## Experimental and comparative oncology: zebrafish, dogs, elephants

One of the exciting things about mathematical oncology is that thinking about cancer often forces me to leave my comfortable arm-chair and look at some actually data. No matter how much I advocate for the merits of heuristic modeling, when it comes to cancer, data-agnostic models take second stage to data-rich modeling. This close relationship between theory and experiment is of great importance to the health of a discipline, and the MBI Workshop on the Ecology and Evolution of Cancer highlights the health of mathematical oncology: mathematicians are sitting side-by-side with clinicians, biologists with computer scientists, and physicists next to ecologists. This means that the most novel talks for me have been the ones highlighting the great variety of experiments that are being done and how they inform theory.In this post I want to highlight some of these talks, with a particular emphasis on using the study of cancer in non-humans to inform human medicine.

## Egalitarians’ dilemma and the cognitive cost of ethnocentrism

Ethnocentrism (or contingent altruism) can be viewed as one of many mechanisms for enabling cooperation. The agents are augmented with a hereditary tag and the strategy space is extended from just cooperation/defection to behaviour that can be contingent on if the diad share or differ in their tag. The tags and strategy are not inherently correlated, but can develop local correlations due to system dynamics. This can expand the range of environments in which cooperation can be maintained, but an assortment-biasing mechanism is needed to fuel the initial emergence of cooperation (Kaznatcheev & Shultz, 2011). The resulting cooperation is extended only towards the in-group while the out-group continues to be treated with the cold rationality of defection.

Suppose that circles are the in-group and squares the out-group. The four possible strategies and their minimal representations as finite state machines is given.

The four possible strategies are depicted above, from top to bottom: humanitarian, ethnocentric, traitorous, and selfish. Humanitarians and selfish agents do not condition their behavior on the tag of their partner, and do not require the cognitive ability to categorize. Although this ability is simple, it can still merit a rich analysis (see: Beer, 2003) by students of minimal cognition. By associating a small fitness cost $k$ with categorization, we can study how much ethnocentric (and traitorous) agents are willing to pay for their greater cognitive abilities. This cost directly changes the default probability to reproduce ($\text{ptr}$), with humanitarians and selfish agents having $\text{ptr} = 0.11$ and ethnocentrics and traitorous agents having $\text{ptr} = 0.11 - k$. During each cycle, the $\text{ptr}$ is further modified by the game interactions, with each cooperative action costing $c = 0.01$ and providing a benefit $b$ (that varies depending on the simulation parameters) to the partner. For more detailed presentation of the simulation and default parameter, or just to follow along on your computer, I made my code publicly available on GitHub. Pardon its roughness, the brunt of it is legacy code from when I first build this model in 2009 for Kaznatcheev (2010).

Number of agents by strategy versus evolutionary cycle. The lines represent the number of agents
of each strategy: blue — humanitarian; green — ethnocentric; yellow — traitorous; red — selfish. The width of the line corresponds to standard error from averaging 30 independent runs. The two figures correspond to different
costs of cognition. The left is k = 0.002 and is typical of runs before the cognitive cost phase transition. The right is k = 0.007 and is typical of runs after the cognitive cost phase transition. Figure is adapted from Kaznatcheev (2010).

The dynamics for low $k$ are about the same as the standard no cognitive cost model as can be seen from the left figure above. However, as $k$ increases there is a transition to a regime where humanitarians start to dominate the population, as in the right figure above. To study this, I ran simulations with a set $b/c$ ratio and increasing $k$ from 0.001 to 0.02 with steps of 0.001. You can run your own with the command bcRun(2.5,0.001*(1:20)); some results are presented below, your results might differ slightly due to the stochastic nature of the simulation.

Proportion of humanitarians (blue), ethnocentrics (red), and cooperative interactions (black) versus cognitive cost for b/c = 2.5. Dots are averages from evolutionary cycles 9000 to 10000 of 10 independent runs. The lines are best-fit sigmoids and the dotted lines mark the steepest point; I take take this as the point for the cognitive cost phase transition. Data generated with bcRun(2.5,0.001*(1:20)) and visualized with bcPlot(2.5,0.001*(1:20),[],1)

Each data-point is the average from the last 1000 cycles of 10 independent simulations. The points suggest a phase transition from a regime of few humanitarians (blue), many ethnocentrics (red), and very high cooperation (black) to one of many humanitarians, few ethnocentrics, and slightly less cooperation. To get a better handle on exactly where the phase transition is, I fit sigmoids to the data using fitSigmoid.m. The best-fit curves are shown as solid lines; I defined the point of phase transition as the steepest (or inflection) point on the curve and plotted them with dashed lines for reference. I am not sure if this is the best approach to quantifying the point of phase transition, since the choice of sigmoid function is arbitrary and based only on the qualitative feel of the function. It might be better to fit a simpler function like a step-function or a more complicated function from which a critical exponent can be estimated. Do you know a better way to identify the phase transition? At the very least, I have to properly measure the error on the averaged data points and propogate it through the fit to get error bounds on the sigmoid parameters and make sure that — within statistical certainty — all 3 curves have their phase transition at the same point.

The most interesting feature of the phase transition, is the effect on cooperation. The world becomes more equitable; agents that treat out-groups differently from in-group (ethnocentrics) are replaced by agents that treat everyone with equal good-will and cooperation (humanitarians). However, the overall proportion of cooperative interactions decreases — it seems that humanitarians are less effective at suppressing selfish agents. This is consistent with the free-rider suppression hypothesis that Shultz et al. (2009) believed to be implausible. The result is egalitarians’ dilemma: by promoting equality among agents the world becomes less cooperative. Should one favour equality and thus individual fairness over the good of the whole population? If we expand our moral circle to eliminate out-groups will that lead to less cooperation?

In the prisoners’ dilemma, we are inclined to favor the social good over the individual. Even though it is rational for the individual to defect (securing a higher payoff for themselves than cooperating), we believe it is better for both parties to cooperate (securing a better social payoff than mutual defection). But in the egalitarians’ dilemma we are inclined to favour the individualistic strategy (fairness for each) over the social good (higher average levels of cooperative interactions). We see a similar effect in the ultimatum game: humans reject unfair offers even though that results in neither player receiving a payoff (worse for both). In some ways, we can think of the egalitarians’ dilemma as the population analogue of the ultimatum game; should humanity favor fairness over higher total cooperation?

I hinted at some of these questions in Kaznatcheev (2010) but I restrained myself to just $b/c = 2.5$. From this limited data, I concluded that since the phase transition happens for $k$ less than any other parameter in the model, it must be the case that agents are not willing to invest much resources into developing larger brains capable of categorical perception just to benefit from an ethnocentric strategy. Ethnocentrism and categorical perception would not have co-evolved, the basic cognitive abilities would have to be in place by some other means (or incredibly cheap) and then tag-based strategies could emerge.

Value of k at phase transition versus b/c ratio. In blue is the transition in proportion of humanitarians, red — proportion of ethnocentrics, and black – proportion of cooperative interactions. Each data point is made from a parameter estimate done using a sigmoid best fit to 200 independent simulations over 20 values of k at a resolution of 0.001.

Here, I explored the parameter space further, by repeating the above procedure while varying the $b/c$ ratio by changing $b$ from 0.02 to 0.035 in increments of 0.0025 while keeping $c$ fixed at 0.01. Unsurprisingly, the transitions for proportion of ethnocentrics and humanitarians are indistinguishable, but without a proper analysis it is not clear if the transition from high to low cooperation also always coincides. For $b/c > 2.75$, agents are willing to invest more than $c$ before the phase transition to all humanitarians, this invalidates my earlier reasoning. Agents are unwilling to invest much resources in larger brains capable of categorical perception only for competitive environments (low $b/c$). As $b$ increases, the agents are willing to invest more in their perception to avoid giving this large benefit to the out-group. This seems consistent with explicit out-group hostility that Kaznatcheev (2010b) observed in the harmony game. However, apart from simply presenting the data, I can’t make much more sense from this figure. Do you have any interpretations? Can we learn something from the seemingly linear relationship? Does the slope (if we plot $k$ versus $b$ then it is about 0.5) tell us anything? Would you still conclude that co-evolution of tag-based cooperation and categorical perception is unlikely?

### References

Beer, Randall D. (2003). The Dynamics of Active Categorical Perception in an Evolved Model Agent. Adaptive Behavior. 11(4): 209-243.

Kaznatcheev, Artem (2010). The cognitive cost of ethnocentrism Proceedings of the 32nd annual conference of the cognitive science society

Kaznatcheev, A. (2010b). Robustness of ethnocentrism to changes in inter-personal interactions. Complex Adaptive Systems – AAAI Fall Symposium.

Kaznatcheev, A., & Shultz, T. R. (2011). Ethnocentrism maintains cooperation, but keeping one’s children close fuels it. Proceedings of the 33rd Annual Conference of the Cognitive Science Society. 3174-3179.

Shultz, T. R., Hartshorn, M., & Kaznatcheev, A. (2009). Why is ethnocentrism more common than humanitarianism? Proceedings of the 31st Annual Conference of the Cognitive Science Society. 2100-2105.

## Generating random k-regular graphs

In a previous post, I gave a brief overview of the role of spatial structure in evolutionary game theory. Along with an explanation of what it means for an agent-based simulation to have such structure, several notable types of networks were introduced, including small-world and scale-free networks, as well as random graphs. At the time, random k-regular graphs were singled out as being of particular practical interest to modelers. Consequently, I would now like to touch on some of the practical considerations involved in generating such graphs. Recall that, in our particular case, a graph’s vertices may be thought to represent agents, whereas the graph’s edges characterize the social or reproductive connections between them.

A random k-regular graph is a set of n vertices randomly connected with k edges incident on each vertex. This specification is rather straightforward, as is the naive approach to generating such a graph: In theory, we must simply consider all possible graphs with n vertices and k degree, and then select one of them at random. Unfortunately, the space of possible k-regular graphs is so large that this approach could never work in practice. As years of research have now shown [1], generating a random k-regular graph by other means is also no trivial task. A variety of algorithms have emerged as a result.

The most popular method to date was first proposed by Bollobás [2], and is called the pairing model (though it is also sometimes referred to as the configuration model). The algorithm itself is simple:

1. Begin with a set of n vertices.
2. Create a new set of nk points, distributing them across n buckets, such that each bucket contains points.
3. Take each point and pair it randomly with another one, until ½nk pairs are obtained (i.e., a perfect matching).
4. Collapse the points, so that each bucket (and thus the points it contains) maps onto a single vertex of the original graph. Retain all edges between points as the edges of the corresponding vertices.
5. Check if the resulting graph is simple. That is to say, make sure that none of the vertices have loops (i.e., self-connections) or multiple edges (i.e., more than one connection to the same vertex). If the graph is not simple, restart.

There are several limitations to this algorithm. The first of these is that nk must be an even number, a requirement that is easily satisfied. The second and more interesting limitation is the need to retain only simple graphs. This is because, if we allow loops or multiple edges, graphs are no longer generated uniformly at random [1], and the randomization process becomes biased . Luckily, in evolutionary game theory, we are typically quite happy with this constraint and, in fact, often deliberately exclude the possibility of self-connections or parallel connections to the same agent. The final drawback is brought about by the occasional need for a restart. Specifically, asgrows, the probability of obtaining a non-simple graph rises as well, eventually making applying this algorithm impractical. Luckily, it has been shown that so long as is a smaller constant, this limitation is of no great concern [1]. Other algorithms that bypass this third limitation have been proposed [3], though they come with a notable drawback: Rather than generating a uniform random regular graph, they typically only generate an asymptotically uniform one. In light of this, there are good reasons for preferring the pairing model, so long as exceptionally large and highly-connected graph structures are not required.

Though the pairing model algorithm is easy to implement, a brief search of the Matlab Central File Exchange reveals that a ready-made implementation is already available for Matlab users. Using it is as simple as loading createRandRegGraph.m, then calling the createRandRegGraph function, using and k as parameters, respectively. The output of this function is an adjacency matrix, using a sparse matrix representation, though this is readily converted to a standard adjacency matrix by applying the full function. For example, entering full(createRandRegGraph(4, 2)); into the command window yields an adjacency matrix that describes a 4 vertex graph of degree 2, such as this one:

$\begin{pmatrix} 0 & 1 & 0 & 1 \\ 1 & 0 & 1 & 0 \\ 0 & 1 & 0 & 1 \\ 1 & 0 & 1 & 0 \end{pmatrix}$

Using a random k-regular graph as the basis for your simulation’s spatial structure, therefore, is as simple as incorporating the output of this function into your work.

### References

1. N. Wormald, Models of random regular graphs. Surveys in combinatorics, 1999 (Canterbury), 239-298, London Math. Soc. Lecture Note Ser., 267, Cambridge Univ. Press, Cambridge, 1999.
2. B. Bollobás, A probabilistic proof of an asymptotic formula for the number of labelled regular graphs. European J. Combin. 1 (1980), no. 4, 311-316.
3. Kim, J. H., & Vu, V. H. (2003). Generating random regular graphs. Proceedings of the thirty-fifth annual ACM symposium on Theory of computing, STOC  ’03 (pp. 213–222). New York, NY, USA: ACM.