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Quantum Mechanics? No, Quantum Epistemology

There is value in eschewing blind faith in data and more in the people who commit to work with uncertainty to provide life-changing results.

Article Jan 05, 2022

Rob Keefer

Trust the science.” We hear this refrain quite often these days, as parties stake out over-politicized positions responding to a pandemic that has taken too much from all of us. Taken in the broadest way possible, “trust the science” suggests a belief that the scientific process is somehow unassailable, based on objective, provable, measured data. Measurement is math, after all, and the numbers don’t lie, right?

Sort of. The equations that brought us those numbers don’t lie. But the invisible biases of the people doing the measuring, and how they do it, can introduce uncertainties into the scientific process. Thought through fully, these uncertainties can cloud what we think we know just enough to make you wonder: what is real?

The sometimes undetected biases in the measurement process, and how they can shake our hold on truth, are central to a fascinating Scientific American article we read recently. In “Quantum Epistemology for Business,” University of Toronto Professor Mihnea Moldoveanu and Boston Consulting Group Senior Partner Martin Reeves suggest a misplaced belief in the infallibility of Big Data as a driver of important business decisions. The reason, they say, is that no matter how many fancy tools you create, you can never eliminate the human impact.

“The data (from the Latin datum—a thing which is given) on which machine learning and analytics are built, are taken as given and incontrovertible,” the authors write. “But what managers, data scientists and social scientists think of as data is in fact not given. It is the outcome of a process of measurement - an interaction between an observer, a technique or apparatus and a context.”

To underscore the problem, Moldoveanu and Reeves invoke the principles of quantum mechanics, which, they explain, utilizes less specific terms in its mode of measurement: indeterminacy, superposition, entanglement, and observer-dependence. In an oversimplified way, the notion is that knowledge exists in bits rather than as data points fixed in place and time. How you evaluate that knowledge depends on where they are, where you are, and your background, processes, and experiences in assessing them.

The indeterminate nature of “data” lies under the surface of many everyday events. Take, for example, the notion of “economic recession.” A recession is officially defined as two consecutive quarters of negative economic growth. We often know when the economic clouds are starting to gather -- the media will report those factors in different contexts. That stream of negative news has an impact. Does it have enough influence to tip an economy that was on the edge into a recession by possibly convincing a household to postpone a trip or other significant expenditure? Was the information reported based on available empirical data or used as an indicator enabling decisions to change the future? Did external forces trigger other changes that made it a self-fulfilling prophecy?

The authors hint at this in the business context of a “transformation program:” just talk of transformation alone is enough to shift the direction of the business through perception alone. It is, the authors note, “treacherous work: the information you need to do the right thing will be highly dependent on the perceptions, incentives, and behaviors of those on whom you rely to provide that information.” And those perceptions, incentives, and behaviors have already changed the “right” thing.

So, then, if everything is constantly changing, and nothing is fixed or can be measured objectively, what does that say about the era of Big Data? Is it built on faulty promises and premises? That’s not what we’re saying at all. We believe that the best decision-making comes from refined interactions between humans and machines. If you think that machines provide all the answers, you inherently limit your thinking and work within artificial constraints. And you may find yourself frustrated when things don’t go your way.

The Scientific American authors put it this way: “We are laboring under the illusion of ‘classical information’ and ‘classical measurement’ when we deal with human and organizational phenomena. Insights from quantum epistemology raise indelible doubts about the given-ness of data. But business people need more than reasonable doubt: they need insights and action prompts. How can we leverage ‘quantum effects’ in human organizations?”

At POMIET, we immerse ourselves inside a problem, challenging ourselves to question everything, to get to a solution. Our recommendations and our approach are based on real-life observations, not just on math. We believe instead in human-centricity: Ascribing a little less exalted blind faith in the data and more in the people who understand the fallibility and commit to working with its uncertainty to provide satisfying and life-changing results. You could even call it our method of “quantum innovation.”

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