The exploration/exploitation trade-off (EE trade-off) describes how, when faced with several competing alternatives, decision-makers must often choose between a known good alternative (exploitation) and one or more unknown but potentially more rewarding alternatives (exploration). Prevailing theory on how humans perform the EE trade-off states that uncertainty is a major motivator for exploration: the more uncertain the environment, the more exploration that will occur. The current article examines whether exploratory behavior in both choice and attention may be impacted differently depending on whether uncertainty is onset suddenly (unexpected uncertainty), or more slowly (expected uncertainty). It is shown that when uncertainty was expected, participants tended to explore less with their choices, but not their attention, than when it was unexpected. Crucially, the impact of this "protection from uncertainty" on exploration only occurred when participants had an opportunity to learn the structure of the task before experiencing uncertainty. This suggests that the interaction between uncertainty and exploration is more nuanced than simply more uncertainty leading to more exploration, and that attention and choice behavior may index separate aspects of the EE trade-off. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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Proc Biol Sci
January 2025
Faculty of Life Sciences, School of Zoology, Tel Aviv University, Tel Aviv 6997801, Israel.
Understanding how wildlife responds to the spread of human-dominated habitats is a major challenge in ecology. It is still poorly understood how urban areas affect wildlife space-use patterns and consistent intra-specific behavioural differences (i.e.
View Article and Find Full Text PDFSci Rep
December 2024
Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, 550025, Guizhou, China.
In response to the challenges faced by the Coati Optimization Algorithm (COA), including imbalance between exploration and exploitation, slow convergence speed, susceptibility to local optima, and low convergence accuracy, this paper introduces an enhanced variant termed the Adaptive Coati Optimization Algorithm (ACOA). ACOA achieves a balanced exploration-exploitation trade-off through refined exploration strategies and developmental methodologies. It integrates chaos mapping to enhance randomness and global search capabilities and incorporates a dynamic antagonistic learning approach employing random protons to mitigate premature convergence, thereby enhancing algorithmic robustness.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
December 2024
Department of Biology, University of Oxford, Oxford OX1 3SZ, United Kingdom.
Chaos
December 2024
School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, USA.
A foundational machine-learning architecture is reinforcement learning, where an outstanding problem is achieving an optimal balance between exploration and exploitation. Specifically, exploration enables the agents to discover optimal policies in unknown domains of the environment for gaining potentially large future rewards, while exploitation relies on the already acquired knowledge to maximize the immediate rewards. We articulate an approach to this problem, treating the dynamical process of reinforcement learning as a Markov decision process that can be modeled as a nondeterministic finite automaton and defining a subset of states in the automaton to represent the preference for exploring unknown domains of the environment.
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September 2024
Faculty of Data and Decision Sciences, Technion-Israel Institute of Technology.
Compared to individuals who are rated as less creative, higher creative individuals tend to produce ideas more quickly and with more novelty-what we call faster-and-further phenomenology. This has traditionally been explained either as supporting an associative theory-based on differences in the structure of cognitive representations-or as supporting an executive theory-based on the principle that higher creative individuals utilize cognitive control to navigate their cognitive representations differently. Though extensive research demonstrates evidence of differences in semantic structure, structural explanations are limited in their ability to formally explain faster-and-further phenomenology.
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