Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Алгоритм Топ-Трейдинг Циклов× | Байесовское равновесие по Нэшу× | |
|---|---|---|
| Область | Теория игр | Теория игр |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 1974 | 1967 |
| Автор метода≠ | Lloyd Shapley, Herbert Scarf | John Harsanyi |
| Тип | algorithm | algorithm |
| Основополагающий источник≠ | Shapley, L. S., & Scarf, H. (1974). On cores and indivisibility. Journal of Mathematical Economics, 1(1), 23-37. DOI ↗ | Harsanyi, J. C. (1967). Games with incomplete information played by Bayesian players, Parts I, II, and III. Management Science, 14(3), 159-182. DOI ↗ |
| Другие названия | TTC, Shapley-Scarf Algorithm, Efficient Exchange | BNE, Perfect Bayesian Equilibrium, Type-Contingent Equilibrium |
| Связанные | 4 | 4 |
| Сводка≠ | Top Trading Cycles (TTC) is an algorithm for allocating indivisible goods to agents such that the allocation is Pareto efficient and individually rational. Developed by Lloyd Shapley and Herbert Scarf in 1974, the algorithm identifies cycles of trades in a preference digraph, executes those trades, and iteratively repeats until no further trades are beneficial. TTC is widely used in kidney exchange and housing allocation due to its efficiency and implementation simplicity. | Bayesian Nash Equilibrium (BNE) extends Nash Equilibrium to games with incomplete information, where players lack full knowledge of others' payoff functions. Introduced by John Harsanyi in 1967, BNE models strategic interaction under uncertainty by representing unknown payoffs as players' private types drawn from a probability distribution. Equilibrium is found by solving for type-contingent strategies that are best responses to all possible type realizations. |
| ScholarGateНабор данных ↗ |
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