Emile is one of the world’s top experts in prediction markets. As founder and CEO of NewsFutures (2000-2010), and then as a managing director of Lumenogic, he pioneered many business applications of prediction markets for dozens of leading companies on four continents.
His work with prediction markets has been featured in the best-selling book The Wisdom of Crowds as well as in leading print media such as The New York Times, the Economist, BusinessWeek, Time, Newsweek, The New Yorker, The Wall Street Journal and Nature.
Emile has lectured widely on the topic of prediction markets and collective intelligence across the globe, including at the World Bank, Wharton Business School, INSEAD, Carnegie Mellon’s Tepper School of Business, the Collège de France, Frankfurt University, the City University of Hong Kong, and the European University at St Petersburg. He is an associate editor of the Journal of Prediction Markets.
Prior to Lumenogic and NewsFutures, Emile co-authored two award-winning CD-Roms featuring the collective intelligence of dozens of world-famous scientists and Nobel prize winners: The Challenge of the Universe (1995) and Secrets of the Mind (1997). As a consultant for the Organization for Economic Co-operation and Development (OECD), he has contributed for 8 years to the research on a new brain-based science of learning. Emile began his career as an artificial intelligence (“AI”) engineer for Ilog, the leading provider of AI-based business solutions (later acquired by IBM).
Emile holds a BS in Applied Mathematics and a PhD in Cognitive Psychology, both from Carnegie Mellon University.
The Marketcast Method for Aggregating Prediction Market Forecasts Avec Pavel Atanasov, Phillip Rescober, Eric Stone, Barbara Mellers, Philip Tetlock, & Lyle Ungar Proceedings of The 2013 International Conference on Social Computing, Behavioral-Cultural Modeling, & Prediction We describe a hybrid forecasting method called marketcast. Marketcasts are based on bid and ask orders from prediction markets, aggregated using techniques associated with survey methods, rather than market matching algorithms. We discuss the process of conversion from market orders to probability estimates, and simple aggregation methods. The performance of marketcasts is compared to a traditional prediction market and a traditional opinion poll. Overall, marketcasts perform approximately as well as prediction markets and opinion poll methods on most questions, and performance is stable across model specifications.
Intelligence Is Collective Lumenogic Research Report, 2012 How does collective intelligence relate to the individual kind? A high?level review of the state of the art in cognitive science suggests that both brains and the best artificial intelligence programs can be described as ?wise crowds? (in the sense of Surowiecki, 2004). Conversely, the most celebrated instances of collective intelligence bear deep similarities to the human mind’s organization and mechanisms. A tentative conclusion is that intelligence is collective to its core and that Surowiecki’s recipe for crowd wisdom provides a framework to unify human, artificial and collective intelligence. Some implications for research, business and individual decision makers are explored.
Trading Uncertainty for Collective Wisdom Book Chapter in Collective Wisdom Landemore & Elster (Eds.), Cambridge University Press, 2012 Prediction markets have captured the public’s imagination with their ability to predict the future by pooling the guesswork of many. This paper summarizes the evidence and examines the economic, mathematical, and neurological foundations of this form of collective wisdom. Rather than the particular trading mechanism used, the ultimate driver of accuracy seems to be the betting proposition itself: on the one hand, a wager attracts contrarians, which enhances the diversity of opinions that can be aggregated. On the other hand, the mere prospect of reward or loss promotes more objective, less passionate thinking, thereby enhancing the quality of the opinions that can be aggregated.
Betting On a Better World International Studies Association, Annual Meeting, New York 2009 As The Economist recently wrote, “the most heeded futurists these days are not individuals, but prediction markets, where the informed guesswork of many is consolidated into hard probability.” This paper explores the principles driving this powerful new form of collective intelligence, and how may it be applied in the field of International Relations.
Prediction Markets: Does Money Matter? avec Justin Wolfers, David Pennock, et Brian Galebach Electronic Markets, 2004, 14:3 The accuracy of prediction markets has been documented for both markets based on real money and those based on play money. To test how much extra accuracy can be obtained by using real money versus play money, we set up a real-world on-line experiment pitting the predictions of TradeSports.com (real money) against those of NewsFutures.com (play money) regarding American Football outcomes during the fall-winter 2003-2004 NFL season. As expected, both types of markets exhibited significant predictive powers, and remarkable performance compared to individual humans. Perhaps more surprisingly, the play-money markets performed as well as the real-money markets. We speculate that this result reflects two opposing forces: real-money markets may better motivate information discovery while play-money markets may yield more efficient information aggregation.
Macro-markets and Environmental Futures The Hague Conference on Environment, Security and Sustainable Development, May 2004 Prediction markets offer a new forecasting method whereby people trade future outcomes as on a stock exchange. This results in dynamic consensus probability estimations of future events whose superior accuracy has been documented in numerous domains including box-office, product sales, political elections, sports, regulatory forecasting, etc. Applied to the forecasting of important environmental outcomes, prediction markets could help deepen public awareness of the problems and possible solutions, cut through the fog of ambiguous scientific discourse, evaluate alternative scenarios of doom or salvation, and identify the more trustworthy experts.
Secrets of the Mind
avec Bruno Lévy Hypermind-Ubisoft, 1997
Do you know how your memory works? Why do we have emotions? Why do you dream? How do you learn? How does a baby’s brain develop? Is there a limit to artificial intelligence? Unlock the secrets of your mind and discover some of the theories that attempt to answer these diverse questions. This in-depth educational software features a wealth of information from 11 pioneering psychologistst and neuroscientists, including Nobel Prize winners Herbert Simon and Eric Kandel, and features interactive psychological experiments to illustrate key concepts.
The Challenge of the Universe
with Bruno Lévy Hypermind, 1995
RESEARCH IN COGNITIVE PSYCHOLOGY
Chunking Processes and Context Effects in Letter Perception Proceedings of the 14th Annual Conference of the Cognitive Science Society, 1992 Chunking is formalized as the dual process of building percepts by recognizing in stimuli chunks stored in memory, and creating new chunks by welding together those found in the percepts. As such, it is a very attractive process with which to account for phenomena of perception and learning. Servan-Schreiber and Anderson (1990) demonstrated that chunking is at the root of the “implicit learning” phenomenon, and Servan-Schreiber (1990; 1991) extended that analysis to cover category learning as well. This paper aims to demonstrate the potential of chunking as a theory of perception by presenting a model of context effects in letter perception. Starting from a set of letter segments the model creates from experience chunks that encode partial letters, then letters, then partial words, and finally words. The model’s ability to recognize letters alone, or in words, pseudowords, or strings of unrelated letters is then tested using a backward masking task. The model reproduces the word and pseudoword superiority effects.
Classification of Dot Patterns with Competitive Chunking Proceedings of the 12th Annual Conference of the Cognitive Science Society, 1990 Chunking, a familiar idea in cognitive science, has recently been formalized by Servan-Schreiber and Anderson (1999) into a theory of perception and learning, and it successfully simulated the human acquisition of an artificial grammar through the simple memorization of exemplar sentences. In this article I briefly present the theory, called Competitive Chunking, or CC, as it has been extended to deal with the task of encoding random dot patterns. I explain how CC can be applied to the classic task of classifying such patterns into multiple categories, and report a successful simulation of data collected by Knapp and Anderson (1984). The tentative conclusion is that people seem to process dot patterns and artificial grammars in the ame way, and that chunking is an important part of the process.
Learning Artificial Grammars with Competitive Chunking avec John R. Anderson Journal of Experimental Psychology: Learning, Memory, & Cognition, 1990, 16:4 When exposed to a regular stimulus field, for instance, that generated by an artificial grammar,subjects unintentionally learn to respond efficiently to the underlying structure (Miller, 1958; Reber 1967). We explored the hypothesis that the learning process is chunking and that grammatical knowledge is implicitly encoded in a hierarchical network of chunks. We trained subjects on exemplar sentences while inducing them to form specific chunks. Their knowledge was then assessed through judgments of grammaticality. We found that subjects were less sensitive to violations that preserved their chunks than to violations that did not. We derived the theory of competitive chunking (CC) and found that it successfully reproduces, via computer simulations, both Miller’s experimental results and our own. In CC, chunks are hierarchical structures strengthened with use by a bottom-up perception process. Strength-mediated competitions determine which chunks are created and which are used by the perception process.
A Connectionist Approach to the Diagnosis of Dementia avec Benoit H. Mulsant Proceedings of the 12th Annual Symposium on Computer Applications in Medical Care, 1988 This paper describes an implemented connectionist network that performs clinical diagnosis in the domain of dementia. During the past decade, connectionism –also called parallel distributed processing or neural processing– has been established as a new cognitive and computational paradigm, with strong claims that it provides powerful mechanisms to bring solutions to problems previously intractable. To study the suitability of connectionist networks to perform a sequential diagnostic classification task under uncertainty, we have implemented a network that learns to diagnose cases of dementia. We describe in detail the implementation, training, and behavior of this network. We also discuss directions for future research suggested by the limitations of this network.