From Fixation Probabilities to d-player Games: An Inverse Problem in Evolutionary Dynamics
The probability that the frequency of a particular trait will eventually become unity, the so-called fixation probability, is a central issue in the study of population evolution. Its computation, once we are given a stochastic finite population model without mutations and a (possibly frequency depe...
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description | The probability that the frequency of a particular trait will eventually become unity, the so-called fixation probability, is a central issue in the study of population evolution. Its computation, once we are given a stochastic finite population model without mutations and a (possibly frequency dependent) fitness function, is straightforward and it can be done in several ways. Nevertheless, despite the fact that the fixation probability is an important macroscopic property of the population, its precise knowledge does not give any clear information about the interaction patterns among individuals in the population. Here we address the inverse problem: from a given fixation pattern and population size, we want to infer what is the game being played by the population. This is done by first exploiting the framework developed in Chalub and Souza (J Math Biol 75:1735–1774,
2017
), which yields a fitness function that realises this fixation pattern in the Wright–Fisher model. This fitness function always exists, but it is not necessarily unique. Subsequently, we show that any such fitness function can be approximated, with arbitrary precision, using
d
-player game theory, provided
d
is large enough. The pay-off matrix that emerges naturally from the approximating game will provide useful information about the individual interaction structure that is not itself apparent in the fixation pattern. We present extensive numerical support for our conclusions. |
doi_str_mv | 10.1007/s11538-018-00566-w |
format | Article |
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2017
), which yields a fitness function that realises this fixation pattern in the Wright–Fisher model. This fitness function always exists, but it is not necessarily unique. Subsequently, we show that any such fitness function can be approximated, with arbitrary precision, using
d
-player game theory, provided
d
is large enough. The pay-off matrix that emerges naturally from the approximating game will provide useful information about the individual interaction structure that is not itself apparent in the fixation pattern. We present extensive numerical support for our conclusions.</description><identifier>ISSN: 0092-8240</identifier><identifier>EISSN: 1522-9602</identifier><identifier>DOI: 10.1007/s11538-018-00566-w</identifier><identifier>PMID: 30635836</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Cell Biology ; Fitness ; Fixation ; Game theory ; Inverse problems ; Life Sciences ; Mathematical and Computational Biology ; Mathematical models ; Mathematics ; Mathematics and Statistics ; Mutation ; Population ; Population number ; Population studies ; Probability ; Reproductive fitness ; Special Issue: Modelling Biological Evolution: Developing Novel Approaches</subject><ispartof>Bulletin of mathematical biology, 2019-11, Vol.81 (11), p.4625-4642</ispartof><rights>Society for Mathematical Biology 2019</rights><rights>Bulletin of Mathematical Biology is a copyright of Springer, (2019). All Rights Reserved.</rights><rights>Copyright Springer Nature B.V. 2019</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c403t-14ff640abd30e7f70e7c1f92bcff30230c1906eb8b3947abd93337e0556ad0453</citedby><cites>FETCH-LOGICAL-c403t-14ff640abd30e7f70e7c1f92bcff30230c1906eb8b3947abd93337e0556ad0453</cites><orcidid>0000-0001-8200-1899 ; 0000-0002-8081-9221</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11538-018-00566-w$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11538-018-00566-w$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51297</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30635836$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Chalub, Fabio A. C. C.</creatorcontrib><creatorcontrib>Souza, Max O.</creatorcontrib><title>From Fixation Probabilities to d-player Games: An Inverse Problem in Evolutionary Dynamics</title><title>Bulletin of mathematical biology</title><addtitle>Bull Math Biol</addtitle><addtitle>Bull Math Biol</addtitle><description>The probability that the frequency of a particular trait will eventually become unity, the so-called fixation probability, is a central issue in the study of population evolution. Its computation, once we are given a stochastic finite population model without mutations and a (possibly frequency dependent) fitness function, is straightforward and it can be done in several ways. Nevertheless, despite the fact that the fixation probability is an important macroscopic property of the population, its precise knowledge does not give any clear information about the interaction patterns among individuals in the population. Here we address the inverse problem: from a given fixation pattern and population size, we want to infer what is the game being played by the population. This is done by first exploiting the framework developed in Chalub and Souza (J Math Biol 75:1735–1774,
2017
), which yields a fitness function that realises this fixation pattern in the Wright–Fisher model. This fitness function always exists, but it is not necessarily unique. Subsequently, we show that any such fitness function can be approximated, with arbitrary precision, using
d
-player game theory, provided
d
is large enough. The pay-off matrix that emerges naturally from the approximating game will provide useful information about the individual interaction structure that is not itself apparent in the fixation pattern. We present extensive numerical support for our conclusions.</description><subject>Cell Biology</subject><subject>Fitness</subject><subject>Fixation</subject><subject>Game theory</subject><subject>Inverse problems</subject><subject>Life Sciences</subject><subject>Mathematical and Computational Biology</subject><subject>Mathematical models</subject><subject>Mathematics</subject><subject>Mathematics and Statistics</subject><subject>Mutation</subject><subject>Population</subject><subject>Population number</subject><subject>Population studies</subject><subject>Probability</subject><subject>Reproductive fitness</subject><subject>Special Issue: Modelling Biological Evolution: Developing Novel Approaches</subject><issn>0092-8240</issn><issn>1522-9602</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kbtOwzAYRi0EglJ4AQZkiYUl8PsSp2arCi1ISDDAwmI5iYOMkrjYCaVvjyFcJIYOtgef7_PlIHRE4IwAZOeBkJRNEiBxQCpEstpCI5JSmkgBdBuNACRNJpTDHtoP4QViSDK5i_YYCJZOmBihp7l3DZ7bd91Z1-J773Kd29p21gTcOVwmy1qvjccL3ZhwgactvmnfjA_mi61Ng22Lr95c3X8WaL_Gl-tWN7YIB2in0nUwh9_rGD3Orx5m18nt3eJmNr1NCg6sSwivKsFB5yUDk1VZnApSSZoXVcWAMiiIBGHySc4kzyImGWOZgTQVugSesjE6HXqX3r32JnSqsaEwda1b4_qgKImvFlwKEtGTf-iL630bb6coIxkISiHdSBEhaAacy0jRgSq8C8GbSi29beIHKALq048a_KjoR335UasYOv6u7vPGlL-RHyERYAMQ4lb7bPzf2RtqPwBzBpnA</recordid><startdate>20191101</startdate><enddate>20191101</enddate><creator>Chalub, Fabio A. 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C. C. ; Souza, Max O.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c403t-14ff640abd30e7f70e7c1f92bcff30230c1906eb8b3947abd93337e0556ad0453</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Cell Biology</topic><topic>Fitness</topic><topic>Fixation</topic><topic>Game theory</topic><topic>Inverse problems</topic><topic>Life Sciences</topic><topic>Mathematical and Computational Biology</topic><topic>Mathematical models</topic><topic>Mathematics</topic><topic>Mathematics and Statistics</topic><topic>Mutation</topic><topic>Population</topic><topic>Population number</topic><topic>Population studies</topic><topic>Probability</topic><topic>Reproductive fitness</topic><topic>Special Issue: Modelling Biological Evolution: Developing Novel Approaches</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chalub, Fabio A. C. 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C. C.</au><au>Souza, Max O.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>From Fixation Probabilities to d-player Games: An Inverse Problem in Evolutionary Dynamics</atitle><jtitle>Bulletin of mathematical biology</jtitle><stitle>Bull Math Biol</stitle><addtitle>Bull Math Biol</addtitle><date>2019-11-01</date><risdate>2019</risdate><volume>81</volume><issue>11</issue><spage>4625</spage><epage>4642</epage><pages>4625-4642</pages><issn>0092-8240</issn><eissn>1522-9602</eissn><abstract>The probability that the frequency of a particular trait will eventually become unity, the so-called fixation probability, is a central issue in the study of population evolution. Its computation, once we are given a stochastic finite population model without mutations and a (possibly frequency dependent) fitness function, is straightforward and it can be done in several ways. Nevertheless, despite the fact that the fixation probability is an important macroscopic property of the population, its precise knowledge does not give any clear information about the interaction patterns among individuals in the population. Here we address the inverse problem: from a given fixation pattern and population size, we want to infer what is the game being played by the population. This is done by first exploiting the framework developed in Chalub and Souza (J Math Biol 75:1735–1774,
2017
), which yields a fitness function that realises this fixation pattern in the Wright–Fisher model. This fitness function always exists, but it is not necessarily unique. Subsequently, we show that any such fitness function can be approximated, with arbitrary precision, using
d
-player game theory, provided
d
is large enough. The pay-off matrix that emerges naturally from the approximating game will provide useful information about the individual interaction structure that is not itself apparent in the fixation pattern. We present extensive numerical support for our conclusions.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>30635836</pmid><doi>10.1007/s11538-018-00566-w</doi><tpages>18</tpages><orcidid>https://orcid.org/0000-0001-8200-1899</orcidid><orcidid>https://orcid.org/0000-0002-8081-9221</orcidid></addata></record> |
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subjects | Cell Biology Fitness Fixation Game theory Inverse problems Life Sciences Mathematical and Computational Biology Mathematical models Mathematics Mathematics and Statistics Mutation Population Population number Population studies Probability Reproductive fitness Special Issue: Modelling Biological Evolution: Developing Novel Approaches |
title | From Fixation Probabilities to d-player Games: An Inverse Problem in Evolutionary Dynamics |
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