Computation can predict group conflict

When conflict breaks out in social groups, individuals make strategic decisions about how to behave based on their understanding of alliances and feuds in the group. Researchers studied fighting among captive pigtailed macaques for clues about behavior and group conflict. (Photo: A. J. Haverman) But it's been challenging to quantify the underlying trends that dictate how individuals make predictions, given they may only have seen a small number of fights or have limited memory. In a new study, scientists at the Wisconsin Institute for Discovery (WID) at UW-Madison develop a computational approach to determine whether individuals behave predictably. With data from previous fights, the team looked at how much memory individuals in the group would need to make predictions themselves. The analysis proposes a novel estimate of "cognitive burden," or the minimal amount of information an organism needs to remember to make a prediction. The research draws from a concept called "sparse coding," or the brain's tendency to use fewer visual details and a small number of neurons to stow an image or scene. Previous studies support the idea that neurons in the brain react to a few large details such as the lines, edges and orientations within images rather than many smaller details. "So what you get is a model where you have to remember fewer things but you still get very high predictive power — that's what we're interested in," says Bryan Daniels, a WID researcher who led the study. "What is the trade-off?