The Keynote “Socionomics and the Science of Surprise” presents the implications of recent work on wave-like patterns in social phenomena for characterizing and predicting the flow of human events and actions, such as the outcome of political elections, trends in films and fashion, the outbreak of war, and the rise and fall of civilizations. The talk will show that all such collective human social events are generated by the changing social mood in a population, and that the changes in this mood follow patterns that are predictable. This fact has led to the emerging field of socionomics, which is nothing less than a “science of surprise”.
The story of the development of socionomics and its mode of forecasting social trends will be presented through numerous examples and stories, as well as illustrations of how socionomics provides a systematic, coherent tool for predicting changing trends in the overall social mood. The talk shows that these trends exist on all time-scales—minutes to decades—and can be measured by the gyrations of financial market indexes, such as the Dow Jones Industrial Average. Thus, the talk illustrates how to actually predict “surprises”, the turning points in social trends. So in a very real sense socionomics provides what amounts to a telescope for seeing the shape of the future.
The “take-home” message from this talk is twofold: The future is predictable in exactly the same probabilistic way that the weather is predictable, and that thoughts cause action and events—not vice-versa! Individual human thoughts are gathered together through the herding instinct hard-wired into every mammalian brain. These individual moods are like “bets” people place about the future. The bets then self-organize into an overall collective social mood, which after an appropriate period of time depending on the nature of the event, gives rise to things like wars, election results and styles in popular culture. This line of argument is exactly the opposite of that usually put forth by academic thinkers, Op-Ed writers, intellectual commentators, and other assorted pundits in their attempts to “explain” the flow of human events. The conventional explanations mostly go from “actions to moods to thoughts” instead proceeding in the “socionomic direction”, which puts things exactly the other way around.
Dr. John Casti received his Ph.D. in mathematics under Richard Bellman at the University of Southern California. He worked at the RAND Corporation in Santa Monica, CA, and served on the faculties of the University of Arizona, NYU and Princeton before becoming one of the first members of the research staff at the International Institute for Applied Systems Analysis (IIASA) in Vienna, Austria. In 1986, he took up a position as a Professor of Operations Research and System Theory at the Technical University of Vienna. Dr. Casti also served as a member of the External Faculty of the Santa Fe Institute in Santa Fe, New Mexico, USA.
He has published eight technical monographs in the area of system theory and mathematical modeling, as well as 11 volumes of popular science, including the book Paradigms Lost, Complexification, Would-Be Worlds, and The Cambridge Quintet. His latest book, Mood Matters: From Rising Skirt Lengths to the Collapse of World Powers, will be published in early 2010.
In 2000 Dr. Casti formed two companies in Santa Fe and London, Qforma, Inc. and SimWorld, Ltd, devoted to the employment of tools and concepts from modern system theory for the solution of problems in business and finance. In early 2005 he returned to Vienna to co-found The Kenos Circle, a professional society that aims to make use of complexity science in order to gain a deeper insight into the future than that offered by more conventional statistical tool. He is also a Senior Research Scholar at the Interational Institute for Applied Systems Analysis (IIASA) in Vienna, working on questions of extreme events in human society, social mood and its impact on collective behavior, the vulnerability of critical infrastructures, and computer modeling of these types of processes.