Game Theory application for world events: Bueno de Mesquita
That’s where Bueno de Mesquita began programming his computer model. It is based loosely on Black’s voter theory, and it works like this: To predict how leaders will behave in a conflict, Bueno de Mesquita starts with a specific prediction he wants to make, then interviews four or five experts who know the situation well. He identifies the stakeholders who will exert pressure on the outcome (typically 20 or 30 players) and gets the experts to assign values to the stakeholders in four categories: What outcome do the players want? How hard will they work to get it? How much clout can they exert on others? How firm is their resolve? Each value is expressed as a number on its own arbitrary scale, like 0 to 200. (Sometimes Bueno de Mesquita skips the experts, simply reads newspaper and journal articles and generates his own list of players and numbers.) For example, in the case of Iran’s bomb, Bueno de Mesquita set Ahmadinejad’s preferred outcome at 180 and, on a scale of 0 to 100, his desire to get it at 90, his power at 5 and his resolve at 90.
Then the math begins, some of which is surprisingly simple. If you merely sort the players according to how badly they want a bomb and how much support they have among others, you will end up with a reasonably good prediction. But the other variables enable the computer model to perform much more complicated assessments. In essence, it looks for possible groupings of players who would be willing to shift their positions toward one another if they thought that doing so would be to their advantage. The model begins by working out the average position of all the players — the “middle ground” that exerts a gravitational force on the whole negotiation. Then it compares each player with every other player, estimating whether one will be able to persuade or coerce the others to move toward its position, based on the power, resolve and positioning of everyone else. (Power isn’t everything. If the most powerful player is on the fringe of an issue, and a cluster of less-powerful players are closer to the middle, they might exert greater influence.) After estimating how much or how little each player might budge, the software recalculates the middle ground, which shifts as the players move. A “round” is over; the software repeats the process, round after round. The game ends when players no longer move very much from round to round — this indicates they have compromised as much as they ever will. At that point, assuming no player with veto power had refused to compromise, the final average middle-ground position of all the players is the result — the official prediction of how the issue will resolve itself. (Bueno de Mesquita does not express his forecasts in probabilistic terms; he says an event will transpire or it won’t.)
The computer model, in short, predicts coalitions. And computers are much better at doing this than humans, because with more than a few players the number of possible coalitions quickly multiplies. With 40 players, the typical size of one of Bueno de Mesquita’s forecasts, there are 1,560 possible pairs to consider just for starters. This is why, he says, his model often produces surprising results. It’s not that it is smarter than humans. But it methodically works through not only the obvious coalitions we know about and expect but also the invisible ones that we don’t.
Then the math begins, some of which is surprisingly simple. If you merely sort the players according to how badly they want a bomb and how much support they have among others, you will end up with a reasonably good prediction. But the other variables enable the computer model to perform much more complicated assessments. In essence, it looks for possible groupings of players who would be willing to shift their positions toward one another if they thought that doing so would be to their advantage. The model begins by working out the average position of all the players — the “middle ground” that exerts a gravitational force on the whole negotiation. Then it compares each player with every other player, estimating whether one will be able to persuade or coerce the others to move toward its position, based on the power, resolve and positioning of everyone else. (Power isn’t everything. If the most powerful player is on the fringe of an issue, and a cluster of less-powerful players are closer to the middle, they might exert greater influence.) After estimating how much or how little each player might budge, the software recalculates the middle ground, which shifts as the players move. A “round” is over; the software repeats the process, round after round. The game ends when players no longer move very much from round to round — this indicates they have compromised as much as they ever will. At that point, assuming no player with veto power had refused to compromise, the final average middle-ground position of all the players is the result — the official prediction of how the issue will resolve itself. (Bueno de Mesquita does not express his forecasts in probabilistic terms; he says an event will transpire or it won’t.)
The computer model, in short, predicts coalitions. And computers are much better at doing this than humans, because with more than a few players the number of possible coalitions quickly multiplies. With 40 players, the typical size of one of Bueno de Mesquita’s forecasts, there are 1,560 possible pairs to consider just for starters. This is why, he says, his model often produces surprising results. It’s not that it is smarter than humans. But it methodically works through not only the obvious coalitions we know about and expect but also the invisible ones that we don’t.
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