We study a longitudinal fit model of adaptation and its association with the longitudinal risk-return relationship. The model allows the firm to adjust its position in response to partial learning about a changing environment characterized by two path-dependent processes—a random walk and a stochastic trend. Computational simulations at low levels of learning in both environmental contexts are consistent with empirical data. However, the results are also consistent when firm behavior appears to be mindless in the form of a random walk. Hence, both imperfect learning and a mindless random walk can lead to the inverse longitudinal risk-return relationships observed empirically. We discuss this apparent paradox and the possible resolution between mindless and conscious behavior as plausible causes of the longitudinal Bowman Paradox.