  The idea is advanced that self-organization in complex systems can be treated
as decision making (as it is performed by humans) and, vice versa, decision
making is nothing but a kind of self-organization in the decision maker nervous
systems. A mathematical formulation is suggested based on the definition of
probabilities of system states, whose particular cases characterize the
probabilities of structures, patterns, scenarios, or prospects. In this general
framework, it is shown that the mathematical structures of self-organization
and of decision making are identical. This makes it clear how self-organization
can be seen as an endogenous decision making process and, reciprocally,
decision making occurs via an endogenous self-organization. The approach is
illustrated by phase transitions in large statistical systems, crossovers in
small statistical systems, evolutions and revolutions in social and biological
systems, structural self-organization in dynamical systems, and by the
probabilistic formulation of classical and behavioral decision theories. In all
these cases, self-organization is described as the process of evaluating the
probabilities of macroscopic states or prospects in the search for a state with
the largest probability. The general way of deriving the probability measure
for classical systems is the principle of minimal information, that is, the
conditional entropy maximization under given constraints. Behavioral biases of
decision makers can be characterized in the same way as analogous to quantum
fluctuations in natural systems.
