<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet type="text/xsl" href="static/style.xsl"?><OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd"><responseDate>2026-09-19T22:17:58Z</responseDate><request verb="GetRecord" identifier="oai:dspace.mit.edu:1721.1/122893" metadataPrefix="dim">https://dspace.mit.edu/server/oai/request</request><GetRecord><record><header><identifier>oai:dspace.mit.edu:1721.1/122893</identifier><datestamp>2021-07-05T14:03:20Z</datestamp><setSpec>com_1721.1_7582</setSpec><setSpec>com_1721.1_7581</setSpec><setSpec>col_1721.1_131023</setSpec></header><metadata><dim:dim xmlns:dim="http://www.dspace.org/xmlns/dspace/dim" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:doc="http://www.lyncode.com/xoai" xsi:schemaLocation="http://www.dspace.org/xmlns/dspace/dim http://www.dspace.org/schema/dim.xsd">
   <dim:field mdschema="dc" element="contributor" qualifier="advisor" lang="en_US">Deb Roy.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="author" lang="en_US">Yuan, An,S.M.Massachusetts Institute of Technology.</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="other" lang="en_US">Program in Media Arts and Sciences (Massachusetts Institute of Technology)</dim:field>
   <dim:field mdschema="dc" element="contributor" qualifier="department" lang="en_US">Program in Media Arts and Sciences (Massachusetts Institute of Technology)</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="accessioned">2019-11-12T17:42:13Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="available">2019-11-12T17:42:13Z</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="copyright" lang="en_US">2018</dim:field>
   <dim:field mdschema="dc" element="date" qualifier="issued" lang="en_US">2018</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="uri">https://hdl.handle.net/1721.1/122893</dim:field>
   <dim:field mdschema="dc" element="identifier" qualifier="oclc" lang="en_US">1126790108</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2018</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Cataloged from PDF version of thesis.</dim:field>
   <dim:field mdschema="dc" element="description" lang="en_US">Includes bibliographical references (pages 101-102).</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="abstract" lang="en_US">Participating in online debate can expose people to diverse viewpoints, and thereby reduce polarization of opinion over controversial issues. However a lot of online debate is hostile and further dividing -we need tools that facilitate meaningful back and forth discussion. For my thesis work I created such a tool in the form of an artificial agent that engages users in debate over controversial issues. By engaging in debates with many users, the agent will start to gain insight into things like: what kinds of arguments do people find persuasive? Or, what can we predict about a person's argumentative behavior from their moral sense? Or, what is the characteristic debate path for someone who becomes persuaded to change his mind completely? The agent will then use what it has learned to help users on either side of an issue better understand each other by exposing them to compelling arguments from both sides. To identify these arguments, the agent develops a model of the user that predicts which arguments the user will like. I measure the agent's performance given different models of the user. I then evaluate the performance of each model against the random agent, which does not attempt to model the user.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="statementofresponsibility" lang="en_US">by An Yuan.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="degree" lang="en_US">S.M.</dim:field>
   <dim:field mdschema="dc" element="description" qualifier="collection" lang="en_US">S.M. Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences</dim:field>
   <dim:field mdschema="dc" element="format" qualifier="extent" lang="en_US">102 pages</dim:field>
   <dim:field mdschema="dc" element="language" qualifier="iso" lang="en_US">eng</dim:field>
   <dim:field mdschema="dc" element="publisher" lang="en_US">Massachusetts Institute of Technology</dim:field>
   <dim:field mdschema="dc" element="rights" lang="en_US">MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.</dim:field>
   <dim:field mdschema="dc" element="rights" qualifier="uri" lang="en_US">http://dspace.mit.edu/handle/1721.1/7582</dim:field>
   <dim:field mdschema="dc" element="subject" lang="en_US">Program in Media Arts and Sciences</dim:field>
   <dim:field mdschema="dc" element="title" lang="en_US">Collective debate</dim:field>
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   <dim:field mdschema="mit" element="thesis" qualifier="degree" lang="en_US">Master</dim:field>
   <dim:field mdschema="mit" element="thesis" qualifier="department" lang="en_US">Media</dim:field>
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   	&lt;Title>Collective debate&lt;/Title>
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   	&lt;PublicationDate>2018&lt;/PublicationDate>
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        	&lt;DisplayName>Yuan, An,S.M.Massachusetts Institute of Technology.&lt;/DisplayName>
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    &lt;Keyword>Program in Media Arts and Sciences&lt;/Keyword>
   	&lt;Abstract>Participating in online debate can expose people to diverse viewpoints, and thereby reduce polarization of opinion over controversial issues. However a lot of online debate is hostile and further dividing -we need tools that facilitate meaningful back and forth discussion. For my thesis work I created such a tool in the form of an artificial agent that engages users in debate over controversial issues. By engaging in debates with many users, the agent will start to gain insight into things like: what kinds of arguments do people find persuasive? Or, what can we predict about a person&amp;apos;s argumentative behavior from their moral sense? Or, what is the characteristic debate path for someone who becomes persuaded to change his mind completely? The agent will then use what it has learned to help users on either side of an issue better understand each other by exposing them to compelling arguments from both sides. To identify these arguments, the agent develops a model of the user that predicts which arguments the user will like. I measure the agent&amp;apos;s performance given different models of the user. I then evaluate the performance of each model against the random agent, which does not attempt to model the user.&lt;/Abstract>
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