Scientific Networks on Data Landscapes: Question Difficulty, Epistemic Success, and Convergence.
Identifieur interne : 000014 ( PubMed/Corpus ); précédent : 000013; suivant : 000015Scientific Networks on Data Landscapes: Question Difficulty, Epistemic Success, and Convergence.
Auteurs : Patrick Grim ; Daniel J. Singer ; Steven Fisher ; Aaron Bramson ; William J. Berger ; Christopher Reade ; Carissa Flocken ; Adam SalesSource :
- Episteme (Edinburgh) [ 1742-3600 ] ; 2013.
Abstract
A scientific community can be modeled as a collection of epistemic agents attempting to answer questions, in part by communicating about their hypotheses and results. We can treat the pathways of scientific communication as a network. When we do, it becomes clear that the interaction between the structure of the network and the nature of the question under investigation affects epistemic desiderata, including accuracy and speed to community consensus. Here we build on previous work, both our own and others', in order to get a firmer grasp on precisely which features of scientific communities interact with which features of scientific questions in order to influence epistemic outcomes. Here we introduce a measure on the landscape meant to capture some aspects of the difficulty of answering an empirical question. We then investigate both how different communication networks affect whether the community finds the best answer and the time it takes for the community to reach consensus on an answer. We measure these two epistemic desiderata on a continuum of networks sampled from the Watts-Strogatz spectrum. It turns out that finding the best answer and reaching consensus exhibit radically different patterns. The time it takes for a community to reach a consensus in these models roughly tracks mean path length in the network. Whether a scientific community finds the best answer, on the other hand, tracks neither mean path length nor clustering coefficient.
DOI: 10.1017/epi.2013.36
PubMed: 24683416
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He primarily researches the nature of epistemic normativity and uses computational models to explore the social structure of inquiry.</nlm:affiliation></affiliation></author><author><name sortKey="Fisher, Steven" sort="Fisher, Steven" uniqKey="Fisher S" first="Steven" last="Fisher">Steven Fisher</name><affiliation><nlm:affiliation>graduate of the University of Michigan in Complex Systems. He is currently working with early stage startups to optimize technology strategies.</nlm:affiliation></affiliation></author><author><name sortKey="Bramson, Aaron" sort="Bramson, Aaron" uniqKey="Bramson A" first="Aaron" last="Bramson">Aaron Bramson</name><affiliation><nlm:affiliation>visiting scientist in the economics department of Gent University and at the Riken Brain Science Institute. His research focuses on complex systems methodology: developing new tools for building and analyzing agent-based, network, and mathematical models.</nlm:affiliation></affiliation></author><author><name sortKey="Berger, William J" sort="Berger, William J" uniqKey="Berger W" first="William J" last="Berger">William J. Berger</name><affiliation><nlm:affiliation>doctoral candidate in the department of political science and NSF IGERT Fellow at the University of Michigan where he studies the institutional aspects of affective trust.</nlm:affiliation></affiliation></author><author><name sortKey="Reade, Christopher" sort="Reade, Christopher" uniqKey="Reade C" first="Christopher" last="Reade">Christopher Reade</name><affiliation><nlm:affiliation>consultant at ThoughtWorks Inc. and a graduate of the Gerald R. Ford School of Public Policy at the University of Michigan.</nlm:affiliation></affiliation></author><author><name sortKey="Flocken, Carissa" sort="Flocken, Carissa" uniqKey="Flocken C" first="Carissa" last="Flocken">Carissa Flocken</name><affiliation><nlm:affiliation>studies Complex Systems at the University of Michigan. As a research assistant at the Santa Fe Institute, she explores the evolution of social network topology using computational methodology.</nlm:affiliation></affiliation></author><author><name sortKey="Sales, Adam" sort="Sales, Adam" uniqKey="Sales A" first="Adam" last="Sales">Adam Sales</name><affiliation><nlm:affiliation>postdoctoral researcher at Carnegie Mellon University. He studies statistical methods for evaluating educational programs using observational data and likes hanging around philosophers.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2013">2013</date><idno type="doi">10.1017/epi.2013.36</idno><idno type="RBID">pubmed:24683416</idno><idno type="pmid">24683416</idno><idno type="wicri:Area/PubMed/Corpus">000014</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000014</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Scientific Networks on Data Landscapes: Question Difficulty, Epistemic Success, and Convergence.</title><author><name sortKey="Grim, Patrick" sort="Grim, Patrick" uniqKey="Grim P" first="Patrick" last="Grim">Patrick Grim</name><affiliation><nlm:affiliation>Distinguished Teaching Professor in Philosophy at Stony Brook and Visiting Professor with the Center for Study of Complex Systems at the University of Michigan. Grim's current work centers on computational modeling and philosophical logic.</nlm:affiliation></affiliation></author><author><name sortKey="Singer, Daniel J" sort="Singer, Daniel J" uniqKey="Singer D" first="Daniel J" last="Singer">Daniel J. Singer</name><affiliation><nlm:affiliation>Assistant Professor of Philosophy at the University of Pennsylvania. He primarily researches the nature of epistemic normativity and uses computational models to explore the social structure of inquiry.</nlm:affiliation></affiliation></author><author><name sortKey="Fisher, Steven" sort="Fisher, Steven" uniqKey="Fisher S" first="Steven" last="Fisher">Steven Fisher</name><affiliation><nlm:affiliation>graduate of the University of Michigan in Complex Systems. He is currently working with early stage startups to optimize technology strategies.</nlm:affiliation></affiliation></author><author><name sortKey="Bramson, Aaron" sort="Bramson, Aaron" uniqKey="Bramson A" first="Aaron" last="Bramson">Aaron Bramson</name><affiliation><nlm:affiliation>visiting scientist in the economics department of Gent University and at the Riken Brain Science Institute. His research focuses on complex systems methodology: developing new tools for building and analyzing agent-based, network, and mathematical models.</nlm:affiliation></affiliation></author><author><name sortKey="Berger, William J" sort="Berger, William J" uniqKey="Berger W" first="William J" last="Berger">William J. Berger</name><affiliation><nlm:affiliation>doctoral candidate in the department of political science and NSF IGERT Fellow at the University of Michigan where he studies the institutional aspects of affective trust.</nlm:affiliation></affiliation></author><author><name sortKey="Reade, Christopher" sort="Reade, Christopher" uniqKey="Reade C" first="Christopher" last="Reade">Christopher Reade</name><affiliation><nlm:affiliation>consultant at ThoughtWorks Inc. and a graduate of the Gerald R. Ford School of Public Policy at the University of Michigan.</nlm:affiliation></affiliation></author><author><name sortKey="Flocken, Carissa" sort="Flocken, Carissa" uniqKey="Flocken C" first="Carissa" last="Flocken">Carissa Flocken</name><affiliation><nlm:affiliation>studies Complex Systems at the University of Michigan. As a research assistant at the Santa Fe Institute, she explores the evolution of social network topology using computational methodology.</nlm:affiliation></affiliation></author><author><name sortKey="Sales, Adam" sort="Sales, Adam" uniqKey="Sales A" first="Adam" last="Sales">Adam Sales</name><affiliation><nlm:affiliation>postdoctoral researcher at Carnegie Mellon University. He studies statistical methods for evaluating educational programs using observational data and likes hanging around philosophers.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Episteme (Edinburgh)</title><idno type="ISSN">1742-3600</idno><imprint><date when="2013" type="published">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">A scientific community can be modeled as a collection of epistemic agents attempting to answer questions, in part by communicating about their hypotheses and results. We can treat the pathways of scientific communication as a network. When we do, it becomes clear that the interaction between the structure of the network and the nature of the question under investigation affects epistemic desiderata, including accuracy and speed to community consensus. Here we build on previous work, both our own and others', in order to get a firmer grasp on precisely which features of scientific communities interact with which features of scientific questions in order to influence epistemic outcomes. Here we introduce a measure on the landscape meant to capture some aspects of the difficulty of answering an empirical question. We then investigate both how different communication networks affect whether the community finds the best answer and the time it takes for the community to reach consensus on an answer. We measure these two epistemic desiderata on a continuum of networks sampled from the Watts-Strogatz spectrum. It turns out that finding the best answer and reaching consensus exhibit radically different patterns. The time it takes for a community to reach a consensus in these models roughly tracks mean path length in the network. Whether a scientific community finds the best answer, on the other hand, tracks neither mean path length nor clustering coefficient.</div></front></TEI><pubmed><MedlineCitation Status="Publisher" Owner="NLM"><PMID Version="1">24683416</PMID><DateCreated><Year>2014</Year><Month>3</Month><Day>31</Day></DateCreated><Article PubModel="Print"><Journal><ISSN IssnType="Print">1742-3600</ISSN><JournalIssue CitedMedium="Print"><Volume>10</Volume><Issue>4</Issue><PubDate><Year>2013</Year><Month>Dec</Month><Day>1</Day></PubDate></JournalIssue><Title>Episteme (Edinburgh)</Title><ISOAbbreviation>Episteme (Edinb)</ISOAbbreviation></Journal><ArticleTitle>Scientific Networks on Data Landscapes: Question Difficulty, Epistemic Success, and Convergence.</ArticleTitle><Pagination><MedlinePgn>441-464</MedlinePgn></Pagination><Abstract><AbstractText>A scientific community can be modeled as a collection of epistemic agents attempting to answer questions, in part by communicating about their hypotheses and results. We can treat the pathways of scientific communication as a network. When we do, it becomes clear that the interaction between the structure of the network and the nature of the question under investigation affects epistemic desiderata, including accuracy and speed to community consensus. Here we build on previous work, both our own and others', in order to get a firmer grasp on precisely which features of scientific communities interact with which features of scientific questions in order to influence epistemic outcomes. Here we introduce a measure on the landscape meant to capture some aspects of the difficulty of answering an empirical question. We then investigate both how different communication networks affect whether the community finds the best answer and the time it takes for the community to reach consensus on an answer. We measure these two epistemic desiderata on a continuum of networks sampled from the Watts-Strogatz spectrum. It turns out that finding the best answer and reaching consensus exhibit radically different patterns. The time it takes for a community to reach a consensus in these models roughly tracks mean path length in the network. Whether a scientific community finds the best answer, on the other hand, tracks neither mean path length nor clustering coefficient.</AbstractText></Abstract><AuthorList><Author><LastName>Grim</LastName><ForeName>Patrick</ForeName><Initials>P</Initials><AffiliationInfo><Affiliation>Distinguished Teaching Professor in Philosophy at Stony Brook and Visiting Professor with the Center for Study of Complex Systems at the University of Michigan. Grim's current work centers on computational modeling and philosophical logic.</Affiliation></AffiliationInfo></Author><Author><LastName>Singer</LastName><ForeName>Daniel J</ForeName><Initials>DJ</Initials><AffiliationInfo><Affiliation>Assistant Professor of Philosophy at the University of Pennsylvania. He primarily researches the nature of epistemic normativity and uses computational models to explore the social structure of inquiry.</Affiliation></AffiliationInfo></Author><Author><LastName>Fisher</LastName><ForeName>Steven</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>graduate of the University of Michigan in Complex Systems. He is currently working with early stage startups to optimize technology strategies.</Affiliation></AffiliationInfo></Author><Author><LastName>Bramson</LastName><ForeName>Aaron</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>visiting scientist in the economics department of Gent University and at the Riken Brain Science Institute. His research focuses on complex systems methodology: developing new tools for building and analyzing agent-based, network, and mathematical models.</Affiliation></AffiliationInfo></Author><Author><LastName>Berger</LastName><ForeName>William J</ForeName><Initials>WJ</Initials><AffiliationInfo><Affiliation>doctoral candidate in the department of political science and NSF IGERT Fellow at the University of Michigan where he studies the institutional aspects of affective trust.</Affiliation></AffiliationInfo></Author><Author><LastName>Reade</LastName><ForeName>Christopher</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>consultant at ThoughtWorks Inc. and a graduate of the Gerald R. Ford School of Public Policy at the University of Michigan.</Affiliation></AffiliationInfo></Author><Author><LastName>Flocken</LastName><ForeName>Carissa</ForeName><Initials>C</Initials><AffiliationInfo><Affiliation>studies Complex Systems at the University of Michigan. As a research assistant at the Santa Fe Institute, she explores the evolution of social network topology using computational methodology.</Affiliation></AffiliationInfo></Author><Author><LastName>Sales</LastName><ForeName>Adam</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>postdoctoral researcher at Carnegie Mellon University. He studies statistical methods for evaluating educational programs using observational data and likes hanging around philosophers.</Affiliation></AffiliationInfo></Author></AuthorList><Language>ENG</Language><GrantList><Grant><GrantID>U54 GM088491</GrantID><Acronym>GM</Acronym><Agency>NIGMS NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="">JOURNAL ARTICLE</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><MedlineTA>Episteme (Edinb)</MedlineTA><NlmUniqueID>101537927</NlmUniqueID><ISSNLinking>1742-3600</ISSNLinking></MedlineJournalInfo></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2014</Year><Month>4</Month><Day>1</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2014</Year><Month>4</Month><Day>1</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2014</Year><Month>4</Month><Day>1</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="doi">10.1017/epi.2013.36</ArticleId><ArticleId IdType="pubmed">24683416</ArticleId><ArticleId IdType="pmc">PMC3968873</ArticleId><ArticleId IdType="mid">NIHMS555787</ArticleId></ArticleIdList><pmc-dir>nihms</pmc-dir>
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