Playing in the Sand Pile of History – Part I

             A problem in modern economics has been the unpredictability of things. A cacophony of voices have tried from the beginnings of civilization to predict the next big thing. Just imagine the problem an Egyptian stone mason had trying to figure out the next big pyramid job. How was he going to know what and when to order stone so the competition didn’t get the jump on the big contract. Mark Buchanan in his book Ubiquity Why Catastrophes Happen tackles the problem head on and in my mind with that single mindedness of the scientist convinced that there is a mathematical equation somewhere that will hold the answer to life, the universe and everything. Those who model these chaotic systems talk about complexity and the greater problem some call upheavability. Buchanan suggests that chaos is limited in its ability to explain extreme events (I would say Black Swans – you recognize that term) because many models do not generate upheavals.

             Buchanan is good enough to suggest that predicting the long-term future of any chaotic system is practically impossible. I will suggest that it is presently quite unlikely that current models and modeling techniques will successfully model financial and economic systems with any degree of success or accuracy. Further, what little successes we may have is inversely proportional to model time horizon and the complexity of the system.  We will tend to have limited modeling success in the short term with simplistic systems. However, Buchanan does have some interesting suggestions for looking at complex systems which I think may help in understanding both the complexity and the pitfalls inherent in economic and financial modeling. He uses the term critical state to suggest  a special kind of organization characterized by a tendency toward sudden changes, maybe radical changes. Using his physics background he suggests that instead of trying to find mathematical equations to describe these complex systems that an alternative is to use mathematical games, much simpler equations, to understand specific portions of complex systems. We are going to explore one of these modeling technique, the sand pile, in later blogs. Today we need to set some parameters.

             We need to look at some basic principles regarding modeling and models. I want to start with what Emanuel Derman and Paul Wilcott, financial quantitative analysts, term the Modelers’ Hippocratic Oath. Derman and Wilcott are considered part of the elite group of financial modelers and have been in the financial industry before, during and after the great recession of 2008. Many feel that quants as the financial modelers are known are responsible for the severity and length of the recession and its attendant losses. In many respects this is accurate. Derman and Wilcott’s Oath shows some of the problems inherent in trying to model complex financial systems that many people seem to forget. Models are tools – blunt, limited, and easily breakable.

 Modelers’ Hippocratic Oath

·       I will remember that I didn’t make the world, and it doesn’t satisfy my equations.
·       Though I will use models boldly to estimate value, I will not be overly impressed by mathematics.
·       I will never sacrifice reality for elegance without explaining why I have done so.
·       Nor will I give the people who use my model false comfort about its accuracy. Instead, I will make explicit its assumptions and oversights.
·       I understand that my work may have enormous effects on society and the economy, many of them beyond my comprehension.
 

             I have a copy of this hanging by my desk. Any time I encounter a financial or economic model or a discussion of one I look at the oath and attempt to see if the author / originator has applied all the points to his model and the information I have about it. If there is any part of the oath that I suspect the author did not consider or incorporate in his model I am immediately suspect of the model, its conclusions and most of all its recommendations. All financial and economic models are suspect, period. Always assume there are errors in the model. Errors in logic, in assumptions, in data points included and excluded, in the equations, and in conclusions. Once you have looked at a model in this light it can be reviewed and analyzed to see if there are portions that may have some value. If you get the sense that models are potential death traps to your financial health and to the forecasting of economic conditions, that is accurate. One doesn’t have to be able to break down models to their component parts but one does need to realize that every model has problems, many of them significant problems. What is a person to do, be very skeptical of the output of any financial or economic model and run away from anyone who says, “trust me, you don’t need to know what is in the ‘black box’ “. If the person can’t or won’t explain the black box (for example, a new financial product guaranteed to get you 25% return in today’s environment) you don’t want to be involved, ever. Remember the old adage, if it’s too good to be true it probably is.

             Next time, how to look at a sand pile –  what can we learn and how can we use it.