and run GPT-modified abstracts through 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 is that any learning must be rationalizable.
Topics: Attention and Perception.
Latest version: April 2024
(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 is that any learning must be rationalizable.
Topics: Attention and Perception.
Latest version: April 2024