Planning, with a difference

Planning for reliability and reliable planning are to be distinguished from each other, in the words of my research colleague, Paul Schulman.

–Much of infrastructure planning is dedicated to planning for a reliable electricity grid, water supply, transportation system, or other core critical service. Planning for infrastructure reliability has it textbooks, courses, and disciplines. Risk analysis in this planning also has established sets of regulatory and professional standards, methods, and “best practices” depending on the infrastructure in question.

–Reliable planning, in contrast, is not about the reliability of infrastructure but rather about the process of planning—a process for selecting appropriate means for achieving expected outcomes in the infrastructure’s safety and reliability. Reliable planning means sensitivity, much as infrastructure control room operators have, to possible errors of forecasting and in basic assumptions for the planning exercise. Electricity transmission planning, by way of example, has to estimate load, generation resources, and policy constraints, say, ten years in advance and at times well beyond.

How then do we render planning more reliable as unpredictability ramifies?

–One part of an answer is to manage expectations across the cycle of infrastructure operations, extending from normal operations, through service disruption, system failure, recovery and establishment of a new normal, if there is to be one. Since that is not always possible, managing setbacks is necessarily part of the answer. Fortunately, some setbacks are positive in illuminating better directions ahead in the face of turbulence.

–Where does this leave us?

In the absence of managing expectations and setbacks, a plan falls short of both planning for reliability and reliable planning. Plans collapse into a by-product of the interplay between contingency and events, i.e., far too often those involved don’t realize that what they confront are not classic cause-and-effect but rather situations and resonances about which they have limited causal understanding.

Principal sources. This blog entry draws directly from Paul Schulman’s work in:

E. Roe & P.R. Schulman (2016). Reliability and Risk: The Challenge of Managing Interconnected Critical Infrastructures. Stanford, CA: Stanford University Press.

P.R. Schulman & E. Roe (2018). “Extending Reliability Analysis Across Time and Scope.” In Ramanujam, R. and Roberts, K. (eds.), Organizing for Reliability. Stanford, CA: Stanford University Press.

Related blog entry: “The New Normal is managing not just negative setbacks but positive ones as well”

It’s more top-down and outside-in than bottom-up or inside-out

–Assume the recommendation is that stakeholders sit down and hammer out a management plan for the landscape. Assume they are community residents, large business- and land-owners, representatives from local non-profits or NGOs, and government officials and planners with duties for the area. Looks bottom-up, at first pass.

What if the businesspeople and large landowners do not live within the landscape; the NGO personnel are headquartered or live elsewhere; and government officials attend if able to travel from the capitol? Stakeholders they are, but do they have the same “stake” in the landscape as do local residents–namely, the only stakeholders in this “bottom-up” exercise that reside in the area to be better managed?

–This kind of stakeholder planning isn’t so much bottom-up as it is outside-in planning. It equates outsiders and insiders as well as experts and residents are stakeholders, full stop. It asserts that the claims of expertise or government duties in the landscape are right up there with the claims arising from full-time presence there.

A different exercise is to come up with recommendations that promote inside-out planning and management, where locals are themselves the professionals and where the planning and management process is initiated and guided by those within the landscape. Policy relevance of information gathered under these conditions is more likely to increase when those who determine what information is needed end up using the information they themselves have gathered.

–Which is to be preferred: (first-pass) bottom-up or (in-practice) inside-out? The answer depends on your stance with respect to the context complexities for planning.

A top-down or outside-in approach to sustainable livelihoods might be grounded in an overall design or foundation said to be better or best for realizing the goals and mandates of “sustainable livelihoods.” In contrast, the goals and mandates that emerge from a bottom-up or inside-out approach are likely to differ, when really-existing practices, rather than macro-designs, accommodate the local contingencies.

The crux for me is whether or not the contingently local practices are directed to reducing the complexities that give rise to having to live sustainably. When sustainable livelihoods are a response to context complexity, insiders must be expected to wonder what are options, if any, to reduce the complexity outright. Inside-out and bottom-up are to be distinguished from each other if their respective practices represent different orientations to accommodating or reducing the complexity.

–A last point. One form of accommodating complexity is to recast a seemingly intractable problem more tractably. Doing so isn’t simplifying or reducing the complexity; indeed, recasting shows the issue to be complex in other ways than thought to that point. A new analogy, for example, illuminates complexity rather than shrinks it.

From my perspective, it’s much better to think of sustainability not so much as ensuring resources for future generations as it is increasing the opportunities of this generation to respond to unpredictable change without killing ourselves in the process. Both tasks are complex; the latter, however, is a necessary albeit not sufficient condition for the former. To repeat this blog’s mantra: If you can’t manage now, why would I believe you can manage later?

Which of these old lists still makes sense?

Does reality exist distributively? or collectively?–in the shape of eaches, everys, anys, eithers? or only in the shape of an all or whole? William James, philosopher

1. Here are two among several principles proposed for evaluating whether a policy is consistent with electricity competition and deregulation (thanks to a 2005 publication):

  • Generation decisions (building and producing) are driven purely by market forces, which include the cost of externalities in the prices to customers.
  • Consumption decisions are driven purely by market forces in which customers have access to relevant data and information and prices include the cost of externalities.

2. These three assumptions among several others were also listed as key in market deregulation (thanks to a 2001 publication):

  • Markets are efficient and follow the One Price Law.
  • Risk can be quantified and therefore uncertainty eliminated through probabilistic statistical analysis.
  • Seismic market shifts, sometimes called outliers, are so rare that they can for all practical purposes be disregarded.

3. Contrast the above with three propositions proposed at about the same time (thanks to a 2004 publication):

  • Belief in the possibility of a public interest, distinct from private interests, is fundamental to the public domain.
  • It follows that the public domain must be protected from the ever-present threat of incursion by the market and private domains.
  • By the same token, the language of buyer and seller, producer and consumer, does not belong in the public domain; nor do the relationships which this language implies. People are consumers only in the market domain; in the public domain, they are citizens.

It’s a fair certainty which of the three lists you consider closest to realistic.

An infrastructure’s regulator of record is in real-time recovery, always

–The repeated criticism made about regulators of record is that they can never keep up with the latest maneuvering of those they regulate. Were that the only problem!

–Since there is so much the regulator has to keep on top of, it is in a permanent state of managing or coping with setbacks in doing that. To see why, I quote at some length from Reliability and Risk (2016), where we describe in schematic terms the full cycle of the real-time operations of a critical infrastructure under regulation:

Figure 7.1 introduces an idealized version of a single infrastructure’s entire operational cycle, covering all system states from the perspective of its control operators and other reliability professionals (including their support staff). . .Beginning from the left side of Figure 7.1, this infrastructure’s control operators typically maintain operations within (de facto and de jure) bandwidths of reliable system and performance conditions. Notice that “normal” does not—cannot, as we have seen—mean “invariant”: adjustments are to be expected within bandwidths to enable adaptation to a stream of contingencies and unpredictabilities. . .

Control operators enter a crisis condition when they confront or anticipate conditions that threaten their cognitive skills to understand a situation in terms of recognizing patterns and to respond to a situation in terms of formulating action scenarios that they then translate into strategies of action. A crisis could begin with loss of communications or some other requirement for the management of their control variables. The crisis starts for control operators when they are pushed to operate at the edge of or beyond their domain of competence, where skills and task requirements are matched.

. . . .[T]his team situational awareness of the crisis might not correspond to an actual disruption or catastrophic loss of service; it may well precede it. The time period for the crisis from the perspective of the operators may be advanced in every phase over public perceptions and even the perceptions of organizational or political leaders. The latter may see the crisis only at the point of loss of service. But by the time that stage is reached, control room operators could be on the road to restoration or even beginning recovery. . . .

The crisis begins a period of intense and focused attention on recognizing what’s actually happening: discovering a pattern or patterns and assessing action scenarios. This activity can restore disrupted service. . . If failure occurred, the recovery takes a different form of problem-solving activity on the part of control room operators. The focus shifts from narrowing attention on understanding the particular character of the failure to expanding the scope of factors attended to, including related infrastructures, in pursuit of recovery strategy. Recovery necessarily involves many outside organizations and personnel whose actions have to be carefully coordinated if it is to be successful. . . .

In nominal terms, the whole cycle of infrastructure operations ends after recovery, when control operators are at a new normal, with scenarios and patterns added to their management repertoire, along with new facilities and equipment as well as with any new regulatory or policy constraints. The “new” in the “new normal” could be above the old normal’s performance effectiveness. But it could also be below. Untried equipment, hasty reorganizations, or new regulatory constraints imposed from a public-accountability more than a reliability perspective may leave control operators and other reliability professionals with far fewer options and more unstudied conditions than they confronted before the crisis.

–While imposition of the last mentioned new regulatory constraints is with respect to the “the new normal,” the regulator of record is present at every horizontal and vertical point in the infrastructure’s whole operations cycle, if only ensuring regulatory compliance. As the entire cycle, including the stage of normal operations, is dynamic, so too it must be for the regulator.

–Focus now on several dynamics. The probability of system failure (Pf) changes along the horizontal and vertical dimensions (Pf often being higher in recovery). As such, talking about risks depends on where you are in Figure 7.1: the risk of disrupted services given crisis conditions in control room management before any actual service disruption, (2) the risk of failed services if disrupted infrastructure services are not restored, (3) the risk of failure in recovering services given the difficulty and complexity of recovery operations, and (4) the fresh risks of a new normal in infrastructure services post-recovery (e.g., is the infrastructure now more resilient or brittle?).

Talk about resilience means as well differentiating the types of resilience by the stage of operations one actually is in during the cycle, e.g., resilience to stay within the bandwidths of normal operations is nothing like the inter-organizational resilience to be evinced at recovery. All along the cycle, latent as well as manifest configurations of interconnectivity with other infrastructures change.

–The upshot? This challenge of the regulator–to be as differentiated in regulation as the whole cycle is for the infrastructure being regulated–isn’t just “a challenge.” Yet labeling it “an impossible task” doesn’t help, for where goes the infrastructure, there must go the regulator of record.

In my view, it is better to say that at best the regulator of record is in permanent setback management; at worst its own activities require the coping behavior associated with emergency management. Either way, the regulator in terms of its own cycle of operations never recovers fully; or if you’re on the optimistic side, recovery is its new normal.

Siding with the wall

On one side. I read a lot because I’d like to think the answer is out there, ready to be stumbled over, because someone smarter has seen it already. More than that, when found, I’d realize that Piece-of-Truth had been right in front of me–the writing on the wall.

On the other. Why does sustained analysis often deepen, rather than dispel, complexity? Answer: It’s less the “analysis” than the “sustained” we call explication. This drive to explicate—to explain so as to explain more and then even more—has been criticized by the wildly different Peter Sloterdijk, philosopher, and Shirley Hazzard, novelist. Further, the more we explicate, the more we feel compelled to name the now more complicated. Surely, the brain must be hard-wired for all this.

Which side? A while back I culled old journal issues that I’d been saving. Partly to see what I had commented on then by way of marginalia, but also to see if what I had read pointed to what I think now. My scribbles were unreadable.

It’s war or peace?

Actually, neither.

The opposite of peace is not-peace. War is one type of not-peace. There are also contraries and contradictories, like “both peace and not-peace” and “neither peace nor not-peace.” If these semiotics were not enough, ordinary language has its own categories. Other people don’t think in twos, but in threes or more, e.g., Virginia Woolf talks about Peace, Love and Hate as the biggies.

Once you’ve got more than a dualism, the contradistinctions go any which way. If Peace is the freedom from extreme love and hate, the Woolf’s threesome become Love, Hate and Freedom from extreme versions of both. And by talking about Peace being “a freedom from,” you eventually stumble into “freedom to,” as in: Why not freedom-to as its own kind of Peace?

–For my part, the better question is: What is neither peace nor not-peace? One answer would be a world so complex that the determination of what is “peace” versus “not-peace” is not possible. Why? Because right now nothing has been concluded, yet. It’s as if when reading World War II entries in John Colville’s Downing Street Diaries, you were also experiencing real time today.

Impact-sheds are not managed systems, except when…

–Conflation of the physical system managed with the area of system impacts should the system fail is common. The spatial area managed by a water supply or electric grid is not the spatial area affected by indefinite loss of water or electricity. Large critical infrastructures may be operated within regions, but regions are not systems managed on their own in the same way critical infrastructures with central control rooms are.

To see how this matters, picture a stylized relationship between the probability of levee failure (Pf, e.g., 1%, 0.1%, 0.01% per annum), the estimated cost per mile of levee stretch to bring it to high safety standard, and the estimated loss in economic value (including foregone earnings due to loss of life), should levee failure occur at a given Pf. One relationship is the diagonal read from the upper right to the lower left (my thanks for Robert Pyke for the figure):

The dotted line assumes that the losses in economic value of a levee breach decline as levees are brought to a higher, more costly standard with reductions in the probability of levee failure, Pf. What is managed directly is maintenance at a levee standard and the associated Pf; only indirectly is the “economic value of levee breach” managed..

–If you counter that this impact-shed is “the system to be managed,” then you beg the larger question: What infrastructure manages the impact-shed in terms of the consequences of levee breach (Cf), including economic losses?

Answer: Cf is most pertinent to the emergency management infrastructure, and not the flood and levee infrastructure as in the illustration. The time period for the former involvement may well be limited (say, six weeks to three months after the disaster), leaving the bulk of the recovery to those infrastructures that manage systems–e.g., roads and waterways–and not the respective impact-sheds.

Coulda, shoulda, woulda

–Have you attended any presentation where the engineer proposes all-benefit-and-no-cost designs and technologies of such fantastification as would bring a failing grade to a student in public policy and management? They’re like Wile E. Coyote, rushing off the cliff and hanging high in the thin air of ifs and maybes, coulds and mights, cans and perhaps. Their slides are a tableau vivant of Revelation pulling the “thing” out of Nothing, the thingamajiggery sacralized as innovation. (As a Renaissance ceiling fresco, such fabled risk-seeking innovators would be little putti wheeling around St Market, upwards into a cerulean sky.)

–When I read criticisms that blame deaths or injuries in a disaster on the “lack of coordination,” I expect to see answers to two immediate questions: (1) can it be demonstrated that the lack of coordination did not arise because the responders knew—or thought so at the time—that they were undertaking activities just as urgent; and (2) can we conclude that the event in question would (not could, should, might or perhaps) have been better responded to had it not been handled the way it was (the classic counterfactual)? Rarely, I find, are answers even attempted, let alone provided. (The counterfactual often has a twofold would. The sociologist, Raymond Aron, ask critics of decisionmakers: “What would you do, in their place, and how would you do it?”)

–What would we be reading now to be as collectively agitated as were early readers of Machiavelli’s Prince, the French classes delving into the Encyclopedia of Diderot and d’Alembert, or Beccaria’s On Crime and Punishment, or those stirred by Michael Harrington’s The Other America?

Or is the point quite the other way round? The “we” is expanding, every day, by agitations of other media?

–Go look for one of those early 20th century American landscape paintings by, e.g., Redmond Granville, of wildflowers spreading across fields or Edgar Payne of a remote lake in the snowy Sierras. Then look at virtually the same painting, but this time with a young woman in her calico dress or cowboy on a horse. In an instant, this painting dates the preceding one. What had been an idealized-now flips to a historicized-then. Public policy is full of such flips: reforms that work on paper but date immediately when real people with real problems in real time enter the picture—both as subject and as frame.

— Samuel Taylor Coleridge argued “matter” was treated like a pincushion whose surface was hidden by all the sensations, thoughts and properties stabbed into it.

You ask today’s version of, “What’s the matter?,” and you get a pincushion of sentences affixed with an “etc.” Each implies the unnamed factors are only critical to the point we needn’t clutter the analysis any further by naming them. “Hail, Muse! Et Cetera,” as the poet, Byron, sarcastically put it in the third canto of Don Juan. Yet, really, why are we reading if not to find out what the writers think are critical enough to name? (Writes Wittgenstein: “Again and again, my ‘etc’ has a limit.”)

–Our experiences “lie jumbled up inside us, and we find we have an inner world like a rubbish bin,” wrote the sociologist and psychotherapist, Ian Craib:

This is a different sort of mess…the flux of the inner life and our emotions, about which we maintain the illusion that it can be made orderly and predictable. We might think that the rubbish bin can be sorted out, but it seems to me what the push is towards emptying it and starting afresh.

But we don’t know how to start all over again, and as such two sets of opposing pressures drive the anxiety of having to sort things out: the centripetal pressures of closing in on what we think we really know (or can know) and the centrifugal pressures of opening up recasting what has been taken as unknowable or for granted.

This is Proust in translation: “What we have not had to decipher, to elucidate by our own efforts, what was clear before we looked at it, is not ours. From ourselves comes only that which we drag forth from the obscurity which lies within us, that which to others is unknown”. We only know that which we create—and with this, the anxiety both at the knowing and at the recasting.

–The first words in Shakespeare’s Hamlet are, “Whose there?” Indeed. And at its end, what life isn’t unfinished? In both cases, arithmetic averages wobble.

On population increase

It’s the crudeness: As if more numbers of people were even a credible unit of analysis, full stop, for policy or management. As if complex could be abbreviated that simply.

Not a scintilla of recognition:

  • that perceptions and management of policies differ, at least, by a person’s age, education, income, gender, and ethnicity;
  • that not-knowing, difficulty and inexperience with respect to these numbers and to perceptions, at the very least, set disciplines, fields and ways of being apart from each other;
  • that what stops further polarization of disciplines, fields and ways of being are not fewer numbers but the not-knowing, difficulty, and inexperience; and
  • that when the numbers do polarize, fear becomes a solipsism, believing itself to be an anti-politics machine.

Recourse to numbers on their own is the Olympian capacity to deoxygenate all living matters.

Heuristics as clues

When Louis XIV saw the new maps of France he sponsored in 1693 he supposedly complained that his cartographers had cost his kingdom more land in a year than foreign armies had done in a century.

(From Paul Slack’s The Invention of Improvement)

–Long-held heuristics, like routines and standard operating procedures, are shorthand ways of doing things without all the uncertainty of reinventing the wheel. Newer heuristics include big data algorithms we don’t understand and policy narratives we think we do, both of which enable making decisions in the face of uncertainty. Both shorthands are treated as good-enough, like a new atlas of maps.

The older heuristics are relied upon because they are said to reduce uncertainty; more recent ones are used to better manage (in the face of) uncertainty that hasn’t been/cannot be reduced, at least for the moment.

One major commonality between both the old and new needs to be highlighted. While typically not taken as such, both are less a shorthand than clues for what to do ahead.

–By way of example, handbooks detailing how to respond to unpredictable floods and famines were written by and for administrators in Imperial China. Over the course of some thousand years, handbooks started to group together what had been learned into tables of maxims (sometime cast in rhyme) for ease of reference by users.

Handbooks “are quick to insist, however, that using the tables is not sufficient in the long run: for the professional administrator they are rather a ‘clue’. . .that indicates where to go in the more complete texts,” writes Pierre-Étienne Will, the most recent and comprehensive bibliographer of handbooks (2020, XLIV). This status of heuristic-as-clue is to alert us to important omissions that require reference beyond any shorthand exposition (Ibid, 568). Occasions when a map proves imperfect and misleading are all too familiar.

Professor Will elaborates in an email: “’clue’ (yinxian 引線. . .) literally means ‘a thread that leads to…’, ‘that can be pulled to get…, or something of the sort. The same character yin is part of the words suoyin and yinde, meaning ‘index’, in modern Chinese. The tables or rhymes are like indexes to the complete texts.”

–I want to apply this notion of heuristic-as-clue more speculatively to the newer algorithms derived from big data. We’re told that, even though the algorithms are not based on models of known cause and effect, they identify complex, albeit opaque, correlations said to be worth relying upon.

But that stops short of the needful. The status of a heuristic as clue underscores that, just as with causal models, there’s also a great deal yet to puzzle out with correlations before going forward. Correlations are not just the start of an analysis. They also are in context and those contexts start the analysis as well. Correlations index spatial-temporal references to be pursued.

–There is no obvious point of entry when it comes to revealing the wider references. For purposes of illustration, start with the canonical index of fire, smoke. In the same way, the heat from server centers (some indeed call it “data exhaust”) indexes the large electricity usage in generating and updating algorithms. But context doesn’t stop there. Other clues are less spatial-temporal and more social for the heuristic inseparable from wider referential meanings.

Again, by way of example, the status of the algorithm-as-heuristic clues you into the underlying assumptions for using big dataset algorithms, not least of exemplify “trust.” Some say, e.g.:

  • algorithms deliver the best result among the other methods and heuristics available;
  • while not free of bias, they do a better job than others by virtue of the huge run of cases and calculations;
  • some kind of result at the scale of big data is better than no result, plus the algorithmic result is often more timely; and
  • anyway, there’s always a danger that the critics of big data algorithms take them more seriously than the users, like consumers who comparison-shop and then make their own decisions.

The wider point here is that the methodological duty of care in using heuristics means treating them as indexes of that which cannot be omitted, yet could have already been omitted, from analysis and practice when usefulness is the question.

Principal sources

Pierre-Étienne Will (2020). “Introduction,” in: Handbooks and Anthologies for Officials in Imperial China: A descriptive and critical bibliography. Koninklijke Brill NV, Leiden, The Netherlands.

The notion of index in the sense of smoke and fire follows that of C.S. Pierce (“purse”), a founder of American pragmatism.

R. Machen and E. Nost (2021). “Thinking algorithmically: The making of hegemonic knowledge in climate governance.” Transactions of the Institute of British Geographers: 1-15.

Julia Velkova (2021). “Thermopolitics of data: Cloud infrastructures and energy futures.” Cultural Studies.