The problems facing 1990s business strategy were quite different than Pelzman’s concerns for IO. At the time, and to this day, there is a lot of “verbal” strategic management theorizing that gets published that can be conceptually sloppy. This doesn’t mean one can’t be very precise. It just so happens that in management and strategy a lot of “theory” and empirical work cashes in big on being vague and mushy. People can read into it whatever they want.

In my opinion then, formal modeling’s marginal benefit to strategy is substantial. (Aside: economists have been doing a lot of solid empirical work relevant to strategy in the last 20 years).

Thus, long time O&M friend S. Postrel is pretty handy to have around if you’re mucking through lots of qualitative theories since he happens to have some very nice models to aid thinking things through.

As a few people above point out the models are more useful if there are some data and expectations grounded in observation to use with them. In the field of strategy this has not been a big problem. In 1980s IO perhaps it was.

]]>Maths can be misleading even in the physical sciences

I have never thought about how interviewing managers might yield data relevant to understanding the relationship between multiple mechanisms for generating profit (such as rivalry restraint and competitive advantage). If you have any ideas, let me know.

Meanwhile, if you want to learn more about this theory as it currently stands, then I would recommend reading the following recent articles (listed chronologically):

Makadok, Richard (2010). “The Interaction Effect of Rivalry Restraint and Competitive Advantage on Profit: Why the Whole Is Less Than the Sum of the Parts.” Management Science 56(2): 356-372.

Chatain, Olivier, & Zemsky, Peter (2011). Value creation and value capture with frictions. Strategic Management Journal, 32(11): 1206-1231.

Makadok, Richard (2011). “The Four Theories of Profit and Their Joint Effects.” Journal of Management 37(5): 1316-1334.

Makadok, Richard and Ross, David Gaddis (2013). “Taking Industry Structuring Seriously: A Strategic Perspective on Product Differentiation.” Strategic Management Journal 34(5): 509-532.

Also, Mike Ryall and his coauthors, Joao Montez and Francisco Ruiz-Aliseda, have written a very interesting not-yet-published working paper on this same topic, where they extend the value-capture style of modeling that he mentioned in his comment above (download from http://works.bepress.com/michael_ryall/20/ ).

Thanks again,

Rich

Thanks for your response, you’ve clarified several points for me. I can certainly appreciate the ability of mathematical models to generate, as you put it, useful insights, especially in relatively new fields of research. Though I didn’t learn much formal modeling in undergraduate B-school, the RBV, Porter’s 5 Forces, and other insights of strategy have had a tremendous impact on my thinking as an economist. I’m happy to work in a field (agricultural and applied economics) that respects B-school perspectives as well as “traditional” economics. While I don’t see myself “bringing my plow over to [your] farm,” I certainly appreciate the invitation and hope I can incorporate the insights of the strategy literature into my research program.

The risk issue originates, I think, with the “need” for developing mathematically-derived theoretical hypotheses for empirical work. Mean-variance modeling is prevalent in the literature likely because it is an elegant way of deriving mathematical hypotheses. If the requirement for mathematically-derived theoretical hypotheses weren’t in place we could begin from what is plainly obvious to all of us, namely that risk aversion is a down-side issue, and use “verbal” theory and whatever empirical method is in line with that theory. I think this is an example of mathematical rigor dictating our (theoretical) understanding of economics. Of course, this isn’t directed at you as a criticism, but at what we both criticize as an over-reliance on formal modeling.

I’m glad you enjoyed the Levins piece. It was presented to me in a research methods class in graduate school. The professor focused on criticisms of the article, but I found Levins’ article quite convincing (and, of course, humorous).

Best,

Levi Russell

]]>This discussion is really useful for early career researchers like me also contemplating adding new quant skills to increase ‘marketabilty’. I do case studies, interviews, ethnography, and follow grounded theory methodology. It appears this abstract model building goes against the grounded methodology where models need to emerge from empirics, and it should be beyond just a ‘relationship between models and data. Going back to Rich’s example on dividing effects into rovalry and comp advantage, wondered what comes in the way of discovering this through indepth interviews and field work. In other words, how could someone observe this dual effect through grounded theory work.

Thanks,

Amer khan

Curtin university Malaysia

What I would like to stress is that there are many theoretical fever-dreams that can only be dispelled with formal modeling. Some of these are my own: For example, I’ve had the idea of a tradeoff between the frequency and severity of downside events as a function of the degree of prevention undertaken, looking for an interior solution of optimal prevention. So far, no dice, and I can see where my initial primitive intuition was in error after trying to fit some functional forms and drawing some pictures. Some of these fever-dreams are others’: I can’t tell you how many theory articles I’ve reviewed where I’ve wanted to shout at the page “Just build a model already!” Precise verbal theorizing is very, very hard to do, and many of the more successful practitioners (e.g. Williamson) freely resorted to drawing curves and diagrams which are–shhh–math. The idea that it’s easier to hide trivial or wrong ideas with math than with words strikes me as empirically false on a massive scale.

The same applies to Peter’s point about the “if” clauses of formal propositions not being verified in application. They are still less verified when they are never specified explicitly or when they can mutate through equivocation to seem to apply or not apply to any situation.

Formalism is a drag to both readers and writers when things that are easy to say in words (with perhaps a couple of hand gestures) must be turned into hard-to-encode-and-decode mathematical expressions. But if you are going to any depth with the analysis, you will quickly find that the things you can say by using the math are very, very hard to say in words without elaborate translation into awkward clauses, clarifications, examples, etc. A diagram may be your best bet. In fact, I’ve occasionally scared myself that some result I had formally derived earlier couldn’t be true based on erroneous verbal arguments or pictorial intuition; only going back to the math restored my understanding.

I certainly believe that formal models should be accompanied at all times by a bodyguard of hypothetical or real examples that keep the relation of the theoretical terms to aspects of reality in view. Oddly, I’ve found that most formal modelers in strategy are much more interested in such “storytelling” than are many purely verbal theorists, who often seem content to float on abstractions.

]]>Of course I agree with everything that was said about the role of formal modeling to generate insights that were unanticipated. This accords with my personal experience developing papers using game theoretical models. I’d just like to add two unrelated observations to this debate.

First the role of the formal model as both a discipline and a spur for your intuition is not to be neglected. For one thing you cannot get any result you want unless you explicitly change your assumptions. This can tell a lot about the value of these assumptions. For another, you sometime get a result which you know is mathematically correct but for which you have absolutely no intuition. You just don’t know what is going on. All the work necessary to figure out where the result is coming from in terms of mechanism is really valuable. And it is tricky too because one can easily give the wrong interpretation to a formula that was correctly derived. But I often feel I learn the most in that phase.

Second, I think it is extremely important to establish the connections between formal theory and empirical work, especially in strategy. When I write a theory paper one of my explicit goals is to help empiricists to do a better job. This can be by providing new hypotheses to test. More subtly, it can be about providing alternative explanations that may help us reconsider the usual interpretation given to empirical patterns (e.g., Lippman and Rumelt’s 1982 paper).

Another way is to provide a toolbox to deeply integrate the theory and the empirics. This is what is starting to appear in the value-based literature as seen in recent workshops at the AOM and at SMS. That one has been a long run effort that seems just to start paying off.

Olivier

]]>Hi, LR.

Thanks for suggesting the amusing “On Farmers” article by Levins, which I had not previously known about.

For the benefit of others who are following this thread, I located an online copy of this article at the following address:

I confess that I am unfamiliar with the fertilizer/risk literature that you mention, so perhaps I am missing or misunderstanding something important, but what you describe sounds more like an empirical measurement issue to me. Am I missing or misunderstanding something?

Also, thanks very much for your pointer to Bryan Caplan’s “economath” blog posts, which I also had not previously known about. I stand behind my argument that insight, rather than precision or rigor, is really what matters. For that reason, I quite agree with you that when formal modeling is pursued so excessively that it no longer yields any meaningful insight, then its return on investment (i.e., investment in training and effort) can certainly be negative. Based on your comments and Caplan’s, it sounds like this may be the case in many subfields of economics, and perhaps even for the economics discipline as a whole. Even Paul Krugman’s DEFENSE of formal modeling (in response to Caplan) starts out by frankly admitting that this is true, as follows: “I don’t really disagree with this statement [i.e., the statement that ‘most economath badly fails the cost-benefit test’]; after all, Sturgeon’s Law — 90 percent of everything is crud — definitely applies to economics, and when it comes to elaborate mathematical models you probably want to up the percentage.”

What makes me happy about this — because it gives me (and Josh and Mike and Olivier and a few others) job security — is the fact that I DON’T live inside an old discipline like economics where formal modeling has been the norm for 40 years and has been dramatically overused to the point where it no longer yields much new insight. Rather, I live in the relatively young field of strategic management where formal modeling is still very new and dramatically underused, so that there will remain, for many years to come, plenty of “low-hanging fruit” where genuinely new insights can be derived via formal models. So, if the “soil” for formal modeling in your area of research has been depleted due to excessive tillage, then I invite you to bring your plow over to our farm, where newly-cleared fields are still fresh and fertile. We could sure use more farmhands over here, so feel free to bring some friends along too.

Thanks,

Rich

Apparently not all that glitters is gold.

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