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Philosophies 2016, 1(3), 245-260;

Harnessing the Computational Power of Fluids for Optimization of Collective Decision Making

WPI Center for Materials Nanoarchitectonics, National Institute for Materials Science, 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan
Network System Research Institute, National Institute of Information and Communications Technology, 4-2-1 Nukui-Kita, Koganei, Tokyo 184-8795, Japan
Earth-Life Science Institute, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro, Tokyo 152-8550, Japan
PRESTO, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan
Author to whom correspondence should be addressed.
Academic Editor: Marcin J. Schroeder
Received: 21 July 2016 / Revised: 24 November 2016 / Accepted: 25 November 2016 / Published: 7 December 2016
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How can we harness nature’s power for computations? Our society comprises a collection of individuals, each of whom handles decision-making tasks that are abstracted as computational problems of finding the most profitable option from a set of options that stochastically provide rewards. Society is expected to maximize the total rewards, while the individuals compete for common rewards. Such collective decision making is formulated as the “competitive multi-armed bandit problem (CBP).” Herein, we demonstrate an analog computing device that uses numerous fluids in coupled cylinders to efficiently solve CBP for the maximization of social rewards, without paying the conventionally-required huge computational cost. The fluids estimate the reward probabilities of the options for the exploitation of past knowledge, and generate random fluctuations for the exploration of new knowledge for which the utilization of the fluid-derived fluctuations is more advantageous than applying artificial fluctuations. The fluid-derived fluctuations, which require exponentially-many combinatorial efforts when they are emulated using conventional digital computers, would exhibit their maximal computational power when tackling classes of problems that are more complex than CBP. Extending the current configuration of the device would trigger further studies related to harnessing the huge computational power of natural phenomena to solve a wide variety of complex societal problems. View Full-Text
Keywords: natural computing; decision making; multi-armed bandit problem; reinforcement learning natural computing; decision making; multi-armed bandit problem; reinforcement learning

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Kim, S.-J.; Naruse, M.; Aono, M. Harnessing the Computational Power of Fluids for Optimization of Collective Decision Making. Philosophies 2016, 1, 245-260.

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