Interdependence

"I know it is a weakness of human nature to become emotionally invested in inconsequential tribal spats, but people who want to be transhumanists need to be able to get past that almost as a prerequisite. In fact, a good portion of the transhumanist ideals are all about shedding this behavior."
- J. Andrew Rogers

When the Boston Red Sox won their first World Series title since 1918 last year, the team had some new blood, including key players Curt Schilling, Orlando Cabrera and Doug Mientkiewicz, to mix with the old and help the team achieve the pinnacle of baseball success.

In a paper to be published April 29 in the journal Science, Northwestern University researchers turned to a different type of team -- creative teams in the arts and sciences -- to determine a team's recipe for success. They discovered that the composition of a great team is the same whether you are working on Broadway or in economics.

The researchers studied data on Broadway musicals since 1877 as well as thousands of journal publications in four fields of science and found that successful teams had a diverse membership -- not of race and gender but of old blood and new. New team members clearly added creative spark and critical links to the experience of the entire industry. Unsuccessful teams were isolated from each other whereas the members of successful teams were interconnected, much like the Kevin Bacon game, across a giant cluster of artists or scientists.

I.B.M. is renowned for its rich storehouse of patented inventions. It once again led the research sweepstakes in America last year, collecting 3,248 patents, more than any other company. And it earned more than $1 billion last year from licensing and selling its ideas.

So why has I.B.M. shifted course recently, giving away some of the fruits of its research instead of charging others to use it? The answer is self-interest.

Diverging from conventional wisdom, the company has calculated that sharing technology can sometimes be more profitable than jealously guarding its property rights on patents, copyrights and trade secrets. The moves by I.B.M., the world's largest supplier of information technology services and computers, are being closely watched throughout the business world.

The evolution of specialization in a multi-agent system is studied both by computer simulation and Markov process model. Many individual agents search for and exploit resources to get global optimization in an environment without complete information. With the selection acting on agent specialization at the level of system and under the condition of increasing returns, the division of labor emerges as the results of long-term optimizing evolution. Mathematical analysis gives the optimum division of agents and a Markov chain model is proposed to describe the evolutionary dynamics. The results are in good agreement with that of simulation model.

Key Words: division of labor, evolutionary dynamics, multi-agent system, emergence.
Zengru Di, Jiawei Chen, Yougui Wang, and Zhangang Han
Department of Systems Science, Beijing Normal University, Beijing, 100875, China

In finding an answer to “perhaps the greatest unsolved ecological riddle,” evolutionists propose that diversity is a testament to there being more than one way to make a living.

The riddle: Why are some habitats loaded with many more species than others?

The answer: Nature and evolution respect that there’s more than one way of doing things.

“What we’ve learned,” said Michigan State University scientist Charles Ofria, “is that if there isn’t just one way to succeed, you’ll see diversity.”

In an article published in the July 2 issue of Science, an interdisciplinary team of scientists at MSU, the California Institute of Technology and Keck Graduate Institute (KGI), with the help of powerful computers, has used a kind of artificial life, or ALife, to gain insight into questions of evolution.

Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies, and Nations

In the summer of 2003, analysts at the Department of Defense had an unusual idea. To predict important events in the world, including terrorist attacks, they would create a kind of market in which ordinary people could actually place bets. The proposed Policy Analysis Market would allow each of us to invest in our predictions about such matters as the growth of the Egyptian economy, the death of Yasir Arafat, and the likelihood of terrorist attacks in the United States. Investors would win or lose money on the basis of the accuracy of their predictions. Predictably, the Policy Analysis Market produced a storm of criticism. Ridiculed as "offensive" and "useless," the proposal was abandoned.

Amid the war on terrorism, why was the Defense Department so interested in the Policy Analysis Market? The answer is simple: it wanted to have some help in predicting geopolitical events, including those that would endanger American interests, and it believed that a market would provide that help. It speculated that if a large number of people could be given an incentive to aggregate their private information, in the way that the Policy Analysis Market would do, government officials would learn a great deal.

Does this idea seem ludicrous? Since 1988, the University of Iowa has run the Iowa Electronic Markets, which allow people to bet on the outcome of presidential elections. As a predictor, the Iowa Electronic Markets have produced extraordinarily accurate judgments, often doing better than professional polling organizations. In the week before each of the last four elections, the predictions in the Iowa market have shown an average absolute error of just 1.5 percentage points, a significant improvement over the 2.1 percentage point error in the final Gallup Polls. Or consider the Hollywood Stock Exchange, in which people predict Oscar nominees and winners, as well as opening weekend box-office successes. Here, too, the level of accuracy has been exceptionally impressive, with (for example) correct predictions of thirty-five out of forty Oscar nominees in 2002.

In fact, prediction markets are springing up all over the Internet, allowing people to make bets on the likely outcomes of sports, entertainment, finance, and political events. On tradesports.com, people have been betting on whether Donald Rumsfeld will resign soon (extremely unlikely), whether Osama bin Laden will be captured by June 2004 (extremely unlikely), whether John Edwards will be selected as John Kerry's running mate (a good chance, but probably not), and whether George W. Bush will be re-elected (more likely than not). One can imagine prediction markets on any number of questions: Will gas prices reach $3 per gallon? Will cellular life be found on Mars? Will smallpox return to the United States? Will there be a sequel to Master and Commander? Will the Federal Communications Commission be abolished? (I didn't make these up; they are actual or proposed questions on existing markets.)

James Surowiecki is fascinated by prediction markets. In his opinion, they demonstrate that crowds are often wise. He rejects the widespread view that groups of ordinary people are usually wrong--and that we do better to ignore them and follow experts instead. Even when individuals blunder, he believes, groups can excel: "Under the right circumstances, groups are remarkably intelligent, and are often smarter than the smartest people in them." This is so even when "most of the people within the group are not especially well-informed or rational." What is wonderful, and surprising, is that "when our imperfect judgments are aggregated in the right way, our collective intelligence is often excellent." Instead of chasing experts, we should consult that collective intelligence.