A paper on applying Game Theory to cancer

Here is a brief summary/exploration of some of the paper:

This is a paper that looks at the GT of cancer: it reads like a proof of concept sitting in literature that already exists on the subject.
The main result is confirming intuitive understanding of proliferation/motility. Increased nutrients implies less motility.
It uses two different approaches: describe an analytic evolutionary model. Note that there is a typo in the game:
\[\begin{pmatrix} b/2 & b - c\\ b & b - c/2 \end{pmatrix}\]
Also note that this is showing the utility of the column player so that’s slightly different from the framework in the course.

The solution comes from:
```
>>> import sympy as sym
>>> p, b, c = sym.symbols("p, b, c")
>>> sym.solveset(p * (b - c / 2) + (1 - p) * (b - c) - p * (b) - (1 - p) * (b / 2), p)
{(b - 2*c)/(b - c)}
```
They also describe how a mixed stable solution will also exist which corresponds to the stability of the replicator dynamics equation.
They implement a Cellular automaton model which is somewhat related to the Moran Process
Improvements could include: - more complex dynamics; - using more phenotypes (strategies); - finite population analytical model;

Here is a blog post on the EGT blog about this paper: https://egtheory.wordpress.com/2013/07/05/motility/