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 a seminar course on entrepreneurship and a lecture class on behavioral economics. In addition, I teach PhD classes 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 a seminar course on entrepreneurship and a lecture class on behavioral economics. In addition, I teach PhD classes on attention and perception and behavioral economics.
Papers on Humans and AI
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: February 2025
Coverage: The Economist, Kellogg Insight, CBC Radio, Communications ACM, Novigi
Extended abstract at EC'24
Improving Human and Machine Classification through Cognitive-Inspired Data Engineering
(with Gunnar Epping, Andrew Caplin, Erik Duhaime, William Holmes, and Jennifer Trueblood)
Summary: We investigate whether ideas from cognitive science can be applied to mitigate the presence of cognitive constraints and cognitive biases in crowdsourced datasets and improve the performance of models trained on these datasets.
Topics: Humans and AI, Attention and Perception.
Latest version: January 2025
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, R&R Management Science
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: January 2025, 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 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: February 2025
Coverage: The Economist, Kellogg Insight, CBC Radio, Communications ACM, Novigi
Extended abstract at EC'24
Improving Human and Machine Classification through Cognitive-Inspired Data Engineering
(with Gunnar Epping, Andrew Caplin, Erik Duhaime, William Holmes, and Jennifer Trueblood)
Summary: We investigate whether ideas from cognitive science can be applied to mitigate the presence of cognitive constraints and cognitive biases in crowdsourced datasets and improve the performance of models trained on these datasets.
Topics: Humans and AI, Attention and Perception.
Latest version: January 2025
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, R&R Management Science
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: January 2025, 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 Paper
Testing Capacity-Constrained Learning
(with Andrew Caplin, Philip Marx, Anastasiia Morozova, and Leshan Xu)
Summary: We introduce the first general test of capacity-constrained learning models and apply this test to the data existing experiments on human perception.
Topics: Attention and Perception.
Latest version: January 2025
(with Andrew Caplin, Philip Marx, Anastasiia Morozova, and Leshan Xu)
Summary: We introduce the first general test of capacity-constrained learning models and apply this test to the data existing experiments on human perception.
Topics: Attention and Perception.
Latest version: January 2025