1. Behavior, Computation and Networks in Human Subject Experimentation A NetSE Workshop held at UCSD July 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.edumkearns@cis.upenn.edu –See workshop report to NetSE