An Exact Cooperation Formula for Introspection Dynamics in the Heterogeneous Public Goods Game

Cooperation in heterogeneous groups, where individuals differ in resources, productivity, and behavioural responsiveness, underpins collective action across many social and biological systems. Introspection dynamics, in which each player compares their payoff to what they would have received under the alternative action, provides a natural learning rule for such asymmetric settings. We study introspection dynamics on multiplayer games in which the payoff difference $\Delta f_i$ evaluated by a player when considering a strategy switch is independent of all other players' current actions, a property we call state-independence. Under this condition the introspection Markov chain decomposes as a random-scan product of $N$ independent two-state chains, one per player, and the stationary distribution is a product measure. As our main application we consider the heterogeneous public goods game, where $N$ players may differ in their contributions $\alpha_i$, public goods multipliers $r_i$, and selection intensities $\beta_i$. We prove that the linear payoff structure implies state-independence, and the long-run cooperation probability admits the exact closed form $$p_C = \frac{1}{N}\sum_{i=1}^{N} \left[\frac{1-\mu_{i0}-\mu_{i1}}{1+e^{\,\beta_i\alpha_i(1-r_i/N)}}+\mu_{i0}\right],$$ with no asymptotic approximation. Several structural consequences follow immediately: a player-specific cooperation threshold at $r_i = N$ (under symmetric mutation), payoff-neutrality under zero selection intensity, and the sign of each player's sensitivity to their own parameters.