Image Source: Photo by Austin Distel on Unsplash

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One of the (admittedly dubious) benefits of having worked for the better part of twenty years in one industry is that my work has now taken in arguably the two largest companies in my industry. At the first one, I was a green-around-the-ears graduate engineer, fresh out of University and excited about the world. At the second, I was a hardy, more cynical “ex-pat”, with the benefit of having weathered work in different cultures in my back pocket. On the surface, both companies could not have been more different: both were in very different local environments steeped in centuries of traditions, one had a reputation for being a slow, lumbering supertanker only adopting cutting edge technology once it was proven elsewhere whilst the other was - and still is to some degree - held up as a paragon of world class performance. With the benefit of hindsight, that perceived dissimilarity was only skin deep; at their core both companies were beholden to an extreme focus on measures of performance, with everything from employee ratings to building reorgs being assiduously measured and monitored in the name of continuous improvement.

In thinking about how both companies could be so similar in spite of the powerful local influences, a number of possible causes come to mind. One consideration is the strong (some might say out-sized) concentration of American Ivy League-type MBAs in the upper and middle echelons of management. Often young relative to the Technical SMEs who delivered actual work, these necktie-wearing, business types - eager to perhaps prove their worth - were more than happy to trot out whatever would get them noticed. Another possible contribution - for different reasons in both contexts - is something Will Storr’s fascinating book calls the Status Game. At the first company, existing as it did within the context of a loose culture, the predominant games seemed to be ones of success, with perceived high-fliers rocketing up the corporate ladder and getting plum assignments every couple of years or so. The tried and tested - ideally by someone else - was as near a guarantee of success as there could be, hence the optimization towards safe, slow change. The wider societal context of a tight culture at the second lent itself more to prestige games. Here the optimization seemed to be towards new, shiny, exciting stuff that could be highlighted upwards as a means to gain the attention of those higher up the status hierarchy.

Of the host of metaphors which exist for organizations - as organisms, as systems with varying degrees of openness, and machines to name a few, I find that of a complex adaptive system the most useful for trying to understand how those two organizations have evolved. At the risk of oversimplification, one may theorize the prevailing culture of any organization as being a dynamic relationship between the wider societal norm in which it exists (tight vs loose), the stated culture of the organization and the behaviour of agents (employees and stakeholders) who relate with it both internally and externally. Marrying this with the idea that we all are involved in status games, one may further theorize that individual agents (of differing influence within the organization) optimize their strategy for different status games (prestige, dominance, virtue, success etc). Taking all of the above into account, it seems reasonable to proffer a third reason for why KPIs and measures are as ubiquitous as they are: organizations are intrinsically complex, and a good KPI is often seen as our best shot at introducing some objectivity into determining performance of both agents and the wider organization.

Circling back to the subject of the tyranny of measures, two things stand out as to why they can be especially dangerous. One is most succinctly captured by Goodhart’s Law, which is perhaps Heisenberg’s Uncertainty Principle for KPIs. Within this framework, the degree to which a target is attained and its usefulness are conjugate variables, a conclusion which is somewhat inevitable once the organization as a complex adaptive system framing is accepted. Of equal importance, at least, is the preponderance of power law type distributions for a number of the things KPIs are usually defined to mitigate such as preparedness for significant adverse effects. Here, the attainment of a target can provide a false sense of security, when in reality it is the relative rarity of the event ostensibly being mitigated that has prevented it from being realized. If there is a moral to this, it is perhaps that the most important time to be vigilant is when a KPI is “green”.

Photo by Lucas Vasques on Unsplash

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It seems to me that one of the defining trends of the past few years has been the death of expertise, occasioned by a deep distrust of experts and what they have come to represent, the entrenched gatekeepers (of knowledge in this case). Like all other such trends, COVID - and what some have cast as the long shadow of government overreach - have only served to accelerate this demise. Prediction is an exercise all experts find difficult, but in continuing to provide dire point estimates which didn’t necessarily pan out in the end, flip-flopping on things like masks and the big ruckus about the efficacy of drugs like hydroxychloroquine, experts in the COVID prediction and management game did little to assure the sceptical that they knew what they were doing and talking about. There is a (politically expedient?) sense in which this sort of disbeliever can easily be dismissed as a nutter, a devotee of a niche conspiracy theory who is more likely to douse themselves in bleach than wear a mask, own a gun and be fervently religious, but to tar all such with that brush does a lot of disservice to the multiple head-winds buffeting experts.

It is, I believe, important to provide some definition and context to the sense in which I am thinking about experts and expertise. To borrow a definition from the Cambridge Dictionary, an expert is a person with a high level of knowledge or skill relating to a particular subject (emphasis mine). I consider this a narrow definition of an expert, focused as it were on a particular subject. A less narrow definition would consider people with skills and knowledge in related subjects as experts in that wider domain. It is from this wider definition that the second main head-wind affecting expertise comes. Again, COVID has has a part of play here with the preponderance of data and folk with time on their hands conspiring to create tons of citizen epidemiologists whose perspectives on things sometimes differed spectacularly from the experts. At the heart of this though is the question of how much domain specific knowledge is required to predict a phenomenon. Is time series data, just data devoid of context? By crunching enough data, can patterns useful for prediction and policy be extracted with a reasonable degree of reproducibility?

The third, perhaps more pertinent, headwind facing experts takes the second to the limit, specifically to what extent Artificial Intelligence and Machine Learning based expert systems can replace human experts. Consider Knowledge Graphs as a subset of such techniques, and if in the extreme one such system were able to ingest the knowledge currently encoded in whatever consists the key texts of a given domain (say Fontana and Greene for the Corrosion Expert). Some input from an actual expert might be required to clarify some fuzzy edge cases but the relationships between material properties, service conditions and the likelihood of corrosion/ failure could easily be learned/ embedded in such a graph. The material selection problem could very easily be solved by such a system somewhat trivially. Such prediction/knowledge domains are ones in which AI/ML systems already outperform humans in to some degree.

There is a personal angle to all of this, being the sort of solidly mid-career expert in a niche domain within the so-called knowledge economy which could conceivably be replaced by such AI/ML expert systems. My industry is notoriously slow to adopting technology (a famour CEO once compared it to a slow turning tanker) but even there the beat of the data science drums are getting louder and more persistent, beaten both by those who truly espouse disruptive technology and those who want to be seen as doing so.

This mid-career guy’s hope though lies in the value the domain-expert still brings to all of this (for now). Data will win in the end though, I am afraid, but till then one must learn to coexist as one half of these hybrid human-AI decision systems. That is perhaps the moral of the Zillow story, if one can be found from that affair.

Photo by jaikishan patel on Unsplash

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An excess of 6,000 thoughts wend their way each day through the average individual’s mind, just under one every 10 seconds by my approximation if we assume the average- if he/she exists - is otherwise asleep for eight hours. By comparison, we breathe about 20,000 times a day and our hearts beat over 100,000 times a day, which begs the question as to why Descartes chose thinking (I think therefore I am) as the defining characteristic of being. Of all the bodily functions - and I recognise by calling thinking a bodily function I am tacitly refusing to acknowledge the mind is a different entity - the long held consensus seems to be to hold thinking to a higher, exalted status. At first glance it seems that a key difference is in how much control one has over the respective processes ; heartbeats and breaths are to a large extent involuntary actions whilst the content and quality of one’s thinking would appear to be firmly within the realms of one’s control. This idea of control though, is an illusion I believe.

I lean towards thinking of thinking as an abstraction layer that hides the complex interplay of knowledge, experience and memory. Depending on which mast you nail your colours to, on the spectrum between a literal seven day creation and a less contrived/designed origin of life, thinking is either a function defined by a designer or one that has emerged from the complex system it abstracts. The common conclusion from either perspective though is that thinking is what enables us first survive and then thrive in the world. Interesting research which finds a surprising connection between thinking and the brain’s navigation system supports this point in my layman’s view. That accepted, what control we have is limited to consciously adding more knowledge and experience to our knowledge-experience pot, such as can tilt the equilibrium towards the desired direction. I imagine that is why thoughts and control of belief are such key parts of religious and political systems - if we can control the source files of thought, the individual and the group can be formed and bent to our will over time.

Thinking is therefore important, as is what we think about and how we think, which is why meta-cognition has become a keen interest of mine recently, especially as I have begun to explore spaces outside the tightly constrained realms of faith and belief I grew up in. I am finding though that what I think I know only becomes clearer when I mull over it consciously over time. In a similar vein, Dinty W. Moore quotes Joan Didon as saying:

I write to find out what I am thinking, what I’m looking at, what I see, and what it means.

This is what (I hope) this space will be for me, a place where the discordant notes of thoughts often coursing through my mind on faith, life, books and a fair few other things can be engaged, each assay like the blow of a chisel which though insignificant by itself, builds on the past and slowly carves out a thing of exquisite beauty.