This research is motivated by problems in urban transportation and labor mobility, where the agent flow is dynamic, non-deterministic and on a large scale. In such domains, even though the individual agents do not have an identity of their own and do not explicitly impact other agents, they have implicit interactions with other agents. While there has been much research in handling such implicit effects, it has primarily assumed controlled movements of agents in static environments. We address the issue of decision support for individual agents having involuntary movements in dynamic environments . For instance, in a taxi fleet serving a city: (i) Movements of a taxi are uncontrolled when it is hired by a customer. (ii) Depending on movements of other taxis in the fleet, the environment and hence the movement model for the current taxi changes. Towards addressing this problem, we make three key contributions: (a) A framework to represent the decision problem for individuals in a dynamic population, where there is uncertainty in movements; (b) A novel heuristic technique called Iterative Sampled OPtimization (ISOP) and greedy heuristics to solve large scale problems in domains of interest; and (c) Analyze the solutions provided by our techniques on problems inspired from a real world data set of a taxi fleet operator in Singapore. As shown in the experimental results, our techniques are able to provide strategies that outperform "driver" strategies with respect to: (i) overall availability of taxis; and (ii) the revenue obtained by the taxi drivers.