Saturday , October 31 2020
Home / Miles Kimball / Getting the Best from Wokeness by Having the Right Mean, Reducing the Variance and Mitigating the Losses from Extreme Values

Getting the Best from Wokeness by Having the Right Mean, Reducing the Variance and Mitigating the Losses from Extreme Values

Summary:
In each graph, the bell curve is is the density function for wokeness and the quadratic function below it is the loss function. If one can only control the mean of wokeness, the optimum is to have the mean of the wokeness distribution at the point where the loss is

Topics:
Miles Kimball considers the following as important:

This could be interesting, too:

Global Economic Intersection Analysis Blog Feed writes COVID-19 Update 27 October 2020: U.S. Vs EU27

MilesCorak writes An Employment Insurance system for the 21st century

Tyler Cowen writes Those old and new service sector jobs

Tyler Cowen writes Friday assorted links

Getting the Best from Wokeness by Having the Right Mean, Reducing the Variance and Mitigating the Losses from Extreme Values
Getting the Best from Wokeness by Having the Right Mean, Reducing the Variance and Mitigating the Losses from Extreme Values
Getting the Best from Wokeness by Having the Right Mean, Reducing the Variance and Mitigating the Losses from Extreme Values
Getting the Best from Wokeness by Having the Right Mean, Reducing the Variance and Mitigating the Losses from Extreme Values

In each graph, the bell curve is is the density function for wokeness and the quadratic function below it is the loss function. If one can only control the mean of wokeness, the optimum is to have the mean of the wokeness distribution at the point where the loss is minimized. And given the symmetry of the two functions, one can tell whether one has too much or too little wokeness by whether (a) there are worse horrors coming from too little wokeness than from too much wokeness or (b) there are worse horrors coming from too much wokeness. Without at all minimizing the horrors from too much wokeness (see for example “John McWhorter on Professors Worrying about the Consequences If They Sound Less Than Totally Woke”), I argue that the horrors from too little wokeness are currently worse.

But this simple model points clearly to two other ways to minimize losses: reducing the variance of wokeness and mitigating losses in any given situation (making the loss function less tightly curved).

A start toward reducing the variance of wokeness is criticizing and educating people with a level of wokeness either higher or lower than the vertex of the loss function. Even if it doesn’t change average beliefs, giving people the facts about statistically discriminating beliefs should be able to reduce the variance of “wokeness.” (My post “The Cost of Variance Around a Mean of Statistically Discriminating Beliefs” is related, but has a somewhat different model.) At any rate, we should try to collectively express strong social disapproval for those at either extreme of the wokeness distribution—obviously with more disapproval expressed on one end if the mean of wokeness is not at the vertex of the loss function. Currently that means more expression of social disapproval for too little wokeness, but there will be some people with enough too much wokeness that they deserve strong expression of social disapproval.

Reducing the losses from any given situation affected by high or low wokeness is often a matter of common sense that too often gets blocked in its application by partisanship of those with quite high or quite low wokeness. Those of us nearer the level of wokeness at the vertex of the loss function (a group in which I optimistically put myself :) need to support common sense measures. What do I mean?

  1. Send in the police to stop looting when folks with too much wokeness, while not doing any looting themselves, are too willing to tolerate looting.

  2. Hold the police to account when they mistreat people.

  3. Call out coded racist statements.

  4. Don’t let people hide behind saying they aren’t racist, they are just statistically discriminating. Because of hysteresis or slow convergence, to get to the right equilibrium in a reasonable time frame, we need to do more than just get rid of out-and-out racism. (See “Enablers of White Supremacy.”)

Quite importantly, note that someone is not a racist for opposing too much wokeness. They are only supporting racism if they promote too much wokeness. The distribution of wokeness has a large enough variance that it would be unlikely indeed that there aren’t some people out there with too much wokeness. So there ought to be some criticism of people for too much wokeness; it would be a bad sign if there weren’t. Those who criticize too much wokeness when it is genuinely left of the vertex are heroes, not racists. But those with too much wokeness will fight back, so those who criticize people for too much wokeness need to be tough heroes.

But given the current mean of wokeness, we need many more heroes to attack too little wokeness than the number of heroes we need to attack too much wokeness. I am heartened to see the number of people taking up the cause of antiracism with vigor. It makes me proud of our country.

Miles Kimball
Miles Kimball is Professor of Economics and Survey Research at the University of Michigan. Politically, Miles is an independent who grew up in an apolitical family. He holds many strong opinions—open to revision in response to cogent arguments—that do not line up neatly with either the Republican or Democratic Party.

Leave a Reply

Your email address will not be published. Required fields are marked *