According to some commentators and critics, all literary plot lines are variations on the single theme of someone wanting something and someone else trying to prevent them from getting it. Similarly, one might see everyday life as a continual sequence of transactions, in which two (or more) parties enter into a negotiation to exchange something for something else, the negotiation itself being a form of conflict resolution. In 1944 John von Neumann and Oskar Morgenstern published their magiste...

How a population/group feels about its future, its so-called. social mood., and how that mood biases collective events of all types is the focus of this article. Through a variety of examples running from trends in clothing styles to the collapse of world powers, the paper argues that the social mood dramatically influences the types of events we can expect to see on all time scales. The argument is advanced that the financial market price movements serve as a good "sociometer" for measuring the social mood on all time scales. Additionally, we present arguments showing that there is virtually no feedback from events to mood; that is, the mood is endogeneous to the population and is not determined by any sort of "outside forces". Finally, the paper concludes with a research program for turning the hypotheses advanced here into a full-fledged scientific theory of collective human behavior.

While aimlessly wandering through the Internet not long ago, I came upon a link to my former employer, The RAND Corporation (named for “Research and Development”), which is the prototype for “think tanks” everywhere.

In 1956 the American Petroleum Institute held their annual meeting in San Antonio, Texas. On the program was a talk titled “Nuclear Energy and the Fossils Fuels” to be presented by M. King Hubbert, a geophysicist at the Shell Oil Research Center in Houston.

In the autumn of 2001, the financial collapse of the Enron Corporation hit the front pages of virtually every newspaper in the world. At the time it was the largest bankruptcy in US corporate history. But as scandals go, it turned out to be just one of many accounting “irregularities” that numerous American corporations had been practicing for the decade or more during the runaway bull market that began in the early 1980s.

Nikolai Kondratiev was a Russian Marxist economist who directed the Conjuncture Institute in Moscow in the 1920s. The principal focus of his institute’s work was the study of business cycles in the capitalist economies, since socialist economies do not have the degrees of freedom needed for such cycles to occur.

21 The Complex Adaptive Systems View of the World 22 The Point of Departure – Futures Research Methodology – V2.0 25 The Sensemaking Model of the Re-Analysis 27 A Re-Analysis of Futures Research Methodology 29 Ideas for Methodology Development 32 References 34 COEVOLUTIONARY INTEGRATION  THE COCREATION OF A NEW ORGANISATIONAL FORM FOLLOWING A MERGER OR ACQUISITION 39 Critical Path of M&A (History) 40 Some Key M&A Statistics 41 The Two Case Studies 43 ‘Designing’ A New Integrated Organisation 44 Communication 48 Making Change Happen 50 Focus and Leadership 51 Integration Across National Cultures 53 Matrix Structure and New Product Development 55 Relationships, Culture and Communication 57 Leadership, Role of Central Team, Management 58 EnF Success 58 Summary: Enablers of Integration Post M&A 59 Acknowledgments 62

This paper will examine the way in which the ability to create surrogate versions of real complex systems inside our computing machines changes the way we do science. In particular, emphasis will be laid upon the idea that these so-called “artificial worlds” play the role of laboratories for complex systems, laboratories that are completely analogous to the more familiar laboratories that have been used by physicists, biologists and chemists for centuries to understand the workings of matter. But these are laboratories in which we explore the informational rather than the material structure of systems. And since the ability to do controlled, repeatable experiments is a necessary precondition to the creation of a scientific theory of anything, the argument will be made that for perhaps the first time in history, we are now in a position to realistically think about the creation of a theory of complex systems.

A STRANGENESS IN THE ATTRACTION A bout 25 years ago, mathematicians and computer scientists began making computer-generated patterns of a strange type. These patterns were christened “fractals” by IBM researcher Benoit Mandelbrot, whose work on mathematically characterizing the geometry of irregular objects like coastlines and clouds dovetailed perfectly with the work of dynamical system theorists studying the socalled “chaotic” processes of weather formation and the rise and fall of insect populations. The system theorists discovered that the long-run behavior of most dynamical processes ended in “attractors” whose geometrical character is far removed from the simple geometries of a point or a closed curve. And the geometers showed that these “strange” attractors had dimensions that were not simple whole numbers like 0 or 1, but had a fractional nature; in short, they are fractals—just the objects that Mandelbrot and others knew described the shapes of natural objects. During the 1980s, something akin to a cottage industry of computer-generated art appeared in a regular stream of books and posters, all aimed at displaying the infinite variety of fascinating shapes and forms that could be created by using simple formulas and feedback in an iterative manner to generate and color points on the plane. Probably the most famous of these “artistic forms” is the well-known Mandelbrot set M shown in Figure 1. The iterative rule for coloring points of the plane black or white to form this set is well chronicled, and the reader can find it in many places. So I won’t repeat it here. The most important property of the Mandelbrot set is that it looks exactly the same when examined under microscopes of greater and greater resolution (technical aside: this property holds strictly only in the neighborhood of certain points; generally speaking, the set M is only what is called “quasiself-similar”). This type of strict (or quasi-)self-similarity on all scales is one of the distinguishing fingerprints of fractal forms and is what leads to these objects having a geometric dimension D in the plane that is greater than a line (D 1) and less than that of the entire plane itself (D 2). Interestingly, in 1991 a Japanese mathematician proved that the boundary of M has geometric dimension 2. This means that not only is the boundary itself a fractal, but it “wiggles” as much as any curve in the plane possibly can. Note, however, that this does not mean that the boundary is a curve that entirely fills up the plane. Whether this is indeed the case remains an open question. But it is known that the dimension D of M equals 2 and that M contains and is contained in a disk. Thus, since a disk has D 2, so does M. JOHN L. CASTI

For centuries humans have speculated on how the technology of the time could be used to carry out mental tasks that mimic, if not surpass, those done by the human mind. The current incarnation of this form of hubris landed on the intellectual landscape in a legendary 1950 paper in which British computer pioneer Alan Turing laid out the agenda for the creation of a “machine that thinks” (Mind 59, 433–460). Just six years later, a summer conference at Dartmouth College in New Hampshire brought together an eclectic group of maverick mathematicians, engineers, psychologists, computer scientists (in today’s terminology), neurobiologists and other assorted denizens of the academic world. They established a research programme in what one attendee, John McCarthy, dubbed “artificial intelligence” (AI), an evocative (and provocative) appellation by which the field has been known ever since. Like all zealots setting up a new religion, the Dartmouth group asserted some pretty amazing claims. Two of the most interesting to catch the public eye ended up as touchstone problems by which progress in AI could be measured. The first was to develop within ten years a computer program that could beat the world chess champion. The second was to develop in about the same length of time a computer program that could translate from one human language to another at a level indistinguishable from that of a professional translator. The rationale for these benchmark problems was that, in both cases, creating a program to carry out the task would teach us many things about the mysterious ways of the human mind. Amusingly, the first goal has now been achieved by the program Deep Blue II — but it taught us nothing about human thought processes, other than that world-class chessplaying can be done in ways completely alien to the way in which human grandmasters do it. The second goal, however, is about as far from being achieved as the number of atoms in the Universe is from infinity.But,strangely perhaps, work on machine language translation has taught us a lot about how human language processing takes place. About 20 years after the Dartmouth meeting, writer Pamela McCorduck, wife of pioneering computer scientist Joseph Traub, was having lunch at Stanford with two AI veterans, McCarthy and Edward Feigenbaum. She suggested getting the thoughts and impressions of the workers in this field down on paper as a kind of historical and sociological account of the emergence and evolution of an entirely new field of intellectual endeavour. In 1979, after several years of interviews and lunchtime conversations with AI researchers, including Marvin Minsky, Lotfi Zadeh, Herbert Simon and Alan Newell, McCorduck published her book, Machines Who Think. This work did not pretend to be an exhaustive account of the entire field, but rather was a kind of eclectic sampling of various schools of thought in AI and how much progress, or lack thereof, had been made towards the ultimate goal of a thinking machine. Being an informed outsider to the field,as well as a gifted writer and storyteller, was a decided advantage to McCorduck. She had no particular axe to grind with regard to championing one approach over another, and was able to tell the human story of AI in terms that made the book fascinating reading — even if you were not especially interested in whether a machine could one day replace your brain. In retrospect, 1979 was a turning point for AI. The technological advances of the preceding two decades, coupled with the appearance that year of Douglas Hofstadter’s Pulitzer-prize-winning book Goedel, Escher, Bach, catalysed a resurgence of ‘bottomup’ AI based on neural networks, or what came to be termed ‘connectionism’ — an approach that had long been displaced by ‘top-down’designed programs.Moreover, in the decades since, much progress has been made in robotics, distributed intelligence, cooperative intelligence and numerous other areas, to go along with an exponential improvement in hardware, which allowed these approaches to be implemented. McCorduck has now reissued her original 1979 book, this time with an afterword of more than 100 pages that tells the story of the past 25 years. If you are interested in how the pioneers of AI approached the problem of getting a machine to think like a human — a story told here with verve, wit, intelligence and perception — there is no better place to go than this book. It is a worthy continuation of the story begun in McCorduck’s 1979 account. One can only hope that she will have the same stamina and enthusiasm to produce an expanded, expanded edition for our entertainment and enlightenment in another decade or two. No one could do it better. ■ John L. Casti is at Complexica, Santa Fe, New Mexico 87505, USA, and the Institute for Monetary Economics, Vienna, Austria. books and arts

In 1999, Time magazine made Albert Einstein its ‘man of the century’ for the work that changed our view of time and space. It's difficult to argue too strenuously with this choice.