Science is not about making predictions or performing experiments. Science is about explaining.

― Bill Gaede

In the modern dogmatic view of high performance computing, the dominant theme of utility revolves around being predictive. This narrative theme is both appropriate and important, but often fails to recognize the essential prerequisites for predictive science, the need to understand and explain. In scientific computing the ability to predict with confidence is always preceded by the use of simulations to aid and enable understanding and assist in explanation. A powerful use of models is the explanation of the mechanisms leading to what is observed. In some cases simulations allow exquisite testing of models of reality, and when a model matches reality we infer that we understand the mechanisms at work in the World. In other cases we have observations of reality that cannot be explained. With simulations we can test our models or experiment with mechanisms that can explain what we see. In both cases the confidence of the traditional science and engineering community is gained through the process of simulation-based understanding.

Leadership and learning are indispensable to each other.

― John F. Kennedy

Too often in today’s world we see a desire to leap over this step and move directly to prediction. This is a foolish thing to attempt. Like fools, this is exactly where we are leaping! The role of understanding in the utility of simulation is vital in building the foundation upon which prediction is laid. This has important technical and cultural imprints that should never be overlooked. The role of building understanding is deep and effective in providing a healthy culture of modeling and simulation excellence. Most essentially it builds deep bonds of curiosity satisfaction within the domain science and engineering community. The experimental and test community is absolutely vital to a healthy approach, and needs a collaborative spirit to thrive. When prediction becomes the mantra without first building understanding, simulations often get put into an adversarial position. For example we see simulation touted as a replacement to experiment and observation. Instead of collaboration simulation becomes an outright threat. This can lead to completely and utterly counter-productive competition where collaboration would serve everyone far better in almost every case.

Understanding as the object of modeling and simulation also works keenly to provide a culture of technical depth necessary for prediction. I see simulation leaping into the predictive fray without the understanding stage as arrogant and naïve. This is ultimately highly counter-productive. Rather than building on the deep trust that the explanatory process provides, any failure on the part of simulation becomes proof of the negative. In the artificially competitive environment we too often produce, the result is destructive rather than constructive. Prediction without first establishing understanding is an act of hubris, and plants the seeds of distrust. In essence by sidestepping the understanding phase of simulation use makes failures absolutely fatal to success instead of stepping-stones to excellence. This is because the understanding phase is far more forgiving. Understanding is learning and can be engaged in with a playful abandon that yields real progress and breakthroughs. It works through a joint investigation of things no one knows and any missteps are easily and quickly forgiven. This allows the competence and knowledge to be built through the acceptance of failure. Without allowing these failures, success in the long run cannot happen.

Raise your quality standards as high as you can live with, avoid wasting your time on routine problems, and always try to work as closely as possible at the boundary of your abilities. Do this, because it is the only way of discovering how that boundary should be moved forward.

― Edsger W. Dijkstra

The essence of the discussion revolves around the sort of incubator that can be created by a collaborative, learning environment focused on understanding. When the focus is understanding of something the dynamic is forgiving and open. No one knows the answer and people are eager to accept failure as long as it is an honest attempt. More importantly when success comes it has the flavor of discovery and serendipity. The discovery takes the role of an epic win by heroic forces. After the collaboration has worked to provide new understanding and guided the true advance of knowledge, simulation sits in a place where it can be a trusted partner in the scientific or engineering enterprise. Too often in today’s world we disallow the sort of organic mode of capability development in favor of an artificial project based approach.

Our current stockpile stewardship program is a perfect example of how we have systematically screwed all this up. Over time we have created a project management structure with lots of planning, lots of milestone, lots of fear of failure and managed to completely undermine the natural flow of collaborative science. The accounting structure and funding has grown into a noose that is destroying the ability to build a sustainable success. We divide the simulation work from the experimental or application work in ways that completely undermine any collaborative opportunity. Collaborations become forced and teaming with negative context instead of natural and spontaneous. In fact anything spontaneous or serendipitous is completely antithetical to the entire management approach. Worse yet, the newer programs have all the issues hurting the success of stockpile stewardship and have added a lot additional program management formality. The biggest inhibition to success is the artificial barriers to multi-disciplinary simulation-experimental collaborations, and the pervasive fear of failure permeating the entire management construct. By leaping over the understanding and learning phase of modeling and simulation we are short-circuiting the very mechanisms for the most glorious successes. We are addicted to managing programs not to ever fail, which ironically sew the seeds of abject failure.

The problem with the current project milieu is the predetermination of what success looks like. This is then encoded into the project plans and enforced via our prevalent compliance culture. In the process we almost completely destroy the potential for serendipitous discovery. Good discovery science is driven by having rough and loosely defined goals with an acceptance of outcomes that are unknown beforehand, but generally speaking provide immense value at the end of the projects. Today we have instituted project management that attempts to guide our science toward scheduled breakthroughs and avoid any chance at failure. The bottom line is that breakthroughs are grounded on numerous failures and course corrections that power enhanced understanding and a truly learning environment. Our current risk aversion and fear of failure is paving the road to a less prosperous and knowledgeable future.

A specific area where this dynamic is playing out with maximal dysfunctionality is climate science. Climate modeling codes are not predictive and tend to be highly calibrated to the mesh used. The overall modeling paradigm involves a vast number submodels to include a plethora of physical processes important within the Earth’s climate. In a very real sense the numerical solution of the equations describing the climate are forever to be under-resolved with significant numerical error. The system of Earth’s climate also involves very intricate and detailed balances between physical processes. The numerical error is generally quite a bit larger than the balance effects determining the climate, so the overall model must be calibrated to be useful.

You couldn’t predict what was going to happen for one simple reason: people.

― Sara Sheridan

In the modern modeling and simulation world this calibration then provides the basis of very large uncertainties. The combination of numerical error and modeling error means that the true simulation uncertainty is relatively massive. The calibration assures that the actual simulation is quite close to the behavior of the true climate. The models can then be used to study the impact of various factors on climate and aid the level of understanding of climate science. This entire enterprise is highly model-driven and the level of uncertainty is quite large. When we transition over to predictive climate science, the issues become profound. We live in a world where people believe that computing should help to provide quantitative assistance for vexing problems. The magnitude of uncertainty from all sources should provide people with significant pause and provide a pushback from putting simulations in the wrong role. It should also not prevent simulation from providing a key tool in understanding this incredibly complex problem.

The premier program for high performance computing simply takes all of these issues and amplifies them to an almost ridiculous degree. The entire narrative around the need for exascale computing is entirely predicated on the computers providing predictive calculations. This is counter to the true role of computation as a modeling, learning, explanation, and understanding partner with scientific and engineering domain expertise. While it is wrong at an intrinsic level the secondary element in the program’s spiel is the simplicity of moving existing codes to new, faster computers for better science. Nothing could be further from the truth on either account. Most codes are woefully inadequate for predictive science first and foremost because of their models. All the things that the exascale program ignores are the very things that are necessary for predictivity. At the end of the day this program is likely to only produce more accurately solved wrong models and do little for predictive science. To exacerbate these issues, the exascale program generally does not support the understanding role of simulation in science.

The long-term impact of this lack of support for understanding is profound. It will produce a significant issue with the ability for simulation to elevate itself to a predictive role in science and engineering. The use of computation to help with understanding difficult problems paves the way for a mature predictive future. Removing the understanding is akin to putting someone into an adult role in life without going through a complete childhood. This is a recipe for disaster. The understanding portion of computational collaboration with engineering and science is the incubator for prediction. It allows the modeling and simulation to be very unsuccessful with prediction and still succeed. The success can arise through learning things scientifically through trial and error. These trials, errors and response over time provide a foundation for predictive computation. In a very real way this spirit should always be present in computation. When it is absent, the computational efficacy will become stagnant.

In summary we have yet another case of marketing of science overwhelming the true narrative. In the search for funding to support computing, the sale’s pitch has been arranged around prediction as a product. Increasingly, we are told that a faster computer is all that we really need to do. The implied message in this sale’s pitch is a lack of necessity to support and pursue other aspects of modeling and simulation for predictive success. These issues are plaguing our scientific computing programs. Long-term success of high performance computing is going to be sacrificed, based on this funding-motivated approach. We can add the failure to recognize understanding, explaining and learning as a key products for science and engineering from computation.

Any fool can know. The point is to understand.

― Albert Einstein

It is quality rather than quantity that matters.

― Seneca
The utility of calculations and scientific computing is taken to be axiomatic, yet we cannot easily articulate why a calculation is useful. By virtue of this dynamic, we also can’t tell you why a calculation isn’t useful either. This axiomatic belief underlies the investment by the nation into high performance computing (HPC), yet the lack of clarity on utility clouds any discussion. Worse yet, the clouding of the reasons for utility produces counter-productive investment decisions and suboptimal programs. Perhaps it is high time to wrestle with this issue and try to see our way clear to some greater clarity.

Useful calculations shed light and understanding on issues existing in the real world. The utility of calculation for scientific investigations lies in their ability to study hypotheses or (help to) explain observations. A successful model of reality implies a certain level of understanding that can be comforting, contrasted with an unsuccessful or highly calibrated that drives new work. With sufficient confidence, the calculation allows one to play “what if” scenarios and study the impact of changes to a physical system. This guides physical studies, measurements and observations, which can yield unequivocal evidence. Computations usually do not provide this, but show the way to finding it. The same thing happens in engineering where calculations are often used in a comparative sense to understand how to optimize designs, or fix problems with existing designs. In other cases, the calculation can help to explain why things don’t work, or broke, or behaved in a manner that was unexpected. For calculations need to take a valued role in science and engineering the demonstration the ability to provide these varied aspects of real world functionality are essential. Once calculations step into the role of advisor, sage, and detective, the confidence, trust and credibility follows. This dynamic is never present in any discourse on HPC and current HPC programs almost callously disregard this legacy. This is dangerous and threatens progress.

When one looks at HPC, the press is drawn to the biggest, most expensive, most time consuming calculations and the science community allows itself to bullshit people on their utility. Plainly put, the giant calculations inhabiting press releases are simply demos at best, stunts at worst and very rarely science of any note. These hero calculations are not useful for science or engineering. As one of my most senior colleagues has quipped, single calculations will never be the right answer for hard problems. These stunts and demos are single, one-off calculations that have no established pedigree and dubious credibility. The problem is that the broader dynamic in HPC is poisoned by the devotion to the myth of utility of hero calculations. At best, these calculations are harbingers of the calculations that might be useful five to ten years from now and little else. These calculations push and pace progress in HPC, but their use for engineering and domain science is minimal.

What we have is another serious case of bullshit hurting our ability does deal with reality. In HPC, the effort and funding is chasing the biggest calculations while the important work done with smaller calculations simply fails to capture the attention and focus of the community. Increasingly the funding follows the bullshit narrative instead of the actual utility narrative, which is undermining the reality of modeling & simulation impact. The danger in the distance between focus and utility is the loss of opportunity to make HPC really matter and produce unambiguous impact. The community has allowed this fiction to persist and color funding decisions for decades. The efficacy of HPC for science and engineering is suffering as a result. The depth of the issue is great and clarity is actually easy to grasp.

One of the clearest issues with HPC utility is the prevalence of faith in individuals being definitive in credibility. Even today, the analysts involved in conducting calculation matter more to real world concerns using modeling & simulation than any technical work underpinning utility. The importance of the analyst also overwhelms the importance of the code itself. We persist with this false narrative around the importance of codes. One of the clearest results of this gap is the continuing lack of impact for verification and validation. In fact I see regression instead of progress in the impact of technical work for credibility and greater focus on the personal aspect of credibility. In other words, it is more important who does a calculation than how the work is done although these two items are linked. This was true 25 years ago with ASCI as it is today. The progress has not happened in large part because we let it, and failed to address the core issues while focusing on press releases and funding profiles. We see the truth squashed because it doesn’t match rhetoric. Now we see lack of funding and emphasis on calculation credibility in the Nation’s premier program for HPC. We continue to trumpet the fiction that the bigger the calculation and computer, the more valuable a calculation is a priori.

Even today with vast amounts of computer power, the job of modeling reality is subtle and nuanced. The modeler who conspires to represent reality on the computer still makes the lion’s share of the decisions necessary for high fidelity representations of reality. All of the items associated with HPC impact a relatively small amount of the overall load of analysis credibility. The analyst decides how to model problems in detail including selection of sub-models, meshes, boundary conditions, and the details included and neglected. The computer power and the mesh resolution usually end up being an afterthought and minor detail. The true overall modeling uncertainty is dominated by everything in the analyst’s power. In other words, the pacing uncertainty in modeling & simulation is not HPC; it is all the decisions made by the analysts. Even with the focus on “mesh resolution” the uncertainty associated with the finite integration of governing equations is rarely measured or estimated. We are focusing on a small part of the overall modeling & simulation capability to the exclusion of the big stuff that drives utility.

As usual, the issue is related to the relative sex appeal of the details in modeling & simulation. All the analyst-controlled details are dull and unexciting while HPC is sexy and exciting. The HPC things are easily marketed and receive funding while the analyst details are boring, but essential. The result is a focus on the sexy HPC stuff while the important work done by analysts goes by with meager, haphazard and disparate support. More deeply, the analyst support is defined purely through application work and generally divorced from the HPC work. As such the divide just grows and grows. Moreover the HPC side of the work can dutifully ignore the analyst stuff that matters because the political weight says that the important details matter little. In the HPC work all the glue between the computer-focused HPC work and applications is poorly funded or not funded at all.

One of the core issues in this entire dynamic associated with the utility of computational modeling and simulation is predictivity. Predictive simulations are a sort of “Holy Grail” for computational science. Predictive calculations are not necessarily useful. Useful computations can come from sources that are far from predictive, and the utility is far more driven by the flexibility of computational capability combined with the ability of analysts to wield the computational power. The utility, flexibility and understanding cannot come from the lumbering computational behemoths driving funding. If a calculation is predictive; so much the better it is for utility. The key to predictivity is it demands a lot of evidence and a systematic investigation, which is the whole practice of verification and validation (V&V).

Where utility ends and decoration begins is perfection.

― Jack Gardner

One of the single greatest issues is a general failure to measure prediction, modeling & simulation uncertainties in a holistic manner. Generally uncertainty estimation is limited to parametric epistemic uncertainty, which is an important, but small part of the overall uncertainty budget. Numerical uncertainty is usually not estimated at all, but declaration is made regarding the lack of mesh dependence, or simply the massive size of the calculation renders numerical errors small by fiat. In many cases systems have intrinsic variability that provides an important source of uncertainty (turbulence canonically comes to mind). This is also rarely estimated. Finally we come to the uncertainty directly associated with the analyst’s decisions. When this issue has been studied, the uncertainty associated with analyst modeling decisions or analyst assumptions tends to huge compared to other sources. The easier and common thing to do is a declaration that the calculation is predictive by definition thus avoids any real quantification of the uncertainty.

The current HPC belief system believes that massive computations are predictive and credible solely by the virtue of overwhelming computational power. In essence they use proof by massive computation as the foundation of belief. The problem is that science and engineering do not work this way at all. Belief comes from evidence and the evidence that matters are measurements and observations of the real World (i.e., this would be validation). Models of reality can be steered and coaxed into agreement via calibration in ways that are anathema to prediction. Part of assuring that this isn’t happening is verification. We ultimately want to make sure that the calculations are getting the right answers for the right reasons. Deviations from correctness should be understood at a deep level. Part of putting everything in proper context is uncertainty quantification (UQ). UQ is part of V&V. Unfortunately UQ has replaced V&V in much of the computational science community, and UQ estimated is genuinely incomplete. Now in HPC most of UQ has been replaced by misguided overconfidence.

This issue is another view of the dynamic where we have allowed alternative facts to displace reality. We are paving the road for a reality where bullshit and facts cannot be separated. It is everyone’s fault for allowing this to happen. Too many of us simply comply with the need for declarative success when admission of failure would suit progress and truth far better. Too often the emphasis is placed on marketing and spin rather than the truth. In the process we have systematically undermined core principles of quality in every corner of life. Perception has been allowed to become more important that truth and reality. Into this vacuum propaganda quickly become the medium of discourse. We may be too far-gone to fix this, and reality will bite back in a viscous manner to restore balance. This restoration will probably be very painful to experience.

At the core of the problem with bullshit as a technical medium is a general lack of trust, and inability to accept outright failure as an outcome. This combination forms the basis for bullshit and alternative facts becoming accepted within society writ large. When people are sure they will be punished for the truth, you get lies, and finely packaged lies are bullshit. If you want the truth you need to accept it and today, the truth can get you skewered. The same principle holds for the acceptance of failure. Failures are viewed as scandals and not accepted. The flipside of this coin is the truth that failures are the fuel for progress. We need to fail to learn, if we are not failing, we are not learning. Instead of hiding, or bullshitting our way through in order to avoid being labeled failures, we avoid learning, and also corrode our foundational principles. We are locked in a tight downward spiral and all our institutions are under siege. Our political, scientific and intellectual elite are not respected because truth is not valued. False success and feeling good is acceptable as an alternative to reality. In this environment bullshit reigns supreme and being useful isn’t enough to be important.

Raise your quality standards as high as you can live with, avoid wasting your time on routine problems, and always try to work as closely as possible at the boundary of your abilities. Do this, because it is the only way of discovering how that boundary should be moved forward.

― Edsger W. Dijkstra

Honest differences are often a healthy sign of progress.

― Mahatma Gandhi

Last week I attended a rather large scientific meeting in Knoxville Tennessee. It was the kickoff meeting for the Exascale Computing Project. This is a relatively huge program ($250 million/year) and the talent present at the meeting was truly astounding, a veritable who’s who in computational science in the United States. This project is the crown jewel of the national strategy to retain (or recapture) pre-eminence in high performance computing. Such a meeting has all the makings for banquet of inspiration, and intellectually thought-provoking discussions along with incredible energy. Simply meeting all of these great scientists, many of whom also happen to be wonderful friends only added to the potential. While friends abounded and acquaintances were made or rekindled, this was the high point of the week. The wealth of inspiration and intellectual discourse possible was quenched by bureaucratic imperatives leaving the meeting a barren and lifeless launch of a soulless project.

The telltale signs of worry were all present in the lead up to the meeting: management of work took priority over the work itself, many traditional areas of accomplishment are simply ignored, political concerns swamping technical ones, and most damningly no aspirational vision. The meeting did nothing to dampen or dispel these signs, and we see a program spiraling toward outright crisis. Among the issues hampering the project is the degree of project management formality being applied, which is appropriate for a benign construction projects and completely inappropriate for HPC success. The demands of the management formality was delivered to the audience much like the wasteful prep work for standardized testing in our public schools. It will almost certainly have the same mediocrity inducing impact as that same testing regime, the illusion of progress and success where none actually exists. The misapplication of this management formality is likely to provide a merciful deathblow to this wounded mutant of a program. Some point in the next couple of years we will see the euthanized project as being the subject of a mercy killing.

There can be no progress without head-on confrontation.

― Christopher Hitchens

The depth of the vision problem in high performance computing (HPC) is massive. For a quarter of a billion dollars a year, one might expect an expressive and expansive vision for a future to be at the helm of the project. Instead the vision is a stale and spent version of the same approach taken in HPC for the past quarter of a Century. ECP simply has nothing new to offer. The vision of computing for the future is the vision of the past. A quarter of a century ago the stockpile stewardship program came to being in the United States and the lynchpin of the program was HPC. New massively parallel computers would unleash their power and tame our understanding of reality. All that was needed then was some faster computers and reality would submit to the power of computation. Today’s vision is exactly the same except the power of the computers is 1000 times greater than the computers that would unlock the secrets of the universe a quarter of a century ago. Aside from the Exascale replacing Petascale in computing power, the vision of 25 years ago is identical to today’s vision. The problem then as now is the incompleteness of the vision and fatal flaws in how it is executed. If one adds a management approach that is seemingly devised by Chinese spies to undermine the program’s productivity and morale, the outcome of ECP seems assured, failure. This wouldn’t be the glorious failure of putting your best foot forward seeking great things, but failure born of incompetence and almost malicious disregard for the talent at their disposal.

The biggest issue with the entire approach to HPC is evident in the room of scientists I sat with last week, the minds and talents of these talented people are not being engaged. Let’s be completely clear, the room was full of immense talent with many members of the National Academies present, yet no intellectual engagement to speak of. How can we succeed at something so massive and difficult while the voices of those paid to work on the project are silenced? At the same time we are failing to develop an entire generation of scientists with the holistic set of activities needed for successful HPC. The balance of technical activities needed for healthy useful HPC capability is simply unsupported and almost actively discouraged. We are effectively hollowing out an entire generation of applied mathematicians, computational engineers and physicists pushing them to focus more on software engineering than their primary disiplines. Today someone working in applied mathematics is more likely to focus on object oriented constructs in C++ than functional analysis. Moreover the software is acting as a straightjacket for the mathematics slowly suffocating actual mathematical investigations. We see important applied mathematical work avoided because software interfaces and assumptions are incompatible. One of the key aspects of ECP is the drive for everything to be expressed in software as products and our raison d’être. We’ve lost the balance of software as a necessary element in checking the utility of mathematics. We now have software in ascendency, and mathematics as a mere afterthought. Seeing this unfold with the arrayed talents on display in Knoxville last week felt absolutely and utterly tragic. Key scientific questions that the vitality of scientific computing absolutely hinge upon are left hanging without attention and progress on them is almost actively discouraged.

When people don’t express themselves, they die one piece at a time.

—  Laurie Halse Anderson

At the core of this tragedy is a fatally flawed vision of where we are going as a community. It was flawed 25 years ago, and we have failed to learn from the plainly obvious lessons. The original vision of computer power uber alles is technically and scientifically flawed, but financially viable. This is the core of the problem as dysfunction; we can get a flawed program funded and that is all we need to go forward. No leadership asserts itself to steer the program toward technical vitality. The flawed vision brings in money and money is all we need to do things. This gets to the core of so many problems as money becomes the sole source of legitimacy, correctness and value. We have lost the ability to lead by principle, and make hard choices. Instead the baser instincts hold sway looking only to provide the support for little empires that rule nothing.

First, we should outline the deep flaws in the current HPC push. The ECP program is about one thing, computer hardware. The issue a quarter of a century ago is the same as it is today; the hardware alone does not solve problems or endow us with capability. It is a single element in our overall ability to solve problems. I’ve argued many times that it is far from being the most important element, and may be one of the lesser capabilities to support. The item of greatest importance are the models of reality we solve, followed bythe methods used to solve these models. Much of the enabling efficiency of solution is found in innovative algorithms. The key to this discussion is the subtext that these three most important elements in the HPC ecosystem are unsupported and minimized in priority by ECP. The focal point on hardware arises from two elements, the easier path to funding, and the fandom of hardware among the HPC cognoscenti.

We would be so much better off if the current programs took a decisive break with the past, and looked to move HPC in a different direction. In a deep and abiding way the computer industry has transformed in the last decade by the power of mobile computing. We have seen cellphones become the dominant factor in the industry. Innovative applications and pervasive connectivity has become the source of value and power. A vision of HPC that resonates with the direction of the broader industry would benefit from the flywheel effect instead of running counter to direction. Instead of building on this base, the HPC world remains tethered to the mainframe era long gone everywhere else. Moreover HPC remains in this mode even as the laws of physics conspire against it, and efforts suffer from terrible side effects of the difficulty in making progress in the outdated approach being taken. The hardware is acting to further tax every effort in HPC making the already threadbare support untenably shallow.

Instead of focusing on producing another class of outdated lumbering dinosaur mainframes, the HPC effort could leap onto clear industry trends and seek a bold resonant path. A combination of cloud based resources, coupled with connectivity could unleash ubiquitous computing and seamless integration with mobile computing forces. The ability to communicate works wonders for combining ideas and pushing innovation ahead would do more to advance science than almost any amount of computing power conceivable. Mobile computing is focused on general-purpose use, but hardly optimized for scientific use, which brings different dynamics. Specific effort to energize science through different computing dynamics could provide boundless progress. Instead of trying something distinct and new, we head back to a mine that has long since born its greatest ore.

Progress in science is one of the most fertile engines for advancing the state of humanity. The United States with its wealth and diversity has been a leading light in progress globally. A combination of our political climate and innate limits in the American mindset seem to be conspiring to undo this engine of progress. Looking at the ECP program as a microcosm of the American experience is instructive. The overt control of all activities is suggestive of the pervasive lack of trust in our society. This lack of trust is paired with deep fear of scandal and more demands for control. Working in almost unison with these twin engines of destruction is the lack of respect for human capital in general, which is only made more tragic when one realizes the magnitude of the talent being wasted. Instead of trust and faith in the arrayed talent of the individuals being funded by the program, we are going to undermine all their efforts with doubt, fear and marginalization. The active role of bullshit in defining success allows the disregard for talent to go unnoticed (think bullshit and alternative facts as brothers).

Progress in science should always be an imperative of the highest order for our research. When progress is obviously constrained and defined with strict boundaries as we are seeing with HPC, the term malpractice should come to mind. One of the clearest elements of HPC is a focus upon management and strict project controls. Instead I see the hallmarks of mismanagement in the failure to engage and harness the talents, capabilities and potential of the human resource available to them. Proper and able management of the people working on the project would harness and channel their efforts productively. Better yet, it would inspire and enable these talented individuals to innovate and discover new things that might power a brighter future for all of us. Instead we see the rule of fear, and limitations governing people’s actions. Instead we see an ever-tightening leash placed around people’s neck suffocating their ability to perform at their best. This is the core of the unfolding research tragedy that is doubtlessly playing out across a myriad of programs far beyond the small-scale tragedy unfolding with HPC.

We can only see a short distance ahead, but we can see plenty there that needs to be done.

― Alan Turing

Fear is the mind-killer.

― Frank Herbert

If one wants to understand fear and how it can destroy competence and achievement take a look at (American) football. How many times have you seen a team undone during the two minute drill? A team who has been dominating the other team defensively suddenly becomes porous when it switches to the prevent defense, it is a strategy born out of fear. They stop doing what works, but is risking and takes a safety first approach. It happens over and over providing the Madden quip that the only thing the prevent defense prevents is victory. It is a perfect metaphor for how fear plays out in society.

Fear is a rather enormous player in societal decision-making. In playing an over sized role fear provides a massive drain on everything we do ultimately costing us more than we can possibly estimate. Fear produces actions that work steadfastly to undermine every single productive bit of work we might do. Fear drives decisions that cause everything we do to be more expensive. Fear costs us time. Fear destroys trust. Fear undermines openness. Fear enslaves us to a pessimistic life always looking for disaster. In the end fear will keep us from succeeding at making the world better. Fear is making the world worse.

Over 80 years ago we had a leader, FDR, who chastened us against fear saying, “we have nothing to fear but fear itself”. Today we have leaders who embrace fear as a prime motivator in almost every single public policy decision. We have the cynical use of fear to gain power used across the globe. Fear is also a really powerful way to free money from governments too. Terrorism is both a powerful political tool for both those committing the terrorist acts, and the military-police-industrial complexes to retain their control over society. We see the rise of vast police states across the Western world fueled by irrational fears of terrorism.

If you want to control someone, all you have to do is to make them feel afraid.

― Paulo Coelho

Fear also keeps people from taking risks. Many people decide not to travel because of fears associated with terrorism, among other things. Fear plays a more subtle role in work. If failure becomes unacceptable, fear will keep people from taking on difficult work, and focus on easier, low-risk work. This ultimately undermines our ability to achieve great things. If one does not focus on attempting to achieve great things, the great things simply will not happen. We are all poorer for it. Fear is ultimately the victory of small-minded limited thinking over hope and abundance of a better future. Instead of attacking the future with gusto and optimism, fear pushes us to contact to the past and turn our backs on progress.

One of the huge downsides to fear-based decision-making is shutting down communication. Good communication is based on trust. Fear is the absence of trust. People are afraid of ideas, and afraid to share their ideas or information with others. As Google amply demonstrates, knowledge is power. Fear keeps people form sharing information and leader to an overall diminishment in power. Information if held closely will produce control, but control of a smaller pie. Free information makes the pie bigger, creates abundance, but people are afraid of this. For example a lot of information is viewed as dangerous and held closely leading to things like classification. This is necessary, but also prone to horrible abuse.

Power does not corrupt. Fear corrupts… perhaps the fear of a loss of power.

― John Steinbeck

A big part of the abuse is retention of power, and used to enhance the power of those holding the power. The issue with this information control is how it inhibits people from working on things that have the greatest value, or simply working allowing people to work on things that others already know don’t work. It keeps people from building productively on the knowledge that others possess. In this and a myriad of other ways the control and failure to share information leads to a diminished future devoid of the potential freedom offers.

He who has overcome his fears will truly be free.

― Aristotle

There are very few truly unique, new ideas. Instead new things and new ideas arise from combining old ideas in new ways or for new purposes. With more ideas on the table and available, the possibilities and discoveries are great and more varied. The entirety of human experience and technology is based on the sharing of information, the combination of old existing ideas over and over. Just as the printing press created the sharing of knowledge and an explosion of creativity, the Internet is doing the same thing today. It can be a force for good and freedom. It can also be a force of evil and chaos as we have seen unfolding in the events of the World. Our job should be make sure that we actively work to make sure information can be harnessed as an agent for good. Fear when added to mix becomes a direct and powerful force for pushing us toward evil and chaos.

Another aspect of modern life and the availability of information is the ever-present worry of scandal and the implications of being part of it. Spurring this fear-based environment is the use of scandal as a political tool and the chaos scandal produces. There are fears of audits and unwanted attention driving decision-making and pushing all sorts of costs. All of this is driven by a general lack of trust across society and the rise of fear as a motivating factor. Instead of being focused on progress and achievement, we see fear of loss and terror at the prospect of scandal forming the basis of decisions. This is captured in the oft-heard comment, “I don’t want to see this featured on the front page of the New York Times.” To avoid this possibility we incur massive costs and horrible performance penalties. The bottom line is that fear is inhibiting our ability to create a better, richer and more abundant future.

Most people do not really want freedom, because freedom involves responsibility, and most people are frightened of responsibility.

― Sigmund Freud

Fear is used because fear works. Fear has become a powerful tool that political forces use to push their agenda, or attack their enemies. The most evident fear-based vehicle is terrorism, which our governments make much more powerful through channeling the fear to support the creation of large pervasive police-surveillance state. Instead of defeating terror, the state amplifies the impact of terror, terrorizes the populace, and becomes the source of terror itself. The greatest weapon against terror is to not be terrorized. Courage and bravery in the face of terror is the cure. Our reaction to terrorism gives it all of its power. By our fearful reaction we insure that more terror will bred out of our fearful reaction to it. This principle is broadly applicable. Our reactions to fear empower the fears and allow them to shape our lives. To overcome fear, we must cease to be afraid. We must be led to not fall prey to fear. Instead we are led to be afraid, and amplify our fears as a means of subservience.

Without leadership rejecting fear too many people simply give into it. Today leaders do not reject fear; they embrace it; they use it for their purposes, and amplify their power. It is easy to do because fear engages people’s animal core and it is prone to cynical manipulation. This fear paralyzes us and makes us weak. Fear is expensive, and slow. Fear is starving the efforts society could be making to make a better future. Progress and the hope of a better future rests squarely on our courage and bravery in the face of fear and the rejection of it as the organizing principle for our civilization.

Our enemy is not terror, it is losing our soul while fighting terror.

— Jeff Lawson

And one has to understand that braveness is not the absence of fear but rather the strength to keep on going forward despite the fear.

― Paulo Coelho