Anarkistisen kauppatieteilijän manifesti (The Manifesto of an Anarchic Management Scholar)

Posted on April 17, 2012 by

The following is a short essay I wrote last year for the course “Developing professional skills in academic work.” In it, I outline some ideas how to do management research that

  • is useless for those who wish to use it to advance commercial interests
  • is indistinguishable from research that actually is useful
  • still produces recognition, grants, and other rewards
  • may collapse the capitalist system.

The essay was originally published in 2011 in Räsänen, Keijo (ed.) Tutkijat kertovat. Kymmenen esseetä akateemisesta työstä.

An English version may follow, or not.

Posted in Academic work, How to, Politics, Random musings, Wisdom of the Elders, Writing | 2 Comments

Is there such a thing as radical innovation?

Posted on March 21, 2012 by

Recently, I came across a working paper by two very big names in design/innovation research – Donald A. Norman and Roberto Verganti. The paper was very interesting, not the least since the hill-climbing paradigm of innovation presented therein is almost exactly what I and Lotta Hassi wrote about nearly three years ago. In addition to this certified streak of pure genius, Norman’s and Verganti’s piece argues that

  • human-centered design is fundamentally incremental and has failed to produce any radical change
  • innovations happen in two dimensions, in technology and in meanings
  • design-driven research can lead to radical innovation in meanings

All of which is great, but during the last year or so, I’ve come to question the whole idea of neatly separating novelty into incremental and radical innovations. My problem, which I ran into while researching the evolution of technology, is this: I cannot, in all honesty, find anything particularly “radical” from the innovations I’ve studied.

Innovations are generally defined as “radical” if they are novel and have a major impact, one way or another. A general yet seldom expressed assumption seems to be that a radical innovation (that is, one which has a significant impact) must somehow be a radical departure from existing practice, i.e. it must also be novel and/or unique. (Yes, there is something of a tautology going on here.)

As an example, Dahlin and Behrens (2005; excellent paper, by the way – all innovation scholars should take note) suggest a measure of radicalness for patents: how many patent citations the patent in question has. The logic is that radical inventions (note, not yet innovations) are so different from “prior art” that they do not have antecedents in the field.

But when we think about innovation process, how do these radically-different-from-prior-art inventions actually come about? Many people seem to believe that in order to find radical inventions, one must “think outside the box” and throw out all the “outdated assumptions” about how the world works. Some, myself included, have even made a tidy profit from advising companies in these matters.

There is some truth to these claims. It is indeed often helpful not to take the assumptions for granted and to think outside the box. But when one looks at the history of actual radical innovations, one doesn’t see much of creativity exercises, wild experimentation, or black turtlenecks (well, except at that one company). Radical innovations simply don’t seem to happen by thinking outside the box, by stimulating creativity, or – for that matter – by hiring members of the “creative class” either.

Instead, most radical innovations seem to result from steady, methodical, even boring work of staid, solid and pragmatic tinkerers. There is experimentation aplenty, to be sure, but it is very much planned and very much non-random. A significant characteristic is that experiments tend to be very small increments to state-of-the-art; simple adjustments may be tested and tweaked and tested all over again, for years in some cases.

As an example, let’s take what is arguably the most radical innovation of the 20th Century (if not all the time): the aeroplane. By the late 1890s, many people around the world had the means and the interest to build the first heavier-than-air flying machine. In general, these people fell into two broad types: those who were really thinking outside the box, and the Wright brothers. (Yes, this is a bit unfair generalization.)

Most of their competitors did just what several innovation gurus seem to implicitly recommend: they thought out more or less wild ideas, built the prototypes, and tested them. Unfortunately, by testing entire flying machines, they were playing a double or nothing game: if the basic idea behind their contraption was unsound, they had just lost a considerable amount of time and money.

The Wright brothers took a different tack. They started to methodically extend the prior art in a series of carefully controlled experiments. Instead of building whole aeroplanes, they tested particular components and their configurations. Bit by bit, they had a better understanding of just what combination of components would make a viable airplane. Only when they had high confidence of actually making the airplane work, they built one.

It worked from the start.

Every time something similar happens, an outside observer might be startled and believe the inventor made a huge conceptual leap, attributable perhaps to the singular, non-replicable genius of the inventor in question. This is understandable, but wrong. In real life, there are few if any great conceptual leaps, when seen from the inside. But there are many examples of small, methodical – incremental – steps leading to a “radical” outcome. We just tend to assume a conceptual leap, because those steps are rarely visible to outsiders. Indeed, sometimes even the inventor may be unaware of the steps that contributed to the invention.

I have trouble believing great inventions can even happen in any other way. Human brains have a lot of trouble trying to keep track of more than a few complex ideas and their combinations. Since radical innovations almost by definition require a significant juggling of complex ideas, making conceptual leaps without any stepping stones on the way seems a bit unrealistic.

So, what I believe is this: all innovations, whether incremental or radical, are fundamentally the same. What we call “incremental” is just a label we affix to those innovations that do not seem to be that consequential to us, the general (or scholarly) public. But many of these incremental improvements pave way, one way or another, to radical innovations.

Those what we call “radical” innovations may simply be a way of labelling an example of so-called self-organizing criticality. When we have a lot of incremental improvement going on, it’s almost inevitable that some improvements will turn out to be far more significant than the others. An avalanche – a cascade – of effects ensues. But when carefully considered, the triggering improvement need not be any different in any real sense from those improvements that failed to make the waves, so to speak.

There is some evidence that a 1/f or lognormal distribution, a fingerprint of self-organizing criticality (SOC), is visible in patent data. For example, Silverberg and Verspagen (2005), discussing the available evidence, conclude that patent citation statistics are extremely skewed, in a 1/f manner: very few patents are cited extremely often, while most are cited hardly at all. They also adapt a computer model that is known to exhibit SOC behavior and generate data that matches the patent data fairly well.

So, the claims that Norman and Verganti make in the paper I started this essay with are not necessarily wrong. But the implication – that incremental improvement and radical innovation require completely different approaches – is, in my opinion, potentially misleading. In particular, the hill-climbing metaphor, originally borrowed from evolutionary biology, oversimplifies things a little bit. Instead of hills, the landscape might resemble more a series of interconnected ridgelines. This “genetic drift” across more or less selection-neutral areas of the landscape is now thought to explain how new features and species actually evolve. In technological terms, the selection-neutral features might be small changes that do not really have an effect on the cost or user experience, but cumulatively may take the product far from its origins. (There is also another matter with the hill-climbing paradigm and its use as a metaphor: it assumes the landscape to be climbed will be stationary. This is not usually the case.)

What I and a colleague from the Netherlands are currently working on relates to these problems. We’re building a computer model of innovation that hopefully sheds a bit of light into how radical can emerge from the incremental. A preliminary paper detailing the problem and the proposed model will be presented at Technoport 2012 conference in Norway, and submissions of more detailed version are in for a couple of other conferences as well. I’ve also been doing some work on more selection-neutral models, but that’s farther off.

But what practical implications can I offer in the meantime? As far as money-making schemes i.e. companies are concerned, I have this to offer: if there is a choice between an “ideas guy” and an “execution guy,” pick the latter. There is a saying about how everyone has an idea, but without relentless, tenacious, stubborn execution, most ideas remain just that. And in my experience, it’s the execution that’s the hard part.

In general, I might suggest amending that popular wisdom, “the best way to have a good idea is to have many ideas.” In my opinion, the best way to have a good idea is to have many experiments. Of which this essay is one, so feel free to comment :) .

References

Dahlin, K. B., D. M. Behrens. 2005. When is an invention really radical? Defining and measuring technological radicalness. Research Policy 34(5) p.717-737.

Silverberg, G., B. Verspagen. 2005. A percolation model of innovation in complex technology spaces. Journal of Economic Dynamics and Control 29(1-2) p.225-244. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0165188904000132 .

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Innovations and evolution of technology, note I: Schumpeter and Usher on innovations

Posted on February 24, 2012 by

Some notes about what previous thinkers have said about innovations and the evolution of technology. We’ll begin by notes I made from Ruttan’s classic 1959 article,  “Usher and Schumpeter on invention, innovation, and technological change.” As the title says, it’s a comparison of Schumpeter’s (known to most students of economics) and Usher’s (known mostly to students of technological change) views on the subject.

Notably, Usher’s theory of “cumulative synthesis” is a remarkably successful theory explaining how inventions and innovations occur. It’s the basis of much of the work I’m currently doing, as it has greatly informed Arthur’s thesis of combinatorial evolution of technology (Arthur 2009).

Schumpeter and Usher on innovations (From Ruttan 1959)

Schumpeter

Schumpeter’s discussion of the role of innovation in economic growth is stated in its most developed form in Chapters III and IV of Business Cycles (Schumpeter 1939). Schumpeter identified innovation as the essential function of the entrepreneur. He made the innovator and the process of innovation one of the three elements (along with credit and profit maximization) of a theory of economic development.

To Schumpeter, innovation and the innovator were quite distinct from invention and the inventor. In his opinion, innovation is possible without anything we should identify as invention, and the social process which produces innovations is distinctly different “economically and sociologically” from the social process which produces inventions.

Schumpeter’s “rigorous” definition of innovation is a change in the form of the production function:

“This function describes the way in which quantity of products varies if quantity of factors vary. If, instead of quantities of factors we vary the form of the function, we have an innovation.” (Schumpeter 1939:87-88)

The inputs were only labor and land – this differs from the neoclassical formulation in that capital is excluded. However, in Schumpeter’s view, innovations cannot be measured through changes in production function, as price changes and non-neutrality of innovation would effectively limit the possibilities of measurement.

NOTE: Schumpeter’s definition is quite close to the definition of technological change as used by economists. See e.g. Solow’s work.

According to Ruttan (1959:599), who was a colleague of Schumpeter, he was “primarily interested in changes in the production functions of the technological leaders – the innovating firms – because of the growth forces which adoption of new methods of production set in motion.” This contrasts to many other economists who have concentrated their attention to the production function, which describes the average performance of the economy or industry.

NOTE: Many computer models of technological evolution, such as NK models, calculate average performance. In a sense, it might be thought of as the production function. 

Schumpeter did not give explicit attention to the process by which innovation is generated. There is nothing in Schumpeter’s works that can be identified as a theory of innovation. Although the business cycle is a direct consequence of the appearance of clusters of innovations, no real explanation for the clusters is given and a theoretical basis is explicitly eschewed. However, three cycles – Kitchen (40 months), Juglar (10 years) and Kondratieff (60 years) – are observed.

Usher (on the emergence of strategic inventions)

Usher’s thesis is most fully presented in Chapter IV of the revised edition of History of Mechanical Inventions (Usher 1954). According to Ruttan (1959), Usher forms the basis of a theory of innovation that is lacking from Schumpeter’s works.

One problem faced by economists is that “invention” is difficult to define. Usher defines inventions as the emergence of “new things” which require an “act of insight” going beyond the normal exercise of technical or professional skills.

“Acts of skill include all learned activities whether the process of learning is an achievement of an isolated adult individual or a response to instructions by other individuals. Inventive acts of insight are unlearned activities that result in new organizations of prior knowledge and experience…” (Usher 1954:526)

“Such acts of insight frequently emerge in the course of performing acts of skill, though characteristically the act of insight is induced by the conscious perception of an unsatisfactory gap in knowledge or mode of action.” (Usher 1954:523)

Usher identifies three general approaches to the problem of explaining the emergence of inventions in contrast with the performance of acts of skill. These are the transcendentalist, the mechanistic process, and the cumulative synthesis.

The transcendentalist approach attributes the emergence of invention to the inspiration of the occasional genius who from time to time achieves a direct knowledge of essential truth through the exercise of intuition. This Usher rejects as unhistorical: acts of insight have not been the rare, unusual phenomenon, and they are not accidents but require a highly specific conditioning of the mind – think Pasteur’s “fortune favours the prepared mind.”

Usher also rejects the mechanistic process, which was espoused by Chicago sociologists such as Ogburn (1922) and Gilfillan (1935). However, he stresses that their empirical results are important. These sociologists demonstrated that the process of invention typically represents a new combination of a relatively large number of indiidual elements accumulated over long periods of time. However, Usher thinks that this process is not merely an instrument of historical necessity: discontinuities cannot, in his opinion, be explained by the mechanistic approach, but require aforementioned acts of insight. Only a limited number of individuals are operating under conditions which bring both an awareness of the problem and the elements of a solution within their frame of reference. Even then, it is not certain that the specific act of insight required for a solution will occur.

Usher’s alternative is the cumulative synthesis approach. Drawing on the insights into metnal and social processes provided by Gestalt psychology, major inventions are visualized as emerging from the cumulative synthesis of relatively simple inventions, each of which requires an individual “act of insight.”

Individual invention comprises of four steps:

1. Perception of the problem, in which an incomplete or unsatisfactory pattern or method of satisfying a want is perceived.

2. Setting the stage, in which the elements or data necessary for a solution are brought together through some particular configuration of events or thought. Among the elements of the solution is an individual who possesses sufficient skill in manipulating the other elements.

3. The act of insight, in which the essential solution of the problem is found. Usher stresses that large uncertainties surround the act of insight. This uncertainty makes it impossible to predict the timing or the precise configuration of a solution in advance.

4. Critical revision, in which the newly perceived relations become fully understood and effectively worked into the entire context to which they belong, possibly calling for new acts of insight.

A major or strategic invention represents the cumulative synthesis of many individual inventions, and will usually involve all the separate steps that may be found in individual inventions.

According to Ruttan (1959:602), Usher’s cumulative synthesis theory provides a unified theory of the social processes by which “new things” Come into existence, and is broad enough to encompass the whole range of activities  characterized by the terms science, invention, and innovation. The artificial distinction between the processes of invention and innovation is no longer required.

Usher’s theory also clarifies the points at which conscious efforts to speed the rate or alter the direction of innovation can be effective. The conscious effort is useful around the second and fourth steps in the aforementioned process – in setting the stage and in critical revision. An appropriate research environment which consciously brings together the elements of a solution can set the stage so that fewer elements are left to chance. In the critical revision stage, many of the elements required – testing, for example – are “acts of skill” rather than “acts of insight.” (“Applied research” or R&D is concerned with this critical revision stage, mostly.)

Transcendentalist approach obscures these possibilities with its dependence of the “great man;” mechanistic process denies the possibility altogether.

A limitation of Usher’s theory, according to Ruttan (1959:605), is that it is not a predictive theory – as Usher himself asserts (Usher 1954:66). However, Ruttan notes that since Usher predicts that focus on the two stages of “setting the stage” and “critical revision” should make inventions more likely, the effective institutionalization of applied research and the growing interest in the problem of creating an institutional environment favorable to “basic research” do provide an operational test that is consistent with Usher’s theory (Ruttan 1959:605).

References

Arthur, B.W., 2009. The Nature of Technology: What it is and how it evolves. Free Press, New York.

Gilfillan, S.C. 1935. The Sociology of Invention. Chicago: Follett Publishing.

Ogburn, William F. 1922. Social Change. New York: Viking Press.

Ruttan, V.W., 1959. Usher and Schumpeter on invention, innovation, and technological change. The Quarterly Journal of Economics 596–606.

Schumpeter, J. A. 1939. Business Cycles, vol. I. New York: McGraw-Hill.

Usher, A. P., 1954. A History of Mechanical Inventions. Cambridge, Harvard University Press.

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OSWC 2012: Big Data Is So 2011, It’s Broad Data Now

Posted on February 22, 2012 by

Visited the Organization Science Winter Conference in Steamboat Springs, Colorado. Very nice conference, well worth visiting even from Finland – not too big, perhaps some 60 people, and only a single track, which made for interesting interactions all around. Not to mention that the location was pretty nice as well :) .

Although the theme of the conference (Formal Organizations Meet Social Networking) was not exactly my speciality, the papers, posters (very good poster sessions BTW) and presentations were quite interesting. A key takeaway for organizational/management/social science researchers out there: Big Data is coming so fast that it’s already “so 2011″ – now the Bleeding Edge is talking about “Broad Data.” If at all possible, aspiring social scientists should grab their nearest friendly physics or informatics graduate and have a nice little chat on how to possibly use these wonderful new opportunities in their own research. It’s not something for everyone, but I do feel that especially those with a quantitative bent in their research should really look into this. And not only those: just think of what you could do with data mining the corpus of Google Books, for example!

Among the presentations, of course, was one of my own about the effects of constraints on the development and adoption of new technologies: See here for the presentation, and below for the working paper.

(The results in brief: constraints are not very good at inspiring companies to develop new technologies, but they can work in pressuring them to adopt existing best practices. The practical takeaway? Don’t expect magical technologies to appear just because there is a demand for them.)

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Of nuclear weapons, nuclear threats, and nuclear-free world

Posted on October 18, 2011 by

Being old enough to dimly remember something about the late 1980s, I’ve always been fascinated by the history of the Cold War. I’ve been researching one aspect in particular: the development, deployment and (so far, with two noted exceptions) the non-use of nuclear weapons. As many who have studied the subject know, there is the perverse thrill of thinking the unthinkable and knowing Things That Are Not Good For A Man To Know: for example, just how close Dr. Strangelove was – and, to a large extent, still is – to reality.

However, there is more at stake than just historical interest and “bomb porn” as one fellow nuclear wonk once put it. Curiously, most people seem to have forgotten that there are still thousands of nuclear warheads lurking beneath the badlands of the USA and the steppes of Russia, mere software update and thirty minutes from their targets. What’s worse, there are those nuclear-wars-waiting-to-happen in Pakistan/India and Israel/Iran, maybe with somewhat smaller but still unimaginably horrifying arsenals (and the distinct possibility of sparking off a world-wide conflagration). And there are still men (usually men, that is) sitting and waiting to twist the keys to Kingdom Come. In fact, the chances of nuclear war, accidental or otherwise, have been estimated to be as great as 1 in 100 – every year.

The surreal, beyond-the-looking-glass wonderland of nuclear weapons and nuclear strategies is so full of unresolvable paradoxes, twisted logic, and simply nauseating facts (the Number, for example: at least one billion dead within six weeks) that I doubt many will want to seriously study the subject. I think more people should; while a world without nuclear weapons might be a practical impossibility, there are very good reasons and a good chance for reducing their numbers and their hair-trigger status so that their use would be less likely and the results would “only” equate to the worst ever natural disaster or the Holocaust, not to the destruction of the human species.

For the elucidation of those beginning their journey, here are some pertinent facts and sources after the jump.

Continue reading →

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Crises are “come as you are” events, not springboards, simulations say

Posted on August 15, 2011 by

My PhD research topic deals with resource constraints and, by implication, resource shocks such as the oil crisis of 1973 – and the current slow-mo energy crisis. What I’m trying to do is to sort out whether sudden constraints, such as the 1973 event, act as springboards of technological advancement. In other words, I’m trying to figure out if we can rely on our ingenuity and opposable thumbs to get us out from the mess they’re getting us into.

After some trouble with initial simulations, I now have a bunch of data from simulated companies solving simulated resource shocks by developing new (simulated) technologies. Granted, the simulated companies are more of a “toy model” kind of a firms (think one step above microeconomic equations) but at least this is a toy model with some pedigree – the fabled NK model to be exact, as seen on Levinthal (1997), Rivkin (2000), Kauffman et al. (2000), Frenken (2001, 2006) and others. For those not so much into artificial societies, this approach should be at least theoretically tolerable, if not exactly universally accepted, by many organizational scientists and technology researchers.

I won’t go into nitty-gritty of technical detail here – I’ll be presenting that in PREBEM 2011 and will post the full paper here later – but for those who are interested in the results and conclusions, more after the jump.

Continue reading →

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Theories of design – is design problem-solving?

Posted on April 29, 2011 by

The field of design theory is separated to two warring camps. On the one side are people following Donald Schön’s (1983) “Reflective Practitioner;” an epistemology of practice based on a close examination of what different practicioners actually do. On the other side, sometimes angrily denounced by Schön and his followers, is Herbert Simon’s “Sciences of the Artificial” (1969, 1996). Simon, one of the founders of cognitive science and perhaps most famous for his theory of bounded rationality, presented an instrumental theory of design: design as fundamentally a problem-solving activity, or “search” for better solutions in a “design space” of all possible designs. (Note that “design” here refers to all kinds of purposeful activities for designing something, from organizations to policies to products.)

Schön, and later authors, attacked Simon’s view of design as problem solving as “technical-rationality” and “optimizing,” which was – according to Schön – something that simply didn’t happen in real life. Later authors in the field of design theory have largely followed Schön’s lead, dismissing Simon and claiming he did not have much anything useful to say about how design happens.

That this is based on a misrepresentation of what Simon was actually saying is evident if one reads a later edition (e.g. 1996) of Simon’s groundbreaking book. Simon’s theory is far from its caricature as naive linear search for solutions. It’s not “programming” as some (e.g. Hatchuel and Weil 2009) claim; it’s not about fixed goals, it’s not about well defined objects. In fact, it’s somewhat ironic that accusations of “optimization” and trust in the rationality of design decisions were levelled against Simon, who after all received his Nobel price in 1978 precisely for pointing out that humans cannot be perfectly rational, as tacitly assumed under most neoclassical economic theories.

What Simon says is simply that design can be conceptualized as a problem-solving activity, that problem-solving activity can be conceptualized as a search process through immensely vast design space of possible solutions, and in principle, search can operate through simple algorithms or bundles of such algorithms. This conceptualization makes purposeful design (as done by humans) directly comparable to generalized Darwinian “design” or problem-space search, also accomplished through repetitive use of relatively simple algorithms. For a very good discussion and a compelling argument that the “design space” of biology and technology are unitary, see Dennett (1995): Darwin’s Dangerous Idea. (Extremely good if philosophically rather heavy reading anyway!)

For a very good reconciliation of Schön’s and Simon’s views, see Meng (2009): Donald Schön, Herbert Simon and The Sciences of the Artificial, Design Studies 30:60-68.

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