Efficient decision-making by volume-conserving physical object

Song Ju Kim, Masashi Aono, Etsushi Nameda

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

Decision-making is one of the most important intellectual abilities of not only humans but also other biological organisms, helping their survival. This ability, however, may not be limited to biological systems and may be exhibited by physical systems. Here we demonstrate that any physical object, as long as its volume is conserved when coupled with suitable operations, provides a sophisticated decision-making capability. We consider the multi-armed bandit problem (MBP), the problem of finding, as accurately and quickly as possible, the most profitable option from a set of options that gives stochastic rewards. Efficient MBP solvers are useful for many practical applications, because MBP abstracts a variety of decision-making problems in real-world situations in which an efficient trial-and-error is required. These decisions are made as dictated by a physical object, which is moved in a manner similar to the fluctuations of a rigid body in a tug-of-war (TOW) game. This method, called 'TOW dynamics', exhibits higher efficiency than conventional reinforcement learning algorithms. We show analytical calculations that validate statistical reasons for TOW dynamics to produce the high performance despite its simplicity. These results imply that various physical systems in which some conservation law holds can be used to implement an efficient 'decision-making object'. The proposed scheme will provide a new perspective to open up a physics-based analog computing paradigm and to understanding the biological information-processing principles that exploit their underlying physics.

Original languageEnglish
Article number083023
JournalNew Journal of Physics
Volume17
Issue number8
DOIs
Publication statusPublished - 2015 Aug 11
Externally publishedYes

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decision making
war games
physics
rigid structures
reinforcement
conservation laws
organisms
learning
analogs

Keywords

  • decision-making
  • natural computing
  • random walk

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Cite this

Efficient decision-making by volume-conserving physical object. / Kim, Song Ju; Aono, Masashi; Nameda, Etsushi.

In: New Journal of Physics, Vol. 17, No. 8, 083023, 11.08.2015.

Research output: Contribution to journalArticle

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