From the Archives: Realizing Possibilities Part II

A revised view of reality and its practical implications

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Jul 22, 2022

Introduction from the Editor

In Part II of Realizing Possibilities, Dave and James go deeper into their discussion about the nature of reality and how to work with it to create change, covering topics ranging from the nature of scientific explanation and the limitations of models to the implications of considering reality as multi-dimensional rather than as unified.

The discussion also gives a deeper explanation of the foundational principle of looking at the world in terms of flux-and-constraint rather than cause-and-effect, which is outlined in Some Common Myths about Change Part II, and how, as a result, the persistence of any pattern requires explanation.

James also makes the important distinction between this method of creating large-scale systemic change and the systems-based or complexity-based approaches that are becoming increasingly popular. Cybernetically-informed minimalist intervention is actually very far from systems thinking because systems thinking is based on models, and our view of change rejects modeling complexity in favor of filtering complexity.

In this part of the interview, it starts to become clear how the methodology of minimalist intervention emerges naturally from a revised conception of reality.

–Ellen

From Cause-and-Effect to Flux-and-Constraint

JW: My Interchange colleagues and I have come to view reality as a fluid, infinitely multi-dimensional world of endless possibilities, an indeterminate and undetermined, fundamentally anarchic and constantly shifting kaleidoscopic universe of form and pattern, flux and constraint, which even in its most idiosyncratic local details is susceptible of rigorous scientific analysis and which is ultimately pliable to our will. For another 17th- and 18th-Century notion, a Baroque invention we have had to abandon as misleading and unnecessary—whether in science or in everyday life—is the conventional modern notion (400 years old and out of date), the notion of cause-and-effect, and again irrespective of whether you are talking about lineal causality or circular causal loops. We replace cause-and-effect with the notion of flux-and-constraint.

DF: Should I assume, then, if the notion of cause-and-effect is to be abandoned, that on your view of reality, when I hit my finger with a hammer when trying to pound a tack into a wall to hang a picture, the smash of the hammer doesn’t cause the pain in my finger?

JW: No, not saying that at all, nor would the explanation likely be much different in your example. But once again I have to take a step backward so I can put this into perspective.

The conventional view just assumes, tacitly, that persistence is always the status quo. So any change needs to be accounted for in terms of specific causes, and therefore just assumes that any desired change must be brought about by somehow causing that change. That’s the picture we discard. Instead, on our own “photographic negative” or figure-ground reversal of the conventional picture, we hold that, all things being equal, we are entitled to expect continuous, random flux everywhere; and the persistence of any particular order or pattern is thus viewed as highly improbable, and it’s this persistence that needs accounting for. This would include situations like your hammer-smash, finger-pain example.

From this perspective, persistence presupposes mechanism. And so any scientific inquiry aims to throw light on the mechanism that accounts for the persistence of any descriptive invariance—any pattern.

Order or invariance—pattern—we view as a function of “constraint,” which is a technical term from cybernetics referring to a particular relation between two sets of possibilities, where only a subset of some wider set of theoretical possibilities are currently realized.

DF: I’m not sure I follow that.

JW: Look, if there were ceaseless, unconstrained, random flux, there would be a far wider set of possibilities that might now be realized just here, yet only a small subset of the logical possibilities are actual, given the constraints in place. Our inquiry is therefore aimed at revealing the nature of these empirical constraints. This type of inquiry can be applied in any situation, including your picture-hanging accident, but the differences there would be fairly trivial—not so in more complex cases requiring explanation and intervention.

The Idiosyncrasy of Constraints and Releasing Change

DF: Isn’t this just a matter of semantics?

JW: Well, it is semantics, but unbelievably powerful and important semantics, especially when the inquiry involves a situation more complex than your hammer and painful finger.

Here’s the thing: in any situation, the constraints may include certain universal, empirically established patterns or invariances, including of course the most overarching ones of all, normally referred to as “laws of nature” or of physics (e.g. that light cannot travel faster than C); these may be sufficient to explain your finger pain.

But in more complex real-world situations, our inquiry need not stop there, with laws of nature, etc.—far from it! We needn’t confine ourselves to citing only such universal invariances as happen to apply.

Because in specifying more fully the set of constraints-on-variance that preclude all states of affairs other than the one we are seeking to account for, we can go on to specify the far more numerous sources of constraint which are not universal at all, but which are quite concrete local constraints, idiosyncratic to the specific situation. For example, it is not a law of nature that prevents me from getting down to Wall Street by 2:00 p.m., it’s the cancelled subway train and the traffic in the streets on this rainy New York day.

DF: Would you seek to express things in such terms—in terms of the idiosyncratic constraints—in any and every scientific inquiry, or only in analyzing practical situations scientifically?

JW: On our revised view, our new question-relative epistemology of form-and-pattern, flux-and-constraint, no scientific explanation is to be regarded as complete until we can satisfy the questioner’s quite specific “why this rather than that.”

If I am in the front quad at my college in Oxford, talking to the Professor of Neurophysiology, and we both see the Politics don running out of the front gate like a bat out of hell, and the neurophysiologist asks me, “Why is John running?” then any answer I try and give in terms of nerve cells firing in his brain and muscles contracting and so on will be regarded as an irritating joke—the Professor of Neurophysiology of course knows all that already. The right answer may be, “To catch the last post,” or “the Headington bus leaves in two minutes and he is already late for tea.” Reductionist approaches rooted in the old epistemology tend to miss this point when they think they can explain behavior in terms of nerve-cell firings.

DF: We’re getting off track again—can you go back to what you were saying about idiosyncratic constraints?

JW: I’m actually still in the middle of saying it: So, to satisfy a questioner’s specific “why this rather than that,” we shall find it essential to specify constraints which may be idiosyncratic to this particular context. Any state-of-affairs is from now, on our new epistemology, only regarded as having been accounted for scientifically when it can be demonstrated to be the only state-of-affairs not currently precluded by the constraints enumerated; and we must rigorously demonstrate how nothing other than what obtains locally is currently possible given the constraints in place.

When explanation is couched in the negative terms of flux-and-constraint rather than in the positive terms of cause-and-effect, it becomes possible to apply scientific rigor to the analysis of completely idiosyncratic, one-off situations, like the ones you deal with every day. And here an analysis in terms of flux-and-constraint becomes indispensable.

DF: Can you say more about why?

JW: Sure. We must ask such negative questions as, “What stops this happening?” and “How is it that the current state-of-affairs is the only state-of-affairs not currently prevented?”

You see where this is going: Change, from this perspective, is never caused, or “brought about,” nor need it be. Desired change needs merely to be released, by pinpointing the specific, idiosyncratic constraints which need to be lifted and/or inserted so that, in place of the existing state-of-affairs, the desired state-of-affairs is now the only state-of-affairs not precluded, given the new sets of constraints put in place; and the constraints identified will be as local and idiosyncratic to the situation as you please.

DF: Is this what makes it possible to rapidly catalyze large-scale transformations with very small interventions?

JW: Precisely. Since any desired state-of-affairs is viewed as already inherent in the existing state-of-affairs and needs merely to be released by carefully removing and inserting constraints, it follows that major transformations can be achieved all at once, in an all-or-none flip from the existing state-of-affairs to the desired state-of-affairs. You shift the configuration of constraints so that the old state-of-affairs is rendered impossible and so that now the new one is the only one possible—it just flips over accordingly.

In fact it is possible to demonstrate—not only in theory (as we have done in our philosophical work) but also in practice (as we have done extensively in our applied scientific work)—that in principle change does not take time, and need not take time in practice. What typically does take time are the longwinded, clumsy ways people normally, habitually attempt to “bring about” or “cause” change, rather than simply pinpointing, releasing and channeling the possibilities that are already there. To paraphrase an old NATO training manual, just because it’s possible, with enough determination, dexterity and ingenuity, to push a pea up the side of a mountain with your nose does not mean that that is a sensible way of getting it there, or that delivering the pea to the peak is the only or even best way of achieving your objective.

DF: A bit worrisome, isn’t it, that our own troops needed to be told that! I’d rather it was the training manual for the bad guys.

JW: Well, actually, it was our NATO computer-programming experts who needed to be told. But yes, worrisome all the same! Still, they’re experts, and the sharp-nosed pea-shifters just kept pushing up the mountainside, one more push of the pea and we’ll be home by Christmas. Once again it’s like Pliny’s cobbler moving on from opining on shoe-latchets to opining on how the greatest painter of the day should portray his models’ legs. The programmers knew how to push peas.

Now, from this perspective too, we can see that any generalized solution to a generalized class of problems (the usual sorts of solution) will on its own typically fail to deal with the only sets of constraints that in point of fact necessarily preclude the desired state-of-affairs—the local, idiosyncratic ones, within which any effective solution must be designed in any case.

This is also why, when the general solution fails for just this reason, or takes great effort to shoehorn into place, this is normally seen as being no fault of the solution itself—just some local interference, or “implementation issues”—and so any number of such failures and any amount of delay and difficulty in implementation is never enough to discredit the general solutions themselves, which haplessly we still try and finesse, force-fit or even hammer into place by force.

The Limitations of Maps and Models

DF: Again, do you mean to imply, as you seem to be implying, that there is no such thing as a general solution to any type of problem? If that’s the case, why do organizations invest so much money paying for consultants to bring their canned solutions in to solve the organization’s problems?

JW: Sure, there are plenty of effective general solutions to types of problem, but they’re never optimal solutions, and as often as not involve a massive waste of time and resources, though some of the time this may not matter. I think it was Bertrand Russell who said that fully nine-tenths of the world’s business is pointless lunacy in the cause of general employment. But more broadly, your question gets to the heart of the matter, since, once again—and it’s easily done—you’ve fallen into the common trap of describing both problems and solutions as falling into categories or types, rather than being singular, idiosyncratic situations that may or may not bear any relationship to anything that has come before or since.

Reality, on our revised view—this fluid, anarchic, infinitely multidimensional world of local, idiosyncratic form and pattern, where cause-and-effect is replaced by flux-and constraint—is a world of endless possibilities which are all too easily obscured by blurry yet concrete-sounding, mid-level abstractions, rigid models, and the search for generic technical solutions couched in terms of these limiting models and metaphors.

DF: How so?

JW: People tend to operate consciously or unconsciously according to more-or-less crude, fairly rigid metaphors for what they are dealing with, in order to help them get a handle on an infinitely multidimensional, constantly shifting situation. However, every metaphor breaks down somewhere, or else it would not be a metaphor at all—it would be a strictly literal description! And the options available to us are limited by the very limitations built into the more-or-less arbitrarily adopted metaphor; so people tend to get stuck in their own chosen metaphors, blinded to all the possibilities obscured by the metaphor.

DF: Sounds disarmingly familiar . . .

JW: Sure it does—in fact, most approaches to managing complexity, from managers’ natural, everyday intuitive, commonsense approaches to the most sophisticated, arcane ‘systems’ approaches (with or without fancy charts with loops and boxes and arrows, or even computer simulations), attempt to address the complexity of situations by constructing ultimately ad hoc maps or models of what we are dealing with, introducing various simplifying assumptions along the way.

DF: But isn’t it true that models at least help us understand complex situations?

JW: No it isn’t, because the model depicts only observable, recognizable descriptive features of the original along with the connections and/or relationships posited or observed to hold between them. In contrast with any truly scientific approach, unobserved features of the situation are not depicted in the map or model, and the models constructed are primarily descriptive rather than explanatory.

Such a model—the usual sort in management—is, therefore, merely a simplified representation, a mere re-description that retains some features of the original while deliberately omitting others.

In explaining all this, Dr D. J. Stewart years ago illustrated the point with the example of a model ship. The model ship is like the real one, he points out, in much of its structure and therefore in the relationship between its various parts, but it’s of a different size, is made of different materials, and so on. A model of a ship will require certain features to be discarded but not others if one is interested in modelling its flotational properties, but a totally different selection will be required if it is intended to show the location of the different cabins on the various decks. Here’s the bridge; this is the engine room. Of course, we incorporate into our model all kinds of features which are neither permanent nor necessary in reality, and which are themselves already dysfunctional; and once enshrined in our model we tend to treat them as semi-permanent fixtures. For we treat all the elements in the model as equally real, part of the territory we are navigating.

DF: What exactly do you mean by that?

JW: Well, for example, you sometimes still see signs in England above sinks in public restrooms saying, “CAUTION, EXTREMELY HOT WATER—DANGER OF SCALDING.” Used to see them at Heathrow!

Recently. Some years ago we had one—you’ll love this story—in the basement of the medieval building in Oxford where I teach. Beautiful plaque with nice graphic design. A very traditional English sign, with the traditional English wording. In everyone’s mental model of the College, there was a restroom, and in the restroom was a sink with taps, and in the taps was extremely hot water, clearly labelled as such. I told them that unless they wanted to put the warning sign in every language under the sun or get sued as well as massively fined for violating Health and Safety regs, shouldn’t they just adjust the boiler temperature down? Which they did right away, as soon as I pointed it out, and they duly took down the sign. Took less than one minute, apparently—to order the sign took them a week, and had been up there for as long as anyone could remember. Probably took longer to take down the sign than to fix the problem it warned you about. But the problem had meanwhile already become a fixture, part of the wallpaper no one noticed any more, nothing that could be addressed, only labelled, and it was labelled in a way that presupposed it could not be addressed.

But before you sneer, I bet you do this all the time yourself. I know I do. We all do. No one had ever asked, “how do we keep the water from getting so hot?” They asked, “how can we get people to exercise caution so they don’t scald themselves here, with this very hot water?” They assumed there was nothing they could do about it. “Different department.” The maintenance people assumed it was fine as it was, or else they’d have been asked to adjust the temperature, since that’s so easy; but apparently the people in charge preferred it the way it was because they put up that beautiful expensive sign instead. So the Oxford dons on the maintenance committee years ago, the ones who ordered the sign, some of the world’s most brilliant minds, may have made the mistake of thinking, “Look, the reality is that someone’s sooner or later going to scald themselves if they’re not careful, so we’d better warn them so they can’t say they weren’t warned!” Wrong reality. Bad models can short-circuit even the best minds.

But what I was saying is that if you build a model ship, there’s a way to build that model if what you’re trying to show is where the engine room is and where’s the bridge, and the lifeboats and whatnot. But that’s all the model can do for you in that case. Show what’s where. It can only answer that one question, “where is what?”

DF: There’s nothing wrong with that. It’s just being pragmatic.

JW: Of course there’s nothing wrong with it, but the choice of which features or properties of the original to retain and which to discard as irrelevant is entirely a function of the use to which the model is to be put, and so entirely a function of the question you are asking. The properties relevant to your particular question are retained, while those deemed irrelevant or immaterial, are not represented in the simplified model. As I said, you’ll need quite different features to be discarded and retained if what you want to model is the ship’s flotational properties—a model showing the bridge and janitor’s closet probably won’t help you. But in the modelling process, Stewart emphasized, what is most important is the selection of which properties to leave out. All of these considerations are particularly evident in those models intended to serve as maps to help one find one’s way around the territory. Like the map of the New York subway.

DF: You make it sound like we should never use models to help us understand complex situations. Is there ever a place for models when dealing with complex problems?

JW: Sure. Nuclear reactors, for one. Look: As with the use of unwitting metaphors, maps or models limit our choice of action to the options available within the surrogate world of the model constructed.

Imagine trying to find your way around any major city on foot using only a subway map!

The infinite range of possibilities for intervention is restricted to the infinitesimally small fraction of possibilities that are represented in the model. What happens is that all the rich, granular detail—which is where, we have learned, the optimal solutions are invariably to found—gets lost in favor of the few rigid sets of possibilities offered by the interconnected abstractions constituting the model or map, which only fudges the real issues. It lulls us into a false sense of understanding what’s going on. When the model-builders try to put some practical situation into a nutshell they typically end up with nothing more than, well, . . . the nutshell!

DF: So I gather then, that making the models and maps more detailed and complex is not going to help either?

JW: It will neither help nor hurt—it’s simply irrelevant. It’s just rearranging the deck chairs on the naval architect’s proposed design for the Titanic. And apparently with the Titanic the problem was that they failed to specify the rivets in sufficient detail, including where they were to be sourced from—so the shipbuilders ended up using a cheap lot of substandard imported fastenings that just sheared, weren’t up to the job. The hull shouldn’t have split when the ship hit the iceberg. The ship’s design was robust enough to take it, but only if that design had included a specification of the manufacturing standard for the rivets—using “No. 4-bar” iron as opposed to “No. 3-bar,” and using techniques that only the largest, most experienced forges were capable of, whereas the rivets were sourced from small, mom-and-pop-shop forges. In short, the naval architect’s model left out some key design features that should have been carefully specified. The devil turned out to be in the granular details. Like in the old proverb about the battle being lost “for want of a nail” in the horse’s shoe.

Anyway, so whether the maps seem almost as complex as the situations being modelled, or hopelessly over-simple and reductionistic, either way, they simply compound the scientifically naive errors of everyday life. After all, we get stuck in situations when the otherwise useful abstractions we’re using are no longer adequate for what we’re trying to do.

At the concrete level there are countless latent possibilities, but these get masked by the mid-level abstractions people tend to deploy, and so the real challenge is how to reveal—and realize—those latent possibilities. Nothing’s to be gained by plastering over the practical cracks with yet another layer of those fancy, mid-level abstractions.

DF: So what’s a person to do?

JW: It should be clear by now, given the radical question-relativity and infinite multidimensionality of reality, that from our perspective, any map or model worth its salt will only be suitable to answer one single question; and that as soon as a different question is asked we would need to throw away our model and construct a new one from scratch.

DF: If that’s the case, won’t I be spending all of my time doing nothing but constructing new models to help me deal with every new situation I encounter?

JW: Only if you really enjoy building models. Otherwise, you won’t get much for your efforts.

In general, the mapping or model-building approach to managing complexity is valuable only if the real thing is too dangerous or too fragile to mess around with—nuclear reactors, as I said earlier—or has not been built yet (ditto—try building one, or any complex structure, without one). Otherwise, though, it is more appropriate and useful to operate directly upon the real world, for instead of constructing analogical models of complex practical situations, we can filter the complexity out of the equation in a non-reductionistic way.

Instead of keeping reality at a distance by working with the surrogate reality afforded by maps and models and mid-level abstractions, my scientific colleagues and I choose “to work directly with pieces of the real world in a close and delicate manner,” as Stewart likes to put it.

The Fabric of Reality

DF: Can you elaborate a bit on how you “…work directly with pieces of the real world in a close and delicate fashion?”

JW: For me that’s the really exciting bit. Basically, on our revised perspective, the real world is not a fixed piece of clockwork, with wheels within wheels, susceptible to being represented in a diagrammatic model, but the ceaselessly shifting, highly patterned, kaleidoscopic reflection of the anarchic interaction of a myriad of independent agents purposefully pursuing countless agendas of their own, each necessarily addressing only their own subjective reality selected from their own point of view from amongst the numberless objective possibilities out there.

So this “fabric of reality” as I am calling it, turns out to have something of a quite specific logical structure to it—or at least we can readily analyze the world in terms of such a structure—a peculiar, contingent structure. I say “contingent” because reality might have had a different logical structure had the universe been made differently, but it appears it doesn’t happen to.

For example, if God had made it that way, the world might have been structured in terms of matter and energy, objects and forces, in the way of conventional reductionist materialism (the view we reject). Or alternatively, reality might have been structured instead in terms of objects and relationships, as it is understood to operate in most systems approaches, where the systemic relationships are considered to be as fundamental to the fabric of reality as the objects so related, and where the world is ultimately to be understood as a function of the non-reductionistic laws governing such systemic relationships, from ecosystems and organizations and everyday life right down to the sub-atomic level (a perspective which seems to be slowly, gradually ousting the received view). My own work, though it certainly had its roots in such systems approaches, eventually took a somewhat different turn, along the same lines but at once more radical and more specific.

DF: OK, but you’ve simply used a different reference frame to describe the reality you work on. Tell me more about the “how” of your work.

JW: In our own mode or idiom of scientific analysis, we analyze anything in the natural world—including not just the biological but even the physical aspects, and of course the social and psychological aspects of the world, not least the world of affairs—in terms of a particular contingent logical structure which we apply to the world like a kind of calculus or template.

DF: Whoa! Why is that template you say you apply to the world not just yet another general model of the kind whose validity you reject?

JW: Fair point. I haven’t explained. The kind of everyday models we reject, the kind so prevalent in the social sciences and especially in management, are just re-descriptions of more or less unfamiliar matters into familiar terms. People describe something, insert the word “because,” and then describe it again, thinking they’ve now explained it. And worse yet, they refer back to their re-description as a map to figure out what to do in all kinds of different situations, using the same model over and over again, consulting it like an oracle. So the models we reject are multipurpose in this way, and they attempt to explain unfamiliar matters by translating them into comfortingly familiar terms.

By contrast, truly scientific models explain familiar matters by translating them into unfamiliar terms, and the models are actually very simple and answer only a very restricted set of questions—for example, like the “rectilinear propagation of light” in physics, the model of light as moving in straight lines, which when it was first propounded sounded like a bizarre, fantastic notion! Lines of light? You must be joking! I don’t see any lines—surely light is all-pervasive, like space! But while that scientific model explained a host of otherwise puzzling phenomena, like why my shadow is shortest at noon, and so on, still other models were required to answer other questions about light, say in regard to the phenomena seen in rainbows and prisms. In our own work we do indeed use a great many scientific models of this kind, which, like the model of the rectilinear propagation of light, are each at a far higher level of abstraction than the mid-level abstractions of commonsense and management models. And each is relatively simple and self-contained, like virtually all scientific models.

DF: How, then, do you use these templates, or models in your work?

JW: All of the scientific models that we deploy slot into an overall paradigm or template, in which we are only interested in certain highly abstracted aspects of situations—the interactional and communication patterns, and so on—analyzed in terms of particular logical units of analysis. That template enables us to cut through the mid-level abstractions to what is going on at a much more fundamental level. By the way, this is just the sort of thing that your accountant or attorney does when looking at your affairs through a highly abstract financial or legal template.

Our own template however, is at a higher level of abstraction still. We look at the very fabric of reality in terms of a particular, abstracted logical structure, which we have found extremely powerful in analyzing and accounting for a wide range of phenomena, and which enables us to apply specific scientific models—many drawn from cybernetics and semiotics, for example, as well as from our own theoretical work—to idiosyncratic situations, each such component model enabling us to answer different specific questions.

In general, then, the form this template, this contingent logical structure of the fabric of everyday reality, takes, goes something like this: The workings of the world can best be understood as an infinitely multidimensional, fractal pattern of what I call agent-patient dyads, where one element of each pair (the agent) is acting upon another element (the patient—we’re using the old philosophical terminology here for “that which acts” and “that which is acted upon”), in such a way as to cancel-out perturbations to some controlled perceptual variable of the agent. For example, . . .

DF: STOP! You’re getting way, way too detailed and complicated here for this conversation. Can you get more practical and bring it back to what you do to “work with pieces of the real world closely and delicately”?

JW: Sorry. Let me just put it this way, then: We have a rigorous way of analyzing the very fabric of what someone, say a manager, is really dealing with when dealing with her “situation,” and we analyze it in terms of the specific, idiosyncratic patterns within this complex fractal structured in such agent-patient dyads. We analyze the constraints that yield the particular patterns observed. These key constraints will mostly take the form of context-markers, the aspects of communication (in the widest sense of the term), that enable each participant to identify at any point which context they are in.

And we are therefore filtering the complex, dynamic communication patterns and loops that make up the rich, idiosyncratic fabric of possibilities and constraints of which that manager’s situation is actually made, the actual and possible constraints and immanent possibilities that will inevitably determine the success or failure of management actions, whether the players are aware of it or not.

This is the very fabric of reality, as it appears on our revised scientific perspective. This is what you are really dealing with every day, at least if you lift the veil of prefabricated abstract categories and models and MBA-speak blinding you to the possibilities for effective action, and blinding you to the very real but unseen idiosyncratic constraints on effective action, and get underneath it all to bring these to the fore.

Your everyday reality is, in fact, an extraordinarily complex, indeterminate and ultimately undetermined, infinitely multidimensional, highly patterned sphere in which a kind of anarchy reigns—a kaleidoscopically shifting, protean, multi-dimensional playing-field, consisting of an infinity of perceptions and communications of all the players involved: an infinity of possibilities and an infinity of constraints, most of them local and idiosyncratic, yet all of it now readily amenable to rigorous, scientific analysis and understanding.

DF: Sounds mind-boggling regardless.

JW: Well, it is just this truly mind-boggling reality, which is curiously very much closer to our experience of everyday life than it is to the mid-level abstractions of management-speak, it is this mind-boggling complexity that we address directly in our applied scientific work within organizations—rigorously analyzing it to secure precise, desired outcomes brought about rapidly through the design of precision-engineered minimalist interventions: small communications that flip the entire pattern from the existing, problematic state-of-affairs to the desired state-of-affairs in one fell swoop, by deftly removing and inserting constraints, to catalyze the desired change and lock it in place.

In the practical applications of our methodologies, we get to work directly with pieces of the real world automatically, insofar as we are already sitting in a room with an executive-who-is-dealing-with-a-situation-as-mapped-by-him, and who has access, given the right questions from us, to the details of those idiosyncratic patterns and constraints and possibilities whose significance he would not have been able otherwise to recognize. Remember too, for us there is no line to be drawn between our conceptions and perceptions of the world and the world itself out there, because those conceptions and perceptions of the world are parts of the real-world territory-as-mapped. Likewise, as those conceptions shift in response to our very different questions, the reality shifts. Right before our eyes, right there in the room.

Next: A closer look at the methodology for creating rapid change

About the Author: David Franzetta

Dave Franzetta, President of Interchange Associates, Inc., is based in Orange County, California. He has been working with Interchange since 1994, co-designing scores of successful minimalist interventions addressing a wide range of business issues, and immersing himself full time not only in the practice of minimalist intervention but in the science behind the analytical technology of Interchange Research.

Holding degrees in science from Michigan State University and management from Farleigh Dickinson University, and with a professional background ranging from science and naval intelligence to accounting and finance, Franzetta joined Interchange Associates Inc. following a distinguished, wide-ranging 30-year career with Prudential Financial, spanning corporate finance, insurance and reinsurance, franchise management, risk management and investment management, with a reputation for effective leadership in business transformation, including the dramatic turnaround and leadership of what became one of Prudential’s best performing operating companies.

Franzetta served as Comptroller of Prudential Investment Corporation and later as Chief Accounting Officer of Prudential Insurance, and also as Chief Financial Officer, Chief Administrative Officer, President and Vice Chairman of several of Prudential Financial’s operating subsidiaries.