Anonymous

Stochastic dynamics in finite populations: Difference between revisions

From EvoLudo
No edit summary
(4 intermediate revisions by the same user not shown)
Line 2: Line 2:
{{TOCright}}
{{TOCright}}


Stochastic differential equations (SDE) provide a general framework to describe the evolutionary dynamics of an arbitrary number of types in finite populations, which results in demographic noise, and to incorporate mutations. For large, but finite populations this allows to include demographic noise without requiring explicit simulations. Instead, the population size only rescales the amplitude of the noise. Moreover, this framework admits the inclusion of mutations between different types, provided that mutation rates, \(\mu\), are not too small compared to the inverse population size \(1/N\). This ensures that all types are almost always represented in the population and that the occasional extinction of one type does not result in an extended absence of that type. For \(\mu N\ll1\) this limits the use of SDE’s, but in this case well established alternative approximations are available based on time scale separation. We illustrate our approach by a [[Rock-Scissors-Paper game]] with mutations, where we demonstrate excellent agreement with simulation based results for sufficiently large populations. In the absence of mutations the excellent agreement extends to small population sizes.
Stochastic differential equations (SDE) provide a general framework to describe the evolutionary dynamics of an arbitrary number of strategic types \(d\) in finite populations, which results in demographic noise, as well as to incorporate mutations. For large, but finite populations this allows to include demographic noise without requiring explicit simulations. Instead, the population size only rescales the amplitude of the noise. Moreover, this framework admits the inclusion of mutations between different types, provided that mutation rates, \(\mu\), are not too small compared to the inverse population size \(1/N\). This ensures that all types are almost always represented in the population and that the occasional extinction of one type does not result in an extended absence of that type. For \(\mu N\ll1\) this limits the use of SDE’s, but in this case well established alternative approximations are available based on time scale separation.  
 
The tutorial on [[2×2_Games/Stochastic_dynamics|stochastic dynamics in \(2\times2\) games]] covers the simpler case with two strategic types, \(d=2\). Here we focus on the general case with \(d>2\) strategic types and illustrate our approach based on the [[Rock-Scissors-Paper game]] with mutations (\(d=3\)). The stochastic dynamics is in excellent agreement with simulation based results for sufficiently large populations. In the absence of mutations the excellent agreement extends to small population sizes.


''This tutorial complements a series of [[#References|research articles]] by [http://www.evolbio.mpg.de/~traulsen/ Arne Traulsen], [http://www.inb.uni-luebeck.de/~claussen/ Jens Christian Claussen] & [http://www.math.ubc.ca/~hauert/ Christoph Hauert]''
''This tutorial complements a series of [[#References|research articles]] by [http://www.evolbio.mpg.de/~traulsen/ Arne Traulsen], [http://www.inb.uni-luebeck.de/~claussen/ Jens Christian Claussen] & [http://www.math.ubc.ca/~hauert/ Christoph Hauert]''
Line 8: Line 10:


==Rock-Paper-Scissors game==
==Rock-Paper-Scissors game==
[[Image:Stochastic dynamics - noise term Cxx, no mutations.png|300px|thumb|Value of the element \(\mathcal C_{xx}(x,y,z)\) of the noise matrix \(\mathcal C(\mathbf x)\) for \(d = 3\) strategies and \(\mu = 0\). \(\mathcal C_{xx}(x,y,z)\) determines how the noise in the \(x\)-direction affects the \(x\)-coordinate. In the case of \(\mu = 0\), this noise vanishes for \(x\to0\). For \(y\to0\) and \(z\to0\) we recover the usual multiplicative noise from one-dimensional evolutionary processes.]]
[[Image:Stochastic dynamics - noise term Cxx, no mutations.png|300px|thumb|Demographic noise in evolutionary dynamics with \(d = 3\) strategic types in the absence of mutations, \(\mu = 0\). The value of the element \(\mathcal C_{xx}(x,y,z)\) of the noise matrix \(\mathcal C(\mathbf x)\) is shown. \(\mathcal C_{xx}(x,y,z)\) determines how the noise in the \(x\)-direction affects the \(x\)-coordinate. In the case of \(\mu = 0\), this noise vanishes for \(x\to0\). For \(y\to0\) and \(z\to0\) we recover the usual multiplicative noise from one-dimensional evolutionary processes.]]
 
The [[Rock-Paper-Scissors game]] exhibits cyclic dominance among its three strategic types: Rock beats Scissors beats Paper beats Rocks etc. In evolving populations this gives rise to oscillations in the abundance of each strategic type. The amplitude of these oscillation may (i) decrease over time resulting in stable equilibrium frequencies, (ii) keep increasing and approaching a heteroclinic cycle along the boundary of the simplex \(S_3\) or result in the extinction of one type and eventual reach an absorbing homogenous state or, (iii) give rise to stable limit cycles.


Comparisons between the deterministic dynamics in infinite populations, the stochastic dynamics in finite populations and individual based simulations focus on the [[Rock-Paper-Scissors game]] with a generic payoff
Comparisons between the deterministic dynamics in infinite populations, the stochastic dynamics in finite populations and individual based simulations are illustrated for a generic payoff matrix


\[\begin{matrix}~&\begin{matrix}\ \ R\quad & S\quad & P\quad\end{matrix} \\
\[\begin{matrix}~&\begin{matrix}\ \ R\quad & S\quad & P\quad\end{matrix} \\
\begin{matrix}R\\S\\P\end{matrix}&
\begin{matrix}R\\S\\P\end{matrix}&
\begin{pmatrix}0 & \frac{s}{2} & -1 \\
\begin{pmatrix}0 & {\textstyle\frac{s}{2}} & -1 \\
-1 & 0 & 2+s \\
-1 & 0 & 2+s \\
\frac{1+s}{3} & -1 & 0\end{pmatrix}\end{matrix}.
{\textstyle\frac{1+s}{3}} & -1 & 0\end{pmatrix}\end{matrix}.
\]
\]


Line 26: Line 30:
[[Image:RSP - ODE.svg|left|200px|link=EvoLudoLab: Rock-Paper-Scissors - ODE]]
[[Image:RSP - ODE.svg|left|200px|link=EvoLudoLab: Rock-Paper-Scissors - ODE]]
====[[EvoLudoLab: Rock-Paper-Scissors - ODE|Replicator dynamics - Attractor]]====
====[[EvoLudoLab: Rock-Paper-Scissors - ODE|Replicator dynamics - Attractor]]====
In the limit \(N\to\infty\) with \(s=1.4\) and without mutations, \(\mu=0\), \(\hat x\) is an attractor of the replicator dynamics. The figure shows a sample trajectory that spirals towards the interior fixed point \(\hat x\).
In the limit \(N\to\infty\) demographic stochasticity arising in finite populations disappears and the dynamics becomes deterministic. For \(s>1\) the interior fixed point \(\hat x\) is a stable focus of the replicator dynamics. All trajectories spiral toward \(\hat x\).
{{-}}
{{-}}
</div>
</div>
Line 42: Line 46:
[[Image:RSP - SDE.svg|left|200px|link=EvoLudoLab: Rock-Paper-Scissors - SDE]]
[[Image:RSP - SDE.svg|left|200px|link=EvoLudoLab: Rock-Paper-Scissors - SDE]]
==== [[EvoLudoLab: Rock-Paper-Scissors - SDE|Stochastic differential equations]]====
==== [[EvoLudoLab: Rock-Paper-Scissors - SDE|Stochastic differential equations]]====
The interior fixed point \(\hat x\) is a stable focus of the replicator dynamics, \(s < 1\). Demographic stochasticity arises from the finite population size of \(N = 100\). In the absence of mutations, the boundaries remain absorbing and even though the interior fixed point is attracting, stochastic fluctuations nevertheless eventually drive the population to the absorbing boundaries.
{{-}}
{{-}}
</div>
</div>
Line 48: Line 53:
[[Image:RSP - SDE Mutations.svg|left|200px|link=EvoLudoLab: Rock-Paper-Scissors - SDE with Mutations]]
[[Image:RSP - SDE Mutations.svg|left|200px|link=EvoLudoLab: Rock-Paper-Scissors - SDE with Mutations]]
==== [[EvoLudoLab: Rock-Paper-Scissors - SDE with Mutations|Stochastic differential equations, with mutations]]====
==== [[EvoLudoLab: Rock-Paper-Scissors - SDE with Mutations|Stochastic differential equations, with mutations]]====
The interior fixed point \(\hat x\) of the replicator dynamics is an unstable focus. Even without stochasticity all trajectories spiral away from \(\hat x\) toward the boundary of the simplex \(S_3\). However, due to mutations, the boundary is repelling, which results in a stochastic analog of a stable limit cycle. For larger mutation rates the interior fixed point becomes stable again even for \(s<1\).
{{-}}
{{-}}
</div>
</div>
Line 55: Line 61:
[[Image:RSP - IBS.svg|left|200px|link=EvoLudoLab: Rock-Paper-Scissors - IBS]]
[[Image:RSP - IBS.svg|left|200px|link=EvoLudoLab: Rock-Paper-Scissors - IBS]]
==== [[EvoLudoLab: Rock-Paper-Scissors - IBS|Individual based simulations]]====
==== [[EvoLudoLab: Rock-Paper-Scissors - IBS|Individual based simulations]]====
The interior fixed point \(\hat x\) is a stable focus of the replicator dynamics. Stochastic fluctuations arise in individual based simulations of populations with a finite size, \(N=1000\). In the absence of mutations, the boundaries are absorbing and even though the interior fixed point is attracting, the population will eventually end up in one of the absorbing homogenous states with all rock, all paper or all scissors.
{{-}}
{{-}}
</div>
</div>
Line 61: Line 68:
[[Image:RSP - IBS Mutations.svg|left|200px|link=EvoLudoLab: Rock-Paper-Scissors - IBS with Mutations]]
[[Image:RSP - IBS Mutations.svg|left|200px|link=EvoLudoLab: Rock-Paper-Scissors - IBS with Mutations]]
==== [[EvoLudoLab: Rock-Paper-Scissors - IBS with Mutations|Individual based simulations, with mutations]]====
==== [[EvoLudoLab: Rock-Paper-Scissors - IBS with Mutations|Individual based simulations, with mutations]]====
The interior fixed point \(\hat x\) of the replicator dynamics is an unstable focus. Even without stochasticity all trajectories spiral away from \(\hat x\) towards the boundary of the simplex \(S_3\). However, due to mutations, the boundary is repelling, which results in a stochastic analog of a stable limit cycle. Larger mutation rates increasingly limit the range of values that can be attained by the mean frequencies of the three strategies. In particular, in the limit \(\mu\to1\) the game payoffs no longer affect the dynamics and the mean of all three strategies is simply \(1/3\).
{{-}}
{{-}}
</div>
</div>
Line 109: Line 117:


==References==
==References==
# Traulsen, A., Claussen, J. C. & Hauert, C. (2012) Stochastic differential equations for evolutionary dynamics with demographic noise and mutations. ''Phys. Rev. E'' in print.
# Traulsen, A., Claussen, J. C. & Hauert, C. (2012) Stochastic differential equations for evolutionary dynamics with demographic noise and mutations. ''Phys. Rev. E'' '''85''' 041901 [http://dx.doi.org/10.1103/PhysRevE.85.041901 doi: 10.1103/PhysRevE.85.041901].
# Traulsen, A., Claussen, J. C. & Hauert, C. (2006) Coevolutionary dynamics in large, but finite populations. ''Phys. Rev. E'' '''74''' 011901 [http://dx.doi.org/10.1103/PhysRevE.74.011901 doi: 10.1103/PhysRevE.74.011901].
# Traulsen, A., Claussen, J. C. & Hauert, C. (2006) Coevolutionary dynamics in large, but finite populations. ''Phys. Rev. E'' '''74''' 011901 [http://dx.doi.org/10.1103/PhysRevE.74.011901 doi: 10.1103/PhysRevE.74.011901].
# Traulsen, A., Claussen, J. C. & Hauert, C. (2005) Coevolutionary Dynamics: From Finite to Infinite Populations. ''Phys. Rev. Lett.'' '''95''' 238701 [http://dx.doi.org/10.1103/PhysRevLett.95.238701 doi: 10.1103/PhysRevLett.95.238701].
# Traulsen, A., Claussen, J. C. & Hauert, C. (2005) Coevolutionary Dynamics: From Finite to Infinite Populations. ''Phys. Rev. Lett.'' '''95''' 238701 [http://dx.doi.org/10.1103/PhysRevLett.95.238701 doi: 10.1103/PhysRevLett.95.238701].


[[Category:Tutorial]]
[[Category:Tutorial]]
860

edits