“In Between”—Complex Adaptive Systems

“The complex adaptive social systems view of the world,” Miller and Page argue, “allows us to explore the spaces between simple and strategic behavior, between pairs and infinities of agents, between equilibrium and chaos, between richness and rigor, and between anarchy and control.” (p. 213) Sound like the spaces occupied by the markets?

The authors look at each of these spaces in turn.

First, in between simple and strategic behavior. The authors contrast tic-tac-toe with chess. Apparently even chickens can be trained to play the optimal tic-tac-toe strategy. (Now that’s scary!) With chess, however, there’s no computer program that can generate the whole game tree; it can produce a tree only toward the end of the game. Instead, chess computer programs employ heuristics (x piece is more valuable than y piece), clever pruning of localized parts of the game tree, and other means to decide on their moves.

Second, in between pairs and infinities of agents. Most social activity takes place in this space. Once again, the markets are a good example.

Third, in between equilibrium and chaos. The authors consider the markets a prime case of this “in between.” “In the starkness of neoclassical models, exchange markets result in a single, stable price equating the quantity supplied with the quantity demanded. Unfortunately, our experiences with real, experimental, and artificial markets indicate that the actual behavior of a market is not so easily captured. In real markets phenomena like clustered volatility and excess trading remain difficult to explain, in experimental markets traders seem to be less strategic and far more irrational than expected, and in artificial markets even minimally rational traders cause the market to achieve high levels of ex post efficiency, even though the observed price path is very noisy.” (pp. 222-23)

Fourth, in between richness and rigor. Or, as the authors suggest, in between qualitative methods and mathematics. Qualitative methods are flexible in the sense that they can analyze many kinds of problems but are often “vague, inconsistent, and incomplete.” (p. 224) Mathematical methods are normally more rigorous but sacrifice the richness of what can be studied.

Fifth, in between anarchy and control. The stock market exemplifies this space. “Our theorems tell us that the market should efficiently aggregate information through the price mechanism. Yet, fluctuations in price appear to far outstrip variations in information. The market sometimes appears to have a mind of its own, yet it does not collapse into complete anarchy. Computational models allow us to mimic such processes. . . . They produce behavior not unlike real markets, and we can use them to begin to experiment with attempts to control such worlds. For example, we can see if increasing the amount that can be bought on margin will reduce or eliminate bubbles.” (p. 225)

Now I’m not suggesting that investors and traders try to mimic computational models in building their own trading models. For one thing, it would be daunting to go beyond the most simplistic construct. And however intellectually challenging the project, I doubt that riches would immediately start to flow. Nonetheless, I think that the “in between” concept is powerful and that imaginative traders and investors might be able to exploit it to their advantage.