Disciplines As Contrarian Correlators

I’m often interested in subjects that fall between disciplines, or more accurately that intersect multiple disciplines. I’ve noticed that it tends to be harder to persuade people of claims in these areas, even when one is similarly conservative in basing arguments on standard accepted claims from relevant fields.

One explanation is that people realize that they can’t gain as much prestige from thinking about claims outside their main discipline, so they just don’t bother to think much about such claims. Instead they default to rejecting claims if they see any reason whatsoever to doubt them.

Another explanation is that people in field X more often accept the standard claims from field X than they accept the standard claims from any other field Y. And the further away in disciplinary space is Y, or the further down in the academic status hierarchy is Y, the less likely they are to accept a standard Y claim. So an argument based on claims from both X and Y is less likely to be accepted by X folks than a claim based only on claims from X.

A third explanation is that people in field X tend to learn and believe a newspaper version of field Y that differs from the expert version of field Y. So X folks tend to reject claims that are based on expert versions of Y claims, since they instead believe the differing newspaper versions. Thus a claim based on expert versions of both X and Y claims will be rejected by both X and Y folks.

These explanations all have a place. But a fourth explanation just occurred to me. Imagine that smart people who are interested in many topics tend to be contrarian. If they hear a standard claim of any sort, perhaps 1/8 to 1/3 of the time they will think of a reason why that claim might not be true, and decide to disagree with this standard claim.

So far, this contrarianism is a barrier to getting people to accept any claims based on more than a handful of other claims. If you present an argument based on five claims, and your audience tends to randomly reject more than one fifth of claims, then most of your audience will reject your claim. But let’s add one more element: correlations within disciplines.

Assume that the process of educating someone to become a member of discipline X tends to induce a correlation in contrarian tendencies. Instead of independently accepting or rejecting the claims that they hear, they see claims in their discipline X as coming in packages to be accepted or rejected together. Some of them reject those packages and leave X for other places. But the ones who haven’t rejected them accept them as packages, and so are open to arguments that depend on many parts of those packages.

If people who learn area X accept X claims as packages, but evaluate Y claims individually, then they will be less willing to accept claims based on many Y claims. To a lesser extent, they also reject claims based on some Y claims and some X claims.

Note that none of these explanations suggest that these claims are actually false more often; they are just rejected more.

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  • arch1

    It is interesting that about 1.5 of these 4 explanations of
    other-discipline belief dysfunction also involve own-discipline belief
    dysfunction (namely #4 and to some degree #2).

  • lump1

    Each standard academic field has its own methods, assumptions, temperament and analytic strategies. These are applied within the field, but I’m pretty sure the practitioners are confident that their disciplinary toolkit would also generate knowledge outside the proper confines of their field. When they contemplate issues that exist at the intersection of disciplines, applying their disciplinary toolkit seems to them like the most natural place for analysis to start.

    Of course, scholars from different disciplines will look at the same interdisciplinary question and feel sure that the most natural place to start analysis is completely different. You pick one toolkit, and everybody else see your work as being methodologically flaky.

    Probably the most important thing that every graduate student learns is the (ever-changing) set of conventions for what counts as a “good move” inside their discipline. It’s natural that they would imbue this standard with more objectivity than it deserves. And by its nature, it’s highly improbable that any truly interdisciplinary project will strike them as satisfying their conception of a “good move”.

  • brendan_r

    In his book “Bureaucracy” James Q Wilson shows all sorts of ways that public choice econ fails to explain much actual bureaucratic behavior. For example, the Farm Admin used to be responsible for administering food stamps, but desperately wanted to divest that (and did), in violation of the assumption that agencies try to maximize their size and scope.

    Wilson found reasons to reject common explanations by examining an enormous range of instances in detail.

    I assume someone who works within a bureaucracy also sees much that makes him skeptical of standard things in public choice econ.

    Picture a guy the size of an electron; he wouldn’t be too impressed with classical mechanics.

    So I’m suggesting that maybe excessive familiarity, expertise, and detailed low level knowledge might cause one to reject useful, conventional, but often wrong higher level generalizations.

  • Zvi Mowshowitz

    On the last sentence: The fourth explanation does point directly towards a reasonable argument that such claims will more often be false, and that it is right for more people to reject them more aggressively, which is the correlation argument: It’s not just that a given person’s views that are correlated, it’s probably the truth as well – it’s almost certain that some fields have mostly (or more often than average, at least) correct standard claims, and that others have largely incorrect (or less often than average, at least) standard claims.

    If as you say, the cross-field claim Z requires that both X1 and Y1 be true, where X and Y are fields, it’s less likely to be true than a claim that relies on X1 and X2, or on Y1 and Y2, provided your prior is P(X1)~P(X2)~P(Y1)~P(Y2).

    Or to put it another way: You’re relying (more or less) on two or more sets of assumptions to basically hold, rather than just one, and that makes it more likely you’re assuming something false.

    People’s instinct to say, if Z requires fields X and Y, it will be harder to convince me of such a claim than a claim in field X, or one in field Y, seems very reasonable to me.

    • Yes I agree that correlation in truth among claims within a field makes claims relying on multiple fields less reliable.

  • Assume that the process of educating someone to become a member of discipline X tends to induce a correlation in contrarian tendencies.

    Apparently you think the assumption is warranted, as it isn’t much bolstered by a successful prediction that can be generated in three other ways. It would be informative to have a sense of what observations justify your assumption that education in a discipline causes you to accept or reject packaged claims.

    In fact, it seems to me that education in a discipline inculcates the ability to refute isolated claims. Amateurs tend to think they must reject or accept entire theories. They don’t know how to dissect them into independent parts.

    Your analysis, moreover, suggests that training in a discipline causes you to take a far-mode view of the discipline; but the language in which they’re described–disciplines closer to you–suggests we take a far-mode view of claims in external disciplines (rather than separating claims).

  • arch1

    If explanation #4 is talking about one package per discipline X, with those who stay in X accepting the package whole, it’s just an extreme subcase of #2.
    If OTOH #4 is talking about *multiple* packages per discipline:
    a) Its justification needs fleshing out (why would Xers have a greater tendency than non-Xers to coarse-grain X’s claims into multiple all-or-nothing packages?)
    b) Its explanatory argument needs fleshing out, too (since it only works to the extent that a typical argument’s X-claims tend to cluster into a smaller-than-chance number of these same X-packages)

    • Smaller packages could include all the claims made in a particular course, or during a particular segment of a course.

      • arch1

        Thanks for these examples which each respond to each of a) and b). I now think #4 both nonredundant and plausible.

      • You’re talking about controvertible claims, and the only way graduate students might plausibly take courses where no claims contradict those in other courses is if the claims are generally accepted. Such mandatory claims exist in physics, no doubt; they don’t exist in sociology and the less biological branches of psychology.

        There may be more of a core of mandatory claims in economics. But there are competing schools of economics (but not in physics) in the intellectual world, if not in U.S. academia. The consensus among academic economists (that you universalize to other soft sciences) seems a bug rather than a feature.

  • Aren’t you omitting the main reason claims across disciplines are more readily rejected: we don’t have good ways to credential expertise outside of disciplines. [How many folks are competent to review your book?]

  • aluchko

    Explanation 5:

    Fewer people feel qualified to evaluate cross-discipline claims. Because they feel unqualified to endorse the claims they reject them to preserve reputation.

    Explanation 6:

    Cross-discipline claims tend to be of lower quality. Either because people who aren’t strong enough to compete in X look for an unexploited sub-discipline X+Y, or because lack of expertise in Y leads even strong investigators to spurious claims. The high volume of poor quality claims leads people in field X+Y to adjust their priors and treat all claims with higher skepticism by default.

  • charlie

    Alan Greenspan actually sort of points this out in his latest book. I recall that he cites statistics about the literal physical size of the economy in like pounds of plant and machinery. It apparently peaked a while ago, even though the ‘size’ of the economy in terms of GDP has obviously been increasing.

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