scholarly journals Balancing exploration and exploitation with information and randomization

2020 ◽  
Author(s):  
Robert C Wilson ◽  
Elizabeth Bonawitz ◽  
Vincent Costa ◽  
Becket Ebitz
Keyword(s):  
Exploration And Exploitation ◽  
Trade Off ◽  
Open Questions ◽  
Computational Perspective ◽  
Computational Properties

Explore-exploit decisions require us to trade off the benefits of exploring unknown options to learn more about them, with exploiting known options, for immediate reward. Such decisions are ubiquitous in nature, but from a computational perspective, they are notoriously hard. There is therefore much interest in how humans and animals make these decisions and recently there has been an explosion of research in this area. Here we provide a biased and incomplete snapshot of this field focusing on the major finding that many organisms use two distinct strategies to solve the explore-exploit dilemma: a bias for information (`directed exploration') and the randomization of choice (`random exploration'). We review evidence for the existence of these strategies, their computational properties, their neural implementations, as well as how directed and random exploration vary over the lifespan. We conclude by highlighting open questions in this field that are ripe to both explore and exploit.

scholarly journals The dimensionality of neural representations for control

2020 ◽  
Author(s):  
David Badre ◽  
Apoorva Bhandari ◽  
Haley Keglovits ◽  
Atsushi Kikumoto
Keyword(s):  
Prefrontal Cortex ◽  
Cognitive Control ◽  
Neural Computation ◽  
Neural Representation ◽  
Future Research ◽  
Trade Off ◽  
Neural Representations ◽  
Open Questions ◽  
The Brain ◽  
Review Current

Cognitive control allows us to think and behave flexibly based on our context and goals. At the heart of theories of cognitive control is a control representation that enables the same input to produce different outputs contingent on contextual factors. In this review, we focus on an important property of the control representation’s neural code: its representational dimensionality. Dimensionality of a neural representation balances a basic separability/generalizability trade-off in neural computation. We will discuss the implications of this trade-off for cognitive control. We will then briefly review current neuroscience findings regarding the dimensionality of control representations in the brain, particularly the prefrontal cortex. We conclude by highlighting open questions and crucial directions for future research.

Exploration and Exploitation in Parallel Problem Solving

2013 ◽  
pp. 201-215 ◽  
Author(s):  
Hua Zhang ◽  
Youmin Xi
Keyword(s):  
Problem Solving ◽  
Strategic Behavior ◽  
Group Performance ◽  
Performance Level ◽  
Exploration And Exploitation ◽  
Trade Off ◽  
Knowledge Process ◽  
Agent Behavior ◽  
Management Implication ◽  
Exploration Exploitation

In previous studies on coordinating exploration-exploitation activities, much attention has been paid on network structures while the roles played by actors’ strategic behavior have been largely ignored. In this paper, the authors extend March’s simulation model on parallel problem solving by adding structurally equivalent imitation. In this way, one can examine how the interaction of network structure with agent behavior affects the knowledge process and finally influence group performance. This simulation experiment suggests that under the condition of regular network, the classical trade-off between exploration and exploitation will appear in the case of the preferentially attached network when agents adopt structure equivalence imitation. The whole organization implicitly would be divided into independent sub-groups that converge on different performance level and lead the organization to a lower performance level. The authors also explored the performance in the mixed organization and the management implication.

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