Stochastic Approximation to Understand Simple Simulation Models
This paper illustrates how a deterministic approximation of a stochastic process can be usefully applied to analyse the dynamics of many simple simulation models. To demonstrate the type of results that can be obtained using this approximation, we present two illustrative examples which are meant to...
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| Veröffentlicht in: | Journal of statistical physics 2013-04, Vol.151 (1-2), p.254-276 |
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| container_end_page | 276 |
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| container_issue | 1-2 |
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| container_title | Journal of statistical physics |
| container_volume | 151 |
| creator | Izquierdo, Segismundo S. Izquierdo, Luis R. |
| description | This paper illustrates how a deterministic approximation of a stochastic process can be usefully applied to analyse the dynamics of many simple simulation models. To demonstrate the type of results that can be obtained using this approximation, we present two illustrative examples which are meant to serve as methodological references for researchers exploring this area. Finally, we prove some convergence results for simulations of a family of evolutionary games, namely, intra-population imitation models in
n
-player games with arbitrary payoffs. |
| doi_str_mv | 10.1007/s10955-012-0654-z |
| format | Article |
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n
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n
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| source | SpringerLink Journals - AutoHoldings |
| subjects | Analysis Computer simulation Computer-generated environments Markov processes Mathematical and Computational Physics Physical Chemistry Physics Physics and Astronomy Quantum Physics Statistical Physics and Dynamical Systems Theoretical |
| title | Stochastic Approximation to Understand Simple Simulation Models |
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