Melioration Learning in Two-Person Games

Melioration Learning in Two-Person Games

Melioration learning is an empirically well-grounded model of reinforcement learning. By means of computer simulations, this paper derives predictions for several repeatedly played two-person games from this model. The results indicate a likely convergence to a pure Nash equilibrium of the game. If no pure equilibrium exists, the relative frequencies of choice may approach the predictions of th...

Anticipatory learning in two-person games: some experimental results

Crawford [Econometrica 42 (1974) 885; J. Econ. Behavior Organ. 6 (1985) 69] has presented a striking example in which plausible adaptive learning rules fail to locate a straightforward mixed-strategy equilibrium. However, Selten [Game Equilibrium Models I. Springer, Berlin 1991, p. 98] argued that such learning rules can be stabilized for some games if there is an anticipation component in the ...

A TRANSITION FROM TWO-PERSON ZERO-SUM GAMES TO COOPERATIVE GAMES WITH FUZZY PAYOFFS

In this paper, we deal with games with fuzzy payoffs. We proved that players who are playing a zero-sum game with fuzzy payoffs against Nature are able to increase their joint payoff, and hence their individual payoffs by cooperating. It is shown that, a cooperative game with the fuzzy characteristic function can be constructed via the optimal game values of the zero-sum games with fuzzy payoff...

Zero-Sum Two Person Games

Maximin Play in Two-Person Bimatrix Games

Since the seminal paper of Nash [7] game theoretic literature has focused mostly on equilibrium and not on maximin (minimax) strategies. We study the properties of these strategies in 2-player non-zero-sum strategic games, whose Nash equilibria are only mixed.