The purpose of life is not to be happy. It is to be useful, to be honorable, to be compassionate, to have it make some difference that you have lived and lived well.
― Ralph Waldo Emerson
As one gets older and enters into the heart of mid-life, it is natural to contemplate ones place in the World. I’m deep into such contemplation. I’ve been blessed with work with meaning for much of my adult life, but that meaning seems to have leaked away recently. Part of my thinking is the decision of whether this is a local or global conditio
n. Are things worse where I am, or better than the average? For most of my adult life, I’ve had far better conditions than average, and been able to find great meaning in my work. Is the steady erosion of the quality of the work environment a consequence of issues local to my institution or organization? Or is it part of the massive systemic dysfunction our society is experiencing?
If the problem is local, I could leave for another organization, or another institution that is functioning better. If it’s a global issue, then its not something I can likely influence (much), and its time to ride the storm out the best I can. Right now my money is on the issue being global, and we ought to all be ready for the shit to hit the fan. My guess it is already happened, the shit storm is in effect and we are headed into deep trouble as a Nation and the World. We have repugnantly dysfunctional National 
government, led by an incompetent narcissistic conman without a perceptible moral compass. Racial tensions, and a variety of white supremacist/right wing ultra-Nationalists are walking the streets. Left wing and anarchist groups are waking up as well. Open warfare may soon be upon us making us long for the days where sporadic terrorist attacks were our biggest worry. A shit storm is actually a severe understatement; this is a fucking waking nightmare. I hope this is wrong and I could simply find a better place to work at and feel value in my labors. I wish the problem was simple and local with a simple job change fixing things.
Work is an important part of life for a variety of reasons. It is how we spend a substantial portion of our time, and much of our efforts go into it. In work we contribute to society and assist in the collective efforts of mankind. As I noted earlier, I’ve been fortunate for most of my life, but things have changed. Part of the issue is a relative change in the degree of self-determination in work. The degree of self-determination has decreased over time. An aspect of this is the natural growth in scope of work as a person matures. As a person grows in work and is promoted, the scope of the work increases, and the degree of freedom in work decreases. Again this is only a part of the problem as the system is working to strangle the self-determination out of people. This is control, fear of failure and generic lack of trust in people. In this environment work isn’t satisfying because the system is falling apart, and the easiest way to resist this is controlling the little guy. My work becomes more of a job and a route to a paycheck every day. Earning a living and supporting your family is a noble achievement these days, and aspiring to more simply a waking dream contracting in the rear view mirror of life.
Creative autonomy is essential for the work I do to be satisfying. It is essential to the work being effective. I can’t be an effective problem solver if most of my best options are off the table. We exist in a system where the solutions are dictated in advance. No one is trusted to solve any real problems, just work toward the solutions that have been pre-ordained. Autonomy is threatening to the system because the trust in people is so intrinsically low. The result is the leaking away of meaning in work. The control that exists only calms the deep fears of a system that is failing. Inside the model where we are teetering on the edge of a societal shit storm, the attempt to control makes sense. The system is desperately trying to hold onto whatever control it has, fearing the unraveling about to unfold. Fear makes us do stupid things, and the fear is simply throwing fuel onto the fire by making everyone simply hate life.
The purpose of life is a life of purpose.
― Robert Bryne
I’m trying to grapple with what is happening in my own experience through the lens of the bigger picture. We see a contraction of the trust and autonomy necessary for me to enjoy work. This is in direct reaction to the fears unleashed by the changes in society, and the terror these changes have induced in much of the population. The old world is coming to an end, but not without a fight. People are genuinely frightened by change, and for most people the most comfortable place is the past. They are holding onto the past with a fervent passion, but the future is unstoppable. In between the two is conflict and pain. For someone like myself who demands work that makes progress, I might have to take a break and simply resign myself to defending the progress that has already been made. No new progress can happen without trust in an environment dominated by fear. We are simply trying to maintain the progress that has already been won.
Imagine what our story would look like if, rather than succumbing to the insistent voices of family or culture, we determined that our vocation was to be a better human.
― James Hollis, Ph.D.
The domination of fear has an extremely large impact on the appetite for risk; there isn’t any. Part of the fearful environment is the inability to accept anything that looks, smells or even hints at failure. Without failure you don’t have learning or achievement. Research depends on failure because research is basically learning in its rawest form. Let me be clear that I’m talking about good failure where you try your best, making a best effort and coming up short. Most of the time a failure leads to learning something new. You tweak your approach or knowledge on the basis of the experience and grow. Without failure you short-circuit expertise. We need to energize failure in many small things to engage success in big things. All of this requires the sort of deep trust that our current World is almost devoid of. A combination of courage and trus
t can unleash people’s full potential through allowing them to fail spectacularly and then fully support the next step forward. Today, cowardice and mistrust dominate and even marginal failure results in punishment. It is corroding the foundation of achievement. It makes work simply a job and life more survival than living.
Since our current World is so deeply arrayed against personal success and growth, it might be wise to seek other avenues of fulfillment. Perhaps work is most healthily viewed as simply a task of mere survival. The current environment is so rife with fear, and patently incompetent that no one can really reach their potential. This isn’t a conclusion I like reaching, but the evidence seems overwhelming. Fear and mistrust have led to overarching control issues that remove any degree of personal control for achievement, or at least control while staying inside the rules. If one is willing to completely ignore the rules, success can be had. If one plays by the rules, success is absolutely impossible. The rules of the game are written to avoid all of the acts necessary for success because these involve risk and danger. Fundamental to success is trust, and trusting someone is beyond our collective ken.
The purpose of life is to contribute in some way to making things better.
― Robert F. Kennedy
comfortable and automatic. In many cases culture is the permanent habits of our social constructs, and often defines practices that impede progress. Accepted cultural practices are usually done without thinking and applied almost mindlessly. If these practices are wrong, they are difficult to dislodge or improve upon.
or fundamental human needs, but most are constructs to help regulate the structures that our collective actions are organized about. The fundamental values, moral code and behaviors of people are heavily defined by culture. If the culture is positive, the effect is resonant and amplifies the actions of people toward much greater achievements. If the culture is negative, the effect can undo and overwhelm much of the best that people are capable of. Invariably cultures are a mixture of positive and negative. Cultures persist for extremely long times and outlive those who set the cultural tone for groups. Cultures are set or can change slowly unless the group is subjected to an existential crisis. When a crisis is successfully navigated the culture that arose in its resolution is enshrined, and tends to persist without change until a new crisis is engaged.
We see all sorts of examples of the persistence of culture. The United States is still defined by the North-South divide that fractured during the Civil War. The same friction and hate that defined that war 150 years ago dominate our politics today. The culture of slavery persists in systematic racism and oppression. The white and black divide remains unhealed even though none of the people who enslaved or who were enslaved are still alive with many generations having passed. The United States is still defined by the Anglo-Saxon Protestant beliefs of the founding fathers. Their culture is dominant even after being overwhelmed in numbers of people and centuries of history. The dominant culture was formed in the crucible of history by the originating crisis for the Nation, the Revolutionary war. Companies and Laboratories are shaped by their original cultures and these habits and practices persist long after their originators have left, retired or died.
(earthquakes, floods, hurricanes, famines, …). These events can stress people and existing cultures providing the sorts of crises that shape the future to be more resilient to future disasters. Human events such as wars, trade, and general political events provide both the impact of culture in causing or navigating events, as well as producing crises that shape cultural responses and evolution. We can continue down this line of thinking to ever-smaller cultures such as organizations and businesses are influenced by crises induced by the larger systems (natural or political). This web of culture continues to smaller and smaller scale all the way to communities (towns, regions, schools, families) each having a culture shaped heavily by other cultures or events. In every case a crisis is almost invariably necessary to induce change, cultures are resistant to change unless something painful provides direct evidence of the incapacity of existing culture to succeed.
culture of the broader scientific community. This culture exists within the broader network of cultures in society with give-and-take between them. In the past science has provided deep challenges to prevailing culture, and induced changes societal culture. Today the changes in main societal culture are challenging science. One key aspect of today’s culture wars is lack of support for expertise. One of the key rifts in society is mistrust of the elite and educated. The broader society is attacking and undermining educational institutions across the board. Scientific laboratories are similar in makeup and similarly under assault. Much of this broader assault is related to a general lack of trust. Some of this is a reaction to the surplus of trust granted science in the wake of its massive contributions to the resolution of World War 2 and the Cold War. These successes are waning in memory and science is now contracting for a distinguished role societally.
ns suffers a bit relative to the other Labs, as does the rigor of computed results. Los Alamos was the birthplace of all three labs and computational work, but always puts computation in a subservient role compared to experiments. This leads to a mighty struggle between validation and calibration. Often calibration wins out so that computed results are sufficiently close to experiment. Sandia excels at process and rigor in the conduct of calculations, but struggles at other aspects (at least in a relative sense). The whole verification and validation approach to simulation quality comes from Sandia reflecting the rigor. At the same time institutional support and emphasis are weaker leading to long-term effects.
All this texture is useful to think about because it manifests itself in every place computational science is done today. The scientific culture of any institution is reflected in its emphasis, and approach to the conduct of science. The culture produces a natural set of priorities that define investments and acceptable quality. We can speak volumes about how computational work should be done, but the specific acuity to the message is related to preconceived notions about the aspects. For example, some places are more prone to focus on computing hardware as an investment. In terms of the competition for resources, the purchase of hardware is a priority, and a typical route for enhancement. This becomes important when trying to move into new “hot” areas. If the opportunity falls in line with the culture, investments flow and if it is out of line the institution will miss it.
omputational science is a relatively new area of endeavor. It is at most 70 years old as practiced at Los Alamos; it is a new area of focus in most places. Sometime it is practiced at an institution and added to the repertoire as a new innovative way of doing work. In all these cases the computational work adopts the basic culture of the institution it exists within. It then differentiates based on the local conditions usually dominated by whatever the first acknowledged success is. One of the key aspects of a culture is origin stories or mythological achievements. Origins are almost invariably fraught situations with elements of crisis. These stories pervade the culture and define what success looks like and how investments in the future are focused.
Where I work at Sandia, the origin story is dominated by early success with massively parallel computers. The greatest success was the delivery of a computer, Red Storm. As a result the culture is obsessed with computer hardware. The path to glory and success runs through hardware; a focus on hardware is culturally accepted and natural for the organization. It is a strong predisposition. At Lawrence Livermore the early stages of the Laboratory were full of danger and uncertainty. Early in the history of the Lab there was a huge breakthrough in weapons design. It used computational modeling, and the lead person in the work went on to huge professional success (Lab Director). This early success became a blueprint for others and an expected myth to be repeated. A computational study and focus was always expected and accepted by the Lab. At Los Alamos all roads culturally lead to the Manhattan Project. The success in that endeavor has defined the Laboratory ever since. The manner of operation and approach to science adopted then is blueprint for success at that Laboratory. The multitude of crises starting with the end of the Cold War, spying, fires, and scandal have all weakened the prevailing culture, and undermined the future.
and knowledge. This always manifests itself with a lack of questioning in the execution of work. Both of these issues are profoundly difficult to deal with and potentially fatal to meaningful impact of modeling and simulation. These issues are seen quite frequently. Environments with weak peer review contribute to allowing confidence with credibility to persist. The biggest part of the problem is a lack of pragmatic acceptance of modeling and simulation’s intrinsic limitations. Instead we have inflated promises and expectations delivered by over confidence and personality rather than hard nosed technical work.
When confidence and credibility are both in evidence, modeling and simulation is empowered to be impactful. It will be used appropriately with deference to what is and is not possible and known. When modeling and simulation is executed with excellence and professionalism along with hard-nosed assessment of uncertainties, using comprehensive verification and validation, the confidence is well grounded in evidence. If someone questions a simulations result, answers can be provided with well-vetted evidence. This produces confidence in the results because questions are engaged actively. In addition the limitations of the credibility are well established, and confidently be explained. Ultimately, credibility is a deeply evidence-based exercise. Properly executed and delivered, the degree of credibility depends on honest assessment and complete articulation of the basis and limits of the modeling.
One of the major sins of over-confidence is flawed or unexamined assumptions. This can be articulated as “unknown knowns” in the famously incomplete taxonomy forwarded by Donald Rumsfeld in his infamous quote. He didn’t state this part of the issue even though it was the fatal flaw in the logic of the Iraqi war in the aftermath of 9/11. There were basic assumptions about Hussein’s regime in Iraq that were utterly false, and these skewed the intelligence assessment leading to war. They only looked at information that supported the conclusions they had already drawn or wanted to be true. The same faulty assumptions are always present in modeling. Far too many simulation professionals ignore the foundational and unfounded assumptions in their work. In many cases assumptions are employed without thought or question. They are assumptions that the community has made for as long as anyone can remember and simply cannot be questioned. This can include anything from the equations solved, to the various modeling paradigms applied as a matter of course. Usually these are unquestioned and completely unexamined for validity in most credibility assessments.
stay the course and make standard assumptions. In many cases the models have been significantly calibrated to match existing data, and new experiments or significantly more accurate measurements are needed to overturn or expose modeling limitations. Moreover the standard assumptions are usually unquestioned by peers. Questions are often met with ridicule. A deeply questioning assessment requires bravery and fortitude usually completely lacking from working scientists and utterly unsupported by our institutions.
personality. This proof by authority is incredibly common and troubling to dislodge. In many cases personal relationships to consumers of simulations are used to provide confidence. People are entrusted with the credibility and learn how to give their customer what they want. Credibility by personality is cheap and requires so much less work plus it doesn’t raise any pesky doubts. This circumstance creates an equilibrium that is often immune to scientific examination. It is easier to bullshit the consumers of modeling and simulation results than level with them about the true quality of the work.
One of the biggest threats to credibility is the generation of the lack of confidence honesty has. Engaging deeply and honestly in assessment of credibility is excellent at undermining confidence. Almost invariably the accumulation of evidence regarding credibility endows the recipients of this knowledge with doubt. These doubts are healthy and often the most confident people are utterly ignorant of the shortcomings. The accumulation of evidence regarding the credibility should have a benefit for the confidence in how simulation is used. This is a problem when those selling simulation oversell what it can do. The promise of simulation has been touted widely as transformative. The problem with modeling and simulation is its tangency to reality. The credibility of simulations is grounded by reality, but the uncertainty comes from both modeling, but also the measured and sensed uncertainty with our knowledge of reality.
n oversold and any assessment will provide bad news. In today’s World we see a lot of bad news rejected, or repackaged (spun) to sound like good news. We are in the midst of a broader crisis of credibility with respect to information (i.e. fake news), so the issues with modeling and simulation shouldn’t be too surprising. We would all be well served by a different perspective and approach to this. The starting point is a re-centering of expectations, but so much money has been spent using grossly inflated claims.
limitations. The difference that simulation makes is the ability to remove the limitations of analytical model solution. Far more elaborate and accurate modeling decisions are available, but carry other difficulties due to the approximate nature of numerical solutions. The tug-of-war intellectually is the balance between modeling flexibility, nonlinearity and generality with effects of numerical solution. The bottom line is the necessity of assessing the uncertainties that arise from these realities. Nothing releases the modeling from its fundamental connection to validity grounded in real world observations. One of the key things to recognize is that models are limited and approximate in and of themselves. Models are wrong, and under a sufficiently resolved examination will be invalid. For this reason an infinitely powerful computer will ultimately be useless because the model will become invalid at some resolution. Ultimately progress in modeling and simulation is based on improving the model. This fact is ignored by computational science today and will result wasting valuable time, effort and money chasing quality that is impossible to achieve.
This is a nice way of saying this is usually a sign that the quality is actually complete bullshit! We can move a long way toward better practice by simply recalibrating our expectations about what we can and can’t predict. We should be in a state where greater knowledge about the quality, errors and uncertainty in modeling and simulation work improves our confidence.
One of the issues of increasing gravity in this entire enterprise is the consumption of results using modeling and simulation by people unqualified to judge the quality of the work. The whole enterprise is judged to be extremely technical and complex. This inhibits those using the results from asking key questions regarding the quality of the work. With the people producing modeling and simulation results largely driven by money rather than technical excellence, we have the recipe for disaster. Increasingly, false confidence accompanies results and snows the naïve consumers into accepting the work. Often the consumers of computational results don’t know what questions to ask. We are left with quality being determined more by flashy graphics and claims about massive computer use than any evidence of prediction. This whole cycle perpetuates an attitude that starts to allow viewing reality as more of a video game and less like a valid scientific enterprise. Over inflated claims of capability are met with money to provide more flashy graphics and quality without evidence. We are left with a field that has vastly over-promised and provided the recipe for disaster.
A great question is an achievement in itself although rarely viewed as such. More often than not little of the process of work goes into asking the right question. Often the questions we ask are highly dependent upon foundational assumptions that are never questioned. While assumptions about existing knowledge are essential, finding the weak or invalid assumptions is often the key to progress. These assumptions are wonderful for simplifying work, but also inhibit progress. Challenging assumptions is one of the most valuable things to do. Heretical ideas are fundamental to progress; all orthodoxy began as heresy. If the existing assumptions hold up under the fire of intense scrutiny they gain greater credibility and value. If they fall, new horizons are opened up to active exploration.
questions have led to the best research, and most satisfying professional work I’ve done. I would love to recapture this spirit of work again, with good questioning work feeling almost quaint in today’s highly over-managed climate. One simple question occurred in my study of efficient methods for solving the equations of incompressible flow. I was using a pressure projection scheme, which involves solving a Poisson equation at least once, if not more than once a time step. The most efficient way to do this involved using the multigrid method because of its algorithmic scaling being linear. The Poisson equation involves solving a large sparse system of linear equations, and the solution of linear equations scales with powers of the number of equations. Multigrid methods have the best scaling thought to be possible (I’d love to see this assumption challenged and sublinear methods discovered, I think they might well be possible).
As a result as problems become larger, the proportion of the solution time spent solving linear equations grows ever larger (the same thing is happening now to multigrid because of the cost of communication on modern computers). I posed the question of whether I could get the best of both methods, the efficiency with the robustness? Others were working on the same class of problems, and all of us found the solution. Combine the two methods together, effectively using a multigrid method to precondition the conjugate gradient method. It worked like a charm; it was both simple and stunningly effective. This approach has become so standard now that people don’t even think about it, its just the status quo.
The second great question I’ll point to involved the study of modeling turbulent flows with what has become known as implicit large eddy simulation. Starting in the early 1990’s there was a stunning proposition that certain numerical methods seem to automatically (auto-magically) model aspects of turbulent flows. While working at Los Alamos and learning all about a broad class of nonlinearly stable methods, the claim that they could model turbulence caught my eye (I digested it, but fled in terror from turbulence!). Fast forward a few years and combine this observation with a new found interest in modeling turbulence, and a question begins to form. In learning about turbulence I digested a huge amount of theory regarding the physics, and our approaches to modeling it. I found large eddy simulation to be extremely interesting although aspects of the modeling were distressing. The models that worked well were performed poorly on the structural details of turbulence, and the models that matched the structure of turbulence were generally unstable. Numerical methods for solving large eddy simulation were generally based on principles vastly different than those I worked on, which were useful for solving Los Alamos’ problems.
I should spend some time on some bad questions as examples of what shouldn’t be pursued. One prime example is offered as a seemingly wonderful question, the existence of solutions to the incompressible Navier-Stokes equations. The impetus for this question is the bigger question of can we explain, predict or understand fluid turbulence? This problem is touted as a fundamental building block in this noble endeavor. The problem is the almost axiomatic belief that turbulence is contained within this model. The key term is incompressible, which renders the equations unphysical on several key accounts: it gives the system infinite speed of propagation, and divorces the equations from thermodynamics. Both features sever the ties of the equations from the physical universe. The arguing point is whether these two aspects disqualify it from addressing turbulence. I believe the answer is yes.
Another poorly crafted question revolves around the current efforts for exascale class computers for scientific computing. There is little doubt that an exascale computer would be useful for scientific computing. A better question is what is the most beneficial way to push scientific computing forward? How can we make scientific computing more impactful in the real world? Can the revolution of mobile computing be brought to science? How can we make computing (really modeling and simulation) more effective in impacting scientific progress? Our current direction is an example of crafting an obvious question, with an obvious answer, but failing to ask a more cutting and discerning question. The consequence of our unquestioning approach to science will be wasted money and stunted progress.
rust in people and science. Trust is a powerful concept and its departure from science has been disruptive and expensive. Today’s scientists are every bit as talented and capable as those of past generations, but society has withdrawn its faith in science. Science was once seen as a noble endeavor that embodied the best in humanity, but generally not so today. Progress in the state of human knowledge produced vast benefits for everyone and created the foundation for a better future. There was a sense of an endless frontier constantly pushing out and providing wonder and potential for everyone. This view was a bit naïve and overlooked the maxim that human endeavors in science are neither good or bad, producing outcomes dependent upon the manner of their use. For a variety of reasons, some embedded within the scientific community, the view of society changed and the empowering trust was withdrawn. It has been replaced with suspicion and stultifying oversight.
The Regularized Singularity 