  We live in a world brimming with uncertainty, where we constantly have to
make a lot of decisions under incomplete information. We are firm believers
that our subjective belief cannot be computed by rigorous mathematical formula;
instead based on Darwin's natural selection (the evolution process is simulated
by machine learning with genetic programming), a proposed computational model
that incorporates insights from quantum theory to describe and explain
decision-making under uncertainty. Unlike other decision-making theories that
explain the decision-making process through probability theory, our proposed
decision theory discovers "laws" of thought by learning observed historical
data. There is no differential equation and no transition probability in our
decision theory, our decision model has an emphasis on machine learning, where
decision-makers build-up their experience by being rewarded or punished for
each decision they make and prepare them for making better decisions in the
future. We do not model with the usual utility function, but with quantum
decision tree that simulates people's decision process. Each quantum decision
tree includes a set of strategies; every time a decision is made, the
decision-maker first chooses a strategy from the quantum decision tree's
strategy pool, and then chooses an action based on the degree of belief which
is obtained by genetic programming based on maximizing expected value.
