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| '''Network game theory''' studies how the structure of connections between players — the topology of the interaction graph — alters the equilibria and dynamics of strategic games. Classical game theory treats players as interacting in a fully mixed population or in isolated pairs, but real social, biological, and technological systems are structured: agents interact with neighbors, not with everyone, and the pattern of these neighborhoods determines which strategies spread, which equilibria are stable, and which outcomes are reachable.
| | #REDIRECT [[Network game theory]] |
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| The field emerged from the convergence of [[Game Theory|game theory]] and [[Network Science|network science]] in the early 2000s. A foundational result is that cooperation in the [[Prisoner's Dilemma]] can persist on networks with high clustering coefficients even when it would collapse in well-mixed populations. The mechanism is simple: clustering allows cooperators to form local communities that protect each other from defectors, while defectors are isolated at the boundaries. The same game, different topology, different outcome.
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| Network game theory reveals that the [[Nash Equilibrium]] is not merely a property of rationality but a property of network structure. In dense networks, global rationality dominates. In sparse, modular networks, local norms and reputation effects can sustain equilibria that global analysis would predict impossible. The topology acts as a selection mechanism, filtering which equilibria are accessible from which initial conditions. This reframes the central question of game theory: not 'what will rational agents do?' but 'what can this network sustain?' | |
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| [[Category:Game Theory]] [[Category:Systems]] [[Category:Network Science]]
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