Model strategic situations where outcomes depend on your choice and others’ choices—then design moves or rules to shift the equilibrium.
John von Neumann & Oskar Morgenstern; John Nash; many successors (Aumann, Schelling, Myerson)
Game theory studies interdependent decisions. Players choose strategies; payoffs depend on everyone’s choices. An equilibrium (often Nash) is a stable profile where no player wants to deviate on their own. The craft is twofold: (1) analyse the given game to predict behaviour; (2) change the game—payoffs, information, or timing—so better outcomes become equilibrium.
Game type – zero-sum (one’s gain is another’s loss) vs non-zero-sum (scope for win–win or traps).
Information – complete/incomplete; perfect/imperfect; simultaneous vs sequential moves.
Equilibria – Nash (no unilateral gain by deviating), subgame perfect (credible in every subtree), mixed strategies when no pure equilibrium exists.
Canonical games – Prisoner’s Dilemma (co-operation trap), Stag Hunt (coordination & trust), Battle of the Sexes (coordination with preference conflict), Matching Pennies (zero-sum).
Dynamic effects – repeated games enable reputation, reciprocity, and trigger strategies; signalling/screening convey or extract private information; commitment and credible threats reshape incentives.
Mechanism design – set rules/contracts/auctions so participants’ incentives produce the outcome you want.
Game type – zero-sum (one’s gain is another’s loss) vs non-zero-sum (scope for win–win or traps).
Information – complete/incomplete; perfect/imperfect; simultaneous vs sequential moves.
Equilibria – Nash (no unilateral gain by deviating), subgame perfect (credible in every subtree), mixed strategies when no pure equilibrium exists.
Canonical games – Prisoner’s Dilemma (co-operation trap), Stag Hunt (coordination & trust), Battle of the Sexes (coordination with preference conflict), Matching Pennies (zero-sum).
Dynamic effects – repeated games enable reputation, reciprocity, and trigger strategies; signalling/screening convey or extract private information; commitment and credible threats reshape incentives.
Mechanism design – set rules/contracts/auctions so participants’ incentives produce the outcome you want.
Define the game – players, feasible actions, timing, what each player values (payoffs).
Build the model – 2×2 payoff matrix (simultaneous) or extensive-form tree (sequential with information sets).
Solve
Eliminate strictly dominated strategies.
Best-response reasoning to find Nash equilibria (use backward induction for sequential games → subgame perfect).
If none in pure strategies, compute mixed (randomised) equilibria.
Stress the assumptions – are payoffs right? what is common knowledge? is the move order accurate?
Change the game
Commit (contracts, capacity, pricing guarantees).
Shift payoffs (carrots, penalties, side-payments, reputation effects).
Change information (signalling, screening, transparency).
Repeat the interaction to support co-operation with trigger strategies.
Pilot & measure – run small tests; watch behaviour, not just stated preferences; iterate the model.
Wrong payoffs – pretty maths with the wrong incentives predicts nothing.
Static when dynamic – solving a one-shot game that is actually repeated.
Incredible threats – strategies that won’t be optimal when the moment arrives (fix with subgame perfection or commitment).
Equilibrium selection – multiple equilibria; use focal points, small frictions, or coordination devices.
Misreading mixed strategies – they model unpredictability, not indecision; often implement as controlled randomisation.
Over-engineering – mechanisms too complex for humans or systems create new failure modes.
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A durable edge that lets you create more value or deliver it at lower cost than rivals — and keep it via isolating mechanisms.
In blockchains, value tends to concentrate at the shared protocol layer rather than the application layer, though modular stacks and wallets can shift where value accrues.
Map internal factors (Strengths, Weaknesses) and external factors (Opportunities, Threats), then convert the grid into a short list of strategic moves.