An examination of market mechanism with redundancies motivated by Turing’s rule selection

With the advent of the new era of Artificial Intelligence, we need to update our inferential methods in economics and the social sciences accordingly. The implementation of a slightly realistic consideration will easily reveal to us a very large domain. In this article, we employ the AI market simulation system called U-Mart to model the efficiency of a realistic futures market. In the actual market, participating agents send orders either randomly or non-intelligently, even though they depend on their own unambiguous strategies. It has been noted that purely random orders often result in the best performance in the market. Thus, the market system may have many redundancies. Although we cannot know in advance an optimal solution in advance, we may form a winning strategy. In a sense different from efficiency market hypothesis, we can thus affirm a certain statement on the efficiency of the market. This kind of analysis is essentially similar to the idea of Fully Random, Rule-Based Interactive Cellular Automata (ICA), which is based on Alan Turing’s rule selection. Based on this hint, we can now find a particular agent set to realize a futures price series almost similar to the spot price series. This agent configuration set has been already identified by Nakajima and Mori (Design of experimental environment for artificial financial market. Mimeo, New York, 2005) and may provide us with a special reference point like the fundamentals. Thus, we call this set the standard agent configuration (StdAC). It should be noted that the traditional “fundamentals” are not internally decided by the market. On the other side, StdAC will play its role as the fundamentals of price formation. We finally employ this set to detect a critical configuration from where the futures price series is divergent. Thus, we confirm a new approach to study market mechanism in this context.