When combined uncertainty and reflexivity greatly limit the power of maximizing and equilibrium to do useful economics. Reflexive relations between future expectations and outcomes are constantly breaking down at times and in ways that no one can predict—about which there is complete uncertainty. Between them, they make the economy a moving target for the economist. Models get into people’s heads and change their behavior, usually in ways that undermine the model’s usefulness to predict. Which models do this and how they work is not a matter of quantifiable risk, but radical uncertainty. Of course it’s not just economist’s models that get into people’s heads, lots of other models—“metaphors” Krugman calls them—do so and at rates and with reflexive effects that are radically uncertain. And that’s what makes the economy, and human affairs generally, a target moving too fast for anything more than simple models, whether its [sic] in economics, political science, sociology, etc. Between them reflexivity and uncertainty make economics into a retrospective, historical science, one whose models—simple or complex—are continually made obsolete by events, and so cannot be improved in the direction of greater predictive power, even by more complication. The way expectations reflexively drive future economic events, and are driven by past ones, is constantly being changed by the intervention of unexpected, uncertain, exogenous ones. No bubble ever repeats, because people remember them. Regulators engaged in preventing their recurrence are just setting the stage for new and different ones. Dodd-Frank is just an incentive to figure out new ways to exploit regulations. There is room for both uncertainty and reflexiveness in Krugman’s philosophy of economics. For he recognizes that economics has to be a historical science ‘History provides “the real issues and the real experiences that need explaining.” Indeed, his best examples of why history matters incorporate both uncertainty and reflexiveness: “In macro, in particular, you need to know about drastic events”–the German hyperinflation for example….Drastic events, ones no one could see coming—uncertain ones–punctuate economic life. All too often they break up a comfortable predictable reflexive relationship between expectations and reality. They make economics the discipline Krugman thinks it is, like history, one we may look to for lessons but not predictions.’—Alex Rosenberg, “Paul Krugman’s Philosophy of Economics, and What It Should Be”
I very much agree with Rosenberg’s observations on the tensions in Krugman’s (evolving) philosophy of economics, and I also agree with much of Rosenberg’s emphasis on reflexivity and uncertainty. (No surprise, I have published on both and also done a lot of blogging on them.) But I do offer two (friendly) qualifications on Rosenberg’s analysis. First, I note a lacuna in Rosenberg’s analysis. What Rosenberg calls “radical uncertainty” (or “complete uncertainty”) used to be called “Knightian uncertainty.” It’s typically understood as the doctrine that prevents us assigning probabilities or measures on the likelihood of future outcomes. Rosenberg notes in his piece that Knight and Keynes both embraced versions of radical uncertainty. In Rosenberg’s hands this is an epistemic doctrine. (I mention this because Keynes also recognized a metaphysical version of uncertainty.)
Now, mathematical economists displaced epistemic uncertainty in the post WWII era (the start of which we can conveniently mark with Samuelson’s Foundations). But they did not just pretend it did not exist as a problem. Rather one way they tamed epistemic uncertainty is by treating (or operationalizing) it as a species of randomness. They could then design decision procedures that would allow for, say, the best of the worst outcomes, or (another example) a rational option price, in a stochastic environment—the environment is treated as uninformative and one places so-called ‘bets against nature.’ (The previous sentence is, of course, a gross simplification.) This is not the only way economists displaced epistemic uncertainty; elsewhere I have discussed how Arrow Commodities did so in a general equilibrium framework (see also).
Undoubtedly, Rosenberg, who pioneered the philosophy of economics, knows all of this. But one reason to mention this taming of uncertainty strategy is that the problems associated with it do not turn on reflexivity. Rather, it turns on (to use philosophical jargon) hidden modal features of the mathematical treatment of randomness. For the mathematical tools that operationalize randomness all make assumptions about possible distributions (that are and are not allowed). There are other subtle (defeasible) assumptions about the way the world might built into these mathematical tricks. The point is that if we focus on reflexivity we might miss some of the non-trivial ways that make it so hard to have a predictive social sciences.
Second, it’s not true that reflexivity prevents predictability. We know, for example, that the existence of opinion polling does not necessarily prevent their predictions from coming true. Even if this is due to them being self-fulfilling or self-reinforcing prophecies, the point still stands. We need not be too in awe of Nate Silver’s successes in predicting electoral outcomes; more than half a century ago, Herbert Simon (among others) had offered an analysis that had explained how this could be possible (see also). But we need to be clear about what made this success possible: it requires a data-rich environment, institutional stability, a slow evolution of demographics, and limited alternatives in one’s choice (there are a lot of explicit and implicit barriers to entry in elections). One reason I could make fun of Silver’s World Cup predictions is that it is a data poor environment and the demographics (the players/teams) evolve rather rapidly. So, we should expect that in some circumstances economists should continue to make reasonably fine-grained predictions in relatively controlled environments (say, rent-control in a city with stable demographics and limited new supply of housing).
In conclusion, my two qualifications do not undercut Rosenberg’s general thrust. But they also generate some chilling possibilities. Computer power is becoming cheap and we are starting to drown in easily available data. This allows, as Oskar Lange foresaw, for massive simulations and modelling. If governments or corporations could limit our options as consumers to the option generated in simulations then barring natural disasters and war, the economy would be predictable. This is not just science fiction tempting to future rulers of Singapore. Whole chunks of the economy are funny hybrids of public/private entanglements (health, education, defense, construction, etc.) or attempts at generating closed corporate networks (cf, Google, Apple, Facebook, etc.).