Next Article in Journal
Political Correctness between Wise Stoicism and Violent Hypocrisy
Next Article in Special Issue
A New Kind of Aesthetics —The Mathematical Structure of the Aesthetic
Previous Article in Journal
The Interaction and Convergence of the Philosophy and Science of Information
Article Menu

Export Article

Open AccessArticle
Philosophies 2016, 1(3), 245-260; doi:10.3390/philosophies1030245

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

1
WPI Center for Materials Nanoarchitectonics, National Institute for Materials Science, 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan
2
Network System Research Institute, National Institute of Information and Communications Technology, 4-2-1 Nukui-Kita, Koganei, Tokyo 184-8795, Japan
3
Earth-Life Science Institute, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro, Tokyo 152-8550, Japan
4
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
View Full-Text   |   Download PDF [2282 KB, uploaded 7 December 2016]   |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Philosophies EISSN 2409-9287 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top