Date of Award

2024

Degree Type

Restricted to Claremont Colleges Dissertation

Degree Name

Economics, PhD

Program

School of Social Science, Politics, and Evaluation

Advisor/Supervisor/Committee Chair

Paul J. Zak

Dissertation or Thesis Committee Member

Greg DeAngelo

Dissertation or Thesis Committee Member

Monic Capra

Dissertation or Thesis Committee Member

Monic Capra

Terms of Use & License Information

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

Rights Information

© 2024 Sean H. Merritt

Keywords

Human behavior, Neurophysiological data, Machine learning, Statistical analyses, Mood disorders

Subject Categories

Economics

Abstract

Predicting human behavior presents significant challenges in various domains. Chapter 1 adopts a novel methodological approach, utilizing neurophysiological responses measured and utilized machine learning to predict hit songs. Statistical analyses reveal the effectiveness of machine learning techniques, showcasing a 97% accuracy in classifying hit songs. This underscores the potential of machine learning applied to neural data in enhancing market outcome predictions. Chapter 2 focuses on the proactive assessment of mood among the elderly using continuous neurophysiological data. By integrating self-reports with neurophysiological measures, the study demonstrates a high predictive accuracy in identifying low mood and energy levels. Applications of such predictions can enable timely interventions to mitigate the risk of depression in vulnerable populations. In Chapter 3, a proactive approach to mental health assessment is explored through the continuous collection of neurophysiological data in the same population as chapter 2. Statistical analyses unveil the predictive power of neurophysiological troughs and peaks in determining daily mood fluctuations, with machine learning models achieving 90% accuracy. The study underscores the viability of commercially available neuroscientific technologies in assessing mood disorders and promoting well-being. Collectively, this dissertation underscores the transformative potential of neuroscientific methodologies in predicting human behavior and experience. By leveraging machine learning and continuous neurophysiological monitoring, these studies offer insights into proactive interventions and personalized approaches to improve societal well-being and prevent mental health crises.

ISBN

9798382742045

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