Michael Kearns Computer and Information Science, Penn & Colin Camerer Economics, Caltech

1. Behavior, Computation and Networks in Human Subject ExperimentationA NetSE Workshop held at UCSDJuly 31 – August 1, 2008 Michael Kearns Computer and Information Science, Penn & Colin Camerer Economics, Caltech

2. Observations on Recent Trends and Activities • Explosion of CS interest in economic/game-theoretic models • complexity of equilibrium computations • realization that modern technological networks are economic systems • selfish routing, free-riding in peering networks, ISP game theory • economics as a design principle for complex systems • algorithmic mechanism design • emerging electronic markets • sponsored search, Internet advertising, affiliate networks • Algorithmic Game Theory (AGT): “scaling up” classical game theory/economics • AGT edited volume Cambridge University Press 2007 • Significant CS-Econ interactions over several years • Hal Varian invited lecture ACM EC 2006 • Many CS plenary talks at last two GT World Congress; e.g. Tim Roughgarden Nash Lecture 2008 • Emerging interdisciplinary resources/programs • Joint postdocs at Caltech; 3 new CS faculty hires in algorithmic game theory at Northwestern • Observation: Practically all AGT work and CS/Econ interaction is theoretical in nature

3. Goals of the Workshop • Bring together researchers with behavioral and computational interests • Introduce a behavioral component to AGT • Examine how AGT can inform/shape behavioral experiments • Discuss opportunities and challenges for larger-scale human subject experiments • CS/AGT strength: system design, theories of scaling, network models • BGT strength: experimental design and methodology, network models

4. Sample Brainstorming Topics • How do computational considerations modify BGT? • How do behavioral considerations modify AGT? • How can we “scale up” behavioral experiments? • examination of (social) network effects • use of web/Internet technology • development of shared experimental infrastructure/platforms • If there were no technology/methodology limits, what experiments would we do? • How can we mix human subjects and artificial agents in interesting ways? • What are the appropriate (statistical) models for collective behavioral data?

5. Workshop Outputs: Research Challenges • Unifying Algorithmic and Behavioral Game Theory • Subfields both seek to modify classical GT towards “realism” • Algorithmic GT: realism = computational tractability • Behavioral GT: realism = conformance with and prediction of actual behavior • Ideally, two should be unified and inform each other • AGT must consider strategic computation related to behavior, not traditional complexity • BGT must develop computational models of behavior (e.g. Camerer’s “cognitive hierarchy”) • Network and Systems Infrastructure for Behavioral Experiments • Internet and Web have opened the possibility of “scaling up” behavioral experiments in sociology, game theory, economics, CS,… • Current status: many independent groups build “one-off” systems for specific experiments • Little/no sharing of code, infrastructure, human subjects, data, methodologies, etc. • Should we have a shared Web/Internet-based platform/infrastructure for large-scale behavioral work? • How general/specific should it be? What about control/management of subjects? • For more detail/info or to give suggestions/ideas: • Email mkearns@cis.upenn.edu • See workshop report to NetSE