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Computational Examination of Strategies for Play in IDS Games

IDS games are multi-agent games where each agent must decide whether or not to invest in risk-mitigating measures. Each agent knows that even if she fully protects herself by investing in such measures, she may still incur losses due to one of the other agents who chose not to invest. Many significant social problems fit into an IDS framework, including investing in airline or port security, taking vaccinations against disease, installing computer security software to protect from viruses, and divisions of firms undertaking risky investments that could cause the entire firm to become insolvent. A key social policy question that arises in all of these examples is how to induce enough agents invest in risk-mitigating measures so that Pareto optimal levels of investment are achieved. In this document we report the results and analysis of computational simulations involving repeated IDS games with two players. We assume that strategies in these games can be represented as algorithms, and we employ a series of computational tournaments to investigate the performance of a diverse set of strategies under varying conditions. Our aims are to investigate what kinds of strategies do well in these games, and to understand what kinds of social policies promote increased investment in riskmitigation.