fullinformation

Full information, often described as complete information, is a concept in game theory and decision theory describing a setting in which all players know the game's structure: the set of players, available actions, payoff functions for each outcome, and the rules governing play. In such a setting, there is no uncertainty about these elements among players; any ambiguity arises only from strategic interaction, not from unknown data about the game itself. Full information is typically assumed as common knowledge: each player knows the payoffs, knows that others know them, and so on.

It contrasts with perfect information and with incomplete information. Perfect information refers to sequential games where every move is observable by all players as it happens; complete information concerns knowledge of the game's structure, not necessarily the moves. A game can have complete (or full) information but not be of perfect information, such as a simultaneous-move game in normal form where payoffs are common knowledge. Conversely, a game with incomplete information involves private data about players, such as types, beliefs, or private signals, leading to Bayesian games.

Examples: Chess is often cited as having complete information and perfect information; rock-paper-scissors has complete information but is typically analyzed as a simultaneous-move game; poker involves incomplete information due to hidden cards.

Applications: Full information models are used in economic analysis, mechanism design, and strategic reasoning to predict equilibria and outcomes when the underlying game is fully known.