Why techies consistently get the social sciences wrong

It’s about a paradigm.

I’m not—at least not today and probably not ever—planning on taking on the memo written by a now-fired Google engineer which attempted to explain why women are not as well represented in high technology. But I would predict we’ll probably see more such memos and that, even if we don’t, the underlying attitudes will persist.

This is a problem for Silicon Valley that goes far beyond questions of workplace culture. It affects the products they produce and probably nearly all its efforts to address social problems.

And to understand it, we have to understand Silicon Valley’s success in a different frame.

Silicon Valley has succeeded brilliantly at positivism, deploying scientific method and quantitative research to produce systems that scale well, massively. Positivism is a paradigm that, among many other things, insists on reproducible, and therefore reliable, results. It assumes linear cause and effect and is highly reductive, meaning that it assumes the whole can be understood by analysis of the parts. While the fallacy of the false dichotomy is well understood, positivism tends to think in ‘either/or,’ and is therefore inherently susceptible to that fallacy.

Positivism is also out of date. Research over the last century has led to systems theory and complexity theory, which view biological and other systems as something different from the sum of their parts, with the difference being labeled ‘emergent properties,’ and see causation as much more often mutual, in which parts of a system affect each other. Systems cannot be understood by analysis of their parts but rather must be observed to see how their parts interact with each other and what they produce.[1]

But because Silicon Valley engineers have succeeded so brilliantly with positivism, they adopt the positivist view that scientific method is the only reliable—even at the expense of validity—way of knowing. The distinction between reliability and validity is crucial: Reliability says you can consistently reproduce particular results. Validity is about whether those results and whether the variables in scientific method actually represent what they are claimed to. Validity is a problem, especially in quantitative research, because it is often hard to find quantifiable variables that actually represent what researchers seek to represent (the selection of such variables is called ‘operationalization’). And when researchers choose variables poorly, which they may be prone to do especially with a positivist and therefore limiting view of the world, the results won’t actually mean what researchers think they do.

Think, for example, of yourself. Are there any numbers that actually convey who you are with real meaning? It’s a pretty low-risk guess that your answer will be no. You are more than your age, your weight, your height, your income, or any other quantifiable demographics. In fact, you are a lot more.

But positivists satisfy themselves with quantitative results because they are reliable.

Real knowledge is a lot less certain. There is, in fact, no theory of truth that holds water. You can find glaring weaknesses in every one of them.[2] Which is to say that thousands of years after philosophers first started wondering what truth is, we still don’t know what it is.

And so-called ‘objective’ knowledge simply doesn’t exist, at least among humans. For it to exist, we would have to have a “God’s eye” view of the world. We would have to be omniscient in every detail. But positivism assumes that objectivity is possible.[3]

In addition to being uncertain, real knowledge is rich and nuanced. That richness and that nuance generally cannot be captured in numbers. And it will, much more often than not, be missed with scientific method. Which is why we have qualitative inquiry.[4]

But positivists insist that their’s is the only legitimate way of knowing.[5] In particularly crude form, this appears when atheists cite ‘science’ as a refutation of religion: God’s existence cannot be ‘proven,’ therefore s/he does not exist. (You won’t hear actual scientists make such a claim because in order to sustain it, they would have to be able to explore every nook and cranny of the entire universe and of, if they exist, all other universes, observable or not, on every level, just to rule out her or his existence.)

In positivism, any attempt to delve beyond reliable results is dismissed as ‘subjective.’ And subjectivity is unreliable. It is not ‘generalizable.’[6]

But with standpoint theory, and even more so with systems theory,[7] we know that the only view we have is subjective. At the end of the day, we are all just individuals looking at our niches, comparing notes, interacting with each other, and jointly constructing a ‘reality.’ And we know relatively little about what lies beyond that construction.

Ultimately, positivism is arrogant. It assumes much that we cannot know. It shouldn’t be dismissed outright: Much of our intellectual progress—technological and scientific—has occurred in this paradigm. We live better lives because of it. But it assumes too much and it excludes too much.

Hence the arrogance of Silicon Valley. And of a bunch of young engineers who still think, with some but certainly not unlimited justification, the world can be a better place if it just does things their way.

  1. [1]Fritjof Capra, The Web of Life: A New Scientific Understanding of Living Systems (New York: Anchor, 1996);Fritjof Capra and Pier Luigi Luisi, The Systems View of Life: A Unifying Vision (Cambridge, UK: Cambridge University, 2014);Joanna Macy, Mutual Causality in Buddhism and General Systems Theory (Delhi, India: Sri Satguru, 1995).
  2. [2]Bradley Dowden and Norman Swartz, “Truth,” Internet Encyclopedia of Philosophy, September 17, 2004, http://www.iep.utm.edu/truth/
  3. [3]Bruce Mazlish, The Uncertain Sciences (New Brunswick, NJ: Transaction, 2007).
  4. [4]Norman K. Denzin and Yvonna S. Lincoln, eds., The SAGE Handbook of Qualitative Research, 4th ed. (Thousand Oaks, CA: Sage, 2011); Bruce Mazlish, The Uncertain Sciences (New Brunswick, NJ: Transaction, 2007).
  5. [5]Bruce Mazlish, The Uncertain Sciences (New Brunswick, NJ: Transaction, 2007).
  6. [6]Bruce Mazlish, The Uncertain Sciences (New Brunswick, NJ: Transaction, 2007).
  7. [7]Edgar Morin, On Complexity (Cresskill, NJ: Hampton, 2008).

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