Better to get hurt by the truth than comforted with a lie.

― Khaled Hosseini

Being honest about one’s shortcomings is incredibly difficult. This is true whether one is looking at their self, or looking at a computer model. It’s even harder to let someone else be honest with you. This difficulty is the core of many problems with verification and validation (V&V). If done correctly, V&V is a form of radical honesty that many simply cannot tolerate. The reasons are easy to see if our reward systems are considered. Computer modeling desires to get great results on the problems they want to solve. Computer modelers are rated on their ability to get seemingly high-quality answers (https://wjrider.wordpress.com/2016/12/22/verification-and-validation-with-uncertainty-quantification-is-the-scientific-method/ ). As a result, there is significant friction with honest V&V assessments, which provide uncertainty and doubt on the quality of results. The tension between good results and honesty will always favor the results. Thus V&V is done poorly to conserve the ability of modelers to believe their results are better than they really are. If we want V&V to be done well an additional level of emphasis needs to be placed on honesty.image008

If you do not tell the truth about yourself you cannot tell it about other people.

― Virginia Woolf

V&V is about assessing capability. It is not about getting great answers. This distinction is essential to recognize. V&V is about collecting highly credible evidence about the nature of modeling capability. By its very nature, the credibility of the evidence means that the results are whatever the results happen to be. If the results are good the evidence will show this persuasively. If the results are poor, the evidence will indicate the quality (https://wjrider.wordpress.com/2017/09/22/testing-the-limits-of-our-knowledge/ ). The utility of V&V is providing a path to improvement along with evidence to support this path. As such, V&V provides a path and evidence for getting to improved results. This improved result would then be supported by V&V assessments. This entire process is predicated on the honesty of those conducting the work, but the management of these efforts is a problem. Management is continually trying to promote the great results outcomes for modeling. Unless the results are actually great, this promotion provides direction for lower quality V&V. In the process, honesty and evidence are typically sacrificed.

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Standards Subcommittee. Provide procedures for assessing and quantifying the accuracy and credibility of computational modeling and simulation. V&V Standards Committee in Computational Modeling and Simulation. V&V-10 – Verification and Validation in Computational Solid Mechanics. V&V-20 – Verification and Validation in Computational Fluid Dynamics and Heat Transfer. V&V-30 – Verification and Validation in Computational Simulation of Nuclear System Thermal Fluids Behavior. V&V-40 – Verification and Validation in Computational Modeling of Medical Devices.

If we want to do V&V properly, something in this value system needs to change. Fundamentally, honesty and a true understanding of the basis of computational modeling must surpass the desire to show great capability. The trends in management of science are firmly arrayed against honestly assessing capability. With the prevalence of management by press release, and a marketing based sales pitch for science money both act to promote a basic lack of honesty and undermine disclosure of problems. V&V provides firm evidence of what we know, and what we don’t know. The quantitative and qualitative aspects of V&V can produce exceptionally useful evidence of where modeling needs to improve. These characteristics conflict directly with the narrative that modeling has already brought reality to heel. Program after program is sold on the basis that modeling can produce predictions of what will be seen in reality. Computational modeling is seen as an alternative to expensive and dangerous experiments and testing. It can provide reduced costs and cycle times for engineering. All of this can be a real benefit, but the degree of current mastery is seriously oversold.image001

Doing V&V properly can unmask this deception (I do mean deception even if the deceivers are largely innocent of outright graft). The deception is more the product of massive amounts of wishful thinking, and harmful group think focused on showing good results rather than honest results. Sometimes this means willfully ignoring evidence that does not support the mastery. In other cases, the results are based on heavy-handed calibrations, and the modeling is far from predictive. In the naïve view, the non-predictive modeling will be presented as predictions and hailed as great achievements. Those who manage modeling are largely responsible for this state of affairs. They reward the results that show how good the models are and punish honest assessment. Since V&V is the vehicle for honest assessment, it suffers. Modelers will either avoid V&V entirely, or thwart any effort to apply it properly. Usually the results are given without any firm breakdown of uncertainties, and simply assert that the “agreement is good” or the “agreement is excellent” without any evidentiary basis save plots that display data points and simulation values being “close”.

If you truly have faith in your convictions, then your convictions should be able to stand criticism and testing.

― DaShanne Stokes

This situation can be made better by changing the narrative about what constitutes good results. If we value knowledge and evidence of mastery as objectives instead of predictive power, we tilt the scales toward honesty. One of the clearest invitations to hedge toward dishonesty is the demand of “predictive modeling”. Predictive modeling has become a mantra and sales pitch instead of an objective. Vast sums of money are allotted to purchase computers, and place modeling software on these computers with the promise of prediction. We are told that we can predict how our nuclear weapons work so that we don’t have to test them. The new computer that is a little bit faster is the key to doing this (they always help, but are never the lynchpin). We can predict the effects of human activity on climate to be proactive about stemming its effects. We can predict weather and hurricanes with increasing precision. We can predict all sorts of consequences and effect better designs of our products. All of these predictive capabilities are real, and all have been massively oversold. We have lost our ability to look at challenges as good things and muster the will to overcome them. We need to tilt ourselves to be honest about how predictive we are, and understand where our efforts can make modeling better. Just as important we need to unveil the real limits on our ability to predict.

A large part of the conduct of V&V is unmasking the detailed nature of uncertainty. Some of this uncertainty comes from our lack of knowledge of nature, or flaws in our fundamental models. Other uncertainty is simply intrinsic to our reality. This is phenomena that is variable even with seemingly identical starting points. Separating these types of uncertainty, and defining their magnitude should be greatly in the service of science. For the uncertainties that we can reduce through greater knowledge, we can array efforts to affect this reduction. This must be coupled to the opportunity for experiment and theory to improve matters. On the other hand, if uncertainty is irreducible, it is important to factor it into decisions and accommodate its presence. By ignoring uncertainty with the practice of default of ZERO uncertainty (https://wjrider.wordpress.com/2016/04/22/the-default-uncertainty-is-always-zero/ ), we become powerless to assert our authority, or practically react to it.

image004In the conduct of predictive science, we should look to uncertainty as one of our primary outcomes. When V&V is conducted with high professional standards, uncertainty is unveiled and estimated in magnitude. With our highly over-promised mantra of predictive modeling enabled by high performance computing, uncertainty is almost always viewed negatively. This creates an environment where willful or casual ignorance of uncertainty is tolerated and even encouraged. Incomplete and haphazard V&V practice becomes accepted because it serves the narrative of predictive science. The truth and actual uncertainty is treated as bad news, and greeted with scorn instead of praise. It is simply so much easier to accept the comfort that the modeling has achieved a level of mastery. This comfort is usually offered without evidence.

The trouble with most of us is that we’d rather be ruined by praise than saved by criticism.

― Norman Vincent Peale

Somehow a different narrative and value system needs to be promoted for science to flourish. A starting point would be a recognition of the value of highly professional V&V work and the desire for completeness and disclosure. A second element of the value system would be valuing progress in science. In keeping with the value on progress would be a recognition that detailed knowledge of uncertainty provides direct and useful evidence to steer science productively. We can also use uncertainty to act proactively in making decisions based on actual predictive power. Furthermore, we may choose not to use modeling to decide if the uncertainties are too large and informing decisions. The general support for the march forward of scientific knowledge and capability is greatly aided by V&V. If we have a firm accounting of our current state of knowledge and capability, we can mindfully choose where to put emphasis on progress.\

image006This last point gets at the problems with implementing a more professional V&V practice. If V&V finds that uncertainties are too large, the rational choice may be to not use modeling at all. This runs the risk of being politically incorrect. Programs are sold on predictive modeling, and the money might look like a waste! We might find that the uncertainties from numerical error are much smaller than other uncertainties, and the new super expensive, super-fast computer will not help make things any better. In other cases, we might find out that the model is not converging toward a (correct) solution. Again, the computer is not going to help. Actual V&V is likely to produce results that require changing programs and investments in reaction. Current management often looks to this as a negative and worries that the feedback will reflect poorly on previous investments. There is a deep-seated lack of trust between the source of the money and the work. The lack of trust is driving a lack of honesty in science. Any money spent on fruitless endeavors is viewed as a potential scandal. The money will simply be withdrawn instead of redirected more productively. No one trusts the scientific process to work effectively.  The result is an unwillingness to engage in a frank and accurate dialog about how predictive we actually are.

It’s discouraging to think how many people are shocked by honesty and how few by deceit.

― Noël Coward

It wouldn’t be too much of a stretch to say that technical matters are a minor aspect of improving V&V. This does not make light of, nor minimize the immense technical challenges in conducting V&V. The problem is that the current culture of science is utterly toxic for progress technically. We need a couple of elements to change in the culture of science to make progress. The first one is trust. The lack of trust is pervasive and utterly incapacitating (https://wjrider.wordpress.com/2013/11/27/trust/, https://wjrider.wordpress.com/2016/04/01/our-collective-lack-of-trust-and-its-massive-costs/, https://wjrider.wordpress.com/2014/12/11/trust-and-truth-in-management/  ). Because of the underlying lack of trust, scientists and engineers cannot provide honest results or honest feedback on results. They do not feel safe and secure to do either. This is a core element surrounding the issues with peer review (https://wjrider.wordpress.com/2016/07/16/the-death-of-peer-review/ ). In an environment where there is compromised trust, peer review cannot flourish because honesty is fatal.

Nothing in this world is harder than speaking the truth, nothing easier than flattery.

― Fyodor Dostoyevsky

The second is a value on honesty. Today’s World is full of examples where honesty is punished rather than rewarded. Speaking truth to power is a great way to get fired. Those of us who want to be honest are left in a precarious position. Choose safety and security while compromising our core principles, or stay true to our principles and risk everything. Over time, the forces of compromised integrity, marketing and bullshit over substance wear us down. Today the liars and charlatans are winning. Being someone of integrity is painful and overwhelming difficult. The system seems to be stacked against honest discourse and disclosure. Of course, honesty and trust are completely coupled. Both need to be supported and rewarded. V&V is simply one area where these trends play out and distort work.download-1

It is both jarring and hopeful that the elements holding science back are evident in the wider world. The new and current political discourse is full of issues that are tied to trust and honesty. The degree to which we lack trust and honesty in the public sphere is completely disheartening. The entire system seems to be spiraling out of control. It does not seem that the system can continue on this path much longer (https://wjrider.wordpress.com/2017/10/20/our-silence-is-their-real-power/ ). Perhaps we have hit bottom and things will get better. How much worse can things get? The time for things to start getting better has already passed. This is true in the broader public World as well as science. In both cases trust for each other, and a spirit of honesty would go a long way to providing a foundation for progress. The forces of stagnation and opposition to progress have won too much ground.

Integrity is telling myself the truth. And honesty is telling the truth to other people.

― Spencer Johnson

 

Nothing is so difficult as not deceiving oneself.

― Ludwig Wittgenstein