The Experts below are selected from a list of 261 Experts worldwide ranked by ideXlab platform
Vishal Sachdev - One of the best experts on this subject based on the ideXlab platform.
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learning bidding strategies with autonomous agents in environments with unstable equilibrium
Decision Support Systems, 2008Co-Authors: Riyaz Sikora, Vishal SachdevAbstract:The role of automated agents for decision support in the electronic marketplace has been growing steadily and has been attracting a lot of research from the artificial intelligence community as well as from economists. In this paper, we study the efficacy of using automated agents for learning bidding strategies in contexts of strategic interaction involving multiple sellers in reverse auctions. Standard game-theoretic analysis of the problem assumes completely Rational and omniscient agents to derive Nash equilibrium seller policy. Most of the literature on use of learning agents uses convergence to Nash equilibrium as the validating criterion. In this paper, we consider a problem where the Nash equilibrium is unstable and hence not useful as an evaluation criterion. Instead, we propose that agents should be able to learn the optimal or best response strategies when they exist (Rational Behavior) and should demonstrate low variance in profits (convergence). We present Rationally bounded, evolutionary and reinforcement learning agents that learn these desirable properties of Rational Behavior and convergence.
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Learning bidding strategies with autonomous agents in environments with unstable equilibrium
Decision Support Systems, 2008Co-Authors: Riyaz T. Sikora, Vishal SachdevAbstract:The role of automated agents for decision support in the electronic marketplace has been growing steadily and has been attracting a lot of research from the artificial intelligence community as well as from economists. In this paper, we study the efficacy of using automated agents for learning bidding strategies in contexts of strategic interaction involving multiple sellers in reverse auctions. Standard game-theoretic analysis of the problem assumes completely Rational and omniscient agents to derive Nash equilibrium seller policy. Most of the literature on use of learning agents uses convergence to Nash equilibrium as the validating criterion. In this paper, we consider a problem where the Nash equilibrium is unstable and hence not useful as an evaluation criterion. Instead, we propose that agents should be able to learn the optimal or best response strategies when they exist (Rational Behavior) and should demonstrate low variance in profits (convergence). We present Rationally bounded, evolutionary and reinforcement learning agents that learn these desirable properties of Rational Behavior and convergence. © 2008 Elsevier B.V. All rights reserved.
Riyaz Sikora - One of the best experts on this subject based on the ideXlab platform.
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learning bidding strategies with autonomous agents in environments with unstable equilibrium
Decision Support Systems, 2008Co-Authors: Riyaz Sikora, Vishal SachdevAbstract:The role of automated agents for decision support in the electronic marketplace has been growing steadily and has been attracting a lot of research from the artificial intelligence community as well as from economists. In this paper, we study the efficacy of using automated agents for learning bidding strategies in contexts of strategic interaction involving multiple sellers in reverse auctions. Standard game-theoretic analysis of the problem assumes completely Rational and omniscient agents to derive Nash equilibrium seller policy. Most of the literature on use of learning agents uses convergence to Nash equilibrium as the validating criterion. In this paper, we consider a problem where the Nash equilibrium is unstable and hence not useful as an evaluation criterion. Instead, we propose that agents should be able to learn the optimal or best response strategies when they exist (Rational Behavior) and should demonstrate low variance in profits (convergence). We present Rationally bounded, evolutionary and reinforcement learning agents that learn these desirable properties of Rational Behavior and convergence.
Riyaz T. Sikora - One of the best experts on this subject based on the ideXlab platform.
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Learning bidding strategies with autonomous agents in environments with unstable equilibrium
Decision Support Systems, 2008Co-Authors: Riyaz T. Sikora, Vishal SachdevAbstract:The role of automated agents for decision support in the electronic marketplace has been growing steadily and has been attracting a lot of research from the artificial intelligence community as well as from economists. In this paper, we study the efficacy of using automated agents for learning bidding strategies in contexts of strategic interaction involving multiple sellers in reverse auctions. Standard game-theoretic analysis of the problem assumes completely Rational and omniscient agents to derive Nash equilibrium seller policy. Most of the literature on use of learning agents uses convergence to Nash equilibrium as the validating criterion. In this paper, we consider a problem where the Nash equilibrium is unstable and hence not useful as an evaluation criterion. Instead, we propose that agents should be able to learn the optimal or best response strategies when they exist (Rational Behavior) and should demonstrate low variance in profits (convergence). We present Rationally bounded, evolutionary and reinforcement learning agents that learn these desirable properties of Rational Behavior and convergence. © 2008 Elsevier B.V. All rights reserved.
Binghong Wang - One of the best experts on this subject based on the ideXlab platform.
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Rational Behavior is a double edged sword when considering voluntary vaccination
Physica A-statistical Mechanics and Its Applications, 2012Co-Authors: H Zhang, Feng Fu, Wenyao Zhang, Binghong WangAbstract:Of particular importance for public health is how to understand strategic vaccination Behavior in social networks. Social learning is a central aspect of human Behavior, and it thus shapes vaccination individuals’ decision-making. Here, we study two simple models to address the impact of the more Rational decision-making of individuals on voluntary vaccination. In the first model, individuals are endowed with memory capacity for their past experiences of dealing with vaccination. In addition to their current payoffs, they also take account of the historical payoffs that are discounted by a memory-decaying factor. They use such overall payoffs (weighing the current payoffs and historical payoffs) to reassess their vaccination strategies. Those who have higher overall payoffs are more likely imitated by their social neighbors. In the second model, individuals do not blindly learn the strategies of neighbors; they also combine the fraction of infection in the past epidemic season. If the fraction of infection surpasses the perceived risk threshold, individuals will increase the probability of taking vaccination. Otherwise, they will decrease the probability of taking vaccination. Then we use evolutionary game theory to study the vaccination Behavior of people during an epidemiological process. To do this, we propose a two-stage model: individuals make vaccination decisions during a yearly vaccination campaign, followed by an epidemic season. This forms a feedback loop between the vaccination decisions of individuals and their health outcomes, and thus payoffs. We find that the two more Rational decision-making models have nontrivial impacts on the vaccination Behavior of individuals, and, as a result, on the final fraction of infection. Our results highlight that, from an individual’s viewpoint, the decisions are optimal and more Rational. However, from the social viewpoint, the strategies of individuals can give rise to distinct outcomes. Namely, the Rational Behavior of individuals plays a ‘double-edged-sword’ role on the social effects.
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Rational Behavior is a ‘double-edged sword’ when considering voluntary vaccination
Physica A-statistical Mechanics and Its Applications, 2012Co-Authors: Hai-feng Zhang, Feng Fu, Wenyao Zhang, Binghong WangAbstract:Of particular importance for public health is how to understand strategic vaccination Behavior in social networks. Social learning is a central aspect of human Behavior, and it thus shapes vaccination individuals’ decision-making. Here, we study two simple models to address the impact of the more Rational decision-making of individuals on voluntary vaccination. In the first model, individuals are endowed with memory capacity for their past experiences of dealing with vaccination. In addition to their current payoffs, they also take account of the historical payoffs that are discounted by a memory-decaying factor. They use such overall payoffs (weighing the current payoffs and historical payoffs) to reassess their vaccination strategies. Those who have higher overall payoffs are more likely imitated by their social neighbors. In the second model, individuals do not blindly learn the strategies of neighbors; they also combine the fraction of infection in the past epidemic season. If the fraction of infection surpasses the perceived risk threshold, individuals will increase the probability of taking vaccination. Otherwise, they will decrease the probability of taking vaccination. Then we use evolutionary game theory to study the vaccination Behavior of people during an epidemiological process. To do this, we propose a two-stage model: individuals make vaccination decisions during a yearly vaccination campaign, followed by an epidemic season. This forms a feedback loop between the vaccination decisions of individuals and their health outcomes, and thus payoffs. We find that the two more Rational decision-making models have nontrivial impacts on the vaccination Behavior of individuals, and, as a result, on the final fraction of infection. Our results highlight that, from an individual’s viewpoint, the decisions are optimal and more Rational. However, from the social viewpoint, the strategies of individuals can give rise to distinct outcomes. Namely, the Rational Behavior of individuals plays a ‘double-edged-sword’ role on the social effects.
Mariusz Wirga - One of the best experts on this subject based on the ideXlab platform.
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Maultsby’s Rational Behavior Therapy: Background, Description, Practical Applications, and Recent Developments
Journal of Rational-Emotive & Cognitive-Behavior Therapy, 2020Co-Authors: Mariusz Wirga, Michael Debernardi, Aleksandra Wirga, Marta Banout, Olga Gulyayeva FullerAbstract:In this article we present Maultsby’s Rational Behavior Therapy (RBT) as a unique and distinct, but underutilized form of cognitive-Behavior therapy, including its origins, theory (with psychosomatic learning theory), basic concepts, and practical applications, as well as never before published recent developments. As readers will see, many of Maultsby’s concepts, while pioneering and beckoning the third wave, still remain fresh, validated by current cognitive neuroscience, and are very relevant to modern psychotherapeutic practice. We describe RBT’s valuable concepts and effective techniques in such a way that readers may readily start using them to complement and enhance any other form of cognitive Behavior therapy. An article comparing RBT with REBT and CBT will follow.
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maultsby s Rational Behavior therapy background description practical applications and recent developments
Journal of Rational-emotive & Cognitive-behavior Therapy, 2020Co-Authors: Mariusz Wirga, Michael Debernardi, Aleksandra Wirga, Marta Banout, Olga Gulyayeva FullerAbstract:In this article we present Maultsby’s Rational Behavior Therapy (RBT) as a unique and distinct, but underutilized form of cognitive-Behavior therapy, including its origins, theory (with psychosomatic learning theory), basic concepts, and practical applications, as well as never before published recent developments. As readers will see, many of Maultsby’s concepts, while pioneering and beckoning the third wave, still remain fresh, validated by current cognitive neuroscience, and are very relevant to modern psychotherapeutic practice. We describe RBT’s valuable concepts and effective techniques in such a way that readers may readily start using them to complement and enhance any other form of cognitive Behavior therapy. An article comparing RBT with REBT and CBT will follow.
