Interface between Computer Science and Economics & Social Sciences

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The histories and intellectual approaches of social and economic science and computer science have been strongly influenced by the crosscurrents among them. Worst-case computational complexity analysis, so prevalent in computer science, is a form of game-theoretic analysis - perhaps not surprising considering that one of the founders of game theory, John von Neumann, was also a pioneering figure in computer science. Game theory is widely used in social and economic science. Social and economic scientists use concepts that are linked to computer science. For example, decision scientists and economists consider the bounded rationality of individuals making economic decisions; one aspect of bounded rationality is that economic agents may be limited by their "computational" resources, for example in evaluating complicated strategic situations. The ubiquity of socio-technical networks has led to new, more intimate ties between these two fields. New kinds of interactions and transactions have been enabled by such networks. Key features of these new transactions include:· parties who do not know or trust each other· parties represented by software agents · real-time adaptation, decision making, and chain reactions by agentsDesigning decision mechanisms that can govern these increasingly important types of transactions in ways that meet criteria such as fairness, revenue maximization, or efficent resource use is a challenge that requires the expertise of both social and economic scientists and computer scientists. Internet traffic (as also physical traffic on our road networks), email, the use of network bandwidth, the allocation of computing resources to competing processes, etc., may be managed using economic and social choice mechanisms to achieve better utilization and reduction of the nuisance and harm caused by intruders and spammers. Good incentive mechanisms are also needed to mediate the interactions among infrastructure providers, service providers, and clients for computing and communication infrastructure. Mechanisms are also important in driving multi-agent software systems towards socially desirable goals. These questions may require a new understanding of simultaneous collaboration and competition among economic agents.Computational thinking has the potential to change the types of questions considered by social and economic scientists. For example, Nash (and other) equilibria lie at the heart of theories about the behavior of economic agents. Computational thinking can help characterize the range and robustness of possible equilibria and markets for which the computation of equilibria is intractable. Theories of strategic learning by computational agents, studied both in economics and computer science, can shed light on the dynamics of how agents arrive at equilibria. Theories of the spread of contagion or gossip in networks can help explain and contain the chain reactions that can arise. Social/behavioral/economic and computer scientists can jointly study the dynamic functioning and evolution of social and economic networks with mutual benefit to both fields of study. Some important examples of such systems are recommender systems, voting systems, and reputation management systems.This program seeks innovative research at this interdisciplinary boundary, including both projects that use computational thinking for economic and social decision problems and/or ideas from economics and other social sciences for computing and communication systems and multi-agents systems. Computational economics research involving simulation and modeling of economic systems is not appropriate for this program. Illustrative examples of the kinds of research this program seeks to support can be found at: http://www.nsf.gov/cise/ccf/ices_pgm.jsp

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