Before the 2020 election, and after attending a number of virtual industry conventions and user meetings, I asked my data science group whether they could represent “bullshit” formulaically.
As in any case that involves people with PhD’s, it is an iterative process. The first formula was rather simple:
B = 1/K
where B = Amount of Bullshit, and where K = Knowledge + Experience about a given topic was inversely proportional to B.
It was pointed out, however, that knowledge and experience are not necessarily one in the same thing. A knowledgeable person – say an executive at Oracle – could be completely inexperienced about the subject he or she is talking about, but still be knowledgeable (or even smart). So, the team re-wrote the formula:
B = 1/k + 1/e
where k = Knowledge, and e = Experience
After reading “On Bullshit” by Harry Frankfurt, I realized that there was something missing. Frankfurt calls it the “inner strain,” which, from my reckoning, was very different from a pulled hamstring, but rather a relentless yearning to make a mark for oneself. To become a hero, to prove you are the best. With this notion, I concluded that competition was proportional to bullshit, and as such, the formula evolved:
B = 1/k + 1/e + C
where C = Competition.
That is, the more competition on a given subject, the higher the likelihood of bullshit. If Oracle, for example, announces a new product, and so does Salesforce and SAP, then the amount of bullshit will proportionally increase.
My team told me to back off on this. Competition can bring out the best of us, as in the case with Olympians, and can influence BS both ways. I knew I was reaching my own limits, so I called in an outside expert – a Russian. He jumped to a new level of insight by stating that competitive pressure can decrease the real effort put into developing a substantive answer (i.e., non-bullshit).
So, it with suggested to add time:
B = 1/k + 1/e + C/(T*S)
where T = Time, and S is a monthly Spend on the issue.
Olympians train (especially leading up to an Olympics). And it takes time to train. However, when Oracle or Amazon announces their product two weeks before Salesforce’s or Microsoft’s, there’s not enough time to think through the various challenges that might befall a newly announced platform, whose leaders have very little experience, knowledge, and time (specifically time in terms of months, which no-one – notwithstanding Covid – has in the technology industry), but have a high degree of competition. This then led to the topic of weighting, in that not all of these factors should be weighted the same. As a result, we added weights: where β is ranked higher than α:
B = α/k + β/e + C/(T*S)
But how to weight these factors was a different question. We stumbled on this for a couple of minutes and our research led us to the notion of context – or what one might call, the culture of the place in which the bullshit occurs. Culture, from my perspective, is typically a top-to-bottom occurrence, and in the case of Oracle and Salesforce, culture and context originate from Larry Ellison and Marc Benioff respectively. Therefore, I decided to create an index of bullshit culture:
B = lb * (α/k + lb* β/e + C/(T*S)
where lb (short for Larry/Benioff) = amount of bullshit innately originating from culture of the company due its leaders.
*
After the election, I started thinking about my next formula. Before I could get very far, one of my colleagues, who’s steeped in physics, asked some important questions.
He pointed out that though the weights were cool, there could be some room for expansion in my formula.
For example, he asked, “Can a person’s knowledge “K”, or lack of it, cause a bias in the bullshit?
Prior K << N
where N = New technology or a new approach, while << means “much less than.”
Furthermore, he asked, is there any specific context “X” – a hidden variable – that might have been a motivator:
X = fn(entropy, $_greed, trend(lies>0.5), denial_trait…
This hidden variable was intriguing, a potential path to the heart of the matter – above and beyond personalities. X would be found by a function (fn), that parsed and computed things like entropy, greed, denial, narcissism, trends of lies, and anti-social behavior.
Or how does age impact BS? For example, unhealthy aging leads to mild cognitive impairment, with little brain plasticity, declining further from various metabolic syndromes due to poor, or hypercaloric and hyperglycemic food habits – habits that potentially lead to a more opinionated bias toward bigger BS!
As such, the following additional attributes can be added to “K” (Knowledge):
K = analytic_facts + unverified_factoids + narrow_experience + ∂*aging_health + Ω*circumventing_events + ß*skill_disillusion + ∆*self_awareness
where {∂, Ω, ß, ∆} = weights.
Such notions, as unverified factoids (a residual effect of our times), narrow experience, aging health, circumventing events, skill disillusion, and self-awareness could be formulated in the following way:
unverified_factoids = diverse_opinions + misleading_info narrow_experience = sparse_eventful_wisdom (a ‘lifer’ in one company) aging_health = cognitive_decline + no_brain_plasticity + unhealthy_lifestyle where unhealthy_lifestyle = continuous_oxidative_stress + metabolic_syndromes + anabolic_hyperglycolysis + … circumventing_events = happiness_threshold + familial_upbringing + adverse_events + competitive_influence skill_disillusion = imagined_skill_level – actual_skill_level self_awareness = self_denial + lacking_introspection + brittle_mentality + poor_self-esteem
Clearly BS creeps into every facet of our lives, and as a result, more work had to be done. I went back to the drawing board, and decided that I wanted to start at the top, namely the Board, the supreme court of a company.
