 This psychological research shows that rational human agents do learn from the past and plan for the future engaging in counterfactual thinking. Many researchers in artificial intelligence have voiced similar ideas (Ginsberg 1985; Pearl 1995; Costello &amp; Mccarthy 1999). But, this view is distinct from a stronger philosophical claim: that the nature of rational agency consists, in part, in the ability to perform counterfactual thinking. Some versions of causal decision theory make precisely this claim, and do so to capture similar patterns of rational behavior. Newcomb’s Problem (Nozick 1969) consists of a decision problem which challenges the standard way of articulating the idea that rational agents maximize expected utility, and, according to some philosophers (Stalnaker 1972 [1980]; Gibbard &amp; Harper 1978), shows that causal or counterfactual reasoning must be included in rational decision procedures—see the entry  causal decision theory  for further details. In a similar vein, work on belief revision theory explores how a rational agent should revise their beliefs when they are inconsistent with something they have just learned—much like a counterfactual antecedent demands—and uses structures that formally parallel those used in the semantics of counterfactuals (Harper 1975; Gärdenfors 1978, 1982; Levi 1988). See  formal representations of belief  for further discussion of this literature.