Harnessing Natural Fluctuations: Analogue Computer for Efficient
  Socially Maximal Decision Making
  Each individual handles many tasks of finding the most profitable option from
a set of options that stochastically provide rewards. Our society comprises a
collection of such individuals, and the society is expected to maximise the
total rewards, while the individuals compete for common rewards. Such
collective decision making is formulated as the `competitive multi-armed bandit
problem (CBP)', requiring a huge computational cost. Herein, we demonstrate a
prototype of an analog computer that efficiently solves CBPs by exploiting the
physical dynamics of numerous fluids in coupled cylinders. This device enables
the maximisation of the total rewards for the society without paying the
conventionally required computational cost; this is because 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. Our
results suggest that to optimise the social rewards, the utilisation of
fluid-derived natural fluctuations is more advantageous than applying
artificial external fluctuations. Our analog computing scheme is expected to
trigger further studies for harnessing the huge computational power of natural
phenomena for resolving a wide variety of complex problems in modern
information society.
