It would be good to have a Bayesian decision theory that assesses our decisions and thinking according to everyday standards of rationality—standards that do not require logical omniscience (Garber, 1983; Hacking, 1967). To that end we develop a “fragmented” decision theory in which a single state of mind is represented by a family of credence functions, each associated with a distinct choice condition (Lewis, 1982; Stalnaker, 1984). The theory imposes a local coherence assumption guaranteeing that as an agent's attention shifts, successive batches of “obvious” logical information become available to her. A rule of expected utility maximization can then be applied to the decision of what to attend to next during a train of thought. On the resulting theory, rationality requires ordinary agents to be logically competent and to often engage in trains of thought that increase the unification of their states of mind. But rationality does not require ordinary agents to be logically omniscient.
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