What do you mean by "third level players" here? You call "second-level strategy" already modeling other players, but
you can model them as third level players and not have to treat them as equals.
... makes me think you maybe meant first-level players?
Anyway, I would model most of my opponents as what you call first-level strategy, with some varying parameters (and extra randomness). And possibly include as part of the population whichever more exotic strategies can systematically beat (various mixes of) those.
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: