  Functional decision theory (FDT) is a fairly new mode of decision theory and
a normative viewpoint on how an agent should maximize expected utility. The
current standard in decision theory and computer science is causal decision
theory (CDT), largely seen as superior to the main alternative evidential
decision theory (EDT). These theories prescribe three distinct methods for
maximizing utility. We explore how FDT differs from CDT and EDT, and what
implications it has on the behavior of FDT agents and humans. It has been shown
in previous research how FDT can outperform CDT and EDT. We additionally show
FDT performing well on more classical game theory problems and argue for its
extension to human problems to show that its potential for superiority is
robust. We also make FDT more concrete by displaying it in an evolutionary
environment, competing directly against other theories.
