Too many top minds have “positive capability” bias. That (unideal) label usefully contrasts with Keats’ “negative capability,” a poetic idea that applies to unpoetic “scientific” experts.
- Keats said “negative capability” formed “the Man of Achievement, especially in Literature.” He was “capable of being in uncertainties … without any irritable reaching after facts and reason.” Shakespeare had negative capability “enormously,” but “Coleridge … would let go by a fine isolated verisimilitude” because he couldn’t be “content with half-knowledge.”
- “Negative capability” was easier for Shakespeare than for Coleridge — Shakespeare wrote before Newton had enthroned the idea of underlying universal mathematical laws, and Coleridge after.
- Colin McGinn says Shakespeare was a “naturalist without being a scientist.” He described what he saw, without presuming underlying universals drove human behavior (whereas Freud’s universalizing led to imagining all unconsciously suffered oedipal desire).
- After Newton “subsumed the universe” to math, our thinking universe changed, to presuming universal mathematical patterns underlay everything. But Newton was lucky — nothing in physics chooses. Or innovates. Or needs game logic. But we do. The language of physics basically needs only four force verbs. The syntax of our reality — how its parts fit — is richer. (See also Newton pattern vs. Darwin pattern.)
- Obviously, I’m not against “facts and reason,” but many thinkers often now reach unthinkingly (and unskillfully) for data, and statistics, and studies. Whereas much that matters isn’t in “the numbers.” And much is true without data (see analytic vs. synthetic truths, and the logic of needism).
- Pureeing “Big Data” in the stats blender can easily mislead— e.g., the average human has one testicle and one ovary. Without skilled qualitative distinctions, quantification can confuse. Statistical tools were developed in fields with relatively stable populations and patterns. Much in our lives isn’t; e.g., we know sports stats aren’t safely predictive.
- Data, like poetry, depends on metaphors. Data typically has at least two: a) Pythagoras’ fruitful, but limiting, “all things are numbers,” and b) that “the numbers” mathematically mimic reality.
- Data in history (or social science) often isn’t like data in physics. People aren’t biological billiard balls: We react varyingly. (As Rochefoucauld said, “At times we are as different from ourselves as we are from others.”)
- Economics needs more “negative capability.” As Noah Smith notes the necessity of “being in uncertainties” arises because “macroeconomic data is too weak.” Experts who don’t advise contingencies accordingly, suffer “positive capability” bias: overreliance on the beloved ideas and tools they’re expert in (see the one-trick hedgehog, and one billionaire’s view). Why assume economic stats are more like those in physics than sports?
- The conceptual skeleton (interconnected deep metaphors) many experts use risk cognitive rigor mortis. The logic of language provides wider metaphors to animate our thinking than math. Perhaps our lives are more grammatical than mathematical, and more polymorphic than physics. E. O. Wilson advises scientists to think more “like poets.”
Blake prayed “May God us keep / From Single vision & Newton’s sleep.” Whether God will or won’t, skilled reasoning should.