Bayesianism — a dangerous superficiality

19 December, 2016 at 12:34 | Posted in Theory of Science & Methodology | 4 Comments

419fn8sv1fl-_sx332_bo1204203200_The bias toward the superficial and the response to extraneous influences on research are both examples of real harm done in contemporary social science by a roughly Bayesian paradigm of statistical inference as the epitome of empirical argument. For instance the dominant attitude toward the sources of black-white differential in United States unemployment rates (routinely the rates are in a two to one ratio) is “phenomenological.” The employment differences are traced to correlates in education, locale, occupational structure, and family background. The attitude toward further, underlying causes of those correlations is agnostic … Yet on reflection, common sense dictates that racist attitudes and institutional racism must play an important causal role. People do have beliefs that blacks are inferior in intelligence and morality, and they are surely influenced by these beliefs in hiring decisions … Thus, an overemphasis on Bayesian success in statistical inference discourages the elaboration of a type of account of racial disadavantages that almost certainly provides a large part of their explanation.

For all scholars seriously interested in questions on what makes up a good scientific explanation, Richard Miller’s Fact and Method is a must read. His incisive critique of Bayesianism is still unsurpassed.

wpid-bilindustriella-a86478514bOne of my favourite “problem situating lecture arguments” against Bayesianism goes something like this: Assume you’re a Bayesian turkey and hold a nonzero probability belief in the hypothesis H that “people are nice vegetarians that do not eat turkeys and that every day I see the sun rise confirms my belief.” For every day you survive, you update your belief according to Bayes’ Rule

P(H|e) = [P(e|H)P(H)]/P(e),

where evidence e stands for “not being eaten” and P(e|H) = 1. Given that there do exist other hypotheses than H, P(e) is less than 1 and a fortiori P(H|e) is greater than P(H). Every day you survive increases your probability belief that you will not be eaten. This is totally rational according to the Bayesian definition of rationality. Unfortunately — as Bertrand Russell famously noticed — for every day that goes by, the traditional Christmas dinner also gets closer and closer …

For more on my own objections to Bayesianism:
Bayesianism — a patently absurd approach to science
Bayesianism — preposterous mumbo jumbo
One of the reasons I’m a Keynesian and not a Bayesian



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  1. That seems to me the overall problem of statistical induction, not specifically bayesianism. Of course induction is weaker than deduction. If i didnt observe your death i can conclude you are immortal, since my sample contains zero death events. But I will deduce ( as opposed to induce ) you are mortal since all human being eventually die.

  2. This is silly.
    Clearly, the probability of the turkey being on any given day is almost an exemplary case of a process which fails to satisfy IID criteria.
    The problem isn’t Bayesianism or Bayesianists. The problem is people who employ statistical tools without reading (and understanding) the warning label printed on the side, and that applies to any statistical technique, methodology, philosophy, or what have you.
    It’s almost a rule in the social sciences to apply statistical techniques which are only valid under IID to problems with no assurance of IID.
    Why, I don’t know. But it certainly isn’t because of Bayesianism.

    • Edit: “the probability of the turkey being *eaten* on any given day”

  3. So-called “representative” samples of economic data for statistical analysis are intrinsically biased in following sense:
    1. temporal bias: only sampling from past data and no future samples.
    2. structural bias: only “flat” random variables and aggregate macro data are structurally defined in terms of more primitive components.
    3. incomplete bias: violate keynes ‘ concern: the need to include a complete list of all the relevant variables at the outset. This can make a joint distribution assumption invalid.

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