Failing organizations are usually over-managed and under-led.

—Warren Bennis

We are living the golden age of management, or at least in the golden age of looking to management for answers. Everything can be managed, and managed better or more completely. It doesn’t matter how poorly it is done, management is the due diligence for everything. It doesn’t matter if all the planning, Gantt charts and other approaches actually lead to worse performance. We must plan. I agree that we need to plan, and we need to be comfortable throwing those plans away. As military greats have said, planning is essential, and those plans are thrown out with the first contact with the enemy.

No battle plan survives contact with the enemy

—Colin Powell

Its been a few years since I was so deeply engaged with a programmatic planning process. This year I’m doing it on several fronts. The experience is has been jarring. Since my last engagement the entire planning activity has gotten further removed from any practical reality. The powers that be seem to be devoted to believing that the planning should be something we are held to. We seem to have to walk a tightrope between the necessary utility of planning and what we will be culpable for. In terms of the quality of research and science, the whole thing ends up being deeply damaging. Management that suits construction projects is being applied to activities it was never intended to work with. We are at once fully engaged in being held to the objectives while not taking them seriously. I am deeply concerned about how damaging this process is to everything it touches.

Last week I saw a fascinating seminar by Kenneth Stanley U. Central Florida) on “Innovation without Objectives”. He made a remarkably persuasive case that innovation is actually hurt by objectives. The planning we do is completely devoted to defining objectives. The ironic thing is that innovation can be made and objectives met, but not necessarily at the things that we try to achieve. Moreover being overly constrained to achieving the objectives actually hurt the ability to achieve that objective. This is because we focus on obvious solutions while ruling out innovative solutions that are more effective. His argument seemed to point toward the conclusion that the management we are devoting ourselves to is nearly completely counter-productive. Of course, implementing anything like this in today ‘s environment is completely unthinkable. There isn’t the necessary trust in science (or anything else for that matter) to let this happen.

The Truth! You can’t handle the truth!

—Colonel Jessup in “A Few Good Men”

We live in an age where the line between truth and lying is blurred in ways that should trouble us. The reality is that you can get yourself into a lot of trouble telling the truth. Structured lying is the new truth. Just ask Jon Gruber whose truth telling created a firestorm. He said that Americans were stupid and the legislation for the ACA (Obamacare for those of you who don’t know what ACA means) wasn’t transparent. Both things are true, and saying this is politically stupid, but completely honest. It almost perfectly demonstrates that lying is the new truth.

Almost every single bit of legislation passed by Congress is completely opaque and full of crap that Congress wants to hide. Its how everything is done whether or not the bill is democratic or republican. Americans probably aren’t “stupid” at least in the official definition of the word, Americans are willfully ignorant, lazy, uniformed, easily duped and a variety of other awful things. Telling them the truth about this is the problem. We have a generation of people that were told that they are special and everyone is a winner. Calling bullshit on this is unimaginable, and this was Gruber’s crime. He spoke truth in a city devoted to lies.

Here we get to the core of things; we can’t tell the truth about things; we can’t innovate and eliminate harmful over-management; we can’t trust anyone. The devotion to lying as a society leads directly to the lack of trust. Leadership is born out of trust therefore we have only ourselves to blame for the lack of it. Until we start to honor truthfulness again we can expect leadership to be absent from our lives.

Men occasionally stumble over the truth, but most of them pick themselves up and hurry off as if nothing ever happened.

– Winston Churchill

Helping others is the secret sauce to a happy life.

― Todd Stocker

I used to work at McDonalds a long time ago. Most people know that a Big Mac uses a secret sauce in dressing the sandwich. It looks like Thousand Island dressing, but rest assured, it is a secret sauce of some sort. Ideally, the secret sauce is the literal trademark of the sandwich, its identity and it’s a secret only known by a hallowed priesthood. Little did I know that in my chosen professional life I would be exposed to a completely different “secret sauce”.

A successful modeling and simulation code is very much the same thing; it has a trademark “secret sauce”. Usually this is the character for the code is determined by how it is made robust enough to run interesting applied problems. Someone special figured out how to take the combination of physical models, numerical methods, mesh, computer code, round-off error, input, output… and figured out how to make it all work. This isn’t usually documented well, if at all. Quite often it is more than a little embarrassing. The naïve implementation of the same method usually doesn’t quite work. This is a dark art, the work of wizards and the difference between success and failure.

The rub is that we are losing the recipes. In many places the people who developed the secret sauce are retiring and dying. They aren’t being replaced. We are losing the community knowledge of the practices that lead to success. We may be in for a rude awakening because these aspects of modeling and simulation are underappreciated, undocumented and generally ignored. Sometimes the secret to make the code work is sort-to-very embarrassing.

This is a very dirty little secret. Rebuilding this knowledge once lost is going to be very expensive. In most cases we don’t even recognize what we’re losing because the modern view of modeling and simulation doesn’t value it. It is essential and the sort of thing that isn’t being funded today.

Conservatives are not necessarily stupid, but most stupid people are conservatives…

I never meant to say that the Conservatives are generally stupid. I meant to say that stupid people are generally Conservative. I believe that is so obviously and universally admitted a principle that I hardly think any gentleman will deny it.

― John Stuart Mill

Yesterday an opinion column appeared in Nature arguing that the scientific community should steer clear of partisan politics (http://www.nature.com/news/science-should-keep-out-of-partisan-politics-1.16473 by Daniel Sarewitz). Dr. Sarewitz’s arguments and sentiments are high-minded, but really only has traction in the ideal World where support and respect for science was bi-partisan. We do not live there. We live in a World were science has been politicized and attacked. These attacks have come predominantly from one side, the conservatives and their dominant organization, the GOP. Until support and attacks on science are more balanced it is inevitable that the scientific community aligns with one side over the other.

As mankind becomes more liberal, they will be more apt to allow that all those who conduct themselves as worthy members of the community are equally entitled to the protections of civil government. I hope ever to see America among the foremost nations of justice and liberality.

― President George Washington

The assault on science and reason by conservatives is seemingly endless. Leading the charge is the denial of climate change and the threat it poses to humanity. The reasoning for the denial is two-fold, the risk action on climate would impart to the ruling corporate class, and the conservatives love of their wasteful, destructive lifestyles (which largely fuel profit to the corporate overlords). Further attacks occur within their embrace of fundamental religious faction’s desire to indoctrinate our children with their myths in place of science (i.e., creationism). In other venues the conservatives attack science that goes against corporate greed be it environmental science, medicine and especially the social sciences. Conservatives deny the underlying biological basis of homosexuality because of its implications for their religious beliefs. Time and time again it is their commitment to traditional religious belief over science and reason that drives a wedge.

The attacks on science and reason are by no means completely one-sided. Both liberals and conservatives fail to repudiate the various science deniers and neo-Luddite factions in their midst. For instance liberal anti-science can be see with anti-vaccine, anti-GMO and anti-nuclear movements. Each of these is based on fear of technology and is fundamentally irrational. For instance, the coupling of liberal environmental leanings and anti-nuclear mindsets undermines support for action on climate change (https://williamjrider.wordpress.com/2014/06/06/why-climate-science-fails-to-convince/).

Of all the varieties of virtues, liberalism is the most beloved.

― Aristotle

Science is liberal and progressive by its very nature. The fundamental character of research puts it at odds with conservatism almost a priori. Nothing is wrong with religion per se, but when it stands in the way of knowledge and progress there is a problem. The conservatives use religion to control the masses and subjugate people as they have for millennia. This takes the form of fundamentalism, which is the enemy of science, progress and humanity at large. Since science and reason threaten the means that conservatives control the masses, it is a threat to their power. This is the core of the GOP’s repeated attacks on science. This is why scientists will line up on the other side against them.

When the experts’ scientific knowledge is legitimated in terms of being rational, logical, efficient, educated, progressive, modern, and enlightened, what analogies can other segments of society . . . utilize to challenge them?

― Martin Guevara Urbina

Where all think alike there is little danger of innovation.

—Edward Abbey

What do you do when you’re in a leadership position for a project that you’re sure is moving in the wrong direction?

If you’re a regular reader you can guess that its high performance computing, and the direction has been wrong for a couple of decades (IMHO). Today and tomorrow we are just kicking the can down the road. Current direction is virtually identical to what we’ve been doing since the early to mid-1990’s. There isn’t a lot of visionary thinking to be had. On the one hand the imminent demise of Moore’s law promises some degree of disruption. We can’t continue to make progress the way we have been, and change will be thrust on us. On the other hand the conditions for visionary thinking, risk taking and progress are absent.

Without deviation from the norm, progress is not possible.

― Frank Zappa

While the business we are doing is stable today, the continued traditional emphasis has an aggregated impact on a host of issues. Being stable also means that the business is stagnant, which isn’t good for the science. Progress in modeling and simulation has been made largely in two regards: computational power available has increased, and the way of conducting studies has matured. The stagnation of progress is most evident in the codes; the methods and models in the codes are simply not moving forward meaningfully. The codes in terms of methods and models are largely the same as we used twenty years ago. Furthermore most of innovative and creative energy has gone into implementing the codes on modern computers. The result is a phalanx of missed opportunity whose implicit costs are massive. I’d like to sketch out what some of these opportunities and the costs associated with missing them.

Societies in decline have no use for visionaries.

― Anaïs Nin

Historically, we have been rewarded greatly by algorithm, methods and models improvements that exceeded the benefits of faster computers. Despite this track record support for continued development along these lines has languished and dropped in intensity. One might surmise that we have picked off the low hanging fruit already and made the easy breakthroughs. I’m far more optimistic that massive improvements and innovation are not just possible, but awaiting relatively easy synthesis into our current work. To achieve these gains we will have to discard some of the limitations we impose on our current execution of projects.

But knowing that things could be worse should not stop us from trying to make them better.

― Sheryl Sandberg

So what is holding us back?

A big part of the problem is the issue of “sunk cost”. The codes are now huge and quite complex. They represent massive investments in resources over years, if not decades. The program management is not interested in the starting over, but rather evolving capability forward. This is rather limited in scope, and largely takes the form of moving the codes whole cloth onto new computing platforms. For people with short time horizons (and/or attention spans) this is a safe path to success. The long-term costs are lost to the risk calculus currently employed. No one realizes that the code is merely a vehicle for intellectual products that can utilize automatic computation. Its value is solely based on the quality of the thinking in its implementation, and the quality of its implementation. Virtually all the effort today is on implementation rather than the thinking itself. Until we overcome this sunk cost mentality, codes will remain intellectually static with respect to their defining applied character.

Restlessness is discontent — and discontent is the first necessity of progress. Show me a thoroughly satisfied man — and I will show you a failure.

― Thomas A. Edison

What are some of the things we are missing? Clearly one of the greatest sacrifices of the “sunk cost” code is static discretizations and models. The numerical methods that implement the physical models in codes are generally completely intertwined with the codes basic structure. Over time, these aspects of the code become a virtual skeleton for everything else the code does. The skeletal replacement surgery usually kills the patient, and that can be allowed. Therefore we get stuck. New discretizations could provide far more accurate solutions, and new models could provide greater fidelity to reality, but this has been taken off the table to maintain continuity of effort. Part of the work that we need to conduct is a better understanding of how practical discretization accuracy is achieved. For most applications we don’t have smooth solutions and the nominal notions of numerical accuracy do not hold. How do discretization choices impact this? And how can these choices be optimized given resources? Furthermore changes in these areas are risky and never sure to succeed, while risk reduction with fear of failure is the preeminent maxim of project management today.

Anyone who says failure is not an option has also ruled out innovation.

—Seth Godin

Moving on to other more technical aspects of computing and potential benefits I’ll touch on two other missing elements. One of these is stability theory. As I noted a couple of posts ago, robustness is a key to a code’s success. At a very deep level robustness is a crude form of stability. The crudeness is a symptom of failings in the current stability theory. This implies that we could be far better with a more extensive and useful stability theory. Part of this is defining a stability that captures the requirements mathematically for producing robust, physical results. Today we simply don’t have this. Stability theory is a starting point, and we have to kludge our way to robustness.

Innovative solutions to new challenges seldom come from familiar places.

—Gyan Nagpal

A second area of progress that we have suffered from not having is numerical linear algebra. We are thirty years on from the last big breakthrough, multigrid. Multigrid is viewed as being the ultimate algorithm given its ideal scaling with respect to the number of unknowns (being linear, and all other methods are super linear). Since then we have moved to using multigrid as a preconditioner for Krylov method improving both methods, and implemented the method on modern computers (which is really hard). Thirty years is a long time especially considering that other advances in this field came on a faster than decadal pace. A good question to ask is whether a sub-linear method can be defined? Is multigrid the ultimate algorithm? I suspect that the answer is sub-linear method can be discovered, and work on “big data” is pointing the direction. Beyond this we typically solve linear algebra far more accurately (very small residuals) than probably necessary. It is done almost reflexively with a better safe than sorry attitude. This is a huge waste of effort, and someone should come up with a sensible way to set solver tolerances and optimize computational resources.

The willingness to be a champion for stupid ideas is the key to greater creativity, innovation, fulfillment, inspiration, motivation, and success.

—Richie Norton

A big area for progress is uncertainty quantification. The methods today are clearly focused on modeling and parametric uncertainties using sampling methods. While sampling is general, it is inefficient. These are epistemic uncertainties reflecting our lack of knowledge. The natural variability or aleatory uncertainty is largely unexplored computationally. This reflects pointedly on the modeling approach we use. Key to this is the generally homogeneous nature of material models even though the materials are quite heterogeneous at the scale of the discretization. This is a clear place where the maintenance of codes over long periods of time works against progress. Most of the potentially more efficient uncertainty methods are deeply intrusive and don’t fit existing code bases. Further complicating matters, the development of these methods has not been focused on models sufficient for applications. It has focused on “toy” problems. To progress we need to take significant risks and tackle real problems using innovative methods. Our system today is not set up to allow this.

Dreamers are mocked as impractical. The truth is they are the most practical, as their innovations lead to progress and a better way of life for all of us.

—Robin S. Sharma

Expect to see a lot of money going into computing to support “extreme” or “exascale” initiatives. It is too bad that this effort is largely misplaced and inefficient. The chosen approach is grossly imbalanced and not indicative of historical perspective. The work we are not doing is risky, but capable of massive benefit. Current management models seem to be immune to measuring opportunity cost while amplifying the tendency to avoid risk and failure at all costs.

Never confuse movement with action.

― Ernest Hemingway

The only thing we have to fear is fear itself.

― Franklin D. Roosevelt

The more I consider the state of affairs in our country, the more I realize that fear is our single greatest weakness. It has become our defining characteristic. Fear is driving everything we do as a nation, and it is choking us. FDR spoke those words to a generation whose early lives spat at fear, but whose actions later in life paved the way to its control. More than the loss of innovation that I wrote about last, we have lost our courage and become a nation frightened cowards. We fight wars against weak nations for terrible reasons. We allow our vastly armed police force to terrorize our citizens. We imprison huge numbers of Americans without any thought to what it implies. We torture using methods we have executed people for. Its all because we are afraid.We are a shadow of the nation that faced facism because we have lost
our nerve.

We are not afraid to entrust the American people with unpleasant facts, foreign ideas, alien philosophies, and competitive values. For a nation that is afraid to let its people judge the truth and falsehood in an open market is a nation that is afraid of its people.

― John F. Kennedy

Both FDR and JFK spoke to an American that has shrank from view and been replaced by backwards looking zealots wishing to retain a country they don’t deserve anymore. Once upon a time our clear problems and threats spurred Americans to action and accepting of sacrifice. We were willing to confront problems and accept challenges. Now we simply fear losing the advantages we have been granted since the end of World War 2. We aren’t even willing to admit that they have already been lost. The World isn’t static, and others have been willing to accept the sacrifices Americans have shied away from. Our body politic is two parts fear and risk aversion and one part pure denial. The denial is American Exceptionalism, which takes everything we do and casts it as divine and immune from all consideration and perspective.

To share your weakness is to make yourself vulnerable; to make yourself vulnerable is to show your strength.

― Criss Jami

We have massive problems in our nation that need immediate attention. Without change the problems will move from festering to metastasizing and exploding. Whether it is the curse of massive economic inequality and its risks to the bulk of the population and its toxic impact on politics, or our continuing racial inequities both are shrinking from any progress. We are allowing inequality to continue undermining any reality of to the increasingly mythical “American Dream” while allowing the elite to buy elections “legally”. We might have an abysmal level of social mobility; if you’re poor you’ll stay poor, if you’re rich you’ll stay rich. Race is continuing stain that will explode soon as the identity of minority and majority switch identity. We run the risk of having the minority rule, which is the recipe for revolution as is the scourge of inequality.

America is great because she is good. If America ceases to be good, America will cease to be great.

― Alexis de Tocqueville

The war on terror is the epitome of our collective fear. While 9/11 was tragic, it shouldn’t have ever resulted in the sort of resources devoted to its response. We have lost much of our soul to it. Terror has bred terror, and America committed torture, murder and other crimes in its wake. We have sacrificed freedom and privacy in the name of fear. Terror kills very few Americans even factoring 9/11 in, or the lives of soldiers fighting overseas. Americans do a much better job of killing other Americans than terrorists be it by gunfire citizen-to-citizen or our completely and utterly out of control police force.

On top of this we have a completely out of control prison system. It has become a new day Jim Crow with its racial imbalances, and a complete lack of perspective on it terribly reflects on all of us. We destroy more lives of fellow citizens with the moronic war on drugs than the war on terror could have ever caused. The criminalization of drugs is mostly about subjugating minorities and little about public safety (alcohol is a very dangerous drug, but the drug of choice for the white power structure). The drug war isn’t about safety; it’s a replacement for Jim Crow.

I explain that Americans at the level of popular culture, at the level of grassroots politics, were thinking very hard about what it would mean to have a country they didn’t believe was God’s chosen nation. What would it mean to not be the world’s policeman? What would it mean to conserve our resources? What would it mean to not treat our presidents as if they were kings? That was happening! And the tragedy of Ronald Reagan most profoundly wasn’t policy — although that was tragic enough — but it was robbing America of that conversation. Every time a politician stands before a microphone and utters this useless, pathetic cliché that America is the greatest country ever to exist, he’s basically wiping away the possibility that we can really think critically about our problems and our prospects. And to me, that’s tragic.

—Thomas Frank

Yesterday while working out I read a stunningly good article from Aeon (http://aeon.co/magazine/science/why-has-human-progress-ground-to-a-halt/) by Michael Hanlon. I thought it was well done and extremely thought provoking. It’s worth the time for you to read it yourself. In a nutshell Hanlon focuses upon the unwillingness to take risks as the thing that is sapping our ability to progress both scientifically and socially.

In times of war or crisis, power is easily stolen from the many by the few on a promise of security. The more elusive or imaginary the foe, the better for manufacturing consent.

― Ronald Wright

Of course part of my attraction to the article was it similarity in thought to a number of my own posts:

https://williamjrider.wordpress.com/2014/11/17/progress-and-the-social-contract/

https://williamjrider.wordpress.com/2014/03/03/we-only-fund-low-risk-research-today/

https://williamjrider.wordpress.com/2013/11/27/trust/

https://williamjrider.wordpress.com/2014/11/12/its-all-about-trust/

https://williamjrider.wordpress.com/2013/12/06/postscript-on-trust-or-trust-and-inequality/

https://williamjrider.wordpress.com/2014/09/26/what-is-the-source-of-the-usas-tilt-to-the-right/.

He did have a significant amount of additional thinking beyond anything I’ve written. I thought his thesis that we have become risk adverse as a society is worth a great deal of consideration. Moreover he didn’t really pull out the big smoking gun with respect to risk adverse behavior, the “War on Terror”. Almost nothing demonstrates our commitment to not taking risks as well as this. Moreover it has become a tool for amplifying fear for those taking distinct and specific advantage of it to feather their own beds.

A crisis is a terrible thing to waste.

― Paul Romer

While I agree with the general nature of Hanlon’s thesis, I think something deeper might be at work.

The 20^th Century was punctuated by three catastrophic crises: World War 1, the great depression and World War 2. The great leap forward for humanity took place in the wake of the Second World War’s carnage powered by American hegemony, a rebuilding industrialized World and the Cold War. The great stagnation that started in the early 1970’s has seen each of those elements come to a halt, and an immense rise of two paired elements massive inequality and conservatism. The rebuilding of the class of oligarchs is destroying the vast middle class that marked that period of great progress. The conservative movements are a direct response to the vast social (and technological) progress. The conservatism is a reaction to the outright fear of change that Hanlon identifies.

At first sign of crisis, the ignorant don’t panic because they don’t know what’s going on, and then later they panic precisely because they don’t know what’s going on.

― Jarod Kintz

Ironically, the oligarchs have relied upon the rejection of progressive ideals for business to power their accumulation of wealth. They have also relied upon the massive societal disruptions associated with technology to provide much of means of creating wealth that is outside the established channels of the social order. The conservatives have come as a reaction to the sort of changes produced in the “Golden Quarter” as Hanlon describes it. Fear of racial equality is driven by the loss of the white majority, and religious fundamentalism reacts to the sorts of freedoms earned during that period. All of this amplified by the discomfort of new technology while the new technology creates change in society that wreck havoc with the traditional social order.

Crisis is Good. Crisis is a Messenger.

― Bryant McGill

What will fix this and result us to the sort of progress that humanity should aspire toward? I fear it will be a new set of calamities that will surely be unleashed on society some time in the (near) future. We are approaching a serious instability and believe the events of 9/11 and the financial crisis in 2008 were merely pre-shocks to what is coming. As before during the 20^th Century these calamities are the results of systematic imbalances and the violent end of eras of excess.

Treat this crisis as practice for the next crisis.

― John Parenti

Old Europe started to die in World War 1 and its wake helped set in motion forces that created the depths of the depression and the cataclysm of World War 2, which marked the end of Old Europe and the birth of that Golden Quarter. One must also remember that the excesses of the hyper-rich and inequality also played a key roll in how WWI and the depression unfolded. These excesses unleashed a torrent of progressive action to fix the damage to society. It seems that the same thing could unfold in the future to end the current era of stagnation and greed. Let’s hope not. One might hope we have the wisdom to turn away before things get so bad.

The bind we are in today is largely about trust and faith in each other. We don’t trust because we know how selfish, self-centered and fundamentally corrupt we are. We assume everyone else is just as untrustworthy. Without trust the ability to do anything important or great simply doesn’t exist. No one is worth investing anything for the good of the whole. Every action has become centered on the good of the self. Crisis and calamity are built by such selfishness. Unfortunately, America is the most selfish place in the world, bar none. You do the math, who is the most likely to trigger the next calamity?

Then the shit hit the fan.

― John Kenneth Galbraith

In preparing for battle I have always found that plans are useless, but planning is indispensable.

― Dwight D. Eisenhower

It is quarterly review time and it is a reminder of how terribly we run the Labs these days. We run our projects really well and simultaneously run the Labs into the ground in the process. Our mode of project/program management and accountability is crushing our ability to do meaningful work. We make plans for our research, which includes milestones, Gantt charts, and the like. While I don’t have anything against planning per se, I have a lot being held to the plans. The quarterly reports are exemplars of being held to a plan that should only be an initial trajectory, and not the final destination.

I will grant you that the approach to project management has its place. A lot of rote construction projects should be done this way. A complex, but often executed project should run this way. I am talking about research. Research is the exemplar of what should absolutely not be run this way. Yet we do it with almost Pavlovian glee.

Remember the two benefits of failure. First, if you do fail, you learn what doesn’t work; and second, the failure gives you the opportunity to try a new approach.

—Roger Von Oech

The biggest problem is that the approach to doing project management along with time reporting is crushing innovation in research. Innovation simply doesn’t work this way, and it’s likely that the management is actually undermining the capacity to do innovative work. We have come up with a way of describing what they ask for “the scheduled breakthrough”. You predetermine what your “breakthrough” will be. Of course research plainly doesn’t work that way, that’s why its research. The more that you are held to the breakthroughs you promised in your plan, the less likely real success will be.

Bureaucracy destroys initiative. There is little that bureaucrats hate more than innovation, especially innovation that produces better results than the old routines. Improvements always make those at the top of the heap look inept. Who enjoys appearing inept?

—Frank Herbert

Perhaps the worst thing about this approach is what the planning is doing to our ability to think outside the box. We are increasingly defining objectives that we know we can accomplish instead of reaching for new things. If an objective looks too difficult or risky, it will be rejected. The more we hold people to their written objectives, the more we rule out innovation and discovery. The only time people put risky objectives down on their plans are when the discovery has already been made. In other words, the plan is do deliver work you’ve already completed. This is even worse than mediocrity, it is stasis.

Following the rules of your industry will only get you so far.

—Max McKeown

This trend has been going on for several decades now. The entire approach has led to the quality of the work at the Labs decreasing. The oversight and attention to detail is directly associated with working in an environment where trust is low. While I believe that my managers trust me, it is clear that our country does not trust the Labs and science as a whole. So we have this system to enforce accountability. Maybe it does that, but it   also undermines the effectiveness of the work. It is choking our science to death. I believe many of the same things are happening more broadly to research that happens at places like universities. We have more accountability and worse results. The system is harming our country seriously, and there is no end it sight.

Where all think alike there is little danger of innovation.

—Edward Abbey

I have the option of being either honest and ineffective or dishonest and effective. What a horrible choice I’m being offered from a horrible, stupid system. This is what happens when trust is low.

Plans are of little importance, but planning is essential.

― Winston Churchill

Recently, I wrote about the priorities in code development putting accuracy and efficiency last in the list of priorities (https://williamjrider.wordpress.com/2014/11/21/robust-physical-flexible-accurate-and-efficient/). Part of the not very implied critique associated with this is that the relative emphasis in development is very close to the opposite of my list. High performance computing and applied mathematics today is mostly concerned with efficiency (first) and accuracy (second). I believe these priorities do us a disservice, represent a surplus of hubris and fail to recognize some rather bold unfinished business with respect to stability theory.

All that it is reasonable to ask for in a scientific calculation is stability, not accuracy.

–Nick Trefethen

I thought about what I wrote a few weeks ago, and realized that when I state robust, I mean almost the same thing as stable. Well almost the same is not the same. Robust is actually a stronger statement since it implies that the answer is useful in a sense. A stable calculation can certainly produce utter and complete gibberish (it may be even more dangerous to produce realistic-looking, but qualitatively/quantitatively useless results). I might posit that robustness could be viewed as a stronger form of stability that provides a guarantee that the result should not be regarded as bullshit.

Perhaps this is the path forward I’m suggesting. The theory of PDE stability is rather sparse and barren compared to ODE theory. PDE stability is really quite simple conceptually, while ODE stability theory is rich with detail and nuance. One has useful and important concepts such as A-stability, L-stability and so on. There are appealing concepts such as relative-stability and order stars, which have no parallel in PDE stability. I might be so bold as to suggest that PDE stability theory is incomplete and unfinished. We have moved toward accuracy and efficiency and never returned to finish the foundation they should be built upon. We are left with a field that has serious problems with determining quality and correctness of solutions (https://williamjrider.wordpress.com/2014/10/15/make-methods-better-by-breaking-them/, https://williamjrider.wordpress.com/2014/10/22/821/).

Maybe a useful concept would be robust stability. What are the conditions where we can expect the results to be physical and nonlinearly stable? Instead the concept of robustness often gets a bad name because it implies tricks and artifices used to produce results securely. A key point is that robustness is necessary for codes to do useful work, yet doing the work of making methods robust is looked down upon. Doing this sort of work successfully resulted in the backhanded compliment/slight being thrown my way:

you’re really good at engineering methods.

Thanks, I think. It sounds a lot like,

you’re a really good liar

In thinking about numerical methods perhaps the preeminent consideration is stability. As I stated, it is foundational for everything. Despite its centrality to the discussion today, stability is a rather later comer to the basic repertoire of the numerical analyst only being invented in 1947 while many basic concepts and methods precede it. Moreover its invention in numerical analysis is extremely revealing about the fundamental nature of computational methods. Having computers and problems to solve with them drives the development of methods.

Recently I gave a talk on the early history of CFD (https://williamjrider.wordpress.com/2014/05/30/lessons-from-the-history-of-cfd-computational-fluid-dynamics/) and did a bit of research on the origin of some basic concepts. One of my suppositions was that numerical stability theory for ODEs must have preceded that for PDEs. Instead this was not true! PDEs came first. The reason for this is the availability and use of automatic computation (i.e., computers). Because of the applications of PDEs to important defense work during and after World War 2, the problem of stability was confronted. Large-scale use of computers for integrating ODEs didn’t come along until a few years later. The origins of stability theory and its recognition are related in a marvelous paper by Dahlquist [Dahlquist], which I wrote about earlier (https://williamjrider.wordpress.com/2014/08/08/what-came-first-the-method-or-the-math/). There I expressed my annoyance at the style of mathematics papers that obscures the necessary human element in science in what I believe to be a harmful manner. The lack of proper narrative allows the history and impact of applied math to be lost in the sands of time.

The PDE stability theory was first, and clearly articulated by John Von Neumann and first communicated during lectures in February 1947, and in a report that same year [VNR47]. These same concepts appeared in print albeit obliquely in Von Neumann and Goldstine [VNG47], and Crank-Nicholson’s classic [CN47]. Joe Grcar gives a stunning and full accounting of the work of Von Neumann and Goldstine and its impact on applied mathematics and computing in SIAM Review [Grcar]. Since Von Neumann had access to and saw the power of computing, he saw stability issues first hand, and tackled them. He had to, it bit him hard in 1944 [MR14]. His stability analysis methodology is still the gold standard for PDEs (https://williamjrider.wordpress.com/2014/07/15/conducting-von-neumann-stability-analysis/).

Another theme worth restating is the roll of (mis-)classification of the early reports had on muddying the history. LA-657, which was the report on the first mention of stability in numerical analysis was classified until 1993 even though the report is clearly unclassified (https://williamjrider.wordpress.com/2014/11/20/the-seven-deadly-sins-of-secrecy/). As it turned out the official unveiling of the ideas regarding stability of PDEs came out in two papers in 1950 [VNR50,CFVN50].

As Dahlquist relays the PDE world had a head start, and other important work was conducted perhaps most significantly the equivalence theorem of Lax [LaxEquiv]. This theorem was largely recreated independently by Dahlquist two or three years later (he reports that Lax gave the theory in a seminar in 1953). The equivalence theorem states that the combination of stability and consistency is equivalent to convergence. Being rather flip about this stability means getting an answer and consistency means solving the right problem.

From there the ODE theory flowered and grew to the impressive tapestry we have today. A meaningful observation is that we have a grasp of the analytical theory for the solution of ODEs that eludes us today with PDEs. Perhaps the PDE theory would flow like water from a breaking dam were such an analytical theory available. I’m not so sure. Maybe the ODE theory is more of the consequence of the efforts a few people or a culture that was different from the culture responsible for PDEs. Its worth thought and discussion.

The investigator should have a robust faith – and yet not believe.

 Claude Bernard

[LaxEquiv] Lax, Peter D., and Robert D. Richtmyer. “Survey of the stability of linear finite difference equations.” Communications on Pure and Applied Mathematics 9, no. 2 (1956): 267-293.

[VNG] Von Neumann, John, and Herman H. Goldstine. “Numerical inverting of matrices of high order.” Bulletin of the American Mathematical Society 53, no. 11 (1947): 1021-1099.

[Dahlquist] Dahlquist, Germund. “33 years of numerical instability, Part I.” BIT Numerical Mathematics 25, no. 1 (1985): 188-204.

[CN47] Crank, John, and Phyllis Nicolson. “A practical method for numerical evaluation of solutions of partial differential equations of the heat-conduction type.” In Mathematical Proceedings of the Cambridge Philosophical Society, vol. 43, no. 01, pp. 50-67. Cambridge University Press, 1947.

[CFVN50] Charney, Jules G., Ragnar Fjörtoft, and J. von Neumann. “Numerical integration of the barotropic vorticity equation.” Tellus 2, no. 4 (1950): 237-254.

[VNR50] VonNeumann, John, and Robert D. Richtmyer. “A method for the numerical calculation of hydrodynamic shocks.” Journal of applied physics 21, no. 3 (1950): 232-237.

[VNR47] VonNeumann, John, and Robert D. Richtmyer. “On the numerical solution of partial differential equations of parabolic type.” Los Alamos Scientific Laboratory Report, LA-657, December 1947.

[Grcar] Grcar, Joseph F. “John von Neumann’s analysis of Gaussian elimination and the origins of modern Numerical Analysis.” SIAM review 53, no. 4 (2011): 607-682.

[MR14] Mattsson, Ann E., and William J. Rider. “Artificial viscosity: back to the basics.” International Journal for Numerical Methods in Fluids (2014). DOI 10.1002/fld.3981

We are our choices.

― Jean-Paul Sartre

Life comes with many choices regarding what to do, what to eat, buy, watch, listen to and so on. Depending on your personal tastes these choices are wonderful or a burden. If you really care about something quite often you demand choices to be happy. You won’t be pleased with limited options when you know something better isn’t even being offered. In other cases where you aren’t emotionally invested, too many choices can be a burden, and unwelcome. You just need something functional and aren’t willing to expend the effort to sift through a bunch of alternatives. This distinction happens over and over across our lives both personal and professional.

What one person demands as a phalanx of options is a crushing affront to another. The demands of choice come from the aficionado who sees the texture and variation among the choices. When no options are available it can be greeted as the acceptance of something awful. This could even be true for the single option, which is acknowledged as the best and would be chosen from many options. On the other hand for someone who doesn’t care about the details, the mediocre is just fine. It isn’t that they wouldn’t like something better; it is that they can’t tell the difference or don’t care. This sort of dichotomy exists with everyone and varies topic to topic. It plays a huge role in science and engineering. I am certainly guilty of this, and I suspect all of you are too.

In any moment of decision, the best thing you can do is the right thing. The worst thing you can do is nothing.
― Theodore Roosevelt

A while back I wrote about what I don’t like about the finite element method (FEM) (https://williamjrider.wordpress.com/2014/08/01/what-do-i-have-against-the-finite-element-method/). Over the long weekend I was thinking about adaptivity and robustness in numerical methods. Some of my thoughts were extensions of the Riemann solver work discussed last week. When my thoughts turned to finite element powered methods, it dawned on me that I didn’t have many options, or more properly the extensive choices offered by other frameworks. The choices I did have were limited in scope and flexibility. Some approaches to method adaptation were simply absent.

I realized that this was what really deeply bothered me about finite elements. It isn’t the method at all; it’s the lack of options available to engineer the method. For a lot of engineers the FEM is a “turn the crank” exercise. You get a mesh, and pick the degrees of freedom, put the governing equations into the weak form and integrate. You have a numerical method and you are done. For complex physics this approach can be woefully inadequate and with the FEM you aren’t left with much to do about it.

Working at Sandia one thing is always true; the code you write will implement the FEM. With a new project and generally it would be very beneficial to have multiple valid discretizations on the same mesh. This would enable a number of things such as error estimation, resilience against hardware errors, and more robust overall algorithms. The problem is that the FEM generally offers a single preferred discretization once the mesh and associated elements are chosen.

To some extent this is overstated. Some FEM methods offer a bit more in the way of options such as discontinuous Galerkin. Additionally, one could chose to over- or under-integrate, lump the mass matrix or apply a stabilization method. Even then, the available options for discretizing are rather barren compared with finite volume or finite difference methods. It feels like a straightjacket by contrast to relative unconstrained freedom. Even the options I once worked with were too constrained compared with the universe of possibilities offered as I discovered in my most recent paper (“Revisiting Remap Methods” DOI 10.1002/fld.3950).

The hardest choices in life aren’t between what’s right and what’s wrong but between what’s right and what’s best.
― Jamie Ford

For people whose job is doing analysis of physical or engineered systems with codes, the options are a burden. They just want something that works and don’t care much about the detail. They graciously accept something better or something improved even if they couldn’t express the reasons for the improvement. With commercial CFD codes this situation has become critical. These codes reflect a relatively stagnant state of affairs with CFD methods.

For me this is a particular issue in the area of shock physics. Most of the users of shock physics codes are completely happy with their options. For some, the code simply needs to run to completion and produce something that looks plausibly realistic. For me this seems like a god-awfully low standard, and I see methods that are antiquated and backwards. The code users usually only notice new methods when something bad happens, the code runs slower, the answer changes from the ones they’ve grown accustomed to, or the code crashes. It is a rarity for the new method to be greeted as a benefit. The result is stagnation and a dearth of progress.

Sometimes you have to choose between a bunch of wrong choices and no right ones. You just have to choose which wrong choices feels the least wrong.
― Colleen Hoover

This trend is fairly broad. As numerical methods have matured, the codes based upon them have stabilized because the users are generally satisfied with the options offered. Improvements in the methodology are not high on their wish list. Moreover, they have a general understanding of the options the codes are based on, and have little interest in the methods that might be developed to improve upon them. As such the bar for improving codes has been raised to a very high level. With the cost of implementing codes on new computer architectures growing, the tide has turned to a focus on legacy methodology on modern computers sapping the impetus to improve.

We have become a community that sees options as a burden. Other burdens such as changes in computers are swallowing the appetite for the options that exist. As time goes by, the blessings seem more and more distant and foreign to the thought process. Moreover the users of codes don’t see the effort put into better methods as a virtue and want to see focus on improving the capacity to model the physical systems they are interested with. Part of this relates strongly to the missing elements in the education of people engaged in modeling and simulation. The impact of numerical methods on the modeling of physical systems is grossly under-appreciated, and leads to a loss of perspective. Methods in codes are extremely important and impactful (artificial, numerical and shock dissipation anyone?). Users tend to come close to completely ignoring this aspect of their modeling due to the esoteric nature of its impact.

When faced with two equally tough choices, most people choose the third choice: to not choose.

― Jarod Kintz

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