Control, surpris’d


“Policy” need assume no such thing; policy with which I am familiar is about managing, because the imposition of control is not possible. Really-existing implementation, operations and shocks surprise any uni-directional, deterministic notions of control.

–More, attempts at direct control produce disruptive surprise. Think of those cases where power politics and material interests led to counterproductive outcomes never foreseen by power-makers. There surely had to be cheaper ways for the US to get the oil than undertaking two wars in Iraq.

–Surprise too can produce power. This is known, though less understood are all the surprising ways surprise leads to power. Psychologist Kevin Dunbar and his colleagues examined a handful of science labs, particularly their regular meetings involving senior researchers, postdocs and grad students:

The analysis of the 12 laboratory meetings yielded 28 research projects, with 165 experiments, and the participants reasoned about a total of 417 results. . . .When we divided the scientists’ results into expected and unexpected findings, we found that over half of their findings were unexpected (223 out of 417 results). Thus, rather than being a rare event, the unexpected finding was a regular occurrence about which the scientists reasoned.

There were, in fact, so many unexpected findings that what distinguished one lab from another was the subset of unexpected findings the labs chose to pursue (Dunbar, personal communication). In the labs examined, unexpected findings were happening all the time, and what was most interesting was how pursuit of some rather than others led to intellectual property and increased economic and/or scientific status (e.g., a Nobel).

–Let’s try a different way to make the same point. Compare algorithmic decisionmaking (ADM) and the current technology for gene editing known by the acronym, CRISPR. When it comes to ADM, the worry is that we don’t know how the algorithm works; it’s all murky. What’s happening in the algorithm, we asked, because of the cultural biases imported via the original data? When it comes to CRISPR, the worry is that, even when we know that this rather that gene is being edited, we’re still not sure it’s right thing to do. The deaf community is not so hot on getting rid of the deafness gene, when deafness is its own culture.

Suppose we had a CRISPR analogue for ADM, i.e., we could go into the algorithm and excise cultural bias. We’d still worry about, e.g., what is bias to some is not to others. Also, is there any doubt whatsoever that some new mechanism promising greater control in addressing one worry wouldn’t produce another worry, equally if not more important? Control cannot answer the questions control poses.

–Still, we see more and more attempts to control directly. Still, people conclude: “Studies of resistance in organizations have largely concluded that it is impossible to effectively resist contemporary regimes of control.” Still, people talk about taking back control from those who are better described as being out of control in the first place.

So, to be clear as possible, this is my point: You have to take control seriously enough to realize it’s–surprisingly–about much more than control. It’s about managing surprise because you can’t control, much as when negotiated bargains replace contracts that cannot be renegotiated.

Principal sources

Courpasson D, Younes D, Reed M. (2021). Durkheim in the neoliberal organization: Taking resistance and solidarity seriously. Organization Theory. doi:10.1177/2631787720982619

K. Dunbar, personal communication, and also: Dunbar, K., and J. Fugelsang (2005). Causal thinking in science: How scientists and students interpret the unexpected. In M. E. Gorman, R. D. Tweney, D. Gooding, & A. Kincannon (Eds.), Scientific and Technical Thinking (pp. 57-79). Mahwah, NJ: Lawrence Erlbaum Associates

Leonard, D. K. (2013). Social contracts, networks and security. In Tropical Africa Conflict States: An Overview, IDS Bulletin 44.1, Brighton: IDS

The good mess in no single, right reading and in the many (more or less) wrong ones

–An illustration that itself performs the difficulty in telling the difference between knowing what are wrong readings of a text while still not knowing what is the right one is that of literary critic, I.A. Richards. I quote from his letter to T.S. Eliot, where Richards free-associates the challenge:

This problem,—that a single line [of poetry] need have no one right reading, yet will have innumerable wrong ones; that among all the many “right” ones some at least will carry, primarily, very different interpretations. . .and yet that we must take some partial meaning, and make it deputize for the whole but without forgetting that we do so—this problem which almost every sentence of good poetry represents to us seems to me a paradigm for all the problems, big and little of life and thought. . .

By “wrong,” Richards means those “that close down other possibilities [i.e., other readings],” not least of which are the exclusionary readings that “claim to be the only right ones”. (I’d add more or less wrong to reflect the more or less complex interpretations.)

–Eliot had an example. While Goethe’s writings on science, particularly the work on plant morphology and color, have been criticized by scientists, Eliot asks:  “Is it simply a question of who was right, Goethe or the scientists? Or it is possible that Goethe was wrong only in thinking the scientists wrong, and the scientists wrong only in thinking Goethe was wrong?”

I take this to be an implication of Eliot’s point: Goethe and the scientists may have been more or less “right” as far as they went, but they in their respective ways did not go far enough, and by falling short both end up with interpretations that were “partially right” in multiple senses of that word “partial.” Seeing that and going further is a good mess to be in.

–So what? It seems to me then that we are looking for professionals less keen on “the right person asking the right questions at the right time—for the right price!—and with the right policy” and more keen on the multiple ways to go more wrong and the good messes of being less wrong and more right, case-by-case.

Can’t we be best anticipatory and resilient at the same time?

Begin with the strategic orientations many have with respect to resilience and anticipation as distinct from each other. Resilience is said to be optimizing the ability to absorb or rebound from shocks, while minimizing the need to anticipate these shocks ahead of time. Anticipation, in contrast, is to optimize the ability to plan ahead and deal with shocks before they happen, while minimizing having to cope with shocks when they do occur. Consider the resulting Table1:

System designers would like managers to be both optimally anticipatory and resilient at the same time—indeed that managers maximize their “readiness” for whatever arises, whenever. These all-embracing demands can, however, reduce the managers’ much-needed capacity to balance anticipation and resilience case by case.

More, the ideal of stabilizing the task environment so as to minimize the need for both anticipation and resilience—a common enough premise (promise?) of macro-designers—is as impossible to realize as it is irresponsible to promote, when the aim is high reliability in real-time operations.

Good-enough criticism

Oh that’s perfect Edmund: you American puritans, you’re always inventing diseases. And one that singles out blacks, drug users and gays – how perfect! Michel Foucault, philosopher, criticizing novelist Edmund White, when warned about AIDS early on

Praxis. . .appears in theory merely, and indeed necessarily, as a blind spot, as an obsession with what is being criticized. Theodor Adorno, a very different philosopher

–For the policy analyst, being relevant means offering an alternative to what is criticized. But there are other ways for criticism to be good-enough, that is, above and beyond the usual kvetching. (And in case it needs saying, offering an alternative is of little use, when no one is listening or couldn’t care less.)

What’s good enough here? Criticism is relevant even when solutions are not in the offer, if the very idea of “offering solutions” would make bad messes worse. There is also the honorable march of permanent critique, which resists anything like aiding and abetting sanctioned modes for “acting practically.” And then there’s bearing witness, which can make silent criticism very loud (e.g., the Black Sash in apartheid South Africa).

It seems to me that that criticism is good enough when it provokes even if discourages, disturbs even when debatable, and sharpens attention even because it goes no further.

–An example. Science and economics have been much chastised as: religion (e.g., each with metaphysics); imperialist (e.g., colonizing the traditional “why” and “how” questions of the humanities); and for being “just” socially-constructed. Also, critiques of science and economics as Big Business stress their producing sufficient Bad as to shadow whatever the Good.

But any weaknesses in such criticisms can serve in the same instant as their strengths, and this isomorphism is too frequently ignored, when not altogether missed–or so it seems to me.

When focusing on downsides of science and economics, you needn’t be: denying the strengths each has; nor arguing that the blind-spots “cancel out” the strengths; nor saying something like the costs outweigh the benefits. I am saying that what works to the Good also works to the Bad and this happens irrespective of context more than supposed.

–So too for complexity’s chief synonyms: difficulty, inexperience and not-knowing. It stops short to say the three spancel the analyst and hobble analysis: They are also the strengths of analysis and at the core of the analyst’s practice. To see the interchange between Foucault and White as anything less than really-existing difficulty, inexperience and not-knowing—MF was wrong! EW was right!—misses the force-field and torsion of blind-spots.

Good-enough criticism, I think, wants to admit this. It differs from the kind of criticism that wants to buttonhole people once and for all. It’s good enough because the other side of a criticizing “no” is “yes, but”.

Table of key entries by topic area

Most Important: “What am I missing?,” “Complexity is the enemy of the intractable,” “Power,” “Interconnected?,” “I believe,” “Wicked problems as a categorized nostalgia,” “Even if what you say is true as far as it goes, it doesn’t go far enough…,” “Triangulating complexity for policy and management,” “Time as sinuous, space as interstitial: the example of total control,” “Keeping it complex. . .,” ““Long-terms, short-terms, and short-termism,” “More on over-complexification,” “Playing it safe, utopia,” “Case-by-case analysis: realism, criteria, virtues,” “Not ‘Why don’t they listen to us?’ but rather: ‘What should we listen for from them. . .’,” “Humanism, by default,” “Mess and reliability: five inter-related propositions,” “Control, surpris’d”

Recasting big policy issues: “Poverty and war,” “Second thoughts on income inequality,” “Surprising climate change,” “COVID-19,” “Missing racism,” “Healthcare,” “To-do’s in the Anthropocene, ” “The market failure economists don’t talk about: Recasting infrastructures and the economy,” “Culling sustainability,” “In a failed state,” “Revolts,” “A colossal inheritance,” “Wicked problems as a categorized nostalgia,” “Making the best of linear thinking, complexly: typologies for reframing ‘coordination’,” “Government regulation,” “Economic consequences of having no must-never-happen events in the financial sector,” and Longer Reads (below)

More recastings: “Policy narratives,” “America’s and Trump’s,” “Recastings #1,” “When the light at the end of the tunnel is the tunnel,” “Public Policy Analysis, c.1970 – c.2020: In Memoriam?,” “Sound familiar? Here’s why,” “A grammar of policy analysis,” “Bluejays, fists and W.R. Bion,” “Policy as magical thinking,” “A different take on ‘traditional agriculture:’ risk-averse v. reliability-seeking,” “Finding the good mess in supply and demand,” “Escaping from Hell Is a Right!,” “Global Climate Sprawl,” “Disaster averted is central to pastoralist development”

Not-knowing and its proxies: “Seeing unknowns,” “Inexperience and central banks,” “Managing inexperience,” “Difficulty at risk and unequal,” “By way of distraction…,” “Shakespeare’s missing lines still matter,” “Humanism, by default”

Ignorance and uncertainty: “When ignorance does more than you think,” “Optimal ignorance,” “Uncertain superlatives,” “Stopping rules and contested regulation,” “To-do’s in the Anthropocene,” “Why aren’t they all running away!,” “Yes, ‘risk and uncertainty’ are socially constructed and historicized. Now what? The missing corollary and 3 examples,” “Killing cognitive reversals,” “Error and Safety,” “Triangulating complexity for policy and management”

Risk, resilience and root causes: “A new standard for societal risk acceptance,” “Easily-missed points on risks with respect to failure scenarios and their major implications,” “Risk criteria with respect to asset versus system scenarios,” “Half-way risk,” “Central role of the track record in risk analysis,” “Resilience isn’t what you think,” “Root causes,” “Frau Hitler, again,” “With respect to what?,” “Yes, ‘risk and uncertainty’ are socially constructed and historicized. Now what? The missing corollary and 3 examples,” “Error and Safety,” “Four macro-design principles that matter—and one that can’t—for risk managers and policymakers,” “Managing-ahead for latent risks and latent interconnectivity,” “Can’t we be best anticipatory and resilient at the same time?”

Regulation: “A few things I’ve learned from the Financial Times on regulation,” “Government regulation,” “Stopping rules and contested regulation”

Infrastructures: “The real U.S. infrastructure crisis,” “Innovation,” “Take-home messages,” “Who pays?,” “When high reliability is not a trade-off,” “The market failure economists don’t talk about: Recasting infrastructures and the economy,” “When ignorance does more than you think,” “Catastrophizing cascades,” “Healthcare,” “Interconnected,” “Stopping rules and contested regulation,” “Achilles’ heel of high reliability management,” “Where distrust and dread are positive social values,” “To-do’s in the Anthropocene,” “Government regulation,” “Killing cognitive reversals,” “Error and Safety,” “Managing-ahead for latent risks and latent interconnectivity,” “What you need to know: Big System Collapse! Or not.”

Environment: “New environmental narratives for these times (longer read, consolidated from following entries),” “Nature,” “Tansley’s ecosystem,” “Radical uncertainty and new environmental narratives,” “Eco-labelling recasted,” “European Union Emissions Trading Scheme, Scenes I and II,” “To-do’s in the Anthropocene,” “Dining on gin and consommé,” “Culling sustainability,” “Lifecycle modeling of species,” “Better fastthinking in complex times”

Rural development: “Disaster averted is core to pastoralist development,” “Optimal ignorance,” “Culling sustainability,” “A different take on ‘traditional agriculture:’ risk-averse v. reliability-seeking,” “Misadventures by design,” “Triangulating complexity for policy and management,” “Next-ism”

Catastrophe and crisis: “Catastrophizing cascades,” “Jorie Graham’s systemcide,” “The shame of it all,” “Next-ism,” “The future is the mess we’re in now,” “Killing cognitive reversals,” “Escaping from Hell Is a Right!,” “Good messes to be had from their catastrophism,” “What you need to know: Big System Collapse! Or not.”

More mess, good and bad: “Mess and reliability: five inter-related propositions,” “A different take on the traffic mess,” “Happiness: The mess,” “Who pays?,” “Misadventures by design,” “. . .and raise my taxes!,” “Top-of-the-list thinking,” “Take-home messages,” “Finding the good mess in supply and demand,” “The New Normal is managing not just negative setbacks but also positive ones,” “Good messes to be had from their catastrophism,” “Can’t we be best anticipatory and resilient at the same time?,” “The good mess in no single, right reading and in the many (more or less) wrong ones”

Betterment and good-enough: “Betterment as ‘yes-but’ through ‘yes-and’,” “It’s better between the James brothers,” “Good-enoughs,” “Good-enough dreamers,” “Professional, amateur, apprentice; Or, As good as the fingernails of Manet,” “‘at sea,’ ‘from on high’,” “Betterment (continued),” “Better fastthinking in complex times,” “Humanism, by default,” “Good-enough criticism”

Policy palimpsests and composite arguments: “Take home messages,” “Blur, Gerhard Richter, and failed states,” “Time as sinuous, space as interstitial: the example of total control,” “More on policy palimpsests: The European Union Emissions Trading Scheme, Scenes I and II,” “Shakespeare’s missing lines still matter,” “Bluejays, fists and W.R. Bion,” “Reflection and sensibility,” and other Longer Reads (below)

Economism: “Economism,” “Keep it simple?,” “Loose ends, #1” “When high reliability is not a trade-off,” “Short and not sweet,” “The missing drop of realism,” “The market failure economists don’t talk about: Recasting infrastructures and the economy,” “Finding the good mess in supply and demand,” “Makes the gorge rise”

Longer Reads: “Ammons and regulation,” “The next Constitutional Convention,” “Recalibrating Politics: the Kennedy White House dinner for André Malraux,” “Blur, Gerhard Richter, and failed states,” “A consultant’s diary,” “A different take on The Great Confinement,” “Market contagion, financial crises and a Girardian economics,” “New environmental narratives for these times (consolidated from Environment entries),” “New benchmark metrics for major risk and uncertainty (consolidated from entries for Risk, resilience and root causes),” “One ‘why’ and four ‘how’s’ to recasting complex policy and management problems (consolidated from earlier entries)”

Something less complex?: “Red in tooth and claw,” “What kdrama has taught me,” “The irony of it all,” “Dining on gin and consommé,” “Five questions everyone should want to answer,” “Distracted anti-utopians”


Here are early warning signals–typically not recognized–that the major critical infrastructures upon which we survive are operating at, or beyond, their performance edge:

  • The infrastructure’s control room is in prolonged just-for-now performance, which means operators there find it more difficult to maneuver out of a corner in which they find themselves. (“Yes, yes, I know this is risky, but just keep it online for now!”)
  • These real-time control operators are working outside their de jure or de facto performance bandwidths, in effect having to work outside their unique domain of competence.
  • The decision rules operators reliably followed before are turned inside out: “Prove we can do that” becomes “Prove we can’t!;” “Ensure a capital cushion to protect against unexpected losses” becomes “From now on, manage for expected losses!”
  • Real-time operational redesigns (control room “workarounds”) of inevitably defective equipment, premature software, and incomplete procedures are not effective as before.
  • Their skills as reliability professionals in identifying systemwide patterns and undertaking what-if scenario become attenuated or no longer apply.
  • Instead of being driven by dread of the next major failure ahead, control room professionals are told that their track record up to now is to be benchmark for system reliability ahead.

Good messes to be had from their catastrophism

It  is  an  interesting  fact  about  the  world  we  actually  live  in  that  no anthropologist, to my knowledge, has come back from a field trip with the following report: their concepts are so alien that it is impossible to describe their land tenure, their kinship system, their ritual… As far as I know there is no record of such a total admission of failure… It is success in explaining culture A in the language of culture B which is… really puzzling.
                                                   Ernest Gellner, social anthropologist

The overall skeptical argument that we know nothing at all about other people’s minds, for instance, is painless, because it is totally theoretical; it is more disturbing to consider that perhaps we know something about other people, but a lot less than we suppose.
                                                 Bernard Williams, philosopher

–Start by observing that, “There is no alternative but to experiment,” imports a silent clause for catastrophists: “There is no alternative but to experiment, even if the consequences of doing so are uncertain.” We face certain catastrophe if we don’t experiment, no matter what.

A parallel exists in psychoanalysis: Just as a traumatic dream represents for Freud an exception to dreaming as wish fulfillment, so too does catastrophism, their wide-awake trauma, represent an exception to thinking more before acting.

–But do think about it. The business of Empire and Finance is to ensure The End continues. For disaster capitalism and speculative finance, more catastrophism means more ways to make a buck (think: artificial pollination after the bees go extinct).

–So, the policy analytic challenge–the challenge of thinking–isn’t to come up with new money-making crisis scenarios for profiteers. It is for us to think through what is instead-of-catastrophism or how the profiteers’ preoccupation with their catastrophism poses good messes for the rest of us.


–What are some good messes to be had in the catastrophism of others? Let me suggest a few:

Who wouldn’t be right in resisting burn-out, early or late, if told repeatedly they haven’t “taken control” of climate change, species extinction and biodiversity loss?

It would be good to know if we are, as some say, nine missed meals away from civil unrest, or is it only four missed meals, as others say.

Isn’t everything-connected-to-everything-else an odd kind of stasis rather than always-primed for worse?

Hasn’t surviving the remains of war been the only real place where everything is directly connected to everything else by virtue of barter (e.g., your fuel for my food)? If, as novelist Henry James put it, what is real is what remains, then the playwright Samuel Beckett’s “nothing” in “Nothing is more real than nothing,” is what remains—before or after—everything has been directly connected? It’s dangerous to assume we don’t manage different interconnections differently, and with different effect.

Conversation stoppers like “human fallibility” and “radical uncertainty” reverberate with original sin and basic evil. Better to say we still find it reasonable to assume we’re not as rational as we thought, even now (a good indication, btw, that the Enlightenment continues).

To say endemic crises have many causes and are overdetermined is like saying there’s too much wind-up for the pitch thrown.

And since when has inequality been best described as a crisis only or even primarily? Norms are different from crises, and inequality is very much the norm. Inequality is better understood as the scandal of the status quo. Shaming, humiliating and ostracizing the profiteers don’t solve crises, but together they are a very important way to respond to inequality.

Compare economist, George Akerlof, who put it that the most efficient way to look as if you’re honest is to be honest, to the poet, Frank O’Hara’s “It’s easy to be beautiful; it’s difficult to appear so”. Can we say that only when it is difficult to be honest does honesty, let alone efficiency, appear beautiful?

How many times have we heard that we must never rely on those who created problems to help solve them. That rules out those working on reducing global climate change who use water from the tap, turn lights on, and run appliances—the very same people who insist on having reliable infrastructures so they can get on with the necessary environmental work

“Would it really be incoherent, then, or an affront to fairness, to impose a penal draft—i.e. to randomly select citizens to serve their country as inmates, probationers or parolees, perhaps under the (all too familiar) pretense that they must be guilty of something?. . . .What better way could there be than some form of ‘prison duty’ to embody our commitment to protecting the guilty from unjust punishment?” (Benjamin Ewing, “Socializing Punishment,” in The Point Magazine)

A doctor tells me that I have 1 out of 5 chances of having a heart attack or stroke within the next ten years. But for the life of me I can’t explain why the lines of poet, A.R. Ammons, seem much more certain:

                                     though I
have not been here long, I can
look up at the sky at night and tell
how things are likely to go for
the next hundred million years:
the universe will probably not find
a way to vanish nor I
in all that time reappear.

Managing-ahead for latent risks and latent interconnectivity

–To insist that “there are hundreds and hundreds of organizations having oversight responsibility for [fill in name the region]” misses the fact that interconnectivity becomes a focus only with respect to specific failure/accident scenarios. Changing the scenario focus over what are the important manifest interconnections means having also changing the focus over what are the latent ones of concern.

–What are latent interconnections? To answer that, we have first to describe latent and manifest risk. If manifest risk is where the probability of failure (Pf) and the consequences of failure (Cf) are known or estimated, “latent risk” is when uncertainty over Pf or over Cf exists. Once the missing estimate is provided, what was latent becomes manifest risk.

High reliability management recognizes that management of latent risk—the management of nonmeasurable uncertainty—should be ahead of the risk becoming manifest. (Think of measurable risk as associated with professionals’ skills in pattern recognition across a run of cases and nonmeasurable uncertainty as associated with their skills when it comes to a one-off, what-if scenario formulation.)

Minimally, this management-ahead is to forestall the realization of risk-with-respect-to scenarios that would decrease options and/or increase volatility of reliability professionals. In other cases, the management-ahead is to help realize risk-with-respect-to scenarios that would manifestly increase options and/or decrease volatility.

–Now to implications for and about latent and manifest interconnectivity. In complex, interconnected systems where high reliability (including high safety) matter as an existential priority, four inter-related factors move center-stage for the managing-ahead of latency:

  • Analytic modeling uncertainties become a major consideration. Not only do analytic models differ in terms of their uncertainties—electricity modeling appears to be better than levee modeling. Even more important, the more interconnected the infrastructures, the more latent risks to be managed in light of input and output variables as well as their joint their control variables, which modelers often miss or do not understand (e.g., waterflows central to real-time services of key interconnected infrastructures).
  • The evolutionary advantage of each control room’s ability to operationally (re)design workarounds to compensate for (emerging) defects in hardware and software under interconnectivity take on added prominence in real time.
  • A key latency—but one often ignored or not recognized outside the infrastructure control room by regulators and legislators—centers around small change/large impact scenarios. For example, the November 2006 disconnection of a single power cable in northern Germany triggered regional blackouts as far away as Portugal. True, but: How many times were such small changes managed before so as not to lead to huge impacts (the disasters averted) and would subsequent “remedies” undermine this prior ability to manage reliably, had the remedies been instituted earlier?
  • A focus on the classic common-mode failure around spatially collocated elements of different infrastructures, such as a shared utility corridor, is misleading when the chokepoints of the respective systems are physically located elsewhere. A chokepoint of one infrastructure tripping over into disrupted or failed operations is profoundly more important if collocated next or adjacent to the chokepoint of another infrastructure with which it is also functionally interconnected.

One “why” and four “how’s” to recasting complex policy and management problems (longer read)

–Let’s start with some examples of recasting. You see jewelry where I see sculpture on a small scale; you see the orchestra conductor conducting, I see that conducting more as a dance. You see the forest; I see a mountain of poison against insects. I witness the birth of the family’s first child; you see the first child give birth to a family. You see the sketched outline of a toy sailboat (or other desideratum); I point out that the boat’s image is the space left behind after other images have inlined it. We both, on the other hand, see the hole without its doughnut.

I ask, when is biotechnology bestiality? You ask, are gardens zoos without the cruelty? Isn’t heroism first violence to oneself? Is burglary a kind of architectural criticism? Are galleries a novel way artists handle storage problems? Does burning down a huge lumber yard mean houses have been destroyed? Isn’t a single performance one story that can’t be plagiarized?

–Continue with more complications. Doesn’t our continuing inability to safely store nuclear weapons waste reveal the Cold War to be the first war in modern times where the continental US took direct hits because of an enemy? What does the US look like when one realizes it is a country where more men are likely raped than women? (Think: its male prison populations). What if those time-consuming studies to model and validate the life-cycles of endangered species become their weapons of mass destruction?

–Let the examples become even further complicated:

  • Bad policy mess: At one point, three to four billion people—up to two-thirds of the world’s population—lived in regions without adequate water supplies or sanitation. Good policy mess: Now that truly is a very, very large number. Indeed, the distribution of people without adequate water supplies is so large that many of them must be doing better than others. That, without being Darwinian about it, means there are tens of millions —hundreds of millions?—of people who have many things to say about how to better survive without adequate water to those millions more who are also trying to survive without it. But where then are the campaigns, e.g., in the World Bank or the IMF, to do just that?
  • Bad policy mess: It has been said that one out of every two young African-American men in major US urban areas is enmeshed in the criminal justice system. But that too is a large number, right? Good policy mess: Why, then, are we not interviewing the other 50 percent of young urban African-American males outside the criminal justice system to find out what they are doing, and what the rest of us could learn from them?
  • Bad policy mess: A reported 11 million people are in the U.S. illegally. Good policy mess: If those numbers are anywhere close to accurate, then there must be thousands—hundreds of thousands? far, far more? —who are already acting as if they were good U.S. citizens.

Examples can easily be extended, but the point remains: There is no one way, let alone one single right way, to look at conditions already complex.

–The world is not one way only because the world’s complexity—its many components, each component with multiple functions (I am a husband, father, blogger…), and the many interconnections between and among components, functions and the wider context in which these are embedded—enable all manner of interpretations.

That is the “why” of recasting: No single interpretation (explanation or description) can cover or exhaust major issue complexity. The upshot of the inexhaustibility is that complex problems can be cast and recast in multiple ways. Or to put the point from the other direction, any complex policy issue that is described as “intractable” is an exaggeration that has stopped short of further recasting.


But, then, how to recast?

–I suggest four how’s. Two recasting operations are familiar to those who undertake case-by-case analysis of issues: the use of multiple typologies and of methodological triangulation. The other two are less familiar, but no less important for recasting: use of counternarrative thinking and the policy palimpsest.

The four come from my own experience and practice. In this way, the discussion below has all the faults of a sample of one. But, since reframing policy issues has long been a topic in my profession, I encourage the reader to search for other methods, approaches, and examples, starting with that keyword, “reframing.”

–Finally, so as avoid confusion, I keep to the operating definition of issue complexity above: A policy or management issue being more or less complex depends on the number of elements to that issue, the different functions each elements has, and the interconnections among functions and components. Global ecosystem restoration is more complex than that of any regional landscape, because the number of different ecosystem elements, the services provided by each element, and the interconnections among functions and elements (e.g., “resource scarcity) are higher globally.

All else considered, the fewer the elements-etc, the less complex the issue; more elements-etc, then more complex. By way of justification, this reflects the definition of social complexity developed and used over the last 40 years, that of political scientist Todd R. La Porte.

Now to four ways to recast that issue complexity.


Multiple typologies. One great irony in taking issue complexity seriously is the usefulness of linear thinking in doing so. Since “linear” is often equated with “simplification,” I quickly add: …when that linear thinking is in the form of multiple typologies considered together for analyzing that issue complexity.

Any two-by-two table (or some such) is easily criticized for being reductionist in the face of a complex reality. This, though, misses what has always been the latent function of typologies when in the plural: to remind us that reality is indeed more complex than lines, boxes and bullet points can portray.

–Multiple typologies are the norm in my profession, policy analysis and management, and to use them in sequence—one after another, different criteria following upon different criteria—is to render a major policy mess granular enough for differing implications to become visible. Multiple typologies are not the pieces that complete a picture puzzle; they make a puzzle detailed enough to see a different puzzle or puzzles already there.

The typologies in my own work come largely from sociology, political science and organization theory. In the most practical sense, you can begin with any typology, the entire point being there is no obvious macro, meso or micro start when it comes to reframing what is complex—say in natural language, what is uncertain, unfinished and disputed—at the same time. The typologies I rely on include:

  • Different types of unpredictability, including measurable probabilities, unmeasurable uncertainties and unknown-unknowns (adapted from Andrew Stirling’s typology of incertitudes);
  • Different types of organizations, where production agencies–by way of example–differ significantly from coping agencies in terms of their observable/unobservable outputs and outcomes (J.Q. Wilson’s typology of agencies)
  • Different types of cases, e.g., “the case out there in reality” versus “the case emerging from your interaction with issues of concern” (Charles Ragin’s typology of cases)
  • Different types of large-scale technological systems whose centralized or decentralized operations vary as a result of system coupling and interactivity (Charles Perrow’s typology of high-risk technologies)
  • Different types of cultures for differentiating ways of life and policy orientations (the four cultures of Mary Douglas, Aaron Wildavsky and their colleagues)
  • Different performance modes—just-in-case, just-in-time, just-this-way and just-for-now—for the real-time high reliability management of large-scale socio-technical systems (a typology of Roe, Schulman and our colleagues)

No major issue emerges unchanged from the seriatim application of these typologies. More, all issues in this blog—inequality, poverty, war, climate change, pandemics, healthcare, others—merit such attention. In case it needs saying, other typologies may be as fruitful in differentiating the complex issue of concern.

–In all this, though, remember the cardinal virtue of applying typologies when it comes to recasting—or if you prefer, reframing, revising, redescribing, rescripting, refashioning, recalibrating—issue complexity:

  • It is to move you from the myriad types of contingent factors at work affecting the major policy and management issue—societal, political, economic, historical, cultural, legal, scientific, geographical, philosophical, governmental, psychological, neurological, technological, religious, or whatnot.
  • It instead is to move you to the many, but less numerous criteria with which to identify and describe the factors that are pertinent. These reframing criteria are, for example, the horizontal and vertical dimensions used in differentiating the cells of a 2 X 2 typology.


Methodological triangulation. Another great irony in taking social complexity seriously is the how easy it is to over-complexify the issue. People typically think the real problem is simplifying that which cannot or should not be simplified. Equally important, though much less recognized, is over-complexifying the already (more or less) complex.

The litmus test that an issue is too complexified or simplified is whether it can be recast in ways that open up fresh options for intervention without gainsaying the complexity. If a simplification can be recast as complex in ways that new interventions are then plausible or if the issue thought to be so complex that no further action is possible can be recast to show otherwise, then truth of the matter has been pushed and pulled beyond current exaggeration.

–Triangulation is particularly useful for assessing the possibilities of recasting what is thought to be too complex for further action. One form is the use of multiple—the “tri” doesn’t mean three only—methods. This triangulation is most successful when “Whatever the direction you look at this issue, you get to this same point. . .”

Familiar examples of methodological triangulation have been convergence on the recognizing the importance in development of women and of the middle class(es) in a country’s development trajectory. Methodological triangulation also figures prominently in other applied fields (though not all!), e.g., practicing policy analysis, marketing, investigative journalism, and participatory rural appraisal.

–To be clear, the goal in such triangulation is for analysts to increase their confidence—and that of their policy audiences—that no matter what position they take, they are led to the same problem definition, alternative, recommendation, or other desideratum. Accordingly, it cannot and must not be assumed that increasing one’s confidence perforce reduces complexity, increases certainty, or gets one closer to the truth of the issue matter.

Issue complexity remains after triangulation; when successful, it means only that when the number of issue elements, functions and interconnections are high, some mutual intersection or overlap may have occurred empirically.

–Triangulation is thought to be especially helpful in identifying and compensating for biases and limitations in any single approach (e.g., reliance on any single typology). Obtaining a second (and third. . .) opinion or soliciting the input of the range of stakeholders or ensuring you interview key informants with divergent backgrounds are three common examples. Detecting bias is fundamental, because reducing, or correcting and adjusting for, bias is one thing analysts can actually do.

Triangulating on a common point is in no way guaranteed just as canceling out biases—be they cognitive, statistical, cultural, other—cannot be assumed to have occurred as a result of triangulation. (Anyway, it remains an open question which biases are most important—material interests, cultural beliefs or built-in cognitive biases, among the many other candidates.)

–That said, failure to triangulate provides useful information. When findings do not converge across multiple orthogonal metrics or measures (populations, landscapes, scales…), the search by analysts becomes one of identifying specific, localized or idiographic factors at work. What you are studying may in reality be non-generalizable—that is, it may be a case it its own right—and failing to triangulate is one way to confirm that.


Counternarrative thinking. If the aim in complex policy and management is to be more prepared for inevitable surprise—the chief feature of social complexity is not direct power, but direct surprises—you have to be more willing and able to undertake counter-expected or counter-desired thinking. (Surprise, in case it needs adding, is what actually pushes us to rethink what we had expected up to that point.)

–If you’re told “a leads to b leads to c,” ask yourself: Can I think of a plausible scenario where “not-a leads to not-b, but not-b still leads to c” or where “a leads to b, but b leads to not-c”?

If you are told that “integrated pest management [IPM] leads to increased agricultural yields, which in turn lead to more sustainable livelihoods,” can you imagine a scenario (1) where IPM and increased crop yields do not lead to sustainable livelihoods or (2) where chemical-based agriculture and lower field yields could nonetheless lead to sustainable livelihoods?

One can in fact imagine situations where the latter would be found, e.g., in regions where field fertility has always been extremely low and soil depletion high, home plots or gardens become the focus of intensive cultivation.

–Once you have a rival narrative, you ask of it: Is it all that surprising? Under what conditions can the counternarrative be treated seriously? Is its realization desirable with respect to policy and management, here and now? In addition to better preparing you for surprise, a virtue of counternarrative thinking is that, even if the rival scenario cannot now be supported, it could serve as a policy option for the future if conditions change.


Policy palimpsest. A few years ago I had the good fortune to study the EU’s CO2 cap-and-trade system, the Emissions Trading Scheme (ETS). Without going into details, the ETS’s first decade and half of operation has been a record of one major problem after another, including but not limited to too many credits issued, adverse economic conditions, and cyber-theft and permit fraud.

Despite this, ETS supporters continued to argue, “Well, what else could we have done? We needed some kind of market, or things would have only gotten worse. . .” Well,  others counter, what you could have done was to search for those better practices that energy infrastructures use in real time to meet such environmental standards. It may be the case that some EU infrastructure control rooms did just that, but no one would ever know that from the existing ETS literature, so focused as it has been on there being no real alternative to the ETS.

I still agree with the gist of the preceding analysis—at least as far as it went—but now understand how a policy palimpsest approach pushes my argument further. That is, my original analysis read as if it were a chronological history of the ETS—”just the facts ma’am”—when in fact it is no such thing.

How so?

–The term, “policy palimpsest,” refers to the social science notion that longstanding controversial policies, like those witnessed in ETS debates over the years, are themselves the composite of policy arguments and narratives that have overwritten and obscured each other.

Any composite argument read off a policy palimpsest reads sequentially—nouns and verbs appear in order and meaning is made—but none of the previous inscriptions come to us clear and whole through the intercalated layers, effacements, fractures, erasures, and lacerations.

Arguments assembled from the palimpsest have been blurred, intertwined and re-rendered for current (often controverted) purposes. The analytic challenge is to read any composite argument with its blur visible in order to acknowledge and probe what has been rendered missing. Why? Because surfacing what is (or has been made) missing may provide potent ways to rewrite currently dominant composite arguments in light of the issue complexity.

–In my original ETS analysis, all I did was conjoined disparate statements together as if each statement were somehow equally important in the sequence I constructed. This happened in the ETS, and then that happened, and still latter some else happened. . .But this is no more logical than the sequence in an alphabet.

To be specific, the meaning in my ETS chronology was not constructed sequentially, but rather paratactically by myself and associatively by the readers at that time. This juxtaposition of dispersed fragments of text into the composite I offered was in reality punctuated by interruptions the readers did not and still do not see. The analytic challenge remains that of making the interruptions visible to the reader—to make evident what is missing in my composite argument by virtue of those earlier debates and points obscured or written out of the record for the ETS.

–I now think the element missing from my initial summation is that open question about just what kind of “fragment” might the ETS itself be. Is it primarily an institutional structure under continual or intermittent construction? Or is it the ruins left behind by techno-managerial elite and New Class of bureaucrats operating well beyond known capacities? Or is the ETS the hollow cypher for all types of environmental hopes that are still unrealistic or evanescent? Or is the answer, maybe all of these? Or maybe none?

To cut to the quick, my original analysis was to be a kind of “last word” on the ETS. That is a temptation policy analysts, including myself, must resist. The palimpsest is always being written over—e.g., a recent EU proposal has been for carbon border taxes (on imports from countries without adequate carbon-pricing systems). Indeed, each effacement of a preceding argument takes the policy audience further away from any kind of “original” beginning, middle and end for the complex policy in question.

–What the notion of policy palimpsest underscores is that a major policy and management issue actually sources all manner of composite arguments, any one is “revisable,” even at the moment policymakers are insisting “this is the right policy for the right time in the right place.”

This is important, since major policy arguments are constantly urged upon us because of their putative elegance, iron simplicity, and seeming import. These must be perilous indeed! They only wink at complexity; they certainly are not to be found via their constitutive policy palimpsest.


To conclude: It is good, not bad, that policy and management are more complex than we often suppose and because they are complex, they can be recast and seen in a new light. The starting assumption should be that an intractably complex issue is one that has yet to be recast. In fact, labeling a complex issue as intractable, full stop, takes the generous notion of intractably human and scalps it both of the why and the how’s.

Principal sources. Material for the above has been culled and revised from earlier, more detailed blog entries: “Complexity is the enemy of the intractable,” “Triangulating complexity for policy and management,” “Humanism, by default,” “Making the best of linear thinking, complexly: typologies for reframing ‘coordination,’” “Blur, Gerhard Richter, and failed states,” and “More on policy palimpsests: The European Union Emissions Trading Scheme, Scenes I and II.”

Mess and reliability: five inter-related propositions

Proposition 1: The more services demanded from a single resource, the greater the demand for reliability in each service and the messier it is to ensure that reliability (reliability defined as that safe and continuous provision of a vital service).

The more we rely on firefighters, the more services we demand from them: First, crews responded to fires; then they had to respond to other emergency calls. Power lines are expected to carry not just electricity, but now broadband internet services. Banks provided accounts and loans; then we required they source other financial instruments. During such service expansions, reliability mandates and service provision suffer growing pains and things get messier.

Proposition 2: The messier it is to provide multiple reliable services from a single resource, the more the services are provided reliably, if at all, in real time only, when the performance standards are clearest.

Police now respond immediately only to 911 calls for activity in progress. The bank shifts from waiting lines in front of few tellers, to many outside ATMs, each accenting the automatic. Performance criteria are clearer in real time: Did the police come at once, did you get your emergency care, and is the cash really there?

Proposition 3: The more the services are reliably provided in real time, the more likely the demand for new services from that single resource and the messier it will be in ensuring any of the services is reliably provided, right now.

Back at that ATM: Before, it provided cash and deposit services; then it became a one-stop for other transactions, ranging from recharging your cellphone, paying your bills, buying stamps, to booking railway tickets. Conditions get even messier when the multi-purpose ATM (and others nearby) are out of order, and none of the expanded services are available now. It’s the same with your multipurpose cell phone when reception is unavailable.

Proposition 4: The more the services and the messier the real-time management, the greater the pressure to decouple one or more services from the resource and the more likely a new or more differentiated resource will be found/created to provide the decoupled service reliably.

Cellphones are no longer just mobile versions of fixed-line telephones, but altogether different instruments with added services. Banks long ago ceased to source financial services sector on their own; all manner of novel financial transactions are provided outside the official banking sector.

Proposition 5: The more reliably the service is provided from the new resource, the greater the pressure to demand more services from that resource. . . and so the dynamic continues.

As illustrated in the lead-up to the 2008 financial mess, not only did the volume of credit derivatives increase, but so too did novel derivatives for other purposes. Credit default swaps came to measure even the creditworthiness of entire governments.

–Should it need saying, it is not obvious what new or more differentiated resources, if any, will emerge nor is there anything inevitable about the propositional dynamic. What can be said is that you’re in it for life, when it comes to managing mess and reliability.