Game Theoretic Analysis for Offense-Defense Challenges of Algorithm Contests on TopCoder

Software crowd sourcing platforms such as TopCoder have successfully adopted the offense-defense based quality assurance mechanism into software development process to deliver high-quality software solutions. TopCoder algorithm contests run single-round matches (SRM) with the challenge phase that allows participants to find bugs in the submitted program and eliminate their opponents. In the paper, we introduce a game theoretic model to study the competitive behaviors in the challenge phase of SRM. By analyzing the Nash Equilibrium of our multiple-person game model, we find that the probability of making a successful challenge and effort cost are the major factors for contestants' decisions. To verify the theoretic result, we make empirical data analysis with the dataset collected from the algorithm challenge phase on TopCoder. And the results indicate that contestants with a high rating are more likely to launch challenges against lower ones. However, contestants with the highest rating may be unwilling to challenge to avoid risks of losing their points in the contests.