I’m still mulling over the contents of Nassim Nicholas Taleb’s book The Black Swan (see earlier post). He spends many pages (too many, perhaps) unpicking the statistical foundations of the predictive models beloved of economists (and which are similar, in many ways, to the models ecologists use). Such models, he argues, have a track record of failing to predict the really significant events, because these, by their very nature, often fall outside the expectations of those of us raised to think in terms of “normal” (Gaussian) statistics. Rather than attempt imperfect predictions of the future, he suggests, we should, build robustness into our institutions to enable them to react to change.
Yet, when I look around, I see the exact opposite happening, as the economic recession (itself the product of Black Swan events such as the subprime mortgage crisis in the USA) forces the public sector to retrench. All of the agencies with which I work in the UK, and many elsewhere in Europe, are having to survive on smaller budgets than was the case a few years ago. The mantra is always “efficiency” but let’s dissect this and examine what it means through the lens that Talib has given us. Efficiency implies a high ratio of useful work to effort expended and, as “effort” has to paid for from our taxes, this all sounds very reasonable. My problem lies is how this is being achieved. You can bring about “efficiency” of a sort by adopting the approaches of manufacturing industry: splitting tasks into discrete steps and thinking about how to optimise each of these. The outcome should be efficient production of a homogeneous product. Think hamburgers. And welcome to “McEcology”.
This is fine if your product is data, as burger chains achieve a level of reproducibility in their outputs that most ecologists only dream about. However, if your product is “knowledge” or “advice”, then perhaps splitting the task into several steps and making the ecologists spend less time in the field, will be counterproductive. It depends if you think of ecologists as data monkeys or as professionals.
Taleb’s term for this process is “naïve optimisation”, though he approaches the topic from a different angle. He believes that organisations cope with the unexpected by having innate robustness and, interestingly, given my context, uses analogies from ecology. Robustness and stability arise from having many interdependencies within systems. This may look like redundancy to an outsider (why do we have two kidneys, for example?) and, therefore, fair game when savings have to be made. Yet viewed from another perspective, this “redundancy” is insurance against things going wrong. The human body can cope, for example, with only one kidney. The “optimised” ecology teams I meet in my work struggle to cope when colleagues are off sick or on maternity leave and, as important for professionals who need to offer advice and opinions, have little time for reflection and ad hoc investigation of issues that fall outside scheduled activities.
These thoughts came together last night when I went to see the American singer Steve Earle. He had a role in the TV series Treme, about New Orleans in the months following Hurricane Katrina and three of the songs in his set were written for the show. Introducing these songs, he reminded us how the aftermath of Katrina – a classic example of a Black Swan event – had been prolonged because the federal agencies responsible had had their budgets slashed by the Bush administration. All looked fine on paper … until the one time they had to respond to a real crisis.