Timeless Decision Theory

Disputes between evidential decision theory and causal decision theory have continued for decades, and many theorists state dissatisfaction with both alternatives. Timeless decision theory (TDT) is an extension of causal decision networks that compactly represents uncertainty about correlated computational processes and represents the decisionmaker as such a process. This simple extension enables TDT to return the one-box answer for Newcomb’s Problem, the causal answer in Solomon’s Problem, and mutual cooperation in the one-shot Prisoner’s Dilemma, for reasons similar to human intuition. Furthermore, an evidential or causal decision-maker will choose to imitate a timeless decision-maker on a large class of problems if given the option to do so. Yudkowsky, Eliezer. 2010. Timeless Decision Theory. The Singularity Institute, San Francisco, CA. The Machine Intelligence Research Institute was previously known as the Singularity Institute.