Updateless Decision Theory (UDT) is a new decision theory meant to deal with a fundamental problem in the existing decision theories: the need to treat the agent as a part of the world in which it makes its decisions. In contrast, in the most common decision theory today, Causal Decision Theory (CDT), the deciding agent is not part of the world model--its decision is the output of the CDT, but the agent's decision in the world context is "magic": in the moment of deciding, no causal links feed into its chosen action. It acts as though its decision was causeless, as in some dualist free-will theories.
Getting this issue right is critical in building a self-improving artificial general intelligence, as such an AI must analyze its own behavior and that of a next generation that it may build.
UDT specifies that the optimal agent is the one with the best algorithm--the best mapping from observations to actions--across a probability distribution of all world-histories. ("Best" here, as in other decision theories, means one that maximizes a utility/reward function.)...