Collective behavior observed in nature has been actively employed in swarm robotics. In order to better respond to external cues, the agents in such systems organize themselves in an ordered structure based on simple local rules. The central assumption, in swarm robotics, is that all agents in the system collaborate to fulfill a common goal. In nature, however, many multi-agent systems exhibit a more complex collective behavior involving a certain level of competition. One representative example of complex collective behavior is a multi-ball Bernoulli-ball system. In this paper, by extracting local force among the Bernoulli balls, we approximated the state-transfer model mapping interaction forces to observed behaviors. The results show that the collective Bernoulli-ball system spent 41% of its time on competitive behaviors, in which up to 84% of the interaction state is unorganized. The rest 59% of the time is spent on collaborative behavior. We believe that the novel proposed model opens new avenues in swarm robotics research.