Unpacking the exploration–exploitation tradeoff: A synthesis of human and animal literatures.
Sabine Mehlhorn,Ben R. Newell,Peter M. Todd,Michael D. Lee,Kate Morgan,Victoria A. Braithwaite,Daniel Hausmann,Klaus Fiedler,Cleotilde Gonzalez +8 more
- Vol. 2, Iss: 3, pp 191-215
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In this article, the authors explore how potential tradeoffs depend on the conceptualization of exploration and exploitation, the influencing environmental, social, and individual factors, the scale at which exploration and exploit are considered, the relationship and types of transitions between the two behaviors, and the goals of the decision maker.Abstract:
Many decisions in the lives of animals and humans require a fine balance between the exploration of different options and the exploitation of their rewards. Do you buy the advertised car, or do you test drive different models? Do you continue feeding from the current patch of flowers, or do you fly off to another one? Do you marry your current partner, or try your luck with someone else? The balance required in these situations is commonly referred to as the exploration– exploitation tradeoff. It features prominently in a wide range of research traditions, including learning, foraging, and decision making literatures. Here, we integrate findings from these and other often-isolated literatures in order to gain a better understand- ing of the possible tradeoffs between exploration and exploitation, and we propose new theoretical insights that might guide future research. Specifically, we explore how potential tradeoffs depend on (a) the conceptualization of exploration and exploitation; (b) the influencing environmental, social, and individual factors; (c) the scale at which exploration and exploitation are considered; (d) the relationship and types of transitions between the 2 behaviors; and (e) the goals of the decision maker. We conclude that exploration and exploitation are best conceptualized as points on a continuum, and that the extent to which an agent’s behavior can be interpreted as exploratory or exploitative depends upon the level of abstraction at which it is considered.read more
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The science of science: from the perspective of complex systems
An Zeng,Zhesi Shen,Zhesi Shen,Jianlin Zhou,Jinshan Wu,Ying Fan,Yougui Wang,Yougui Wang,H. Eugene Stanley +8 more
TL;DR: In this paper, the authors review the recent advances in science of science (SOS) aiming to cover the topics from empirical study, network analysis, mechanistic models, ranking, prediction, and many important related issues.
Journal ArticleDOI
Deconstructing the human algorithms for exploration.
TL;DR: It is shown that two families of algorithms can be distinguished in terms of how uncertainty affects exploration, and computational modeling confirms that a hybrid model is the best quantitative account of the data.
The Paradox of Choice : Why More is Less (Mengapa Lebih Itu Justru Kurang)
TL;DR: The Paradox of Choice as mentioned in this paper argues that too much choice can lead to clinical depression, and suggests that eliminating choices can greatly reduce stress, anxiety, and busyness of our lives.
Journal ArticleDOI
The algorithmic architecture of exploration in the human brain
Eric Schulz,Samuel J. Gershman +1 more
TL;DR: This work reviews recent studies that have identified multiple algorithmic strategies underlying exploration, which rely on different brain systems, have different developmental trajectories, and are sensitive to different task manipulations.
Journal ArticleDOI
Social learning strategies modify the effect of network structure on group performance
Daniel Barkoczi,Mirta Galesic +1 more
TL;DR: It is argued that understanding how communication networks affect group performance requires taking into consideration the social learning strategies of individual team members, and it is shown that efficient networks outperform inefficient networks when individuals rely on conformity by copying the most frequent solution among their contacts.
References
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Book
Reinforcement Learning: An Introduction
TL;DR: This book provides a clear and simple account of the key ideas and algorithms of reinforcement learning, which ranges from the history of the field's intellectual foundations to the most recent developments and applications.
Book
A Theory of Cognitive Dissonance
TL;DR: Cognitive dissonance theory links actions and attitudes as discussed by the authors, which holds that dissonance is experienced whenever one cognition that a person holds follows from the opposite of at least one other cognition that the person holds.
Journal ArticleDOI
Exploration and Exploitation in Organizational Learning
TL;DR: In this paper, the authors consider the relation between the exploration of new possibilities and the exploitation of old certainties in organizational learning and examine some complications in allocating resources between the two, particularly those introduced by the distribution of costs and benefits across time and space.
Journal ArticleDOI
Reinforcement learning: a survey
TL;DR: Central issues of reinforcement learning are discussed, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.
Posted Content
Reinforcement Learning: A Survey
TL;DR: A survey of reinforcement learning from a computer science perspective can be found in this article, where the authors discuss the central issues of RL, including trading off exploration and exploitation, establishing the foundations of RL via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.