Takeaways:
1. “Holobiont” is a word. What a cool word!
2. I love the quote used as the epigraph:
When we try to pick out anything by itself, we find it hitched to everything else in the Universe.
—John Muir
3. This take on our—or at least, my—obsession with taking photos seems to contain at least a grain of uncomfortable truth:
This is a despair of our own making, according to the German sociologist Hartmut Rosa, not because of technology, but because of a futile yearning to make the world controllable. The categorial imperative of late modernity, Rosa writes, is straightforward but bleak: “Always act in such a way that your share of the world is increased.” Relationships become a means to an end, reducing a magically networked existence into mere “networking.” The writer and former nun Karen Armstrong shares this uneasiness, noting that when people visit museums, they no longer simply absorb being next to an object with world-historical implications. Rather, they take a photograph with their phones and move on, seeking “to own it in some way, as though it does not become real to them until they have a virtual copy.” But that aspiration for control is misguided, Rosa argues, for “it is only in encountering the uncontrollable that we really experience the world. Only then do we feel touched, moved, alive.” Even in life’s planned celebrations, we most remember the unplanned flourishes.1
4. This result is too over-the-top for me to confidently believe but also too hilarious not to share:
Millions now treat undirected contemplation as a waste of time, a frivolity to be life hacked out of your goal-driven schedule. A drive or commute must be filled with radio, chatter, mindless games, music, or podcasts—but rarely silence. Even thirty empty seconds while waiting in line at the grocery store beckons many of us to our smartphones. (To these charges, I, too, plead guilty.) In one recent study, when participants were left alone for between six and eleven minutes in a room that was empty except for a device that could give them a painful electric shock, many opted to shock themselves rather than to sit alone with their thoughts. One man shocked himself 190 times in less than ten minutes.2
(Do we think the one guy just really hated reflecting on his life? Or was this the moment he discovered he had genes for streamlined wireheading?)
5. Klaas suggests a connection between optimization and instability, a connection which means modern societies tend to create conditions that empower seemingly-small events to have drastic, unpredictable consequences:
Because of our relentless drive for ruthless, perfected optimization, most modern social systems have little slack—such as our economies and our politics—and the levels of interconnection are now so great that even minor perturbations can create major shocks. We, by design, race toward the cliff edge, but continue to be surprised when we fall off it.3
6. Chapter 11 has some thought-provoking discussion of how difficult it is for social science to make good predictions. For one thing, Klaas thinks that when a prediction turns out to be wrong, there’s often a “crucial, unanswerable question: Was the original theory wrong, or did the world change?”4 Did we mis-analyze a given context, or did we shift to a new context?
Klaas also seems skeptical that we can make meaningful probability estimates for some kinds of events:
Silver is a world-leading expert at estimating whether polls are accurately capturing public attitudes and then putting together a model based on that data combined with a rigorous set of assumptions. But he’s no better than the rest of us at anticipating what’s known as epistemic uncertainty, gazing into the future to predict highly contingent events (such as whether a foreign government will hack a political data server or whether a sex-offender politician’s unrelated computer files will prompt FBI director James Comey to reopen a federal investigation days before the election). Yet, all of Silver’s analysis has the veneer of hard science because it’s incredibly sophisticated statistically, using thousands of simulations to prove his points. But there aren’t thousands of elections. There’s just one. It’s inherently uncertain. We don’t know whether the outcome we experienced—Trump winning—was an average outcome, an extreme outlier, or anything in between because we can’t rerun history. You can find out that the underlying probability of flipping a heads in a coin toss is roughly 50 percent by simply flipping the coin over and over and observing the results. But can you tell whether a coin is fair or biased if all you have is a single flip that comes up tails? Obviously not, but for one-off events in a highly particular context, we too often try, and fail, to make that judgment.
When Clinton lost, Silver pointed to his model as a defense: 71.4 percent isn’t 100 percent! There was nearly a 30 percent chance of Clinton losing in the model, so the model wasn’t wrong—it was just something that would happen nearly a third of the time! If you say we were wrong, you don’t understand math! This raises the obvious question: Could Nate Silver’s model ever be “wrong” in that election? When the model predicts something with a low probability and it happens, then it’s just the world that’s being weird, not the model being incorrect. It’s unfalsifiable, impossible to disprove.5