Good call, I was pretty sure that there weren't any Nash equilibria other than constant slacking, but everyone using group 4's strategy is also a Nash equilibrium, as is everyone hunting with those with reputation is exactly equal to their own. This makes group 4 considerably harder to exploit, although it is possible in most likely distributions of players if you know it well enough. As you say, group 4 is less foolish than the slackers if there are enough of them. I still think that in practice, strategies that could be part of a Nash equilibrium won't win, because their success relies on having many identical copies of them.
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: