<?xml version="1.0" encoding="utf-8"?><resource xmlns="http://datacite.org/schema/kernel-3" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://datacite.org/schema/kernel-3 http://schema.datacite.org/meta/kernel-3/metadata.xsd">
  <identifier identifierType="DOI"/>
  <creators>
    <creator>
      <creatorName>Magnani, Jacopo</creatorName>
    </creator>
    <creator>
      <creatorName>Friedman, Daniel</creatorName>
    </creator>
    <creator>
      <creatorName>Sinervo, Barry</creatorName>
    </creator>
  </creators>
  <titles>
    <title>Maximum Likelihood Estimate of Payoffs from Time Series</title>
  </titles>
  <publisher>UC Santa Cruz</publisher>
  <publicationYear>2015</publicationYear>
  <subjects>
    <subject>Maximum Likelihood</subject>
    <subject>Time Series</subject>
    <subject>Estimating Payoff Matrix From Time Series Data</subject>
  </subjects>
  <contributors>
    <contributor contributorType="DataManager">
      <contributorName>Sinervo, Barry</contributorName>
    </contributor>
  </contributors>
  <relatedIdentifiers/>
  <resourceType resourceTypeGeneral="Software">Software</resourceType>
  <sizes>
    <size>36961</size>
  </sizes>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International (CC-BY 4.0)</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">We fit the data to a structural model estimated via maximum likelihood (ML) techniques. 
The model in the matlab code joins discrete time replicator dynamics with a stochastic structure, given by the Dirichlet distribution or, alternatively, by the logistic distribution. </description>
    <description descriptionType="Methods">This software can be used in the MatLab to estimate payoff matrices from time series data on the frequency of strategies over generations (discrete). Full details can be found in the book by Daniel Friedman and Barry Sinervo, &quot;Evolutionary Games in Natural, Social, and Virtual Worlds&quot;, (Publisher: Oxford University Press, 2016). </description>
    <description descriptionType="Other"/>
  </descriptions>
</resource>