March 2010, New Scientist runs an article,
where game theory suddenly sounds a lot like PsychoHistory:
Bruce Bueno de Mesquita (,) is a professor of politics at New York University and a senior fellow at the Hoover Institution, Stanford.
He has founded a company, Mesquita & Roundell, that specializes in making political and foreign-policy forecasts
using an unpublished and proprietary computer model based on game theory and rational choice theory.
From the New Scientist article:
In his new book, The Predictioneer (The Predictioneer's Game in the US), he describes a computer model based on game theory which he - and others -
claim can predict the future with remarkable accuracy.
Over the past 30 years, Bueno de Mesquita has made thousands of predictions about hundreds of issues from geopolitics to personal problems.
Overall, he claims, his hit rate is about 90 per cent. So how does he do it?
He had designed a mathematical model to examine the choices people could make. (E.g. in electing the next PM in India).
(Then) He compiled a list of all the people who would try to influence the appointment of the next prime minister, what their preference was
and how much clout they had. He fed this information into his computer programme,
asked it to predict how the negotiations would play out and left it to run overnight. His own hunch was that the deputy prime minister, Jagjivan Ram,
would take over. Many other experts on Indian politics thought the same thing.
The following morning, he checked the computer and found to his surprise that it was predicting a politician
called Chaudhary Charan Singh would be the next prime minister. It also predicted that he would be unable
to build a working coalition and so would quickly fall.
A few weeks later, Singh became prime minister. Six months on his government collapsed.
"The model had come up with the right answer and I hadn't," says Bueno de Mesquita.
"Clearly there were two possibilities: the model was just lucky, or I was on to something."
Bueno de Mesquitas use of Nash's game theory:
Bueno de Mesquita confines himself to "strategic situations" where relatively small numbers of people are haggling over a contentious decision.
"I can predict events and decisions that involve negotiation or coercion, cooperation or bullying," he says. That includes domestic politics,
foreign policy, conflicts, business decisions and social interactions.
In the 1950s, mathematician John Nash, subject of the film A Beautiful Mind, created a more realistic formulation of game theory
in which players are out for themselves and can bully, lie, bluff or renege on their word to achieve their desired outcome.
Bueno de Mesquita uses Nash's assumptions: Players are motivated by self-interest and will do whatever they can to get what they want - or at least to block an undesired outcome.
In its simplest form the model works like this. First, Bueno de Mesquita decides what question to ask - for example, will Iran build a nuclear warhead.
At that point, the "negotiations" begin. Say there are five players, A, B, C, D and E. To arrive at a result, every player is paired with every other
and their positions compared. When A is paired with B, for example, A must decide whether to support or resist the central proposal ("Iran should build a nuclear weapon")
or offer a counter-proposal, taking into account B's position and the likelihood of getting C, D or E's support. B either agrees, negotiates or bullies in return,
all the while taking the positions of the other three players into account.
Once every possible combination has been played out, each player sorts through the various proposals or demands they received, and evaluates the credibility of any threats made against them.
Players may then shift position accordingly. At the end, the model calculates the group's overall position as a number between 1 and 100. This is taken to be the "result".
When five players are involved there are 120 possible interactions. But the complexity soon skyrockets. If you jump to 10 players there are 3.6 million potential interactions. 
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