by Paul Ormerod
In this first Complexity Forum post, we’re looking at Agent-Based Modelling. Thoughts, questions, issues from the “Complexity Community” are very welcome.
There is nothing mystical or magical about Agent Based Models, they are simply computer algorithms.
An important question is: in what sense can an ABM, or indeed any algorithm, discover things which humans previously did not know?
A simple example is the game of chess, where computers (using algorithms) now play at a level where even the strongest human has little chance of defeating them in a one-off game. Over a series of games, the chance is effectively zero. But this is essentially because of their ability to calculate far more variations than humans, not to any superior creativity.
They have discovered things. For example, all – all – positions in which there are only 6 pieces on the board are now solved completely. A non-trivial proportion of specific positions are far beyond the ability of humans to decide unequivocally. For example, at the highest level, complete games of more than 100 moves are rare, and there are almost no examples of such games with more than 150 moves. But in many of these specific positions with only 6 pieces, the computer will discover things such as ‘White to play and win in 186 moves’. The longest sequence involving optimal moves on both sides is 517 moves.
They are also capable of generalising. So, for example, certain combinations of pieces were in general thought to lead to a draw, but computers have established that, instead, the side with the material advantage in these combinations usually wins.
So, given the rules of chess, they have discovered things unknown to humans. But they have not been creative. They cannot devise new games, unless the human programmer instructs them how to do it.