# The Azimuth Project Invasion fitness in moment-closure treatments (Rev #1)

This page is a blog article in progress, written by Blake Stacey.

Consider a lattice, each site of which can be empty ($0$), occupied by a selfish individual ($S$) or occupied by an altruist ($A$). The difference between selfishness and altruism is encoded in the choice of transition rules representing death, birth and migration. We can get an aggregate measure of the situation by finding the probability that a randomly chosen site will be in state $a$, where $a$ can take values in the set $\{0, A, S\}$. A finer degree of distinction is provided by the conditional probabilities $q_{a|b}$, where, for example, $q_{S|A}$ denotes the probability that a randomly chosen neighbor site to a randomly chosen altruist is selfish. Note that if a selfish mutant is injected into a population of altruists and its selfish offspring form a geographical cluster, $q_{S|S}$ can be much larger than $p_S$: few individuals are selfish overall, but the probability of a selfish life-form interacting with one of the same behavior type is high.

The pair dynamics of the system involves the time evolution of the probabilities $p_{a b}$, that is, the probability that a randomly selected lattice edge will have $a$ on one end and $b$ on the other. The differential equation for $d p_{S 0}/d t$, for example, will have terms reflecting the processes which can form and destroy $S0$ pairs: $00\rightarrow S 0$, $S 0\rightarrow 00$, $S0\rightarrow S S$, $S S\rightarrow S 0$, $S 0\rightarrow S A$ and $S A\rightarrow S 0$, let us say. In language more like we’ve used in the Azimuth discussions of stochastic Petri net theory, we’ve moved beyond just creating and annihilating altruists and selfishists, and now we’re dynamically changing the number of “altruist–altruist” and “altruist–selfish” pairs. So, we probably could make a Petri net diagram for these processes, but the labels on the boxes might start looking a little funny.

Each term in our differential equations will have a transition rate dependent upon a conditional probability of the form $q_{a|b c}$, denoting the probability that a $b$ of a $b c$ pair will have a neighbor of type $a$. The differential equations for the pair probabilities $p_{a b}$ thus depend on triplet probabilities $p_{a b c}$, which depend upon quadruplet probabilities and so forth. To make progress, we truncate this hierarchy in a manner reminiscent to cutting off the BBGKY hierarchy in statistical mechanics: we impose the pair approximation that

$q_{a|b c} \approx q_{a|b}.$

This approximation destroys information about spatial structure and thereby introduces bias which in an ideal world ought to be accounted for.

Invasion fitness is judged in the following manner. We start with a lattice devoid of altruists ($p_{A a} = 0$) and find the equilibrium densities $p_{S S}^*$ and $p_{S 0}^*$ by setting

$\frac{d p_{S0}}{d t} = \frac{d p_{S S}}{d t} = 0.$

The exact form of $p_{S S}^*$ and $p_{S0}^*$ will depend upon interaction details which do not concern us here. We then inject an altruistic strain into this situation; as the altruists are initially rare, we can say they do not affect the large-scale dynamics of the selfish population. Summarizing the pair probabilities $p_{A a}$ with the shorthand $\underline{p}$, we write the differential equation in matrix form

$\frac{d\underline{p}}{d t} = M(q_{a|b c})\underline{p},$

where the matrix $M(q_{a|b c})$ encapsulates the details of our chosen behavior modes. The pair approximation, in which we discard correlations of third and higher order, lets us simplify this to

$\frac{d\underline{p}}{d t} = M(q_{a|b})\underline{p}.$

A result supported by numerical investigation is that the conditional probabilities $q_{a|A}$ equilibrate. That is to say, even if the global density of altruists $p_A$ changes, the local statistical structure of an altruistic cluster remains constant. This is the key statement which allows us to linearize the dynamics and write the behavior of $\underline{p}$ in terms of eigenvectors and eigenvalues:

$\underline{p}(t) = C\underline{e}_A \exp(\lambda t).$

The dominant eigenvalue $\lambda$ of $M$ is the “invasion exponent” which characterizes whether an invasion will fail ($\lambda \lt 0$) or succeed ($\lambda \gt 0$). The eigenvector $\underline{e}_A$ associated with $\lambda$ describes the vehicle of selection for the altruist’s particular genetic variation, by summarizing the structure of the altruists’ cluster.

All of this, of course, is only as good as our linearization!

### References

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• B. Allen, Studies in the Mathematics of Evolution and Biodiversity. PhD thesis, Boston University, 2010. (web)
• J. A. Damore and J. Gore (2011). “Understanding microbial cooperation”. Journal of Theoretical Biology DOI:10.1016/j.jtbi.2011.03.008 (pdf).
• B. C. Stacey, A. Gros and Y. Bar-Yam (2011), “Beyond the mean field in host-pathogen spatial ecology”, arXiv:1110.3845 [nlin.AO].

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