Minimax with alpha-beta pruning over a real game tree, plus an equilibrium solver for small normal-form games - optimal play against an adversary, and against a rational one.
Two related pieces of strategic reasoning in one codebase. The first is a complete adversarial search engine: minimax over a game tree, alpha-beta pruning, depth limiting, and a heuristic evaluation function, instrumented to report how many nodes pruning actually removes so the reader sees the saving rather than being told about it. The second is a solver for small normal-form games that finds pure and mixed-strategy equilibria, applied to the standard cases - the prisoner's dilemma, matching pennies, and coordination games - where the equilibrium and the jointly best outcome come apart. Together they cover the two regimes the articles distinguish: strictly opposed play, where minimax is the whole story, and partly aligned play, where it is not. Implementation is in progress and no source repository has been published yet.
How a program plays a game against an opponent who is trying to beat it: the minimax value, why alpha-beta pruning reaches the same answer while examining fewer nodes, and a game tree pruned move by move.
Strategic reasoning when players are not strictly opposed: dominant strategies, the prisoner's dilemma worked from its payoff matrix, Nash equilibrium, Pareto optimality, and why equilibrium and efficiency can conflict.