Biological Cooperation as a Model for Stable AI Alignment: An Evolutionary Game Theory Approach

This paper proposes a novel theoretical framework for AI alignment grounded in evolutionary game theory and inspired by biological cooperation mechanisms. We model multi-agent AI systems as populations of agents in a Prisoner's Dilemma, where misalignment is operationalized as defection from a cooperative equilibrium. Using replicator dynamics and agent-based simulation (N=500, 50 runs), we investigate the conditions under which cooperation remains stable and how biologically inspired mechanisms — altruistic punishment, reputation tracking, and network topology — can be embedded into AI incentive architectures.Key findings: (1) Punishment mechanisms exhibit sharp threshold behavior at a critical monitoring density of ~15%, below which cooperation collapses; (2) reputation-based alignment achieves equilibrium cooperation of 0.83 but is fragile to signal degradation below accuracy θ=0.6; (3) combined mechanisms yield robust cooperation in 99.2% of runs (equilibrium frequency 0.97); (4) strategic deception — the primary robustness vulnerability — maps directly to the deceptive alignment problem and to molecular mimicry in immune evasion.The paper argues that alignment can be engineered as an emergent, self-stabilizing property of internal incentive architecture rather than purely as an externally imposed constraint. Design principles include redundant monitoring mechanisms, threshold-based enforcement, high-fidelity communication infrastructure, and structured interaction topology. The paper also proposes an expanded empirical validation programme in multi-agent reinforcement learning environments including Melting Pot, AI safety gridworlds, and LLM-based multi-agent systems.