Yiling Chen (firstname.lastname@example.org) is Gordon McKay Professor of Computer Science at Harvard University, Cambridge, MA. Arpita Ghosh (email@example.com) is an associate professor of information science at Cornell University, Ithaca, NY. Michael Kearns (firstname.lastname@example.org) is a professor and National Center Chair of Computer and Information Science at the University of Pennsylvania, Philadelphia, PA. Tim Roughgarden (email@example.com) is an associate professor of CS at Stanford University, Stanford, CA. Jennifer Wortman Vaughan (firstname.lastname@example.org) is a senior researcher at Microsoft Research, New York, NY.
We pay particular attention to the design process, highlighting the objectives and properties that are important in the design of good prediction mechanisms. Whereas game theorists ask what outcome results from a game, mechanism designers ask what game produces a desired outcome. In this sense, game theorists act like scientists and mechanism designers like engineers. In this article, we survey a number of mechanisms created to elicit predictions, many newly proposed within the last decade. We focus on the engineering questions: How do they work and why?
Mao, Andrew (Harvard University) | Chen, Yiling (Harvard University) | Gajos, Krzysztof Z. (Harvard University) | Parkes, David C. (Harvard University) | Procaccia, Ariel D (Carnegie Mellon University) | Zhang, Haoqi (Harvard University)
With the proliferation of online labor markets and other social computing platforms, online experiments have become a low-cost and scalable way to empirically test hypotheses and mechanisms in both human computation and social science. Yet, despite the potential in designing more powerful and expressive online experiments using multiple subjects, researchers still face many technical and logistical difficulties. We see synchronous and longitudinal experiments involving real-time interaction between participants as a dual-use paradigm for both human computation and social science, and present TurkServer, a platform that facilitates these types of experiments on Amazon Mechanical Turk. Our work has the potential to make more fruitful online experiments accessible to researchers in many different fields.
Computational studies of voting are mostly motivated by two intended applications: the coordination of societies of artificial agents, and the study of human collective decisions whose complexity requires the use of computational techniques. Both research directions are too often confined to theoretical studies, with unrealistic assumptions constraining their significance for real-world situations. Most practical applications of these results are therefore confined to low-stakes decisions, which are of great importance in expanding the use of algorithms in society, but are far from high-stakes choices such as political elections, referenda, or parliamentary decisions, which societies still make using old-fashioned technologies like paper ballots. In this paper I argue in favour of conceiving "voting avatars", artificial agents that are able to act as proxies for voters in collective decisions at any level of society. Besides being an ideal test-bed for a large number of techniques developed in the field of multiagent systems and artificial intelligence in general, agent-mediated social choice may also suggests innovative solutions to the low voter participation that is endemic in most practical implementations of electronic decision processes.