kinetic energy, not supply & demand

Eighteen years later the answer from many observation posts along the byways of public discourse appears to be: yes, they most certainly do, and so do their fellow travelers, business and financial markets experts.  After all, we just barely survived the most devastating economic event of our times, deeper and more far-reaching than any downturn since the Great Depression, and all the high priests of the field can do is shake their heads and say “wow, I sure didn’t see that coming.”  Distrust of fancy math is rampant in all walks of business life.  That presents a real problem for enterprise decision-makers at a time when they need smart quantitative tools – yes, fancy math and all – more than ever.  Markets are more complex than at any time in human history.  Giant waves of transactional data inundate marketing managers with new information every day.  Managers need science to help them gain valuable insights into the markets for their products and services – but how do they know that the growing number and variety of scientific marketing tools out there aren’t infected with the nasty symptoms of physics envy?

It’s a good question, and one that any decision-maker should ask before embarking on a quantitative marketing solution.  Here are three important questions the manager should ask of any solution being offered:

(a) Is the solution inextricably wedded to a single model of how the world works? This was one of the really fatal flaws in the thinking and modeling practices of economists and financiers in the lead-up to 2008, a flaw most poignantly confessed to by the highest of the high priests of rational economics, Alan Greenspan, in his post-crash testimony to Congress.  Models are supposed to simplify the real world, but not to the point of completely misinterpreting and distorting the behaviors and practices that actually prevail in the world.  Robust quantitative models need to be flexible, adaptable and agnostic in regard to any one single theory that, like rational economics, can become more of a rigid ideology than an objective attempt to explain how the world works.

(b) Is the solution measuring the right thing? Here is where even marketing models with no ideological baggage and with the best of intentions can fall into a trap.  For the past thirty-odd years marketers have tried to define their demand environments through approaches like customer segmentation – identifying demographic segments and then marketing and pricing to the perceived “average” customer in that segment.  A similar approach is segmentation by geography, otherwise known as the “country strategy”.  “How can we optimize our profits in country X?” goes a common problem definition.  But what if the model’s independent variables are (as is often the case) limited to country and product line – answering the question above with a formulation like “sell more turbo widgets in Country X to optimize profits” when in fact the most important influencing variable is actually something else, say a macroeconomic variable like growth in per-capita GDP?  In a world where products proliferate and sales cycles become ever shorter there are numerous variables that influence demand (and hence profitability), and decision-makers need to know these variables at a very granular level – ideally at the level of each potential interaction of customer and product.

(c) Are the right tools being used to measure the right thing? A third pitfall on the road to scientific marketing excellence is the danger of using the wrong tools from the toolkit.  There is a propensity among technology vendors to say things like “our algorithm is better than our competitors’ algorithms”.  In truth there is no one right algorithm because there is no one-size-fits-all solution to anything as complex as markets where product-customer combinations number in the tens of billions.  In this environment there is no such thing as an “average” customer and there is no one single scientific formulation that will solve the problem of making decisions that optimize firmwide performance goals like profitability or market share.  Solutions need to be customized to reflect the unique and constantly evolving contours of each enterprise’s market for its goods and services.

There may be no one perfect, fail-proof screen to detect and avoid the lurking ill effects of physics envy in the market for quantitative business solutions.  But answering a few simple questions like the ones suggested here may go a long way to helping decision-makers avoid the dangers of mathematics for its own sake – and appreciate the value of what mathematical methods can do when applied in the right way for the right reasons.

But let’s take a different look at this apparent tight coupling of mathematics and dire outcomes. Our recent correspondence with an author who has been widely published on the subject of Wall Street’s use of mathematical models recently offered to us an interesting opinion. His point was that the problem with the models was not so much their complexity, but rather that they were models in the first place. His argument was that you can’t ever perfectly hedge model risk.  Now, I agree with that observation: a model by definition selects some aspects of reality to represent and omits others, and the choice of what to include and what to omit is subject to human error, therefore fallible and not perfectly hedgable.  But I take issue with the idea that the fault lies in the existence of the models themselves.  Models can be misused – I think that much is clear. But the notion that models are all doomed to failure obscures a deeper truth about the goals of predictive modeling; namely that you can seek either to reduce the world or truly explain it. By trying to elegantly reduce the world to as few predictor variables as possible, you are more likely to be sowing the seeds of future failure, because complexity and actual drivers of outcomes are taken out of the equations to make them more solvable (or perhaps sellable, as in the case of the Gaussian copula function that was behind Wall Street’s demise, as we discussed in a previous posting “You Can’t Punt Away the Dimensionality Curse”). Predictive modelers don’t have to go down that road, however: they can also set out with the goal not of reducing an entire system to a single neat, tractable equation, but to quantify and explain all of the relationships that dictate outcomes to the absolute fullest extent possible. Tractability and computability are things to address later in the process, through technological means, but they should not dictate the fundamental mathematical approach at the outset.

As I see it, the problem with the financial market meltdown is not that David Li published an article in the Journal of Fixed Income Securities on the Gaussian copula function, or even that in his article Li, then an analyst with JPMorganChase, identified the price of credit default swap (CDS) contracts as a seemingly elegant proxy for the mortgage market – a proxy that greatly reduced the immense complexity of modeling values and risks in this market but, as it turned out, lost a great deal of critically important information along the way.  No – the real problem was with the incremental decisions practitioners made to adopt this model wholesale, to leverage it up to 50 or more times the worth of the underlying assets, and ultimately to heedlessly employ it as a path to untold riches.  In other words it was the people who used the model, not the model itself.  It was the rating agencies who, in conferring the AAA ratings without which the securities would have never been as widely distributed as they were, assumed that housing prices would never go down.  It was the investment bankers who successfully shouted down the warnings of their internal credit risk departments so that they could sell ever higher volumes of CDOs, with ever-higher levels of leverage, in order to maximize their year-end bonuses.

You can’t fully hedge model risk – that is true.  But you can mitigate model risk through the application of robust decision-making processes.  A model did not take down Wall Street. Models do not “screw up” – they do exactly what they are supposed to do once they have their inputs. The screw-ups occur solely in our application of models to inappropriate situations or to situations which we do not fully understand. The predictions may not reflect reality outcomes as precisely as we wish, but that possibility of error needs to be accounted for by the ultimate decision makers. The output of a model should be an input in any decision process, not the entire decision process. For that reason, the emerging generation of predictive technology solutions being employed by all varieties of business seeks to marry model holism (by including ALL of the relevant variables, rather than the most computationally feasible), computational firepower, and above all, ranges of probability-based recommendations, rather than a single output. It may sound heretical at the moment given the present economic calamity, but as the world gets ever more complex, models will become more valuable to decision makers, not less. Informed, prudent decision-making in regard to those models will not be a luxury, but an absolute necessity.