If there is an 'm' reward, you get the same reward whether or not you choose to hunt? I'm confused how this adds incentive to hunt when your goal is to "get more food than other players," not "get food."
My reading of the rules is that the "m" reward is additional to the hunts, not instead of them.
The effect is to reduce the rate at which players on the way to starving get eliminated. If you are one of the leading players, you will want the weaker players to be eliminated as soon as possible and have an incentive to prevent the m reward from happening. If you are one of the losing players, you want the m reward, to get more time to recover.
ETA: But how do you tell where you rank, from the information your program is given? Every act of defection ...
TL;DR = write a python script to win this applied game theory contest for $1000. Based on Prisoner's Dilemma / Tragedy of the Commons but with a few twists. Deadline Sunday August 18.
https://brilliant.org/competitions/hunger-games/rules/
The choices are H = hunt (cooperate) and S = slack (defect), and they use confusing wording here, but as far as I can tell the payoff matrix is (in units of food)
What's interesting is you don't get the entirety of your partner's history (so strategies like Tit-Tit-Tit for Tat don't work) instead you get only their reputation, which is the fraction of times they've hunted.
To further complicate the Nash equilibria, there's the option to overhunt: a random number m, 0 < m < P(P−1) is chosen before each round (round consisting of P−1 hunts, remember) and if the total number of hunt-choices is at least m, then each player is awarded 2(P−1) food units (2 per hunt).
Your python program has to decide at the start of each round whether or not to hunt with each opponent, based on: