tl;dr

The practice of verification is absolutely essential for modeling and simulation quality. Yet, verification is not a priority; quality is not a priority. It is ignored by scientific research. This is because verification is disconnected from modeling. Also, it is not a part of active research. The true value of verification is far greater than simple code correctness. With verification, you can measure the error in the solution with precision. Given this, the efficiency of simulations can be measured accurately (efficiency equaling effort for given accuracy). Additionally, the resolution required for computing features can be estimated. Both of these additions to verification connect to the broader scientific enterprise of simulation and modeling. This can revitalize verification as a valued scientific activity.

“Never underestimate the big importance of small things” ― Matt Haig

The Value of Verification

“Two wrongs don’t make a right, but they make a good excuse.” ― Thomas Szasz

Conceptually, verification is a simple prospect. It has two parts; is a model correct in code and how correct is it? Verification is structured to answer this question. Part one about model correctness is called code verification. Part two is about accuracy called solution verification. This structure is simple and unfortunately lacks practical priority. This leads to the activity being largely ignored by science and engineering. It shouldn’t be, but it is. I’ve seen the evidence in scientific proposals. V&V is about evidence and paying attention to it. There is a need to change the underlying narrative around verification.

Under the current definition, code verification relies upon determining the order of accuracy for correctness. There is nothing wrong with this. The order of accuracy should match the design of the code (method) for correctness. This is connected to the fundamental premise of advanced computers. More computing leads to better answers, This is the process of convergence where solutions get closer to exact. This produces better accuracy. Today this premise is simply assumed and evidence of it is not sought. Reality is rarely that simple. Solution verification happens when you are modeling and do not have access to an exact solution. It is a process to estimate the numerical error. These two things complement each other.

“The body of science is not, as it is sometimes thought, a huge coherent mass of facts, neatly arranged in sequence, each one attached to the next by a logical string. In truth, whenever we discover a new fact it involves the elimination of old ones. We are always, as it turns out, fundamentally in error.” — Lewis Thomas

In code verification, you can also computer the error precisely. The focus on the order of convergence dims the attention to error. Yet the issue of error in science is primal. Conversely, the focus on errors in solution verification dims the order of convergence there. In doing work with verification both metrics need to be focused upon equally. Ultimately, the order of accuracy and diminishing of error should be emphasized.

As will be discussed later, the order of accuracy influences the efficiency mightily. A broad observation from my practical experience is that the order of accuracy in application modeling is low. It is lower than expected in theory. It is lower than the method designed going into codes. Thus, the order of convergence actually governs the efficiency of numerical modeling. This is combined with the error to determine the efficiency of the simulation.

“The game of science is, in principle, without end. He who decides one day that scientific statements do not call for any further test and that they can be regarded as finally verified, retires from the game.” — Karl Popper

Why Verification is Broken?

Verification is broken because it is disconnected from science. It has been structured to be irrelevant. In reviewing more than 100 proposals in modeling and simulation over a few years this is obvious. Verification as an activity is beneath mentioning. When it is mentioned it is out of duty. It is simply in the proposal call and mentioned because it is expected. There is little or no earnest interest or effort. Thus the view of the broader community is that verification is an empty activity, not worth doing. It is done out of duty, but not out of free will.

I should have seen this coming.

“True ignorance is not the absence of knowledge, but the refusal to acquire it.” — Karl Popper

Back in the mid-oughts (like saying that!) I was trying to advance methods for solving hyperbolic conservation laws. I had some ideas about overcoming the limitations of existing methods. In doing this work it was important to precisely measure the impact of my methods compared with existing methods. Verification is the way to do this. I highlighted this in the paper. The response to the community via the review process was negative,… very negative,… very fucking negative.

In the end, I had to remove the material to get the paper published. I got a blunt message from an associate editor, “if you want the paper published, get that shit out of your paper”. By “shit” they meant the content related to verification. I’ll also say this is someone I know personally, so the familiarity in the conversation is normal. Even worse this comes from someone with a distinguished applied math background with a great record of achievement. You find that most of the community despises verification.

I will note in passing that this person’s work actually does very well in verification. In another paper, I confirmed this. For more practical realistic problems it does far better than a more popular method. It would actually benefit greatly from what I propose below. What had become the publication standard was a purely qualitative measure of accuracy for the calculations that matter. Honestly, this attitude is stupid and shameful. It is also the standard that exists. As I will elaborate shortly, this is a massive missed opportunity. It is counter-productive to progress and adoption of better methods.

I found this situation to be utterly infuriating. It was deeply troubling to me too. When I stepped back to look at my own career path I realized the nexus of the problem. Back in the 1990’s I got into verification. I used verification to check the correctness of the code I wrote, but it was not the real value. I used verification to measure the efficiency and errors in the methods I developed. I used it to measure the error in the modeling I pursued. The direct measure of error and its comparison to alternatives was the reason I did it. It provides direct and immediate feedback on method development. These notions are absent from the verification narrative. Measuring and reducing errors is one of the core activities of science. It is the right way to conduct science.

Verification needs to embrace this narrative for it to have an impact.

How to Fix Verification?

“I ask you to believe nothing that you cannot verify for yourself.” — G.I. Gurdjieff

As noted above, the key to fixing verification is to keep both orders of accuracy and numerical accuracy in mind. This is true for both code and solution verification. The second part of the fix is expanding the utility of verification. Verification can measure the efficiency of methods. What I mean by efficiency requires a bit of explanation. The first thing is to define efficiency.

Simply put, efficiency is the amount of computational resources used to achieve a certain degree of accuracy. The resource would be defined by mesh size and number of time steps (degrees of freedom). The algorithm used to solve the problem would combine to define the amount of computer memory and a number of operations to use. Less is obviously better. Runtime for a code is a good proxy for this. Lower accuracy is also better. The convergence rate defines the relationship between the amount of effort and accuracy. The product of these two defines the efficiency. Lower is better for this composite metric.

The first time I published something that exposed this was with Jeff Greenough. We compared two popular methods on a set of problems. One was the piecewise linear method using Colella’s improvements (fourth-order slopes). It was a second-order method. The second method is the very popular Weighted ENO method, which is fifth-oder in space and third-order in time. Both of these methods are designed to solve shock wave problems. One might think that the fifth-order method should win every time. This is true if you’re solving a problem where this accuracy matters. The issue is that all the applications of these methods are limited to first-order at best.

“Science replaces private prejudice with public, verifiable evidence.” — Richard Dawkins

This is where the accepted practice breaks down. When Gary Sod published his test problem and method comparison the only metric was runtime. Despite having an analytical solution, no error was measured. Results for the Sod shock tube problem are always qualitative. Early on, results were bad enough that qualitative mattered. Today all the results are qualitatively good and vastly better than nearly 50 years ago. This is accepted practice and implies that there is no difference quantitively. This is objectively false. At a given mesh resolution the error differences are significant. I showed this with Jeff for two “good” methods. As I will further amplify in what follows, at the lower convergence rates in shocks the level of error means vast differences in efficiency. This is true in 1-D and becomes massive in 3-D (really 2-D and 4-D when you add in time).

“The first principle is that you must not fool yourself and you are the easiest person to fool.” — Richard P. Feynman

It turns out that the 5th-order WENO method is about six times as expensive as the 2nd-order PLM scheme. This was true of the desktop computers of 2004. It is close to the same now. The WENO method might have better computational intensity and have advantages on modern GPUs. What we discovered was that the second-order method produced half the error of the WENO method on simple problems (Sod’s shock tube). Thus the WENO method didn’t really pay off. At first-order convergence, this would mean that WENO would need about 24 times the effort to match the accuracy of PLM. For problems with more structure, the situation gets marginally better for WENO. In terms of efficiency, WENO never catches up with PLM, ever. As we will shortly see in 3-D the comparison is even worse. The cost of refining the mesh is much more costly and the accuracy advantage grows.

“Whatever you do will be insignificant, but it is very important that you do it.” ― Mahatma Gandhi

If This is Done, Verification’s Value Skyrockets

“The truth is rarely pure and never simple.” ― Oscar Wilde

Let’s consider a simple example to explain. Consider a method that is twice as expensive and twice as accurate as another method. The methods produce the same order of convergence. The order of convergence matters a great deal in determining efficiency. Consider three-dimensional time-dependent calculations. If the methods are fourth-order accuracy there is a break even. For any lower order of convergence the higher cost, more accurate method wins. The lower the order of convergence the greater the difference. For first-order the advantage is a factor of eight. By the time you drop to half-order convergence, the advantage grows to 128 times.

This example provides a powerful punchline to efficiency. If the order of accuracy is fixed, the level of accuracy makes a huge difference in efficiency. This points to the power of both algorithms and verification in demonstrating the metrics. It is absolutely essential for verification to amplify its impact on science.

“It’s easy to attack and destroy an act of creation. It’s a lot more difficult to perform one.” — Chuck Palahniuk

For problems solving hyperbolic PDEs, the convergence rates are well defined by theory. For the nonlinear compressible structures, the rate is defined as first-order. For linear waves (that Lax defined as linearly degenerate) the convergence rate is less than one. Thus the impact of accuracy is greater. In my experience, first-order accuracy is optimistic for practical application problems. Invariably the accuracy for practical problems codes are applied to are low order. Thus the accuracy for smooth problems where code verification is done has little relevance. This can show that the method is correct as derived, but not relate to the method’s use.

Code verification needs to focus on results that relate more directly to how methods are used practically. This is a challenge that needs focused research. Rather than a check done before use practically, code verification needs utility in the practical use. Today this is largely absent. This must change.

The other great use is the study of efficiency. With Moore’s law, dead and buried algorithms are the path to computational progress. In addition, the use of verification is needed for the expanding use of machine learning (ML, the techniques used for artificial intelligence). The greatest gaps for ML are the absence of theory to support verification. This is closely followed by a lack of accepted practice. Again, this supports algorithm development, which is the path to progress when computing and data are limited.

“The important thing is not to stop questioning. Curiosity has its own reason for existing.” ― Albert Einstein

Greenough, J. A., and W. J. Rider. “A quantitative comparison of numerical methods for the compressible Euler equations: fifth-order WENO and piecewise-linear Godunov.” Journal of Computational Physics 196, no. 1 (2004): 259-281.

Rider, William J., Jeffrey A. Greenough, and James R. Kamm. “Accurate monotonicity-and extrema-preserving methods through adaptive nonlinear hybridizations.” Journal of Computational Physics 225, no. 2 (2007): 1827-1848.

Sod, Gary A. “A survey of several finite difference methods for systems of nonlinear hyperbolic conservation laws.” Journal of computational physics 27, no. 1 (1978): 1-31.

Colella, Phillip. “A direct Eulerian MUSCL scheme for gas dynamics.” SIAM Journal on Scientific and Statistical Computing 6, no. 1 (1985): 104-117.

Jiang, Guang-Shan, and Chi-Wang Shu. “Efficient implementation of weighted ENO schemes.” Journal of computational physics 126, no. 1 (1996): 202-228.

Majda, Andrew, and Stanley Osher. “Propagation of error into regions of smoothness for accurate difference approximations to hyperbolic equations.” Communications on Pure and Applied Mathematics 30, no. 6 (1977): 671-705.

Banks, Jeffrey W., T. Aslam, and William J. Rider. “On sub-linear convergence for linearly degenerate waves in capturing schemes.” Journal of Computational Physics 227, no. 14 (2008): 6985-7002.

tl;dr

A faster computer is always a good thing. It is not the best way to get faster or better results. A better program (or method or algorithm) is as important, if not more so. A new algorithm can be transformational and create new value. The faster computer also depends on a correct program, which isn’t a foregone conclusion. Demonstrating that things are done right is also difficult. Technically, this is called verification. Here, we get at the challenges of doing verification across the computing landscape. This is especially true as machine learning (AI) grows in importance where verification is not possible today.

“The man of science has learned to believe in justification, not by faith, but by verification.” ― Thomas H. Huxley

The Basic Premise of Better Computing

All of us have experienced the joy of a faster computer. We buy a new laptop and it responds far better than the old one. Faster internet is similar and all of a sudden streaming is routine and painless. If your phone has more memory you can more freely shoot pictures and video at the highest resolution. At the same time, the new computer can bring problems we all recognize. Often our software does not move smoothly over to the new computer. Sometimes a beloved program is incompatible with the new computer and a new one must be adopted. This sort of change is difficult whenever it is encountered.

Each of these commonplace things has a parallel issue in the more technical professional computing world. There are places where proving the improvement from the new computer is difficult. In some cases, it is so difficult that it is actually an article of faith, not science. It is in these areas where science needs to step up and provide means to prove. My broad observation that the faster, bigger computer as a good thing is largely an article of faith. Without the means to prove it, we are left to believe. Belief is not science or reliable. By and large, we are not doing the necessary work to make this a fact. This means recognizing where the gaps result in the faith is being applied dangerously.

A more critical issue is the recession of algorithmic progress. As we struggle to have faster computers as in the past, algorithms are the means to progress. Instead, we have doubled down on computers as they become worse paths to progress. This is just plain stupid. Algorithmic progress requires different strategies for adopting risky failure-prone research. Progress in algorithms occurs in leaps and bounds after long fallow periods. It also requires investments in thinking particularly in mathematics.

“If I had asked people what they wanted, they would have said faster horses.” ― Henry Ford

Verification in Classical Computational Science

“Trust, but verify.” ― Felix Edmundovich Dzerzhinsky

Where this situation is the clearest is traditional computational science. In areas where computers are employed to solve science problems classically, the issues are well known. To a large extent, mathematical foundations are firmly established and employed. The math is a springboard for progress. A long and storied track record of achievement exists to provide examples. For the most part, this area drove early advances in computing and laid the groundwork for today’s computational wonders. For most of the history of computing, scientific computing drove all the advances. All of it is built on a solid foundation of mathematics and domain science. Today progress lacks these advantages.

In no area was the advance more powerful than the solution of (partial) differential equations. This was the original killer app. Computers were employed to design nuclear weapons, understand the weather, simulate complex materials, and more. These tasks produced the will to create generations of supercomputers. It also drove the creation of programming languages and operating systems. Eventually, computers leaked out to be used for business purposes. Tasks such as accounting were obvious along with related business systems. Still scientific computing was the vanguard. It is useful to examine its foundations. More importantly, it is useful to see where the foundations in other areas are weak. We have a history of success to guide our path ahead.

The impact of computing on society today is huge and powerful It forms the basis of powerful businesses. The incredible run-up of the stock market is all computing. The promise of artificial intelligence is driving recent advances. Most of this is built on a solid technical foundation. In key areas of progress, the objective truth of improvements is flimsy. This is not good. We are ignoring history. In the long run, we are threatening the sustainability of progress and economic success. We need sustained strategic investment in mathematical foundations and algorithmic research. If not, we put the entire field at extreme risk.

If one goes back to the origins of computational science, the use of it showed promise first. First in application to nuclear weapons then rapidly with weather and climate. Based on this success the computers were advanced as the technology was refined. As these efforts began to yield progress mathematics joined. One of the key bits of theoretical work was conditions for proper numerical solutions of models. Chief among this theory was the equivalence theorem by Peter Lax (along with Robert Richymyer). This theorem established conditions for the convergence of solutions to the exact solution of models. Convergence means that as more computing is applied, the solution gets closer to exact.

This is the theoretical justification for more computing. More computing power produces more accuracy. This is a pretty basic assumption made with computing, but it does not come for free. To get convergence the methods must do things correctly. In the same breath, the theory of how to do things better as well. Just as importantly, the theorem gives us guidance on how to check for correctness. This is the foundation for the practice of verification.

In verification, we can do many things. In its simplest form, we get evidence of the correctness of the method. We can get evidence that the method is implemented and provides the accuracy advertised. This is essential for trustworthy, credible computational results. With these guarantees in place, the work done with computational science can be used with confidence. This confidence then allows it to be invested in and trusted. The verification and theory provided confident means to improve methods and measure the impact. For 70 years this has been a guiding light for computational science.

We should be paying attention to its importance moving forward. We are not.

Machine Learning and Artificial Intelligence Are An Issue

“If people do not believe that mathematics is simple, it is only because they do not realize how complicated life is.” ― John von Neumann

More recently, the promise of artificial intelligence (AI) has grabbed the headlines. Actually, the technical foundation for AI is machine learning (ML). The breakthrough of generative AI with Large Language Models (LLMs) has rightly captured the world’s imagination and interest. A combination of algorithm (method) advances with high-end computing, powers LLMs. These LLMs are one of the strongest driving forces in the World economically. Their power is founded partly on the fruits of computational science discussed above. This includes computers, software, and algorithms. Unfortunately, the history of success is not being paid sufficient attention to.

The current developments and investments in AI/ML are focused on computers (Big Iron following the exascale program’s emphasis). Secondarily software is given much attention. Missing investments and attention are algorithms and applied math. We seem to have lost the ability to provide focus and attention on laying the ground for algorithm advances. A key driver for algorithmic advances is applied mathematics where the theory guides practice.

For the formative years of computational science applied math gave key guidance. Theoretical advances and knowledge are essential to progress. Today that experience seems to be on the verge of being forgotten. The irony is that the LLM breakthrough in the past few years was dominated by algorithmic innovation. This is the transformer architecture. The attention mechanism is responsible for the phase transition in performance. It is what produced the amazing LLM results that grabbed everyone’s notice. Investments in mathematics could provide avenues for the next advance.

What is missing today is much of the mathematical theory driving credibility and trustworthy methods.

One of the essential aspects of computational science is the concept of convergence. Convergence means more computation yields better results. Mathematics provides the theory underpinning this idea. The process to demonstrate this is known as verification. In verification, convergence is used to prove the correctness and accuracy of algorithms. One of the biggest problems for AI/ML is the lack of theory. This problem is a lack of rigor. Thus the process of verification is not available. Furthermore, the understanding of accuracy for AI/ML is similarly threadbare. Investments and focus to fill these gaps is needed and long overdue.

One of the problems is that this research is extremely difficult and success is not guaranteed. It is likely to be failure-prone and takes some time. Nonetheless, the stakes of not having such a theory are growing. Moreover, success would likely provide pathways for improving algorithms. Many essential ML methods are ill-behaved and perform erratically. Better mathematical theory involving convergence could pave the way for better ML. The theory tells us what works and how to structurally improve techniques. This is what happened in computational science. We should expect the same for AI/ML. Likely, this work would significantly improve trust in systems as well. The combination of trust, efficiency, and accuracy should sufficiently inspire investments. This is if a logical-rational policy was in place.

It is not. Either by the government or the private enterprise. We will all suffer for this lack of foresight.

“Today’s scientists have substituted mathematics for experiments, and they wander off through equation after equation, and eventually build a structure which has no relation to reality. ” ― Nikola Tesla

Algorithms Win and Verification Matters

Anyone who has read my writing knows that algorithms are a clear winning path for computing. Verification is the testing and measurement of algorithmic performance. If one is interested in better computing algorithmic verification is a vehicle for progress. Verification is about producing evidence of correctness and performance. This provides a concrete measurement of algorithmic performance, which can be an engine for progress. In a future without Moore’s law algorithms are the path to improvement.

“Pure mathematics is in its way the poetry of logical ideas.” ― Albert Einstein

As I’ve written before, algorithmic improvement is currently hampered by a lack of support. Some of this is funding and the rest is risk aversion. Algorithmic research is highly failure prone and progress is episodic. A great deal of tolerance for risk and failure is necessary for algorithm advances. All of this can benefit from a focused verification effort. This can measure the impact of work and provide immediate feedback. The mathematical expectations underpinning verification can also provide the basis for improvements. This math provides focus and inspiration for work.

“We can only see a short distance ahead, but we can see plenty there that needs to be done.” ― Alan Turing

tl;dr

There is a fundamental problem with leadership in the USA. They don’t seem to exist. The supposed national leaders like Trump and Musk aren’t. They destroy in the name of greed and corruption. They pose as leaders. Our supposed leaders aren’t honest or direct with people. In their own way, all of them lie to us. It is time to have leaders who deal with objective reality and fix problems. People are hungry for real leaders, and current events will only fuel that hunger.

“The challenge of leadership is to be strong, but not rude; be kind, but not weak; be bold, but not bully; be thoughtful, but not lazy; be humble, but not timid; be proud, but not arrogant; have humor, but without folly.” ― Jim Rohn

The Problem

I had an entirely different topic teed up for the week. I was thinking about the balance between nostalgia and progress. Then the week happened. I needed to address the rampaging elephant in the room. We have a “leader” who is destroying our Nation through an avalanche of stupidity, raw power, and greed. On the other hand, I was locally offered an example of how current leadership fails. We get more toxic positivity and failure to directly address reality. Instead, we get a filter where only positive things are spoken openly.

“He who cannot be a good follower cannot be a good leader.” ― Aristotle

I always worry when I’m writing from the heart and my heart is angry. I am definitely angry these days. This anger comes from the event we all see unfolding in the news. Trump and Musk are trampling the Constitution with impunity. The USA may not be a democracy in weeks if it still is. Our government is being fed into the chipper shredder. Cruelty, stupidity, ignorance, and incompetence are on display almost beyond bounds. I am also angry at the impotence of the Democrats in the face of this. I am angry at the cowardice and meekness of my work leaders. How anyone with a working brain can see business as usual today is beyond imagination. Yet, this is what I see. Work is just business as usual while a Category 5 Political-Hurricane is making landfall. Disaster and catastrophe are upon us.

It is very clear that most voters are fed up with the Nation’s direction. A clear majority thinks things are broken. I am among them. The whole secret to Trump’s success is that he is different. To many he seems strong and challenges all our norms. This is less about Trump’s qualities than the failures of existing leadership. Enough voters simply ignored his legion of faults and put him in charge. They ignored his endless lies and corruption along with a lifetime of criminal behavior. The reason they did this is a scathing indictment of our pathetic leadership class. A reality TV show star is seen as being better.

“Never confuse movement with action.” ― Ernest Hemingway

The Problem Up Close

“Failure doesn’t define you. It’s what you do after you fail that determines whether you are a leader or a waste of perfectly good air.” ― Sabaa Tahir

I got to see another example of how pathetic our institution’s leaders are recently. We had a great internal workshop that brought together one of our big programs. It was great to see the work and all the people. The program has many wonderful, smart, and talented people. The problem is that our leadership is pretty standard these days. Therefore they are shitty and weak. There is little honesty or courage to be seen. It mirrors all the issues responsible for putting Trump in office. It is worth taking a look at why. Once the carnage that Trump and Musk transpires we might get to rebuild something to be proud of. That is if we have leaders worth a shit.

Why is the leadership so shitty? One way of pointing to it is a habit of practicing toxic positivity. Basically, profound and obvious problems are simply ignored in public. The public face is unremittingly positive and any voice that speaks reality is refuted. You cannot talk about problems openly. If you do, you are criticized. The end result is a lack of credibility for the leadership. Whenever they talk about something you really know about, you see the lies. You see the omissions of problems. If you know the ground truth, the gaps with reality are vast. I remember talking to someone at a class I took who told me a story. They had an all-hands meeting where a certain program was lauded. In the afternoon that program was cancelled. Management credibility was too.

They are bullshitting people or they aren’t competent enough to see actual problems. I’m not sure what is worse?

I asked a specific question about an important topic, artificial intelligence. I pointed out that security issues have undermined all the greatest breakthroughs in information technology locally. Search is the primal example from the late 90’s. When Google came out and introduced it, their search was an epiphany. The internet opened up and became something magical. Conversely, our internal search sucks. It sucks because it can’t see the data that would make it work well. We’ve also blown the benefits of smartphones and social media. I wondered “Are we going to blow it with AI too?” It is a reasonable question. Given their track record, we should assume they will fuck this up too. They will certainly fuck this up if they don’t admit the faults of the past.

Yes, I politely asked this without any expletives. I’ll note in passing that this is another way Trump’s norm-breaking signals that he’s different.

Our leader’s response was “Well that was harsh”.

It’s amazing how incapable of taking any critique they are. If they were actual leaders this wouldn’t happen. Rather than welcome someone being honest and direct, they deflect. These “leaders” are more interested in making everything sound good and in control. This was followed by the usual chickenshit bullshit answer too. They’re always on top of things, yet things never get fixed. So, it was another plea for new leadership. It is another reason to think they’ll never make things better. In summary, these attitudes render capable people completely incompetent and unfit to lead. Our culture is prizing toxic positivity, and the comfort of institutions over actual leadership.

Suddenly, I looked at what was happening to the Nation and it became clear. The absolute failure of our usual leaders paved the way for our budding dictatorship. It is on the verge of triggering a Constitutional crisis that will end the USA as a functioning democracy. All because our leaders won’t lead and won’t deal with a reality teeming with problems.

The Solution is a Bigger Problem

“The reasonable man adapts himself to the world: the unreasonable one persists in trying to adapt the world to himself. Therefore all progress depends on the unreasonable man.” ― George Bernard Shaw

Because we have lived with these pathetic leaders for decades, we now sit at the edge of catastrophe. For years people have increasingly hungered for change and actual leaders. This has laid the groundwork for the horrible choice Americans made recently. People are so hungry for leadership that they chose a fake reality show CEO as President. Worse yet, someone devoid of deep ideas with an endless appetite for corruption, law-breaking, and cruelty. Instead of finding brave competent leaders who set an example and solve problems, we have the opposite. The cure is almost certain to be worse. We sit at the precipice of a catastrophe that may effectively destroy a nation.

This seems to be the desire for action and movement of any sort. Most of the leaders today simply cannot deal with the balance between good and bad news. Their collective approach of highlighting the good while ignoring the bad has fueled a slow steady decline. Problems are allowed to fester and grow without relief year after year. In my own institutions, I have witnessed the constant erosion year after year. People across the country see the same as a slow decline. We see the decline in our institutions and feel a growing malaise. It is this feeling and lack of action by traditional leaders that has brought us to the brink of disaster. It has become a habit and is accepted without complaint.

We have the problem of growing inequality and our leadership is drawn from those benefiting from it. They do not feel the weight of our collective problems. Their world is different from those they lead. The additional complication is that fixing things is really hard. The status quo is easy. Destroying things is also very easy. Since lazy greedy people have been in charge they take the easy route. The new easy route is destruction. Destruction is what we are going to get. For many Americans this destruction is going to look like action. This action will ultimately meet reality and that reality is going to be brutal.

Americans are well-to-do and generally prosperous. A big component of this is delivered by the stability of the governance. Without this stability we will start to see economic shocks and prosperity will begin to suffer. The tech base of the country is driven by science funded by the government. This is true of our computer-based tech and medical treatments. Without stability, the economic engines created will start to unravel. Without the government and stable infrastructure, the economy will begin to sputter. National security and defense provide safety and protection. This will begin to crumble too. All of this will begin to hurt people and support for the destruction will end. The better option would be to address problems carefully keeping stability and safety intact while fixes are pursued.

Reality-Based Leadership

What is the change in leadership needed?

We need leadership that deals squarely with objective reality. They need to stop thinking that they can wish cast reality into existence. Instead, they should step up and confront reality and challenge us to fix problems directly. This is the change we need. People are sick and tired of leaders who try to message success and the reality they want. Leaders should be honest and direct asking people to do the hard work and sacrifices to implement genuine repairs. For too long our leaders have pretended that things can fixed easily with minimal effort.

These leaders are rejected by institutions. They challenge the status quo and produce discomfort. For too long this has been rejected as unacceptable. Instead, the institutions should adopt a spirit of continuous improvement. Do not rest on past accomplishments, but seek new advances. If we were always seeking to be better, we might have avoided today’s crisis. Ultimately the thing missing is the sense of progress. The stagnation and decline are the seeds of destruction now being harvested. We need to have the opposite. If people know that things are being fixed and improvement is afoot they will go with that. Ignoring reality and wish casting success is not the path to stability.

“Management is doing things right; leadership is doing the right things.” ― Peter Drucker

tl;dr

There is no doubt that our government is wildly inefficient. The USA could benefit greatly from dramatic improvement. At the same time, virtually every function of government is necessary and desired. The government is the large-scale structure of order for humanity. Our government is inefficient primarily due to a lack of trust. This leads to many expensive checks on everything done. Changes by untrustworthy corrupt leadership will do little to fix anything. It will just make the underlying problems worse. In addition, the destruction of the government yields chaos and suffering. Instead, we need to fix government by getting at the core of mistrust.

The State of Things Today

“Efficiency is doing the thing right. Effectiveness is doing the right thing.” ― Peter F. Drucker

I work for the government and I can see gross inefficiency everywhere I look. The situation is dire and needs to be addressed. The work I do is itself inefficient and my support has a vast overhead that does little or nothing productive. Nothing would make me happier than seeing this improve dramatically.

Right now, the fight for government efficiency is being taken up by DOGE led by Elon Musk. I have no doubt that it will be a complete failure and disaster.

The reason why DOGE will fail is twofold. People like Trump and Musk are part of the reason the government is so inefficient. Much of what we do is related to corruption and greed. The second reason is their desire to simply remove government from business’ way. Regulation and government are a societal hedge against business irresponsibility toward society when profit is at stake. This will simply create destruction and in the long run power the very reasons government is so inefficient to new heights. We will just see the government gutted with corruption and greed supercharged. Of course, it is most likely that DOGE is a trojan horse for a huge executive power grab and assault on the separation of powers. Efficiency is simply a marketing label to make DOGE seem like a good idea to people who aren’t paying attention.

I work for a contractor doing government work supporting our National defense. We are a GOCO, government-owned contractor operated. As such we have to comply with all sorts of regulations, directives, and laws. We are incredibly inefficient. Many of our employees spend their effort executing low-value work defined by the government’s directives. Those directives are usually a combination of executive and legislative actions. In that sense, it is the will of the American people. A good bit of these directives directly impact me and the time I spend at work. These are a whole bunch of no-value actions that you must do. They add nothing to the science-engineering product I work for.

The key question is why do I have to do all this shit? Most of this shit has low value and detracts from my real efforts. In addition, this all costs a lot of money to pay “support” people. Time away from being productive is another drain on money. Together it equals government waste and lack of efficiency. Moreover, these burdens of time and money have grown without bound my entire career. When I examine the underlying reasons for virtually every inefficiency there is a common thread. All of this stuff is related to trust; or more specifically, a lack of it.

One of the big drains on my time is mandatory training. All the training is based on executing various regulations to show compliance. The training is generically horrible and takes a lot of energy to pay attention to. We have other processes like our timecard, our accounting, document control, and information classification that all cost a lot of time and money. Our purchasing process is extremely time and documentation-heavy. This costs extra money. When you examine the underlying thought process it all equals a lack of trust. It requires a mentality that anything we do needs to be checked. We need to make sure I don’t fuck up or do something wrong. My professional experience, judgment, and knowledge are suspect and useless. It is really simply a vast statement of lack of respect too.

I cannot be trusted to make decisions.

What is Government For?

Humans are social animals. Groups of humans working together is the sole reason for our success. Physically, humans are truly weak. Yes, we have great intellect, but most of it is useless without other people. If humans were just individuals they would be food, and likely gone extinct. At the largest scale, the ability for humans to cooperate is government. It is essential for a town, state, or nation. The government is the very identity of our collective groups. The USA is defined by the government. This makes the hate of the government by so many, basically self-hatred.

Humans have a distinct limit on their capability to interact with others deeply. Evidence is that the limit is on the order of 100-150 people. The success of humanity is operating at the level of thousands or millions of people. To do this we need systems and institutions we all can coordinate our actions through. The assemblage of these systems and institutions is government. The government operates through a rulebook we know as the law. Thus the government coordinates the actions of the entire population. It cannot be distinguished from the people themself. The government is us. All of this should run on faith in the institutions. This is trust. What we really have in the United States today is a complete lack of trust.

Ideally, the government should be working for the best outcomes for the population. This is not happening. The way this has occurred is a draining of societal trust. It is a combination of social media, wealth inequality, corruption, and change that’s undermined it. The reaction to all of this is regulation. Every break in trust is met with regulation and regulation is expensive. A big part of this is the behavior of corporations. Basically corporate interest is now purely greed. That greed is not moderated by ethics or social cost. If they can make more money they will lie, cheat and steal. They will discriminate or pollute without any limits. Because of this we regulate and use that as the hedge to unbridled greed. The problem is that regulation is a very inefficient way to achieve the necessary limits.

“A nation of sheep will beget a government of wolves.” ― Edward R. Murrow

Breaking the System

“Democracy must be something more than two wolves and a sheep voting on what to have for dinner.” ― James Bovard

In all likelihood what the Trump administration will do is destroy much of our system. We can expect much of government to be shattered and broken in a couple years. The function of everything broken will simply drop. The need for these functions will not disappear. Every part of our current government is built to solve a societal problem. The inefficiency of the delivery is a completely different issue. If the government function is destroyed the problem will assert itself anew. It will not be efficient; it simply won’t be there.

We do need to recognize that our current inefficiency is the product of largess. After World War 2 the USA stood atop the world economically with wealth and power unrivaled. The only hedge against this supremacy was worry about the threat of the Soviet Union and nuclear armageddon. This allowed the USA to be extremely inefficient and get away with it. Those days are gone now. The USA is extremely rich, but also extremely unequal. The wealth is concentrated amongst a very small group. This state is corrosive to social trust and the seeds of revolution. It leads to instability that either leads to chaos or authoritarian rule. Neither outcome is good for a Nation.

“A really efficient totalitarian state would be one in which the all-powerful executive of political bosses and their army of managers control a population of slaves who do not have to be coerced, because they love their servitude.” ― Aldous Huxley

How a grifter like Trump or a greedy oligarch like Musk can fix this defies logic. People like them are the very reason the inefficient system exists. At the end of their destruction of government, the likely response is even less trust. That lack of trust will yield even less efficiency. The outcome will be chaos and suffering followed by even less trust and greater needless cost. Their efforts will not end well. Because the real root of the inefficiency is not addressed, the problem will simply compound. In the end, the population as a whole will suffer.

“People shouldn’t be afraid of their government. Governments should be afraid of their people.” ― Alan Moore

Fixing the System

“The best way to find out if you can trust somebody is to trust them.” ― Ernest Hemingway

Americans would be far better off if we fixed the system by repairing it. As always to fix something, you need to know what is wrong with it. Fortunately, the root cause of government inefficiency is simple. A lack of trust. Or more to the point the lack of trust. The thing to realize is that failure of trust is extremely expensive. The government red tape is mostly related to not trusting our fellow Americans. We waste most of our money making sure other Americans aren’t wasting or stealing money.

“Government has three primary functions. It should provide for military defense of the nation. It should enforce contracts between individuals. It should protect citizens from crimes against themselves or their property. When government– in pursuit of good intentions tries to rearrange the economy, legislate morality, or help special interests, the cost come in inefficiency, lack of motivation, and loss of freedom. Government should be a referee, not an active player.” ― Milton Friedman

Milton Friedman is the architect of shareholder value being the sole purpose of business. He is the founder of the level of inequality we see today. One of his mistakes was that wealth provides a way to pay off the ref. Today, the refs are owned by the business. His perspective like almost all extreme points of view is out of balance. Today, we are living with the consequences of this oversight and mistake. It is important to see how this mistake contributes to government being inefficient. Government becomes the friction society needs to control excesses of greed and power.

There are a bunch of steps we should be taking (and won’t any time soon). Chief among these is corporate governance. We would be far better off if corporate interests were aligned with the population’s interests. If greed was tempered with social and societal good, regulation could be moderated. Instead, we exist in a world where corporate decision-making is pure greed. Shareholder value is their North Star. Regulation is the only thing standing between them and ethical and moral crimes. Now corporate interests and oligarchs can buy the law. If the regulatory environment (the administrative state) is wrecked they will run wild. A catastrophe will follow at some point. Trust will plummet further and the reaction will end up being opposite to the intent. Society will be harmed and the population will suffer. This will not end well at all.

“None of us knows what might happen even the next minute, yet still we go forward. Because we trust. Because we have faith.” ― Paulo Coelho