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.
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.
Recent working papers
AI Oversight and Human Mistakes: Evidence from Centre Court
(with David Almog, Romain Gauriot, and Lionel Page)
Summary: We provide the first field evidence this AI oversight carries psychological costs that can impact human decision-making. We investigate one of the highest visibility settings in which AI oversight has occurred: the Hawk-Eye review of umpires in top tennis tournaments.
Topics: Attention and Perception. Methods: Revealed Preference.
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: Attention and Perception. Methods: Experiments, Revealed Preference.
Latest version: January 2024
Previous working paper: NBER Working Paper 30600
Rational Inattention in Games: Experimental Evidence
(with David Almog)
Summary: To investigate whether attention responds rationally to strategic incentives, we experimentally implement a buyer-seller game in which a fully informed seller makes an offer to a buyer who faces cognitive costs to process information about the offer's value.
Topics: Attention and Perception. Methods: Experiments, Revealed Preference.
Latest version: November 2023
Previous working paper: SSRN Working Paper 2674224
Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals
(with Nir Chemaya)
Summary: The emergent abilities of Large Language Models (LLMs) have produced both excitement and worry about how AI will impact academic writing. We investigate whether academics view it as necessary to report AI use in manuscript preparation and how detectors react to the use of AI in academic writing.
Topics: Information Disclosure. Methods: Experiments.
Latest version: November 2023
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. Methods: Revealed Preference.
Latest version: July 2023
(with David Almog, Romain Gauriot, and Lionel Page)
Summary: We provide the first field evidence this AI oversight carries psychological costs that can impact human decision-making. We investigate one of the highest visibility settings in which AI oversight has occurred: the Hawk-Eye review of umpires in top tennis tournaments.
Topics: Attention and Perception. Methods: Revealed Preference.
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: Attention and Perception. Methods: Experiments, Revealed Preference.
Latest version: January 2024
Previous working paper: NBER Working Paper 30600
Rational Inattention in Games: Experimental Evidence
(with David Almog)
Summary: To investigate whether attention responds rationally to strategic incentives, we experimentally implement a buyer-seller game in which a fully informed seller makes an offer to a buyer who faces cognitive costs to process information about the offer's value.
Topics: Attention and Perception. Methods: Experiments, Revealed Preference.
Latest version: November 2023
Previous working paper: SSRN Working Paper 2674224
Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals
(with Nir Chemaya)
Summary: The emergent abilities of Large Language Models (LLMs) have produced both excitement and worry about how AI will impact academic writing. We investigate whether academics view it as necessary to report AI use in manuscript preparation and how detectors react to the use of AI in academic writing.
Topics: Information Disclosure. Methods: Experiments.
Latest version: November 2023
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. Methods: Revealed Preference.
Latest version: July 2023