Tag Archives: organization theory

Even rats are complex organizations?

More on balancing exploration and exploitation, this time from a Science article reviewed by the New York Times.  I have not read the original article, but here’s an excerpt from the New York Times review, entitled “Brain Is a Co-Conspirator in a Vicious Stress Loop“:

Nuno Sousa of the Life and Health Sciences Research Institute at the University of Minho in Portugal and his colleagues described experiments in which chronically stressed rats lost their elastic rat cunning and instead fell back on familiar routines and rote responses …

… regions of the brain associated with executive decision-making and goal-directed behaviors had shriveled, while, conversely, brain sectors linked to habit formation had bloomed.

In other words, the rodents were now cognitively predisposed to keep doing the same things over and over, to run laps in the same dead-ended rat race rather than seek a pipeline to greener sewers. “Behaviors become habitual faster in stressed animals than in the controls, and worse, the stressed animals can’t shift back to goal-directed behaviors when that would be the better approach,” Dr. Sousa said. “I call this a vicious circle.”

This could turn out to be a very vicious circle, since those conditions that seem likely to cause stress–environmental turbulence, deteriorating performance, anticipated threats–are exactly those conditions that require exploratory, adaptive responses.  In fact, my colleagues and I argue that deliberately stressing and destabilizing processes (“deliberate perturbation“) may be necessary to sustain exploration in mature organizations.

People are organizations, too

Nelson and Winter’s respected volume An Evolutionary Theory of Economic Change makes a significant contribution to the development of an algorithmic, symbol system model of economic behavior.  One of my favorite lines from the book:

the idea that “individuals are complex organizations too” has considerable power. (1982: 72)

Minsky provides one interpretation of the individual-organization duality in Society of Mind.  Another perspective surfaced in a recent op-ed piece titled “Your Baby is Smarter than you think” in the New York Times.  The article hints that the dynamics of exploration, exploitation, learning, and maturation, which my colleague Brad Staats and I study in the context of organizations, may also apply to human beings.  From the article:

Adults focus on objects that will be most useful to them. But as the lever study demonstrated, children play with the objects that will teach them the most. In our study, 4-year-olds imagined new possibilities based on just a little data. Adults rely more on what they already know. Babies aren’t trying to learn one particular skill or set of facts; instead, they are drawn to anything new, unexpected or informative.

Part of the explanation for these differing approaches can be found in the brain. The young brain is remarkably plastic and flexible. Brains work because neurons are connected to one another, allowing them to communicate. Baby brains have many more neural connections than adult brains. But they are much less efficient. Over time, we prune away the connections we don’t use, and the remaining ones become faster and more automatic. Moreover, the prefrontal cortex, the part of the brain that controls the directed, planned, focused kind of intelligence, is exceptionally late to mature, and may not take its final shape until our early 20s.

So perhaps individuals also face a “productivity dilemma“?

New version of "Wellsprings of Creation" available on SSRN

A new version of my paper with Brad Staats, Mike Tushman, and Dave Upton on how deliberate perturbation can sustain innovation in mature organizations is available for download from SSRN.  Here is the abstract:

Organizations struggle to balance simultaneous imperatives to exploit and explore, yet theorists differ as to whether exploitation undermines or enhances exploration. The debate reflects a gap: the missing theoretical mechanism by which organizations break free of old routines and discover new ones. We propose that the missing link is perturbation: novel stimuli that disrupt the execution of specialized routines. Perturbation creates opportunities for organizations to invoke exploratory, general-purpose problem-solving routines. In mature organizations, exogenous perturbations become increasingly scarce to the point that exploration is stifled and inertia sets in. We theorize that mature organizations can sustain exploration by deliberately inducing perturbations in their own processes. Our theory yields testable hypotheses about the relationships between exploitation, perturbation, and exploration. We provide illustrations from The Toyota Motor Company to show how deliberate perturbation enables efficient exploration in the midst of intense exploitation.

Knowledge: A Short Essay and an Annotated Reading List

One of my colleagues asked me what to read to learn about knowledge. The answer requires a bit of explanation.

There are two approaches to knowledge. On one hand, there are the epistemologists. The epistemologists have spent many centuries developing criteria for evaluating whether a belief qualifies as knowledge. On the other hand, there are the computer scientists and organization theorists, who tend to focus on how knowledge affects the performance of problem solving systems (i.e., humans, computers, organizations). These two approaches can be reconciled as follows.

For the computer scientists and organization theorists, knowledge is anything that improves the performance of a problem solving system, except for information processing capacity. If two systems execute the same number of symbolic operations but one system gets a better answer, it must know something the other system doesn’t.

Epistemologists, by contrast, want perfect knowledge that will never lead a problem solving system to act in ways that betray its own goals. Such perfect knowledge is difficult to obtain, and perhaps even more difficult to define. After a few millennia, epistemologists still haven’t come up with a satisfactory definition. This is not to say that the field has failed: epistemology can help us evaluate the quality of knowledge and acquire better knowledge.

A short example may help clarify the matter. To a computer scientist, “what goes up, must come down” is a reasonably good piece of knowledge. It tells a problem solving system not to throw a water balloon straight up in the air. To an epistemologist, this isn’t knowledge at all, because it isn’t true. If I launch a rocket into space, it doesn’t need to come down. In fact, up and down are not even valid except within very limited frames of reference. The computer scientist has a tolerant, inclusive philosophy of knowledge, while epistemologists have an exacting, exclusive philosophy of knowledge.

For those of us concerned with understanding the performance of problem solving systems, the problems raised by epistemologists are not of primary importance.We are better served with an inclusive definition of knowledge that asks not whether the knowledge is true, but whether it is useful. Those interested in this view of knowledge may find the following books and articles useful.

How organizations represent and exploit knowledge

March, J. G. and H. A. Simon. Organizations. 2nd ed. Cambridge, MA: Blackwell, 1993.

Simon, H. A. The Sciences of the Artificial. 2d ed. Cambridge, MA: MIT P, 1981.

Organizations and The Sciences of the Artificial are essential introductions to the science of problem solving systems (equivalently, symbol systems or information processing systems). Chapters 6 and 7 of Organizations are especially important, because they describe the functioning of performance programs (equivalently, routines), which are one of the most important ways that problem solving systems represent knowledge. Make sure to get the second edition, which has useful commentary after each chapter. Read these books several times.

How organizations learn

Mukherjee, A. S. and R. Jaikumar. “Managing Organizational Learning: Problem Solving Modes Used on the Shop Floor.” 1992.

Bohn, R. and R. Jaikumar. “The Structure of Technological Knowledge in Manufacturing.” Working paper. 1992.

Clark, K. B., R. Henderson, and R. Jaikumar. “A Perspective on Computer Integrated Manufacturing Tools.” Boston, MA, 1988.

Jaikumar had a wonderfully precise grasp of how knowledge, learning, and problem solving interact and drive system performance. The first paper describes the mechanics of unstructured problem solving, which is closely related to learning. The second paper demonstrates how theoretical models can be used to investigate the way knowledge functions. The third paper sheds light on how computers influence learning. Although the studies focus on manufacturing, the principles generalize. Unfortunately, these excellent papers are not easily available

Darr, E. D., L. Argote, and D. Epple. “The Acquisition, Transfer, and Depreciation of Knowledge in Service Organizations: Productivity in Franchises.” Management Science 41, no. 11 (1995): 1750-62.

Edward Feigenbaum often says that knowledge usually comes in thousands of grains of gold dust rather than in large nuggets. This elegant empirical study beautifully captures this aspect of organizational knowledge, and provides an example of one technique for quantitatively analyzing knowledge, learning, and knowledge decay.

Epistemological perspectives

Newell, A. “The Knowledge Level.” AI Magazine 2, no. 2 (1981): 1-20, 33.

Nonaka, I. “A Dynamic Theory of Organizational Knowledge Creation.” Organization Science 5, no. 1 (1994): 14-37.

Lenat, D. B. and E. A. Feigenbaum. “On the thresholds of knowledge.” Artificial Intelligence 47, no. 1-3 (January 1991): 185 – 250.

These three articles by leading experts on problem solving and knowledge provide theoretical foundations for the inclusive, computer science/organization theory approach to knowledge. None provides a complete theory, but when read together they provide a great deal of insight. The significance of the ideas cannot be grasped without considerable reflection. It may help to read them repeatedly, perhaps interspersed with the other readings on the list.

Mechanics of knowledge systems

Davis, R, H. Shrobe and P. Szolovitz. “What Is a Knowledge Representation.” AI Magazine, Spring (1993), 17-33.

Feigenbaum, E. A., B. G. Buchanan, and J. Lederberg. “On Generality and Problem Solving: A Case Study Using the DENDRAL Program.” In Machine Intelligence, edited by B. Meltzer and D. Michie, 165-90: Edinburgh UP, 1971.

Feigenbaum, E. A. “Knowledge Engineering: The Applied Side of Artificial Intelligence.” Proc. of a symposium on Computer culture: the scientific, intellectual, and social impact of the computer. New York Academy of Sciences, 1984.

To understand the mechanics of knowledge, one must dig into questions of representation and inference. Davis’s article provides a useful overview of the issues involved in representation. Feigenbaum’s articles on DENDRAL and knowledge engineering describe the nuts and bolts of working with knowledge.

Insourcing at Apple

In my doctoral dissertation, I develop a theory of computer-assisted organizing that links the rise of computer systems to the shift from integrated organizational capabilities to decentralized capability ecosystems.  Although I believe the predictions of the theory probably hold true in aggregate, Apple’s recent move to bring chip design in-house illustrates how firm-level strategic considerations influence the division of information processing tasks across ecosystem participants.  Presumably Apple won’t in-source all chip design: they’ll focus on domains where they can develop capabilities that strongly complement the firm’s software and hardware design.

Does HP need deliberate perturbation?

In an article titled “Does H.P. Need a Dose of Anarchy?”, the New York Times asks whether Mark Hurd’s management is causing exploitation to drive out exploration–the classic “productivity dilemma“. From the article:

Mr. Hurd, hired four years ago in the wake of Carleton S. Fiorina’s tumultuous departure as chief executive, forced a steady, boring diet of performance benchmarks, heavy-handed cost-cutting and data-mining down H.P.’s corporate throat.  …

But with the most brutal cuts behind it, H.P. faces a fresh set of challenges as the second stage of Mr. Hurd’s tenure begins. Most pressing is widespread concern that Mr. Hurd has built an inflexible, solipsistic giant so obsessed with schematics and data-driven fiscal machinations that it has lost the ability to deliver that prized and perennial Silicon Valley trick: to surprise and astound.

This is a clear statement of what my colleagues Brad Staats, Mike Tushman, and Dave Upton and I label the “conflict school” in our working paper “Wellsprings of Creation: How Deliberate Perturbation Sustains Exploration in Mature Organizations”.  Conflict school theorists argue that the very tools organizations use to exploit their accumulated knowledge–standardized, stable, and streamlined operating procedures–also squelch innovation.  Consequently, the most efficient and productive organizations adapt poorly to environmental changes, leaving them vulnerable to attacks by more creative (albeit perhaps less streamlined) competitors.  In HP’s case, those competitors appear to include Apple, Amazon, and Acer:

It arrived late with a line of netbooks, the low-cost, compact laptops that have taken the world by storm, opening doors for its rival Acer.  …

With its software gurus, its newfound penchant for design and its deep ties to retailers, H.P. might have been expected to disrupt the cellphone market with new devices or even to concoct an electronic book reader that would complement its printer business. Instead, it’s Apple and Amazon that built vibrant new businesses around such products. 

According to the article, Mr. Hurd is not too worried:

“In spite of the fact that there are things we could always do a better job on, innovating and so forth, I don’t think we have ever felt stronger about our portfolio of products and services and our opportunity to serve the market,” Mr. Hurd says. “I don’t think we think we’re confused about what the market wants.”

Perhaps he should be more concerned: academic research suggests that the productivity dilemma is very difficult to overcome.  Our research on Toyota suggests that organizations can sustain exploration in the midst of intense exploitation, but it’s extremely hard to do.  Unless HP is tackling the problem head on, rigidity and inflexibility may be real risks.

What HP probably doesn’t need, however, is anarchy.  Anarchy would simply negate the impressive efficiency gains that the company has made over the past few years.  Instead, we would recommend deliberate perturbation: selectively and strategically destabilizing processes throughout the organization.  The mechanics of deliberate perturbation are not yet well understood, but we try to provide some ideas in our paper.

Limits and dangers of social sciences

In a dense passage in a thick book (On Organizational Learning, 2nd ed.), Chris Argyris makes a point that should deeply concern social scientists including, perhaps especially, economists and organization theorists.

It is important for social sicentists to study double-loop change because if they focus only on single-loop change, they may unwittingly become servants of the status quo…

This consequence holds negative outcomes for social science as a science.  It is becoming evident that there may be a paradox embedded in the goal that social science should be descriptive of the world as it is.  If social scientists aspire to study individuals and systems as they are, they will inevitably fall short of their goal: a complete description of things as they are would have to include a valid description of the capacity to make significant changes, and of the mechanisms by which these changes will occur.  Knowledge of these mechanisms will also produce valid generalizations about constraints to double-loop organizational change.  Such significant changes require changes in the organizational governing variables and master programs, that is, double-loop changes.  But double-loop changes cannot occur without unfreezing the models of organizational structures and processes now in good currency.  These models, in turn, cannot be unfrozen without a model of a significantly different organizational state of affairs: otherwise, toward what is the organization to change?  If these models are genuinely new, then they do not now exist.  if they do not now exist, then their invention and their use is an act of proscription, a normative stance.  Yet if the logic is correct, the normative stance is needed to get at the inner nature of the present double-loop features and potentials of the organization.  Hence, a full description of the world as is requires the intervention of stimuli from a world that presently is largely theoretical. (70)

By “double-loop change” or “double-loop learning”, Argyris means “questioning or altering the underlying values of the system” (68).  He uses the term in opposition to “single-loop learning” which “is designed to identify and correct errors so that the job gets done and the action remains within stated policy guidelines” (151-2).  In single-loop learning, “the underlying program is not questioned” (151).

To paraphrase: the potential range of social system behavior can be known only by construction, not by description.  Purely descriptive approaches to social science will underestimate the range of the possible and, to the extent that these descriptions are used by agents within the system to shape its development, description may ultimately constrain the possible.

"Perspectives on the Productivity Dilemma"

The Journal of Operations Management recently published this invited article that I co-authored with Paul Adler, Mary Benner, John Paul MacDuffie, Emi Osono, Brad Staats, Hiro Takeuchi, Mike Tushman, and Sid Winter.  The article revisits the so-called “productivity dilemma” identified by Abernathy in 1978:  attempts to increase efficiency in the short term tend to inhibit innovation in the long term.  Mike and Mary attribute the problem to the “dynamic conservatism” of tightly coupled systems, while Paul Adler emphasizes the tendency of profit-seeking to undermine the trust required for cooperative innovation.  Brad and I argue that overcoming the problem requires selectively re-introducing variance into mature processes, a phenonenon that we term “deliberate perturbation”.  We are working on a theory paper with Mike that further develops the concept of deliberate perturbation.  [The paper, entitled “Wellsprings of Creation: How Perturbation Sustains Exploration in Mature Organizations”, is now available here — DJB, 24 Aug 2009]

Simon on routinization

A great deal of organization theory literature distinguishes between routine and nonroutine activity.  Routine activity is programmed, while nonroutine activity is ad hoc.  But is any activity really unprogrammed?  As usual, Simon has the answer:

In what sense, then, can we say that the response of a system to a situation is nonprogramed? Surely something determines the response.  That something, that collection of rules of a procedure, is by definition a program.  By nonprogramed I mean a response where the system has no specific procedures to deal with a situation like the one at hand, but must fall back on whatever general capacity it has for intelligent, adaptive, problem-oriented action. (Herbert A. Simon, The New Science of Management Decision, 1977 p. 47)

In a strict sense, all organizational behavior is programmed, hence routinized.  Seemingly nonroutine activity (ad hoc problem solving, improvising) simply involves less specialized routines.  As organizations learn, they develop more specialized routines.