Our goal in this research is to collect a large volume of time-evolving data from a system composed of human agents vying for resources in a common environment, with the eventual aim of guiding the development of computational models of human resource allocation. We have developed an experimental platform that allows a large number of human participants to interact in real-time within a common virtual world. Two resource pools were created with different rates of replenishment. The participants’ task was to obtain as many resource tokens as possible during an experiment. In addition to varying the relative replenishment rate for the two resources (50-50, 65-35, 80-20), we manipulated whether agents could see each other and the entire food distribution, or had their vision restricted to food in their own location. As a collective, the agents would optimally harvest the resources if they distribute themselves proportionally to the distribution of resources. Empirical violations of global optimality were found. First, there was a systematic underutilization of the more preponderant resource. For example, agents distributed themselves approximately 75% and 25% to resources pools that had relative replenishment rates of 80% and 20%, respectively. The expected pay-off per agent was larger for pools with relatively high replenishment rates. Second, there were oscillations in the harvesting rates of the resources across time, particularly when agents’ vision was restricted. Perceived underutilization of a resource resulted in an influx of agents to that resource. This sudden influx, in turn, resulted in a glut of agents, which then led to a trend for agents to depart from the resource region. This cyclic activity in the collective data was revealed by a Fourier analysis showing prominent power in the range of about 50 seconds per cycle.