BEIC （Bayesian equilibrium by iterative conjectures） analyzes games with players forming their conjectures about what other players will do through iterative reasoning starting with first order uninformative conject...BEIC （Bayesian equilibrium by iterative conjectures） analyzes games with players forming their conjectures about what other players will do through iterative reasoning starting with first order uninformative conjectures and keep updating their conjectures iteratively with game theoretic reasoning until a convergence of conjectures is achieved. In a BEIC, beliefs about the other players＇ strategies are specified and they are consistent with the equilibrium strategies they supported. A BEIC is therefore a perfect Bayesian equilibrium and hence a refinement of Nash equilibrium. Through six examples, the BE1C solutions are compared with those obtained by the other refining criteria of payoff-dominance, risk-dominance, iterated admissibility, subgame perfect equilibrium, Bayesian Nash equilibrium, perfect Bayesian equilibrium and the intuitive criterion. The outstanding results from the comparisons are that the BEIC approach is able to pick the natural focal point of a game when the iterated admissibility criterion fails to, the BEIC approach rules out equilibrium depending upon non credible threat, and that in simultaneous and sequential games of incomplete information, the BEIC approach not only normally narrows down the equilibriums to one but it also picks the most compelling equilibrium compare with Bayesian Nash equilibrium or perfect Bayesian equilibrium or intuitive criterion.展开更多
Accurate prediction of wind power is significant for power system dispatching as well as safe and stable operation. By means of BP neural network, radial basis function neural network and support vector machine, a new...Accurate prediction of wind power is significant for power system dispatching as well as safe and stable operation. By means of BP neural network, radial basis function neural network and support vector machine, a new combined method of wind power prediction based on cooperative game theory is proposed. In the method, every single forecasting model is regarded as a member of the cooperative games, and the sum of square error of combination forecasting is taken as the result of cooperation. The result is divided among the members according to Shapley values, and then weights of combination forecasting can be obtained. Application results in an actual wind farm show that the proposed method can effectively improve prediction precision.展开更多
文摘BEIC （Bayesian equilibrium by iterative conjectures） analyzes games with players forming their conjectures about what other players will do through iterative reasoning starting with first order uninformative conjectures and keep updating their conjectures iteratively with game theoretic reasoning until a convergence of conjectures is achieved. In a BEIC, beliefs about the other players＇ strategies are specified and they are consistent with the equilibrium strategies they supported. A BEIC is therefore a perfect Bayesian equilibrium and hence a refinement of Nash equilibrium. Through six examples, the BE1C solutions are compared with those obtained by the other refining criteria of payoff-dominance, risk-dominance, iterated admissibility, subgame perfect equilibrium, Bayesian Nash equilibrium, perfect Bayesian equilibrium and the intuitive criterion. The outstanding results from the comparisons are that the BEIC approach is able to pick the natural focal point of a game when the iterated admissibility criterion fails to, the BEIC approach rules out equilibrium depending upon non credible threat, and that in simultaneous and sequential games of incomplete information, the BEIC approach not only normally narrows down the equilibriums to one but it also picks the most compelling equilibrium compare with Bayesian Nash equilibrium or perfect Bayesian equilibrium or intuitive criterion.
文摘Accurate prediction of wind power is significant for power system dispatching as well as safe and stable operation. By means of BP neural network, radial basis function neural network and support vector machine, a new combined method of wind power prediction based on cooperative game theory is proposed. In the method, every single forecasting model is regarded as a member of the cooperative games, and the sum of square error of combination forecasting is taken as the result of cooperation. The result is divided among the members according to Shapley values, and then weights of combination forecasting can be obtained. Application results in an actual wind farm show that the proposed method can effectively improve prediction precision.
