Metrics: Something That Is Measurable Across Contexts?

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The difference between evaluation and a metric lies in the fact that a metric proceeds from the shared application of a measurement procedure that can be executed by different people across different contexts. For something to be considered a metric, the measurement input procedure must be exportable across context, and the criteria for the metric must be something that many people can share and understand. However, there are many standards of evaluation that don't allow for this cross-contextual universalization, posing a challenge similar to the barrier of meaning in artificial intelligence and the elusive nature of trans-contextuality in machine learning.

There are maps, and there are territories, and humans frequently confuse the two. No matter how insistently this point has been made by cognitive neuroscience, epistemology, economics, and a score of other disciplines, one common human error is to act as if we know what we should measure, and that what we measure is what matters. But what we value doesn’t even always have a metric. And even reasonable proxies can distort our understanding of and behavior in the world we want to navigate. Even carefully collected biometric data can occlude the other factors that determine health, or can oversimplify a nuanced conversation on the plural and contextual dimensions of health, transforming goals like functional fitness into something easier to quantify but far less useful. This philosophical conundrum magnifies when we consider governance at scales beyond those at which Homo sapiens evolved to grasp intuitively: What should we count to wisely operate a nation-state? How do we practice social science in a way that can inform new, smarter species of political economy? And how can we escape the seductive but false clarity of systems that rain information but do not enhance collective wisdom?

Welcome to COMPLEXITY, the official podcast of the Santa Fe Institute. I’m your host, Michael Garfield, and every other week we’ll bring you with us for far-ranging conversations with our worldwide network of rigorous researchers developing new frameworks to explain the deepest mysteries of the universe.

This week on the show we talk to SFI External Professor Paul Smaldino at UC Merced and University of Utah Professor of Philosophy C. Thi Nguyen. In this episode we talk about value capture and legibility, viewpoint diversity, issues that plague big governments, and expert identification problems…and map the challenges “ahead of us” as SFI continues as the hub of a five-year international research collaboration into emergent political economies. (Find links to all previous episodes in this sub-series in the notes below.)

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Mentioned & Related Links:

Transparency Is Surveillance
by C. Thi Nguyen

The Seductions of Clarity
by C. Thi Nguyen

The Natural Selection of Bad Science
by Paul Smaldino and Richard McElreath

Maintaining transient diversity is a general principle for improving collective problem solving
by Paul Smaldino, Cody Moser, Alejandro Pérez Velilla, Mikkel Werling

The Division of Cognitive Labor
by Philip Kitcher

The Unreasonable Effectiveness of Mathematics in The Natural Sciences
by Eugene Wigner