Decision-Making from Lower Forms of Life

Researchers Show that an Iron Bar is Capable of Decision-Making

Lisa Zyga | August 24, 2015

Decision-making—the ability to choose one path out of several options—is generally considered a cognitive ability possessed by biological systems, but not by physical objects. Now in a new study, researchers have shown that any rigid physical (i.e., non-living) object, such as an iron bar, is capable of decision-making by gaining information from its surroundings accompanied by physical fluctuations.

Source: https://www.youtube.com/watch?v=G0Tcd3pWjPk

..."The most important implication that we wish to claim is that the proposed scheme will provide a new perspective for understanding the information-processing principles of certain lower forms of life," Kim, from the International Center for Materials Nanoarchitectonics' National Institute for Materials Science in Tsukuba, Ibaraki, Japan, told Phys.org. "These lower lifeforms exploit their underlying physics without needing any sophisticated neural systems."

<more at http://phys.org/news/2015-08-iron-bar-capable-decision-making.html; related links: https://www.youtube.com/watch?v=G0Tcd3pWjPk (Researchers show that an iron bar is capable of decision making. Published on August 24, 2015) and http://iopscience.iop.org/article/10.1088/1367-2630/17/8/083023 (Efficient decision-making by volume-conserving physical object. Song-Ju Kim, Masashi Aono and Etsushi Nameda. New Journal of Physics, Volume 17, August 2015. [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.]>