Entropy, Computing and Rationality
  Making decisions freely presupposes that there is some indeterminacy in the
environment and in the decision making engine. The former is reflected on the
behavioral changes due to communicating: few changes indicate rigid
environments; productive changes manifest a moderate indeterminacy, but a large
communicating effort with few productive changes characterize a chaotic
environment. Hence, communicating, effective decision making and productive
behavioral changes are related. The entropy measures the indeterminacy of the
environment, and there is an entropy range in which communicating supports
effective decision making. This conjecture is referred to here as the The
Potential Productivity of Decisions.
  The computing engine that is causal to decision making should also have some
indeterminacy. However, computations performed by standard Turing Machines are
predetermined. To overcome this limitation an entropic mode of computing that
is called here Relational-Indeterminate is presented. Its implementation in a
table format has been used to model an associative memory. The present theory
and experiment suggest the Entropy Trade-off: There is an entropy range in
which computing is effective but if the entropy is too low computations are too
rigid and if it is too high computations are unfeasible. The entropy trade-off
of computing engines corresponds to the potential productivity of decisions of
the environment.
  The theory is referred to an Interaction-Oriented Cognitive Architecture.
Memory, perception, action and thought involve a level of indeterminacy and
decision making may be free in such degree. The overall theory supports an
ecological view of rationality. The entropy of the brain has been measured in
neuroscience studies and the present theory supports that the brain is an
entropic machine. The paper is concluded with a number of predictions that may
be tested empirically.
