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<dc:title>Stochastic Approximation to Understand Simple Simulation Models</dc:title>
<dc:creator>Izquierdo, Segismundo S.</dc:creator>
<dc:creator>Izquierdo Millán, Luis Rodrigo</dc:creator>
<dc:subject>Stochastic approximation</dc:subject>
<dc:subject>Mean dynamic</dc:subject>
<dc:subject>Markov models</dc:subject>
<dc:subject>Evolutionary games</dc:subject>
<dc:description>This paper illustrates how a deterministic approximation of a stochastic process&#xd;
can be usefully applied to analyse the dynamics of many simple simulation models. To&#xd;
demonstrate the type of results that can be obtained using this approximation, we present two&#xd;
illustrative examples which are meant to serve as methodological references for researchers&#xd;
exploring this area. Finally, we prove some convergence results for simulations of a family&#xd;
of evolutionary games, namely, intra-population imitation models in n-player games with&#xd;
arbitrary payoffs.</dc:description>
<dc:date>2015-09-16T11:28:03Z</dc:date>
<dc:date>2015-09-16T11:28:03Z</dc:date>
<dc:date>2013-04</dc:date>
<dc:type>info:eu-repo/semantics/article</dc:type>
<dc:identifier>0022-4715</dc:identifier>
<dc:identifier>http://hdl.handle.net/10259/3842</dc:identifier>
<dc:identifier>10.1007/s10955-012-0654-z</dc:identifier>
<dc:language>eng</dc:language>
<dc:relation>Journal of Statistical Physics. 2013, V. 151, n. 1, p. 254-276</dc:relation>
<dc:relation>http://dx.doi.org/10.1007/s10955-012-0654-z</dc:relation>
<dc:relation>info:eu-repo/grantAgreement/MICINN/CSD2010-00034</dc:relation>
<dc:relation>info:eu-repo/grantAgreement/MICINN/DPI2010-16920</dc:relation>
<dc:relation>info:eu-repo/grantAgreement/MEC/JC2009-00263</dc:relation>
<dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
<dc:publisher>Springer Verlag (Germany)</dc:publisher>
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