Measuring the Collective Potential of Populations From Dynamic Social Interaction Data
In any society, is the way in which individuals interact, intentionally or unintentionally, designed to maximize global benefit, or does it result in a fundamentally non-egalitarian stratification of society, where a small number of individuals inevitably dominate? Our ability to observe and record...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers
2011
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| Online Access: | http://hdl.handle.net/1721.1/67652 https://orcid.org/0000-0002-8053-9983 |
| _version_ | 1826210427302838272 |
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| author | Cebrian, Manuel Lahiri, Mayank Oliver, Nuria Pentland, Alex Paul |
| author2 | Massachusetts Institute of Technology. Media Laboratory |
| author_facet | Massachusetts Institute of Technology. Media Laboratory Cebrian, Manuel Lahiri, Mayank Oliver, Nuria Pentland, Alex Paul |
| author_sort | Cebrian, Manuel |
| collection | MIT |
| description | In any society, is the way in which individuals interact, intentionally or unintentionally, designed to maximize global benefit, or does it result in a fundamentally non-egalitarian stratification of society, where a small number of individuals inevitably dominate? Our ability to observe and record interactions between individuals in real populations has improved dramatically with modern technological improvements, but it is still a difficult task to use this data to model cooperation and collaboration between individuals, and its global effect on the entire population. To shed light on these questions, we model an individual's value in society as an epistatic mathematical function of a set of binary choices, and the collective potential of a population as the expected value of an individual over time. Individuals try to selfishly improve their societal value by adopting the choices of their neighbors, constrained by the actual observed interaction topology and order. As a result, we are also able to investigate how far natural populations are from an optimal regime of functioning. We show that interaction topology has a large impact on collective potential, but the relative order of specific interactions seems to have a negligible effect. |
| first_indexed | 2024-09-23T14:49:42Z |
| format | Article |
| id | mit-1721.1/67652 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:49:42Z |
| publishDate | 2011 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/676522022-09-29T10:48:35Z Measuring the Collective Potential of Populations From Dynamic Social Interaction Data Cebrian, Manuel Lahiri, Mayank Oliver, Nuria Pentland, Alex Paul Massachusetts Institute of Technology. Media Laboratory Program in Media Arts and Sciences (Massachusetts Institute of Technology) Pentland, Alex Paul Pentland, Alex Paul Cebrian, Manuel In any society, is the way in which individuals interact, intentionally or unintentionally, designed to maximize global benefit, or does it result in a fundamentally non-egalitarian stratification of society, where a small number of individuals inevitably dominate? Our ability to observe and record interactions between individuals in real populations has improved dramatically with modern technological improvements, but it is still a difficult task to use this data to model cooperation and collaboration between individuals, and its global effect on the entire population. To shed light on these questions, we model an individual's value in society as an epistatic mathematical function of a set of binary choices, and the collective potential of a population as the expected value of an individual over time. Individuals try to selfishly improve their societal value by adopting the choices of their neighbors, constrained by the actual observed interaction topology and order. As a result, we are also able to investigate how far natural populations are from an optimal regime of functioning. We show that interaction topology has a large impact on collective potential, but the relative order of specific interactions seems to have a negligible effect. 2011-12-13T21:25:56Z 2011-12-13T21:25:56Z 2010-08 2009-10 Article http://purl.org/eprint/type/JournalArticle 1932-4553 INSPEC Accession Number: 11415794 http://hdl.handle.net/1721.1/67652 Cebrian, M. et al. “Measuring the Collective Potential of Populations From Dynamic Social Interaction Data.” IEEE Journal of Selected Topics in Signal Processing, 4.4 (2010): 677-686.© 2010 IEEE. https://orcid.org/0000-0002-8053-9983 en_US http://dx.doi.org/10.1109/jstsp.2010.2053093 IEEE Journal of Selected Topics in Signal Processing Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers IEEE |
| spellingShingle | Cebrian, Manuel Lahiri, Mayank Oliver, Nuria Pentland, Alex Paul Measuring the Collective Potential of Populations From Dynamic Social Interaction Data |
| title | Measuring the Collective Potential of Populations From Dynamic Social Interaction Data |
| title_full | Measuring the Collective Potential of Populations From Dynamic Social Interaction Data |
| title_fullStr | Measuring the Collective Potential of Populations From Dynamic Social Interaction Data |
| title_full_unstemmed | Measuring the Collective Potential of Populations From Dynamic Social Interaction Data |
| title_short | Measuring the Collective Potential of Populations From Dynamic Social Interaction Data |
| title_sort | measuring the collective potential of populations from dynamic social interaction data |
| url | http://hdl.handle.net/1721.1/67652 https://orcid.org/0000-0002-8053-9983 |
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