In today's blog I want to set some groundwork for future blogs related to a Simple Rules (2015) book that I'm currently reading. You can can also read the article Simple Rules for a Complex World for a synopsis of some of the ideas contained in the book. The book advocates the use of domain-specific simple rules to manage decision making in those domains. For example, instead of trying to compute an optimal diet using food databases and combinatorial algorithms, you could also use Micheal Pollan's simple rule to Eat food. Not too much. Mostly plants. Following the latter rule would likely lead to as much or more success in deciding what to eat than using some diet optimization technique.

The impetus to use simple rules is that the world is complex and simple rules often capture the most significant features to pay attention to. Often they can be shown to be effective if not optimal according to some criterion. Often the optimal decision is not clear and/or we don't have the computational resources to figure it out. Defining and attending to simple domain specific rules can help us to make adaptive decisions in many aspects of our lives.

Herb Simon won a Nobel Prize in Economics in part because he criticized a foundational assumption of economics that humans are rational actors attempting to make optimal decisions, the so-called "Rational Man" assumption. One of his best critiques of this "Rational Man" viewpoint can be found in chapter 2 his book The Sciences of the Artificial (3rd Ed., 1996). That chapter is titled "Economic Rationality: Adaptive Artifice".

One of Simon's arguments against the rational man assumption involves a critique of Game Theory as a method to compute an optimal strategic move in business or other interactions. One problem you can run into if you set two big-brained computers against each other is the problem of mutual outguessing. If I think A is going to do X, but A knows that I know she might do X, then I should instead do Y, but A might also anticipate this, so perhaps I should do Z and so on. This chain of reasoning can go on indefinitely when two competitive big-brained computers are trying to find an optimal strategic move. Simon drew the following conclusion from this mutual outguessing problem in Game Theory:

Market institutions are workable (but not optimal) well beyond that range of situations [monopoly and perfect competition] because the limits of human abilities to compute possible scenarios of complex interaction prevent an infinite regress of mutual outguessing. Game theory's most valuable contribution has been to show that rationality is effectively undefinable when competitive actors have unlimited computational capabilities for outguessing each other, but that the problem does not arrive as acutely in a world, like the real world, of bounded rationality.

If we are not the optimizing machine that the Rational Man image from economics suggests, then how do we go about solving problems in the real world? Simon calls this the problem of "Adaptive Rationality" and he makes the following suggestions and observations:

If the adaptation of both the business firm and biological species to their respective environments are instances of heuristic search, hence of local optimization or satisficing, we still have to account for the mechanisms that bring the adaptation about. In biology the mechanism is located in the genes and their success in reproducing themselves. What is the gene's counterpart in the business firm?

Nelson and Winter suggest that business firms accomplish most of their work through standard operating procedures - algorithms for making daily decisions that become routinized and are handed down from one generation of executives and employees to the next. Evolution derives from all the processes that produce innovation and change in these algorithms. The fitness test is the profitability and growth rate of the firm. Profitable firms grow by the reinvestment of their profits and their attractiveness for new investment.

Nelson and Winter observe that in economic evolution, in contrast to biological evolution, successful algorithms may be borrowed by one firm from another. Thus the hypothesized system is Lamarkian, because any new idea can be incorporated in operating procedures as soon as its success is observed, and hence successful mutation can be transferred between firms. Transfer is of course not costless, but involves learning costs for the adopting firm. It may also be impeded by patent protection and commercial secrecy. Nevertheless, processes of the kinds just described play a large role in the gradual evolution of an economic system composed of business firms. (p. 48).

The purpose of this blog is provide some context for the idea of simple rules. Simple rules, bounded rationality, and satisficing can be contrasted with the vision of humans as fully informed and always optimizing - the rational man viewpoint. I do not want to completely dismiss the rational man viewpoint as we do in fact have broad range of useful analytic techniques for computing optimal outcomes (inventory management, route planning, scheduling, etc...); however, the rational man viewpoint can be taken too far if we view it as being able to account for or guide all our economic decision making. Given our bounded rationality and the complexity of the decisions we have to make daily, it makes sense to seek out and rely upon simple rules as a method to achieve "economic adaptation". Indeed, sometimes these simple rules perform as well or better than complex optimizing rules (see Naive Diversification vs Optimization). Finally, Herb Simon made some interesting observations about the importance of standard operating procedures and the Lamarkian nature of business evolution that offer some ideas on what Simple Rules in business might consist of (standard operating procedures, routines) and how they might spread and evolve over time (copying and mutation).