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11
Evolving Aspirations and Cooperation
 Journal of Economic Theory
, 1998
"... This paper therefore builds on [3], in which a model of consistent aspirationsbased learning was introduced ..."
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Cited by 56 (3 self)
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This paper therefore builds on [3], in which a model of consistent aspirationsbased learning was introduced
AspirationBased Reinforcement Learning In Repeated Interaction Games: An Overview
, 2001
"... In models of aspirationbased... This paper provides an informal overview of a range of such theories applied to repeated interaction games. We describe different models of aspiration formation: where (1) aspirations are fixed but required to be consistent with longrun average payoffs; (2) aspiratio ..."
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Cited by 14 (1 self)
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In models of aspirationbased... This paper provides an informal overview of a range of such theories applied to repeated interaction games. We describe different models of aspiration formation: where (1) aspirations are fixed but required to be consistent with longrun average payoffs; (2) aspirations evolve based on past personal experience or of previous generations of players; and (3) aspirations are based on the experience of peers. Convergence to nonNash outcomes may result in either of these formulations. Indeed, cooperative behaviour can emerge and survive in the long run, even though it may be a strictly dominated strategy in the stage game, and despite the myopic adaptation of stage game strategies. Differences between reinforcement learning and evolutionary game theory are also discussed.
M (2008) Reinforcement learning dynamics in social dilemmas
 http://jasss.soc.surrey.ac.uk/11/2/1.html JANSSEN, J and Manca, R (2006). Applied SemiMarkov Processes
"... social dilemmas. In particular, we provide further insight into the solution concepts that they describe, illustrate some recent analytical results on the dynamics of their model, and discuss the robustness of such results to occasional mistakes made by players in choosing their actions (i.e. trembl ..."
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Cited by 7 (3 self)
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social dilemmas. In particular, we provide further insight into the solution concepts that they describe, illustrate some recent analytical results on the dynamics of their model, and discuss the robustness of such results to occasional mistakes made by players in choosing their actions (i.e. trembling hands). It is shown here that the dynamics of their model are strongly dependent on the speed at which players learn. With high learning rates the system quickly reaches its asymptotic behaviour; on the other hand, when learning rates are low, two distinctively different transient regimes can be clearly observed. It is shown that the inclusion of small quantities of randomness in players ' decisions can change the dynamics of the model dramatically. Keywords:
Aspiration learning in coordination games
 in IEEE Conference on Decision and Control
, 2010
"... Abstract — We consider the problem of distributed convergence to efficient outcomes in coordination games through payoffbased learning dynamics, namely aspiration learning. The proposed learning scheme assumes that players reinforce well performed actions, by successively playing these actions, oth ..."
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Abstract — We consider the problem of distributed convergence to efficient outcomes in coordination games through payoffbased learning dynamics, namely aspiration learning. The proposed learning scheme assumes that players reinforce well performed actions, by successively playing these actions, otherwise they randomize among alternative actions. Our first contribution is the characterization of the asymptotic behavior of the induced Markov chain of the iterated process by an equivalent finitestate Markov chain, which simplifies previously introduced analysis on aspiration learning. We then characterize explicitly the behavior of the proposed aspiration learning in a generalized version of socalled coordination games, an example of which is network formation games. In particular, we show that in coordination games the expected percentage of time that the efficient action profile is played can become arbitrarily large. I.
Satisficing, Optimization, and Adaptive Systems
"... Abstract An aspect of complex adaptive systems is an observation that they typically operate far from equilibrium and optimality. This provokes investigation into frameworks into such decisionmaking processes. Satisficing describes a rational decision making process in economics where deciding age ..."
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Abstract An aspect of complex adaptive systems is an observation that they typically operate far from equilibrium and optimality. This provokes investigation into frameworks into such decisionmaking processes. Satisficing describes a rational decision making process in economics where deciding agents accept solutions that achieve a minimum level of satisfaction. This theory differs from previously traditional rational decisionmaking in which the agent seeks to maximise or optimize utility from the choices faced. This work investigates some of the impact this altered perspective on the decision making process has had in economics, game theory, control theory, and evolutionary biology. We see it is the latter case that that may be most interesting to the study of complex adaptive systems and resultant models, such as those used in biologically inspired computation.
Reinforcement Behavior in Repeated Games
, 1998
"... This paper describes behavior conventions that are stable long run outcomes of reinforcement behavior rules in twoperson repeated games. Each player plays the repeated game with a fixed but endogenous aspiration, a payoff level that is considered “satisfactory”. Choice probabilities are modified ..."
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This paper describes behavior conventions that are stable long run outcomes of reinforcement behavior rules in twoperson repeated games. Each player plays the repeated game with a fixed but endogenous aspiration, a payoff level that is considered “satisfactory”. Choice probabilities are modified by experience: satisfactory payoff experiences positively reinforce probability weights on chosen actions, while unsatisfactory experiences cause other actions to be tried. Our equilibrium notion requires consistency, equality of aspiration levels with long run average payoffs, and stability, robustness of outcomes with respect to random perturbations of each player’s state. Our main result identifies the set of equilibrium pure strategy conventions: this comprises all efficient, strongly individually rational outcomes, and protected Nash equilibria. Extensions to mixed strategy conventions, and applications to games of coordination, cooperation and oligopoly are discussed.
c ○ 2013 Society for Industrial and Applied Mathematics ASPIRATION LEARNING IN COORDINATION GAMES ∗
"... Abstract. We consider the problem of distributed convergence to efficient outcomes in coordination games through dynamics based on aspiration learning. Our first contribution is the characterization of the asymptotic behavior of the induced Markov chain of the iterated process in terms of an equival ..."
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Abstract. We consider the problem of distributed convergence to efficient outcomes in coordination games through dynamics based on aspiration learning. Our first contribution is the characterization of the asymptotic behavior of the induced Markov chain of the iterated process in terms of an equivalent finitestate Markov chain. We then characterize explicitly the behavior of the proposed aspiration learning in a generalized version of coordination games, examples of which include network formation and commonpool games. In particular, we show that in generic coordination games the frequency at which an efficient action profile is played can be made arbitrarily large. Although convergence to efficient outcomes is desirable, in several coordination games, such as commonpool games, attainability of fair outcomes, i.e., sequences of plays at which players experience highly rewarding returns with the same frequency, might also be of special interest. To this end, we demonstrate through analysis and simulations that aspiration learning also establishes fair outcomes in all symmetric coordination games, including commonpool games.
ADVANCING LEARNING AND EVOLUTIONARY GAME THEORY WITH AN APPLICATION TO SOCIAL DILEMMAS
, 2007
"... for finding me whenever I’ve been lost, for lifting me up whenever I’ve fallen down, for using up all his bright light to get me through my darkest nights, and fill with colour and joy every minute of my life. ..."
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for finding me whenever I’ve been lost, for lifting me up whenever I’ve fallen down, for using up all his bright light to get me through my darkest nights, and fill with colour and joy every minute of my life.
68131 MannheimAsset Prices in an Overlapping Generations Model with CaseBased Decision Makers with Short Memory
, 2004
"... and Klaus Ritzeberger for helpful discussions and comments. Financial support by the DFG is greatefully acknowledged. ..."
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and Klaus Ritzeberger for helpful discussions and comments. Financial support by the DFG is greatefully acknowledged.
c © World Scientic Publishing Company ASPIRATIONBASED REINFORCEMENT LEARNING IN REPEATED INTERACTION GAMES: AN
, 2001
"... In models of aspirationbased reinforcement learning, agents adapt by comparing payos achieved from actions chosen in the past with an aspiration level. Though such models are wellestablished in behavioural psychology, only recently have they begun to receive attention in game theory and its appli ..."
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In models of aspirationbased reinforcement learning, agents adapt by comparing payos achieved from actions chosen in the past with an aspiration level. Though such models are wellestablished in behavioural psychology, only recently have they begun to receive attention in game theory and its applications to economics and politics. This paper provides an informal overview of a range of such theories applied to repeated interaction games. We describe dierent models of aspiration formation: where (1) aspirations are xed but required to be consistent with longrun average payos; (2) aspirations evolve based on past personal experience or of previous generations of players; and (3) aspirations are based on the experience of peers. Convergence to nonNash outcomes may result in either of these formulations. Indeed, cooperative behaviour can emerge and survive in the long run, even though it may be a strictly dominated strategy in the stage game, and despite the myopic adaptation of stage game strategies. Dierences between reinforcement learning and evolutionary game theory are also discussed. 1.