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.

The Black Swan – Story Possibilities (Part IV)

             We have spent a few posts looking at Black Swans in economics and politics. Look at them as possible story lines or back story items. This can be either in world building or single situations. Can the reader see it if seen from the correct angle? For example, one can see a Black Swan if one is the Thanksgiving turkey (see Part I – Are We Unknowingly the Turkey) but there really isn’t any Black Swan from the farmers standpoint. The turkey is dumbfounded but the farmer is full and satisfied. Is the reader the turkey or the farmer?

             L.E. Modesitt, Jr.’s series titled The Ecolitan Matter consists of four books; The Ecolitan Operation, The Ecologic Secession, The Ecologic Envoy and The Ecolitan Enigma. These books explore economic, political and ecologic ideas and concepts. I highly recommend all four as a good example of economic and political ideas. The books were written from 1986 through 1997, during a time of several stock market crashes (1987, 1992 and 1997), political upheaval and a renewed concern for the environment. I will admit it is not hard to write books during times of stock market crashes since they tend to happen every four to eight years. Modesitt does a masterful job of including economic and political problems into his stories. I find Modesitt’s use of economic and political principles very well done and very true to form. Notice how he leaves much of the material as back story or world building materials but enough is in the stories to make it easy to follow and enjoy. He doesn’t burden the reader with too much detail.

             Remember, economics and social sciences are full of Black Swans. It is their very nature to be uncertain and unpredictable. Review The Black Swan’s Mask (Part II) to see how we rationalize our incorrect or missed predictions. Can a story be developed from looking backwards through our missed prediction to a situation (reverse the process by creating an outcome and looking backwards through a false prediction to the original situation- which may be incorrect). Remember every world is full of floods that happen only once in a 1,000 years. In The Black Swan – but I saw that Coming (Part III) we looked at the outlier and the uncertain or unknown nature of such.  Again, the most important point is that economics and political science are not true sciences in the way we think of science as being measurable and predictable. Both are full of Black Swans, the unknown and unknowable. Use that to your advantage.

The Black Swan – But I Saw That Coming (Part III)

           We have spent a couple of posts looking at the problem of the Black Swan – the impact of the highly improbable. We have discussed the turkey and the Thanksgiving feast in part 1 of this set. One doesn’t want to be a surprised turkey. We have looked at the problem of induction or inductive knowledge which includes how can we logically go from specific instances to reach general conclusions. According to Nassim Taleb there are traps built into any kind of knowledge gained from observation. He also suggests that those who believe in the unconditional benefits of past experience cannot project into the future. Remember back to our turkey example, was the farmer surprised by the outcome?

           Having made the above observation, I will say there are some situations where we can use past experiences to draw conclusions about future observations. Suppose you are asked to find the average height of the all men in the United States. You could take a sample of say 10,000 men and draw some fairly detailed conclusions. You would be able to take that data and make some conclusions regarding height of men in general. Now if you were to measure one more man from the population in general your estimate of his height would likely be within the parameters you had established. The same would be true for weight. Another way to state this is, one more observation will not significantly impact the predicted results. Now suppose you sample people who are worth more than $10 million. Let’s suppose that your sample does not include Bill Gates. Your sample size may be quite large, relatively speaking, but if it doesn’t include Bill Gates then as soon as Bill Gates is included in the analysis we have a Black Swan situation. A deviation so large that it blows up the study. So, you decide that Bill Gates must be an aberration… (sorry, let me be specific, in the data only), and toss that data point out. Then the next data point you sample includes Steve Job’s estate. The difference between sampling height and let’s say millionaires is one set of data follows a bell curve (predictable or regular) or a variation of such things and one doesn’t. Many who have studied this feel that economics and social sciences cannot be defined by bell curves and its like. Many in econometrics feel that the world can be defined by regular occurring events or activities. Taleb suggests that “a nerd is someone who thinks exceedingly inside the box” or is blind to Black Swans.

           Some other themes arise from our blindness to the Black Swan. Again I turn to Taleb and his book, The Black Swan the Impact of the Highly Improbable. He suggests the following ideas. We tend to focus on preselected segments of the seen and generalize from it to the unseen. We fool ourselves with stories that cater to our thirst for distinct patterns. We behave as if the Black Swan does not exist, human nature is not programmed for Black Swans. What we see is not necessarily all that is there. History hides Black Swans from us and gives us a mistaken idea about the odds of these events. We “tunnel”, that is we focus on a few well-defined sources of uncertainty at the expense of the others that do not easily come to mind.

           As you listen to the news and look at economic projections remember that economists and their associates are working in a world full of Black Swans or full of Bill Gates type situations. It is important to realize that studies done on how successful economists have been in their predictions have shown economists have an exceptionally poor track record of being able to predict things. Just because an economist is able to show past data to support his position may have little or no relationship to future events. It all depends on where the Black Swans are.