This paper analyses the effects of disease and war on the accumulation of human and physical capital. We employ an overlapping generation framework in which young adults, motivated by old-age provision and possibly altruism, make decisions about investments in schooling and capital. A poverty trap exists for a wide range of constant war losses and premature adult mortality. If parents are altruistic and the sub-utility function for own consumption is more concave than that for their evaluation of their children’s full income in adulthood, the only possible steady-state growth path involves full education. Otherwise, steady-state paths with incompletely educated children may exist. When mortality and destruction rates are stochastic, the initial boundary conditions and agents’ beliefs have a strong influence on the paths generated by a sequence of shocks. Calibrating the model to Kenya, simulations for stochastic settings yield the finding that a trap exists and is always avoided, but the chances of a slow recovery are substantial.

Planning a sequence of robot actions is especially difficult when the outcome of actions is uncertain, as is inevitable when interacting with the physical environment. In this paper we consider the case of finite state and action spaces where actions can be modeled as Markov transitions. Finding a plan that achieves a desired state with maximum probability is known to be an NP-Complete problem. We consider two algorithms: an exponential-time algorithm that maximizes probability, and a polynomial-time algorithm that maximizes a lower bound on the probability. As these algorithms trade off plan time for plan quality, we compare their performance on a mechanical system for orienting parts. Our results lead us to identify two properties of stochastic actions that can be used to choose between these planning algorithms for other applications.