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From the Machine Mind to the Human M...

Chen, Chen.

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From the Machine Mind to the Human Mind: Using Machine Learning to Understand (Ir)Rationality, Bias and Polarization in Human Beings.

Record Type:	Electronic resources : Monograph/item
Title/Author:	From the Machine Mind to the Human Mind: Using Machine Learning to Understand (Ir)Rationality, Bias and Polarization in Human Beings./
Author:	Chen, Chen.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	208 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-08, Section: B.
Contained By:	Dissertations Abstracts International82-08B.
Subject:	Health care management. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28314097
ISBN:	9798557092517

From the Machine Mind to the Human Mind: Using Machine Learning to Understand (Ir)Rationality, Bias and Polarization in Human Beings.
Chen, Chen.

From the Machine Mind to the Human Mind: Using Machine Learning to Understand (Ir)Rationality, Bias and Polarization in Human Beings. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 208 p.

Source: Dissertations Abstracts International, Volume: 82-08, Section: B.

Thesis (Ph.D.)--Boston University, 2021.

This item must not be sold to any third party vendors.

My dissertation, titled "From the machine mind to the human mind: using machine learning to understand (ir)rationality, bias and polarization in human beings," investigates ways in which human minds operate and seeks to uncover the causes of biasedness, limited rationality, and polarization of human minds, to eventually devise tools to compensate for such human limitations. Chapter 2 of the thesis focuses on the evaluation of information and decision making under enormous information asymmetry, in the setting of patients evaluating doctors' medical advice. Patients were found to be poor evaluators who were unable to distinguish good from bad due to their lack of medical expertise, and unable to overcome their own irrationality and bias. I emphasize the ramification of such limited rationality, which might lead to the adoption of suboptimal or bad medical opinions, and propose ways to improve this situation by redesigning some features of the platform, and/or implementing new policies to help good doctors on the platform. Chapter 3 focuses on developing a new metric that reliably measures the ideology of the US elites. This metric was developed based on congressional reports which made it unique and relatively independent from established metrics based on roll call votes, such as DW-NOMINATE. First, I leveraged a neural network-based approach to decompose the speech documents into frames and topics components, with all ideological information funneled into the frames component. Eventually, two different ideology metrics were obtained and validated: an embedding vector and an ideological slant score. Later I showed that our new metrics can predict party switchers and trespassers with high recall. In chapter 4, I applied the newly obtained metric (mainly slant scores) to investigate various aspects of the congress, such as the heterogeneity of ideology among the members, the temporal evolution of partisan division, the bill passing, and the re-election strategy of the senators.

ISBN: 9798557092517Subjects--Topical Terms:

2122906
Health care management.
Subjects--Index Terms:

Machine learning

From the Machine Mind to the Human Mind: Using Machine Learning to Understand (Ir)Rationality, Bias and Polarization in Human Beings.
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520 $a My dissertation, titled "From the machine mind to the human mind: using machine learning to understand (ir)rationality, bias and polarization in human beings," investigates ways in which human minds operate and seeks to uncover the causes of biasedness, limited rationality, and polarization of human minds, to eventually devise tools to compensate for such human limitations. Chapter 2 of the thesis focuses on the evaluation of information and decision making under enormous information asymmetry, in the setting of patients evaluating doctors' medical advice. Patients were found to be poor evaluators who were unable to distinguish good from bad due to their lack of medical expertise, and unable to overcome their own irrationality and bias. I emphasize the ramification of such limited rationality, which might lead to the adoption of suboptimal or bad medical opinions, and propose ways to improve this situation by redesigning some features of the platform, and/or implementing new policies to help good doctors on the platform. Chapter 3 focuses on developing a new metric that reliably measures the ideology of the US elites. This metric was developed based on congressional reports which made it unique and relatively independent from established metrics based on roll call votes, such as DW-NOMINATE. First, I leveraged a neural network-based approach to decompose the speech documents into frames and topics components, with all ideological information funneled into the frames component. Eventually, two different ideology metrics were obtained and validated: an embedding vector and an ideological slant score. Later I showed that our new metrics can predict party switchers and trespassers with high recall. In chapter 4, I applied the newly obtained metric (mainly slant scores) to investigate various aspects of the congress, such as the heterogeneity of ideology among the members, the temporal evolution of partisan division, the bill passing, and the re-election strategy of the senators.
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