Foraging in Virtual Worlds

April 13, 2006

The Percepts and Concepts Laboratory (Directed by Chancellor’s Professor Robert Goldstone, also Director of the Indiana University Cognitive Science Program) applies formal computational and mathematical tools used to study complex systems in biology and physics to understanding human collective behavior. People participate in group-level patterns that they may not understand, or even perceive. Our goals are to conduct experiments that reveal the patterns that groups of people spontaneously create, and to develop computational models that show how these patterns emerge from simple interactions among people.

One common situation that we have formally explored is how groups of people distribute themselves to valuable resources. Morel hunters forage their environment for mushrooms, drivers patrol downtown for convenient parking spaces, web-users surf the internet for desired data, and businesses mine the land for valuable minerals. When an organism forages in an environment that consists, in part, of other organisms that are also foraging, then interesting complexities arise. The resources available to an organism are affected not just by the foraging behavior of the organism itself, but also by the simultaneous foraging behavior of all of the other organisms.

In a series of experiments, we have developed a novel experimental technique for studying human foraging behavior (Goldstone & Ashpole, 2004; Goldstone, Ashpole, & Roberts, 2005). We have created an experimental platform that allows many human participants to interact in real-time within a common virtual environment. Resource pools are created within this environment, participants vie for these resources, and we record the moment-by-moment exploitation of these resources by each participant. The participants’ task is to obtain as many resource tokens as possible during an experiment.

Groups of animals generally distribute themselves well to resource patches. For example, mallard ducks, cichlid fish, and dung beetles all approximately match their numbers to the amount of resource. If twice as much bread is thrown in one pond location than another, then about twice as many ducks will spontaneously go to the more plentiful location. Our groups of humans, recruited from psychology courses, are fairly efficient and about as smart, collectively speaking, as ducks, fish, and dung beetles. However, we also find two important collective inefficiencies in their harvesting.

First, we find that people do not distribute themselves in an extreme enough manner. For example, if one pool produces 80% of the tokens and the other pool produces 20%, people distribute themselves in about a 73%/27% fashion. People who harvest the richer resource patch tend to earn more tokens than those harvesting the poorer patch. If this proves general, our advice is for people to try harvesting the richer patch: fish in pond locations known to be plentiful, study for professions that are hot, and visit bars with attractive people. Even though rich patches will attract more competitors foraging for the same resources, the number of people will not keep up with the patch’s advantage if our experiments generalize.

Second, we find cycles in the harvesting rates over time. In our experiment, these cycles come in 50 second waves of migration into and out of patches. Due to random fluctuations, more people will end up at one patch than another. The people in this over-crowded patch will tend to become dissatisfied with their token earnings, and will decide to leave the patch for hopefully greener pastures elsewhere. However, if they cannot see other people’s movements, they do not realize that what has made them decide to leave is influencing others as well. The result is roughly synchronized waves of migration. Ironically, it is precisely because people share the desire to avoid crowds that migratory crowds emerge! When people can see where other people are in the virtual world, then these waves of crowding do not arise.

We have developed a computational model of foraging behavior that reproduces the results from our experiments (Roberts & Goldstone, 2005). In this model, we create simple rules for each of the agents in a population, and observe the collective patterns that emerge. The assumptions that are critical for getting human-like results are: 1) people are lazy (agents tend to go for tokens that are close), 2) people have inertia (agents tend to keep moving toward a selected token once they have started), 3) people go where the gold is (as the number of tokens in a patch increases, agents will congregate there), 4) people avoid crowds (when agents can see the other agents and all of the tokens in the virtual world, they tend to avoid crowds), and 5) people act like buzzards (when agents can see each other but not the tokens, then they use the presence of other agents to indicate that tokens might be nearby).

Web-citizens can experience these experiments for themselves by visiting¬† This site offers several ongoing experiments that run continuously 24 hours per day. Participants are automatically grouped together into experiments and play in 4-minute rounds. If there aren’t enough human participants at any given time, then we generate artificially intelligent ‘bots’ to keep the humans company in the virtual worlds. As it turns out, these bots are exactly our computational models of how people forage for resources. Like the ‘human be-in’ events of the 1960s and modern flash mobs, people participating in these experiments can experience what it feels like to be part of a collective mind that adapts to its environment.

Goldstone, R. L., Ashpole, B. C., & Roberts, M. E., (2005). Knowledge of resources and competitors in human foraging. Psychonomic Bulletin & Review, 12, 81-87.

Goldstone, R. L., & Ashpole, B. C. (2004). Human foraging behavior in a virtual environment. Psychonomic Bulletin & Review, 11, 508-514.

Roberts, M. E., & Goldstone, R. L. (2005). Explaining resource undermatching with agent-based models. Proceedings of the Twenty-seventh Annual Conference of the Cognitive Science Society. Hillsdale, New Jersey: Lawrence Erlbaum Associates.