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A Unified Model Of Adaptive Learning In Normal Form Games, Naoki Funai
A Unified Model Of Adaptive Learning In Normal Form Games, Naoki Funai
Naoki Funai
We investigate an adaptive learning model which nests several existing learning models such as payoff assessment learning, valuation learning, stochastic fictitious play learning, experience-weighted attraction learning and delta learning with indirect payoff information in normal form games. In this paper, we consider adaptive players each of whom (i) assigns payoff assessments to his own actions, (ii) chooses an action which has the highest assessment with some perturbations, and (iii) updates the assessments using observed payoffs, which may include payoffs from unchosen actions, in each period. Utilising the asynchronous stochastic approximation method introduced by Tsitsiklis (1994), we provide conditions under which …
Adaptive Learning In Finitely Repeated Games, Naoki Funai
Adaptive Learning In Finitely Repeated Games, Naoki Funai
Naoki Funai
This paper investigates the way in which adaptive players behave in the long-run in finitely repeated games. Each player assigns subjective payoff assessments to his own actions and chooses the action which has the highest assessment at each of his information sets. After receiving payoffs, players update their own assessments of chosen actions using the realized payoffs in an adaptive manner; we consider the updating rules of Watkins and Dayan (1992) and Sarin and Vahid (1999). When players experience random shocks on their assessments, players' behavior strategies converge to a unique agent quantal response equilibrium (McKelvey and Palfrey, 1998) if …