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oai:dspace.mit.edu:1721.1/1507202023-05-16T03:09:19Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022

Bar-Yam, Yaneer Roy, Deb Kardar, Mehran Siegenfeld, Alexander F. Massachusetts Institute of Technology. Department of Physics 2023-05-15T19:34:55Z 2023-05-15T19:34:55Z 2022-05 2023-05-10T22:36:04.861Z https://hdl.handle.net/1721.1/150720 https://orcid.org/0000-0003-2041-2742 The standard assumptions that underlie most conceptual and quantitative frameworks do not hold for many complex physical, biological, and social systems. Complex systems science clarifies when and why such assumptions fail and provides alternative frameworks for understanding the properties of complex systems. We review some of the basic principles of complex systems science and provide a mathematical formalism for complexity profiles. We also illustrate general modeling principles using examples from pandemic response, including an illustration of how pandemics can be stably eliminated with a combination of social distancing measures and travel restrictions and how bad science led to bad policy regarding the use of face masks. Applications to democratic elections are also described. We define the concepts of negative representation and electoral instability, demonstrating that United States’ presidential elections underwent a transition from a stable to an unstable regime in the 1970s and have since become increasingly unstable. We also consider the implications of geographic political polarization on multi-scale electoral systems. Ph.D. Massachusetts Institute of Technology In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ Developments in Complex Systems Science with Applications to Political Systems and Pandemic Response Thesis application/pdf Doctoral Doctor of Philosophy Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="cc242778-029c-40a4-a899-dc9e078effe0"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Title>Developments in Complex Systems Science with Applications to Political Systems and Pandemic Response</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2022-05</PublicationDate> <Authors> <Author> <DisplayName>Siegenfeld, Alexander F.</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://rightsstatements.org/page/InC-EDU/1.0/</License> <Abstract>The standard assumptions that underlie most conceptual and quantitative frameworks do not hold for many complex physical, biological, and social systems. Complex systems science clarifies when and why such assumptions fail and provides alternative frameworks for understanding the properties of complex systems. We review some of the basic principles of complex systems science and provide a mathematical formalism for complexity profiles. We also illustrate general modeling principles using examples from pandemic response, including an illustration of how pandemics can be stably eliminated with a combination of social distancing measures and travel restrictions and how bad science led to bad policy regarding the use of face masks. Applications to democratic elections are also described. We define the concepts of negative representation and electoral instability, demonstrating that United States’ presidential elections underwent a transition from a stable to an unstable regime in the 1970s and have since become increasingly unstable. We also consider the implications of geographic political polarization on multi-scale electoral systems.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>