One of my favourite parts of Philip Tetlock’s Expert Political Judgment is his chapter examining the reasons for “radical skepticism” about forecasting. Radical skeptics believe that Tetlock’s mission to improve forecasting of political and economic events is doomed as the world is inherently unpredictable (beyond conceding that no expertise was required to know that war would not erupt in Scandinavia in the 1990s). Before reading Expert Political Judgment, I largely fell into this radical skeptic camp (and much of me still resides in it).
Tetlock suggests skeptics have two lines of intellectual descent - ontological skeptics who argue that the properties of the world make prediction impossible, and psychological skeptics who point to the human mind as being unsuited to teasing out any predictability that might exist. Below are excerpts of Tetlock’s examinations of each (together with the occasional rejoinder by Tetlock).
Path dependency and punctuated equilibria
Path-dependency theorists argue that many historical processes should be modeled as quirky path-dependent games with the potential to yield increasing returns. They maintain that history has repeatedly demonstrated that a technology can achieve a decisive advantage over competitors even if it is not the best long-run alternative. …
Not everyone, however, is sold on the wide applicability of increasing-returns, path-dependency views of history. Traditionalists subscribe to decreasing-returns approaches that portray both past and future as deducible from assumptions about how farsighted economic actors, working within material and political constraints, converge on unique equilibria. For example, Daniel Yergin notes how some oil industry observers in the early 1980s used a decreasing-returns framework to predict, thus far correctly, that OPEC’s greatest triumphs were behind it. They expected the sharp rises in oil prices in the late 1970s to stimulate conservation, exploration, and exploitation of other sources of energy, which would put downward pressure on oil prices. Each step from the equilibrium is harder than the last. Negative feedback stabilizes social systems because major changes in one direction are offset by counterreactions. Good judges appreciate that forecasts of prolonged radical shifts from the status quo are generally a bad bet.
Embracing complexity theory, they argue that history is a succession of chaotic shocks reverberating through incomprehensibly intricate networks. To back up this claim, they point to computer simulations of physical systems that show that, when investigators link well-established nonlinear relationships into positive feedback loops, tiny variations in inputs begin to have astonishingly large effects. …
McCloskey illustrates the point with a textbook problem of ecology: predicting how the population of a species next year will vary as a function of this year’s population. The model is x__t+1 = f(xt), a one-period-back nonlinear differential equation. The simplest equation is the hump: x__t+1 = βxt [1 – xt], where the tuning parameter, β, determines the hump’s shape by specifying how the population of deer at t + 1 depends on the population in the preceding period. More deer mean more reproductive opportunities, but more deer also exhaust the food supply and attract wolves. The higher β is, the steeper the hump and the more precipitous the shift from growth to decline. McCloskey shows how a tiny shift in beta from 3.94 to 3.935 can alter history. The plots of populations remain almost identical for several years but, for mysterious tipping-point reasons, the hypothetical populations decisively part ways twenty-five years into the simulation.
We could endlessly multiply these examples of great oaks sprouting from little acorns. For radical skeptics, though, there is a deeper lesson: the impossibility of picking the influential acorns before the fact. Joel Mokyr compares searching for the seeds of the Industrial Revolution to “studying the history of Jewish dissenters between 50 A.D. and 50 B.C.
Radical skeptics can counter, however, that many games have inherently indeterminate multiple or mixed strategy equilibria. They can also note that one does not need to buy into a hyperrational model of human nature to recognize that, when the stakes are high, players will try to second-guess each other to the point where political outcomes, like financial markets, resemble random walks. Indeed, radical skeptics delight in pointing to the warehouse of evidence that now attests to the unpredictability of the stock market.
If a statistician were to conduct a prospective study of how well retrospectively identified causes, either singly or in combination, predict plane crashes, our measure of predictability—say, a squared multiple correlation coefficient—would reveal gross unpredictability. Radical skeptics tell us to expect the same fate for our quantitative models of wars, revolutions, elections, and currency crises. Retrodiction is enormously easier than prediction.
Preference for simplicity
However cognitively well equipped human beings were to survive on the savannah plains of Africa, we have met our match in the modern world. Picking up useful cues from noisy data requires identifying fragile associations between subtle combinations of antecedents and consequences. This is exactly the sort of task that work on probabilistic-cue learning indicates people do poorly. Even with lots of practice, plenty of motivation, and minimal distractions, intelligent people have enormous difficulty tracking complex patterns of covariation such as “effect _y_1 rises in likelihood when _x_1 is falling, _x_2 is rising, and _x_3 takes on an intermediate set of values.”
Psychological skeptics argue that such results bode ill for our ability to distill predictive patterns from the hurly-burly of current events. …
We know—from many case studies—that overfitting the most superficially applicable analogy to current problems is a common source of error.
Aversion to ambiguity and dissonance
People for the most part dislike ambiguity—and we shall discover in chapter 3 that this is especially true of the hedgehogs among us. History, however, heaps ambiguity on us. It not only requires us to keep track of many things; it also offers few clues as to which things made critical differences. If we want to make causal inferences, we have to guess what would have happened in counterfactual worlds that exist—if “exist” is the right word—only in our imaginative reenactments of what-if scenarios. We know from experimental work that people find it hard to resist filling in the missing data points with ideologically scripted event sequences.
People for the most part also dislike dissonance … Unfortunately, the world can be a morally messy place in which policies that one is predisposed to detest sometimes have positive effects and policies that one embraces sometimes have noxious ones. … Dominant options—that beat the alternatives on all possible dimensions—are rare.
Need for control
[P]eople will generally welcome evidence that fate is not capricious, that there is an underlying order to what happens. The core function of political belief systems is not prediction; it is to promote the comforting illusion of predictability.
The unbearable lightness of our understanding of randomness
Our reluctance to acknowledge unpredictability keeps us looking for predictive cues well beyond the point of diminishing returns. I witnessed a demonstration thirty years ago that pitted the predictive abilities of a classroom of Yale undergraduates against those of a single Norwegian rat. The task was predicting on which side of a T-maze food would appear, with appearances determined—unbeknownst to both the humans and the rat—by a random binomial process (60 percent left and 40 percent right). The demonstration replicated the classic studies by Edwards and by Estes: the rat went for the more frequently rewarded side (getting it right roughly 60 percent of the time), whereas the humans looked hard for patterns and wound up choosing the left or the right side in roughly the proportion they were rewarded (getting it right roughly 52 percent of the time). Human performance suffers because we are, deep down, deterministic thinkers with an aversion to probabilistic strategies that accept the inevitability of error. … This determination to ferret out order from chaos has served our species well. We are all beneficiaries of our great collective successes in the pursuit of deterministic regularities in messy phenomena: agriculture, antibiotics, and countless other inventions that make our comfortable lives possible. But there are occasions when the refusal to accept the inevitability of error—to acknowledge that some phenomena are irreducibly probabilistic—can be harmful.
Political observers run the same risk when they look for patterns in random concatenations of events. They would do better by thinking less. When we know the base rates of possible outcomes—say, the incumbent wins 80 percent of the time—and not much else, we should simply predict the more common outcome.