orgtheory.net

Loyal orgheads know that Foucault is one of my underground favorites (see page 5/July 2007 newsletter). But I was kind of stumped when Dan the Orgtheory Man asked me where Foucault was in modern orgtheory. At first, I thought he meant the absence of Foucault on this blog. He meant *all* of org theory.

Now, Dan wasn’t quite right. There is actually a critical organization studies wing,* where Foucault is right at home. Foucault is also popular among ed school org folks, at least the skeptics who view schooling as partly (or completely) a form of social control.

These pockets of interest don’t explain the lack of Foucault in the rest of org studies  and mainstream sociology, though he does have fans in sexuality studies. Of all the fancy French theorists, you’d think he’d get a bit more attention. He wrote books on hospitals (Birth of the Clinic), prisons (Discipline and Punish) and state formation (Studies in Governmentality). Basically, he’s the orghead of the Continental tradition. The organization was the site of power. I would say that he’s had more to say about organizations than any other major European social theorist since Weber.

So what gives? I mumbled an answer to Dan. Here’s what I tried to say:

• Timing: Foucault had an impact in the English speaking world in the 1970s/1980s. He’d have to compete with neo insitutionalists, ecologists, network theorists, contingency folks, and Carnegie school people for attention. Not easy.
• Hard to reproduce I: The power of Foucault’s writing emerges from a combination of historical vision, deep skepticism toward modern notions of freedom, and a solid acquaintance with the history of philosophy. How many American sociologists would even try to pull that off?
• Hard to reproduce II: Foucault doesn’t offer any simple variables for you to work with like cultural capital, class position, network centrality, etc. “Episteme” and “discourse” are too big and broad for most people to handle.
• The swing away from fancy theory: Foucalt *really* took off in the 1980s - when his major works had been translated and picked up in America. At that time, sociology (and management) was moving away from super big theory and shifting towards middle range Merton style sociology. Blame Parsons and Marx in equal parts. And the GSS. Very un-Foucault.

In the end, any explanation has to concede the general distaste that Americans have for fancy social theory (see this post on the decline of “high theory” generally) and specifically why orgtheorists would just ignore the one guy who would have much to say. I’d probably split it 50/50.

* Organizations and Markets routinely trashes these people in the “Pomo Periscope” series.

I’m a big fan of the emerging artificial intelligence applications, including chatter bots.  Though the projections in AI have always outpaced actualities, nonetheless the emerging technologies and attempts are amazing and interesting.

The crowning test of AI of course is the Turing test: can an autonomous agent mimic human intelligence, for example in conversation?  The Loebner prize puts this to the test, and the 2008 winner will be selected in ten days.  This year’s chatter bot finalists are Elbot, Eugene Goostman, Brother Jerome, Jabberwacky, Alice and Ultra Hal.

I gave them all a quick spin the other night (if you’d like to try this, just search for each bot via google) — just an exchange of five-six sentences with each.  The winner?  The most realistic and entertaining — though a bit hokey and very slow — was Brother Jerome. And, here’s some of the interesting meta-thinking that goes into putting together a realistic conversation.

What is interesting about these bots is the analytical rigor that goes into something seemingly simple (well, depending on what it’s about), in this case, conversation.

Given the early links between AI and organization theory (via Herbert Simon and others), I’m surprised  that there no longer seems to be a similar programmatic effort to make links between the two.  So, what might be learned by re-linking AI and organization theory?  I’ll try to later — this’ll require a longer post — post some ideas about what this type of AI/org theory program might look like.  I think some folks are definitely working in this space, though questions remain.

A snapshot of a representation of scientific paradigms — click here to view the full poster.  Looks like the orgtheory sweet spot (topically — well, we’ve probably mused about everything on the map) is somewhere around the nine, ten o’clock area.

The map “was constructed by sorting roughly 800,000 scientific papers into 776 different scientific paradigms (shown as red and blue circular nodes) based on how often the papers were cited together by authors of other papers. Links (curved lines) were made between the paradigms that shared common members, then treated as rubber bands, holding similar paradigms closer to one another when a physical simulation forced them all apart: thus the layout derives directly from the data. Larger paradigms have more papers. Labels list common words unique to each paradigm.” More details here.

Attention, networkers: According to the soc net email list, there is a small, but important, coding error in the 2004 General Social Survey that casts doubt on a well known empirical result of McPhereson, Smith-Lovin, and Brashears. You might remember that they showed that the 2004 GSS reported that people’s networks were smaller. A vigorous debate ensued.

Turns out that this might attributed to a mistake in the coding of the survey data. 41 people who were missing data were coded as having zero social contacts. It’s got to be emphasized that this was a NORC error, not an error from the authors. The original research team issued this note over the Soc Net list this morning:

Since our 2006 ASR paper using these data got so much attention, it’s worth noting our initial take on the impact of this discovery.  The unweighted, uncorrected percentage of NUMGIVEN=0 in 2004 is about 27%, as opposed to about 9% in 1985.   If we  recode the 41 mis-coded 2004 respondents from 0’s to missing and use the correct weights for the sampling frame of the study (as we did in the 2006 paper), we get about 22% isolated in 2004 and about 10% isolated in 1985.   Our re-estimated models from the 2006 ASR paper, which also continue to correct for fatigue, uncooperativeness and other factors (as well as demographic shifts), still show a substantively and statistically significant difference between 1985 and 2004.   None of our results from the ASR models change substantively because of the newly discovered data problem, but we will publish corrected tables and figures as soon as ASR allows. We are also analyzing the results of a re-interview of some of the 2004 respondents, which will be forthcoming later.

Of course, the skeptics quickly came back. Claude Fischer writes on the same list:

The 2004 GSS Finding of Shrunken Social Networks: An Artifact? ABSTRACT: In 2006, McPherson, Smith-Lovin, and Brashears (MS-LB) reported that Americans’ social networks had shrunk precipitously from 1985 to 2004. They found that respondents to the 2004 General Social Survey (GSS) provided dramatically fewer names when asked to list the people with whom they discussed important matters than respondents to the 1985 GSS had given to the same question. Critically, the percentage of respondents who provided no names at all increased from about 10 percent in 1985 to about 25 percent in 2004. In this memo, I present anomalies found in the 2004 GSS network item which strongly imply that this dramatic increase in apparent social isolation is an artifact. I speculate that the artifact may be the result of random error. With as yet no complete explanation for these anomalies, scholars at this time should draw no inference from this GSS question as to whether American social networks changed substantially from 1985 to 2004 – they probably did not – and should be cautious in using the 2004 network data.

Fischer says he’ll post his paper on his personal website: sociology.berkeley.edu/faculty/fischer/

Elster has a quote in Nuts and Bolts for the Social Sciences that reads: “Reduction is at the heart of progress in science.”  I’m fairly sold on the general point (and in fact have cited the quote in papers with Nicolai).  I think specifying and understanding the micro-elements, and their interaction, a la Coleman, of any whole (organization, community, etc) tends to make for better theory.  That might just be cookie-pushing on my part, though it appears to be pretty close to an axiom in science (though in the social sciences it clearly requires defense).

I wonder, though, if there are disciplines where reduction has not led to progress? Or, have some disciplines taken the opposite tack — finding progress in ‘conflation’ (I’m sure there’s a better word)?   Indeed, if you broadly look at the recent focus in the organization sciences, it appears that the emphasis (well, if you can characterize a whole field!) is actually increasingly moving to higher, rather than lower, levels of analysis: alliances, community, institutions, and so forth.  Is organization theory, then, an exception?

That said, perhaps the natural progression is first to focus on various higher-level correlations and then we begin to ask questions about their origins, which tends to naturally privilege and demand an understanding of micro-elements and their interaction.

This appears to be happening in networks research, for example.  First we note all the brilliant, good stuff that comes from networks: information, knowledge, resources, etc.  However, when imputing ‘all things good’ to networks, we also concurrently make the rather tenuous assumption that the nodes themselves are randomly distributed and homogeneous, and thus superfluous — everything is ascribed to the network itself (the macro rather than micro).  But now, presumably, we are starting to focus on the micro-elements as well, the nodes, recognizing that much, or at least some, of the network-effect might be endogeneous.  Clearly there’ll be some intermediate interaction effects and complex, reflexive (between-level) relationships which need to be specified.  (Usually these are handled with a wave of the hand and quick reference to “emergence.”)

Another example of progression through reduction might be illustrated by Durkheim.  Read the rest of this entry »

Categorizing is dangerous: one might mislabel, forget, over-generalize and simplify or just be plain wrong.  Nonetheless, Nicolai and I have put together a table, with simplified categories, for an upcoming Academy of Management presentation on models of rationality that show up in (or rather, are implied by) extant strategy and organizational theories. (The table might be more applicable for the strategy setting.  For example,  I am not quite sure where the “institutional agent” would really fit in, though it has some overlaps with the boundedly rational model.)

Well, the table is crude, somewhat redundant, it needs revising, but nonetheless it’s a rough first cut at things for the presentation. (In the presentation, we’ll build on some insights from Popper’s ambitious essay “Of Clouds and Clocks: An Approach to the Problem of Rationality and the Freedom of Man.”)

Also, it’s ironic that scholarly conceptions of human rationality are often different from scholars’ conceptions of themselves.  Thus, we should perhaps add an über alles “scholar agent” to the mix as well.

James Evans (U of Chicago, Sociology) has a nice piece in the recent issue of the journal Science about how access to online journals has shaped and narrowed and perhaps hampered the way scientific knowledge is created.  A very interesting piece — here’s the abstract.

Online journals promise to serve more information to more dispersed audiences and are more efficiently searched and recalled. But because they are used differently than print—scientists and scholars tend to search electronically and follow hyperlinks rather than browse or peruse—electronically available journals may portend an ironic change for science. Using a database of 34 million articles, their citations (1945 to 2005), and online availability (1998 to 2005), I show that as more journal issues came online, the articles referenced tended to be more recent, fewer journals and articles were cited, and more of those citations were to fewer journals and articles. The forced browsing of print archives may have stretched scientists and scholars to anchor findings deeply into past and present scholarship. Searching online is more efficient and following hyperlinks quickly puts researchers in touch with prevailing opinion, but this may accelerate consensus and narrow the range of findings and ideas built upon.

There’s of course also a positive side to scientific consensus and variance reduction, right?  If online journal access not only “puts researchers in touch with prevailing opinion,” but also the right opinion, then presumably science progresses faster; variance reduction can be good.  The acceleration of science via various technologies — print, publications, books, academic exchange, travel, conferences, the Internet — throughout history can be seen as a process of variance reduction.  Wouldn’t things have progressed faster (and variance reduced) if we’d given up on ‘phlogiston’ or an earth-centric view earlier? (Though, I don’t really like to disparage historical best efforts at true facts either — at one point these represented the cutting edge of knowledge.  I suppose science progresses, a la Popper, from one approximate truth to a better approximate truth.)

Now, if prevailing scientific opinion is wrong, then variance reduction of course is a problem.  We’ve then got ourselves a deeper epistemological problem here about how we define knowledge: scientific consensus, logic, observation, social construction, etc.  Interesting issues though!

The article (abstract, article gated).  And, here’s a summary article from the most recent issue of The Economist.

Important article alert! The March issue of Administrative Science Quarterly has an article by Jerker Denrell and Balázs Kovács dealing with the selective sampling of empirical settings. While they are not the first to examine the problems associated with selection bias (there is a significant literature on this dating back to the 70s), they are one of the first to draw attention to the problems associated with choosing empirical settings (rather than just cases) that have atypically positive outcomes. As examples, they talk about two very common sorts of selective sampling: studies of practice diffusion and studies examining density-dependence in organizational populations. In the former, researchers typically study practices that have diffused widely rather than studying practices that failed to diffuse at all or that diffused at some intermediate level. In the latter, scholars typically study populations that have become sufficiently legitimate and have high density levels.

Denrell and Kovács, through both simulation and empirical demonstration, show that the selective sampling of empirical settings can lead to biased results. Diffusion studies typically underestimate the contagion effect. Ecological studies potentially find spurious density-dependent effects. That is, many of the U-shaped mortality curves observed by ecologists over the years may be completely spurious as a result of only studying populations that have survived long enough to generate a sufficiently high population density. Thus, the conclusive and consistent finding of density-dependence isn’t as conclusive as we once thought!

This study urges caution among scholars trying to make vast generalizations when studying a limited population. In fact, the paper suggests that it often isn’t enough to study only one population at a time. These examples are important in light of the excitement generated during earlier discussions on this blog about studying unique or extreme cases. While I wholeheartedly agree with my co-bloggers that it’s important to think about the theoretical implications of the extremes (especially when building new theory), testing those theories requires data covering a broad array of outcomes.

But how does one avoid selective sampling bias? The authors offer several suggestions, including changing the dependent variable. One obvious solution is to study multiple populations chosen for reasons other than the average value of the dependent variable. For example, in the forthcoming issue of the American Journal of Sociology, Sarah Soule and I have a paper that uses resource partitioning theory to explain levels of specialization among social movement organizations in three different social movement industries. The first design advantage is that we study three different industries, rather than just one. Including multiple populations reduces the potential of selective sampling bias. Also, rather than choosing to study industries because they were more or less specialized (on average) than other industries, we chose the industries based on their overall prominence during a 30-year time span. Presumably this prominence was uncorrelated with specialization levels. The data show that specialization actually fluctuates quite a bit over time. Finally, rather than only looking at the hazard rate of mortality among organizations (as ecological studies often do), we also included the level of tactical and goal specialization as a dependent variable in the analysis. Interestingly, some of our results support resource partitioning hypotheses (e.g., yes, industry concentration increases the probability that any single organization will specialize), but we also find some surprising results. You’ll have to read the paper to find out what they are (see an online version here).

Unfortunately we finished our paper before I had a chance to read Denrell’s and Kovács’s study and so we didn’t write much in the paper about the problems of selective sample bias and the design advantages of studying multiple industries (although we do talk about it somewhat in this paper!). My hope though is that the contribution will be obvious and people will cite it in the future. I can always hope right?

Yesterday, I wrote about the importance of case studies and general therories, and how they are really two sides of the same coin. In a comment, Mike asks:

I like the argument you’re making, and I am highly supportive of and interested in the study of singular or rare cases. Yet, I wonder if a Popperian might argue that studies of singular events are more difficult to falsify and thus outside the realm of science at worst or on the fringe at best.

Once again, let’s apply this sort of Popperian logic to actual science. If you can’t study singular cases, then the following studies are fringe science:

• You can’t study the platypus because there is no other mammal like it.
• You can’t study the human brain, because it is the only one capable of higher abstract functions like counting and language.
• You can’t study integer numbers, or even fractions, because most numbers are actually irrational.
• You can’t study the planet earth, because it’s the only one, so far, that appears to have life on it. Heck, for a long time, it was the only planet we knew about!

Once again, the reasoning is absurd because the premise is false. The deeper lesson is that facts don’t exist by themselves. Facts usually exist in relation to theories that predict certain kinds of distributions. Ignoring cases leads to bad conclusions because you toss out information about what the distribution is really like. Multiple studies of odd cases can clarify what those limits are. Theories also have assumptions, and cases can reveal the problems with the assumptions.

For example, one biological prediction is that “all mammals give live birth.” If we acted in a thoughtless statistical fashion (a la population fallacy), then we’d either simply not record the platypus as a data point because it’s a case study, or simply code it into a variable. Then we’d look at the population average and conclude “all mammals give live birth,” even when it isn’ true.  Another example, if we found life on a single other planet (so far, Earth = n =1!!), it would greatly change our theories. According to the anti-case study people, the NASA scientists studying Mars are fools because even if they found life, the sample size would only be N=2! The point of studying Mars is that if we found life, it would help us understand the limits of earth focused biology, even if we couldn’t immediately generalize to a more comprehensive theory of life.

I’ll conclude by offering another hypothesis: the anti-case study bias is driven by mathematism, the wrong belief that math is an essential quality of real science. Here’s the real truth - math is sometimes used to refine and verify ideas created through observation and thought experiment. However, real science comes from observation, thought experiments, and critique. Statistics can tell you about trends, but only observation and reason can tell you about the underlying structure and quality of your theory - and that’s where case studies come in.

Mark wrote a very interesting post on the study of rare cases. Let me the take the opportunity to offer my own take on a related topic, what I call the population fallacy:

It’s better to study generic, typical processes than unusual, rare events. If something is statistically unusual, then you should “move up” to studying the entire population.

Here’s my argument against this principle: if we took this statement seriously, there would literally be nothing to study because nearly anything interesting can be easily reframed as an extreme case of a larger population. Don’t believe me? Let the reducto ad absurdum begin:

• We shouldn’t study dominant firms, like Google, because most firms are short term failures.
• We shouldn’t study firms, because most attempts to create firms don’t get off the ground.
• We shouldn’t study people who try to start firms, because most people don’t try to start firms.
• We shoulnd’t study people, because most living things aren’t even people.
• We shouldn’t study living things, because most stuff is inanimate.
• We shouldn’t study inanimate stuff, because most of the universe is actually empty space.

Sound crazy? It’s supposed to be. The initial premise is faulty. But what’s the problem with the anti-rare case bias? It’s this: from a broad point of view, the world is a fairly unordered and structured mess. The “average” state of the universe is often uninteresting or disorderly. However, scientists (including social scientists) are interested in situations where processes come together to create order and structure.

Science is inherently dual in purpose - you study the dull average so you can better understand the structure that you do have. Rare cases can also illuminate what is average, if properly framed. You can summarize this in what I call the transition principle:

Science is about identifying the properties of broad classes of objects and then uncovering the emergence of subclasses of objects. Science is about descriptions that allow one to formulate theories of the transition from general processes to interesting specific cases, and then back again. One without the other leads you to either to the population fallacy, or the mistake of inferring general principles from unusual cases.

The focus is neither on the special case, or the population. It’s on how you can switch between the two. And if you look at the cumulative record of science, you usually see that this level switching is present in most successful research paradigms. Case studies and large N statistics are used to reinforce each other to build a theory that you can have confidence in.

The publication of Harrison White’s notes in Sociologica is one of the coolest things I’ve ever seen in an academic journal! The notes themselves are fascinating, although if you’ve read some of Identity and Control and Markets from Networks they’re not entirely new. Perhaps more fascinating though are the commentary around the notes. Marco Santoro offers one of the most impressive and succinct summaries of the history of network analysis that I’ve seen (by focusing on White’s early contribution), and Michael Schwartz’s reflections on the class are really interesting.

What’s remarkable about White’s notes, in retrospect, is how complete his view of networks and categories were for their time. That’s not to say that White had it all figured out way back in the early 1960s, but all of the necessary parts were there. The “elementary types of social structure” - networks, categories/identities, frames - are all present in these notes. It’s also remarkable that it took so long for structural sociology to reincorporate all of these elements after a long period of networks-dominated structural theory.

Read the rest of this entry »

In a classic discussion of scientists sampling the ground in the Amazon rainforest, Bruno Latour details the process through which physical bits of soil are turned into recorded measurements and data points for comparison and analysis. He remarks,

Stage by stage, we lost locality, particularity, materiality, multiplicity, and continuity, such that, in the end, there was scarcely anything left but a few leaves of paper. … But at each stage we have not only reduced, we have also gained or regained, since, with the same work of representation, we have been able to obtain much greater compatibility, standardization, text, calculation, circulation, and relative universality, such that by the end, inside the field report, we hold not only all of Boa Vista (to which we can return), but also the explanation of its dynamic.

Now, via Andrew Gelman, a fascinating story from Quirin Schiermeier at Nature about the social production of data.

Read the rest of this entry »

Stanley Fish had a long-ish, interesting New York Times post yesterday related to language and performativity, “French theory in America.” 

Previous orgtheory posts on  controversial statistical topics: interaction terms, Bayesian statistics, p-values and survey response rates.

I’ve come to the conclusion that a common statistical practice - the reporting of an R-sqaured for a logit or other categorical data model - is lame and we should immediately stop. Why? Let’s count the ways:

• Categorical data models estimate the effects of unobserved latent variables. You can’t compare the estimate with this purely theoretical object. All R2 are weird concoctions.
• There’s literally seven different R-squared statistics (at least) and they don’t always correlate well. Most people don’t know the difference between them. Even statisticians disagree on which one you should use.
• Pseudo-R2 measures usually do not measure variance, but they measure changes in likelihood and related quantities. For example, McFadden’s R2 measures changes in likelihood functions, which have no obvious interpretation. Unless the pseudo-R2 is either 0 or 1, the statistic is uninterpretable in relation to your data.
• The pseudo-R2 is not an analog to the OLS R2 and it’s misleading to say so. But folks believe it is.
• The pseudo-R2 for some models, like the Tobit, actually create *neagtive* values and are thus confusing for many readers.
• Reporting pseudo-R2 leads to the reader to think that you are directly measuring goodness of fit. Wrong.
• There is no decent way to decide what is good or bad fit. A psuedo-R2 measure of .02 tells you nothing about the match between predicted and observed quantities.

For the full run down, check out this 2007 article by Illinois b-school’s Glenn Hoetker in the Strategic Management Journal. Thanks to my colleague Scott Long, for bringing this article to my attention.

I try to find something interesting to listen to while doing such mind-numbing activities as cleaning up my references section etc. TED usually has something interesting (though, I’m running out of stuff there); today I listened to this excellent Timothy Gowers presentation on “the importance of mathematics” (there are a total of 8 parts to the talk). (Here’s his blog, and web site) 

The talk has something for everyone (including some asides with graph theory, etc), though he largely makes a persuasive argument for why mathematics has critical downstream importance to practice despite the motivations of individual mathematicians themselves (who are interested in interesting rather than practical problems, and interested in such nebulous factors as beauty rather than application).  The presentation not only has some nice public policy implications, but it also implicates how we might assess research and its importance in any discipline. 

Omar

In the latest issue of Econ Journal Watch (the site appears to be currently [4/16/08 10:30am EST]) down for some reason), there is a very interesting piece (link here) by Philip Coelho and James McClure, amusingly entitled “The Market for Lemmas: Evidence that omplex Models Rarely Operate in Our World.” The purpose of the paper is to investigate the “scientific returns” to complexity in math-heavy argumentation in economics. Their argument, drawing on classic Marshall, is that there is that long, complex chains of mathematical reasoning are likely to be less that optimal because (1) long and complex chains of reasoning are more likely to be wrong (”truth content declines as an exponential function of the number of links in the reasoning chain”), and (2) less likely to have empirical implications (”less likely to contain operational statements” as they put it in a rather arcane Vienna-Logi-Po phrasing).

Their analysis looks at a bunch of articles published in the main Econ journals (AER, JPE, etc.) throughout the century, with a particular focus on the last three decades. They find two things: first, the number of articles that contain the word “lemma” has increased dramatically over-time. Their JSTOR search yields about 22 articles who contain the word lemma in the 1960s, as compared to 353 in the 1990s. From this they surmise that the math-complexity of the average Econ mathematical-theoretical article has been increasing over time. Second, they find that mathematically complex articles (their “operational criterion”, is [hence the title] simply counting the number of “lemmas” that the article includes; they define “high mathematical complexity” as containing five or more lemmas) are less likely to either contain empirical statements or to attempt to provide empirical assessments of the derived propositions. Finally, they show that mathematically complex articles in economic theory as defined above are less likely to be cited in comparison to lemma-heavy econometric articles. The conclude that a lot of economic theory, in its removal from empirical reality an entanglement in a skein of lemmas, runs the risk of being “not even wrong” (a fate worse that just being plain wrong in their view).

This article is interesting and got me to think about an organizational theory that I like very much, but that has become increasingly lemmalized in the last few years (Fabio has a nice assessment of the most recent book here). While Hannan, Polos, Carroll et al, see the increasing formalization of the theory as a good thing, and they rely on more flexible styles of logical formalization (non-monotonic, fuzzy, etc) that are much less constraining that the usual first-order logic of most economic theory, if Coelho and McClure are correct, we should find that the impact of the new and improved lemma-heavy version of org ecology should decline with the addition of each lemma. However, one good thing that Hannan et al have going for them is that they are trained sociologists (Polos is a logician of course) and they don’t mind including “operational statements” and (god forbid) writing a grant to get some data to test the empirical implications of the theory.

Teppo

Issues of causality are critical to science, and org theory obviously is no exception.  Most any research project needs to appropriately think through issues of causality and correlation, proximate versus ultimate causes, causal chains, etc. 

The most recent issue of Philosophy in the Social Sciences has a nice piece titled “Weighted Explanations in History” (link is to author working paper version).  The article wrestles with many key issues on causality, specifically as causality relates to historical explanation.

Two other sources on causality and history:

1. Tolstoy, like a good philosopher, carefully wrestles with various issues around causality in his epilogue to War and Peace (see part II).  Very interesting.
2. David Lewis (specifically, see his 1986 collected papers book) is also elightening:

Any particular event that we might wish to explain stands at the end of a long and complicated causal history.  We might imagine a world where causal histories are short and simple; but in the world as we know it, the only question is whether they are infinite or merely enormous (Lewis, 1986: 214).

Here’s the abstract for the PSS piece:

Weighted explanations, whereby some causes are deemed more important than others, are ubiquitous in historical studies. Drawing from influential recent work on causation, I develop a definition of causal-explanatory strength. This makes clear exactly which aspects of explanatory weighting are subjective and which objective. It also sheds new light on several traditional issues, showing for instance that: underlying causes need not be more important than proximate ones; several different causes can each be responsible for most of an effect; small causes need not be less important than big ones; and non-additive interactive effects between causes present no particular difficulty.

Key Words: causation • explanation • history • interaction • proximate • underlying

Teppo 

This quote, by Polya, sounds like many of the papers that I really like:

The principle is so perfectly general that no particular application of it is possible.

Teppo

Last week I raised the issue of using “trump cards” in scholarly dialogue and debate, specifically “language games” as a trump card.

Another trump card that I have seen some scholars (not nobel prize winners themselves) wield is “the nobel prize.”  It is, as if, somehow the granting of the nobel prize makes the associated arguments of that person beyond reproach, somehow transcendent, part of the canon: you’d be an idiot to disagree with spontaneous order, biased decision-making, etc.  Perhaps folks using the nobel prize trump card do not feel that way — particularly since Nobel Laureates of course have disagreed on various issues — but I have seen the nobel prize be used as a similar show-stopper as “language games.”  The problem, of course, similar to language games, is that it makes the arguments themselves fall wayside, not allowing for constructive dialogue about the issues at stake.  So, before you (or I) pull either the “language games” or “nobel prize” trump card out, it might be more worthwhile to try to engage on the issues (or alternatively, just admit that we don’t know what we’re talking about). 

That said, perhaps some things indeed are part of the canon, true, taken for granted, even sacred.  And, perhaps that is where our two trump cards meet: challenging the canon or nobel-prize-related arguments — if the nobel is where the canon gets canonized for some – then may have to emerge exogeneously, from another game.  

 Or, perhaps its back to Max Planck:

A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it.

Teppo

For several years now I have been having a running debate and discussion (and friendship) with JC Spender about various issues related to organizations.  We rarely agree, but always have fun.  And, invariably we end up in various metatheoretical and epistemological debates, usually with JC pulling out the ultimate trump card: “language games.”

Here’s a (slightly edited) response that I recently wrote to him (perhaps more on the context later):

Now, I am of course not completely naive.  I recognize that theoretical insights from different disciplines can be, and often are, contradictory, sometime wildly so, as illustrated by the re-emergence of the neoclassical economics versus organization theory clash (Ferraro et al. 2005; cf. Pfeffer, 1997).  But, being the naive realist that I am, I believe that these clashes can and ought to be settled via the merits of the respective arguments rather than merely referring to ‘language games.’  In other words, we must realize that reasoning and science relies on that “massive core of human thinking which has no history” (Strawson, 1959: 10).  Thus, referencing language games is simply an act of academic cowardice — an effort to avoid truly engaging with the issues, logic and arguments at stake.  Furthermore, citing language games inherently does not recognize that some theories and arguments and facts simply are false, period.  And, vetting the ‘true’ and ‘false’ of arguments, proposed facts and theories is at the very heart of science.