I was struck by a recent article published on the Substack venue, Heterodox STEM: Warnings of the Past, Foreshadowing the Future https://hxstem.substack.com/p/warnings-of-the-past-foreshadowing This article is about the post-modernist ideology souring the public's opinion of science, perhaps inevitably leading to the current funding cuts. It is true that this post-modernist nonsense has soured the public on academia and science. But, it is not the only factor. The pandemic and its response revealed some rot in the scientific enterprise, with one person in particular claiming that he personally was synonymous with "the science". Many in the public scoffed, and they were correct. This looked, and was, ridiculous. Climate change science has become entangled with politics, much to its detriment. Now the scientists have to dance to the tune of the activists and politicians. Now, for the third time that I am aware of, our best data in this area, the Keeling atmospheric carbon dioxide data, is at risk of losing funding. The previous two threats, amazingly, came from the Clinton-Gore administration and the Biden administration. If we are ever to consider terraforming or geoengineering, we need to understand these planetary systems. A wag has stated, "Geoengineering is a bad idea whose time has come". There are many of these efforts being considered, or currently underway, but here is a recent example I became aware of: After decades of fighting sulfur pollution, @MakeSunsets is pumping sulfur dioxide into the atmosphere to cool the planet! https://x.com/MatthewWielicki/status/1900939051454394669 CO2 is a pollutant but this isn't? I am more than a bit skeptical of these attempts, which might be charitably classified as premature and ill-advised, perhaps. On top of this, numerous figures, including Peter Thiel, are noticing a decline in research and development productivity according to a variety of metrics: This Is the Final Taboo & It Can No Longer Be Denied | Peter Thiel https://www.youtube.com/watch?v=kjliH3DMGLE Science is in trouble and it worries me. https://www.youtube.com/watch?v=QtxjatbVb7M I was asked to keep this confidential https://www.youtube.com/watch?v=shFUDPqVmTg No Scientific Innovation Since the 1920s… https://www.youtube.com/watch?v=guQIkV6yCik In addition, numerous speeches and comments by retired General John E. Hyten, former Vice Chairman of the Joint Chiefs of Staff, US Government Department of Defense about the current state of innovation and research and development in the defense arena describe a variety of deficiencies. And these are just the tip of the iceberg. It is why I have been considering a new approach to R&D that might reinvigorate the process. We might do well to return to what worked previously, instead of staying mired in our current morass.

Assorted Topics in Research and Development A: R&D General Information A.2: Hard and Soft Money Expressions that are perhaps unfamiliar to those who are not involved with R & D are the phrases "hard money" and "soft money". Hard money refers to funding that is irrevocably associated with a position, without the need to constantly write proposals and get grants and contracts. So if one has a salary that they get just as part of their employment, that is "hard money". They have a "hard money" position. For example, many faculty positions in North America are '9 month "hard money" positions'. Faculty are paid a salary for 9 months of the year by the university or other academic institution. If most faculty want to get a salary for the remaining 3 months of the year, they must get grants and contracts they apply for to obtain these remaining 3 months of salary. Of course, if they teach during those 3 summer months, they can get "hard money" for those 3 months' salary as well. If instead they are "doing research" for those 3 months, they would normally have to secure "soft money" from grants and contracts to be paid a salary for those 3 months. Any money or assistants or equipment usually comes out of these grants and contracts. However, new positions often come with some "startup" funds or assistants and equipment to help the incoming faculty member establish a research program. Soft money usually has to be applied for with proposals to external funding agencies and corporations and foundations. The soft money usually comes in the form of grants or contracts. Depending on the type of funding and projects, there might be very little effective difference between grants and contracts, or there might be a large difference between the two. In most cases, for applied work, contracts specify exactly what is to be done. There is very little room for any speculation or innovation in most R & D contracts. Grants usually allow more conjecture and open-ended projects and innovation, but this is not always true. Government bureaucrats and other funders frequently favor soft money approaches to providing resources for R&D. The reason is that they think they have more control if R&D is funded with short term grants and contracts. However, a problem arises in who is making the decisions about what to fund, and who to fund.[1] If standard funding agencies like the National Science Foundation (NSF) and the Department of Energy (DOE) are compared to the government organization known as ARPA/DARPA (Advanced Research Projects Agency/Defense Advanced Research Projects Agency), the differences are quickly apparent. Funding officers at DOE, NSF, the Office of Naval Research (ONR) and other typical government funding agencies are long term employees of a government agency. They typically do not have much pressure on them, aside from bureaucratic pressure to conform to whatever nonsense is going on in their particular organization at the moment. Usually people who accept these jobs are nowhere close to our best and our brightest, or our highest performers by any means. However these jobs are not that stressful and it is not easy to terminate anyone in one of these jobs. The standards that are required for one of these jobs are pretty minimal. So this makes them attractive to low performers, and we end up with a sort of feedback loop, or a kind of self-fulfilling prophecy. Only incompetent people take these jobs, which creates a poor reputation for these positions. So only incompetent people apply for these jobs or accept these positions. And this reinforces the negative impressions and stereotypes everyone has. Also, often incompetent people cannot judge what is a good proposal or what is a lousy proposal. They cannot tell who is reasonable to fund, and who is more problematic.[1] So the incompetent funding officers often fund other incompetent people. And the problems are continued, compounded and exacerbated. DARPA (sometimes known as ARPA) operates quite differently and gets markedly better results. Most of the funding officers at DARPA are on temporary tours, and have real permanent positions in challenging academic departments or other R&D organizations. DARPA temporary funding officers are only on a rotation at DARPA for 2-5 years, and then they return to their normal jobs. One does not attract low performers to these positions, for the most part. People do not become stale in these jobs, because they have to remain competitive in their regular positions. And DARPA/ARPA gets much better results than other funding mechanisms. This is so startling that other communities are attempting to copy the DARPA/ARPA model in healthcare (HARPA) and in the intelligence community (IARPA) and possibly other segments of the R & D space. A.2.1: Notes [1] "You can't expect the sheep to respect the best and the brightest. They don't know the difference, really. The vast majority of them [humans] do not possess the ability to judge who is and who isn't a really good scientist. That is the main problem with science, in this century. Science is being judged by people, [and] funding is being done by people, who don't understand it." -- Nobel Prizewinner Kary Mullis

Assorted Topics in Research and Development A: R&D General Information A.1: Categories of Research and Development A.1.1: Introduction to Categories of R&D There are numerous ways to categorize and classify research and development (R&D) efforts. The boundaries between these different categories are somewhat vague and flexible. In other words, the borders are not at all firm. Certain R&D work overlaps a boundary or two. Also, as a project progresses, its classifications and characteristics will often evolve. Different people with different perspectives might disagree and classify a given R&D project as falling on one side of a border, or the other. So for example, sometimes one person's pure research will be another person's applied research. Some of these categories are judged to have a higher status than others. But to some degree, these classifications are sort of arbitrary. They have limited validity, and were just invented by humans to organize their work and to assist their understanding somewhat. A.1.2: Short Term and Long Term Research and Development Most research and development activity falls into the "short term" R&D category. Short term R&D is usually directed and definite, with clear objectives. Some innovation can be required. However, this is not frequently true. Innovation is often not necessary in short term R&D. Examples of short term R&D include safety and requirements testing, and product improvements. Long term R&D is often referred to by a suite of other terms, such as "blue sky" research or "open-ended" research. Some denigrate long term R&D by calling such efforts "fishing expeditions", since the participants and investigators often do not have clear goals for their explorations. Many (if not most) of these long term R&D projects fail and produce nothing of interest. However, sometimes they result in unexpected discoveries and can even create paradigm shifts. Long term R&D might involve trying to solve problems that could be impossible, or which many think are impossible. It can include projects like looking for elementary particles which might not exist, or long term monitoring of the skies with new telescope designs. It is common in "blue sky" long term R&D for the investigators to have at least some say in the problems they choose to tackle. It is even more productive and appealing for the participants to have complete control over the subjects they are exploring.[1] It is much more difficult to justify long term R&D efforts. Frequently organizations dedicated to long term R&D are eviscerated or abolished, since many think they are a waste of resources. Over the years, many long term R&D departments and organizations have been eliminated for "cost-cutting" purposes, or because someone thought they were wasteful and irritating.[3] Examples of this would include the Xerox Palo Alto Research Center, and Bell Labs Area 11, which was effectively gutted.[4] A.1.3: Pure and Applied Research and Development Pure STEM R&D is typically viewed as some activity in science, technology, engineering and mathematics that is pursued purely for the joy and beauty of discovery. Applied STEM is thought of by some as more "grubby" and practical, and devoted towards some pragmatic goal. One of the surprising things which many discover when they begin their STEM journeys, through the many years of classes and studies and internships, is that there is a large "prestige chasm" between pure and applied STEM. Everyone in STEM sort of looks down on applied physics and applied mathematics and other "applied fields". Even engineering, a very noble profession, is the subject of many wisecracks and jokes from others in the "purer" parts of STEM. Most if not all of engineering is an inherently applied discipline. The same is true of medicine. However, it can be an incredible challenge and quite satisfying to discover applications and uses for ideas that are generated throughout STEM R&D. Most people do not agree with this opinion, one might notice. In addition, this pursuit is far from trivial. The majority of attempts will probably fail. However, the sense of achievement when finally a practical use for some obscure, abstruse aspect of STEM is revealed is hard to beat. In addition, uncovering these gems can be quite remunerative in a variety of ways. One of the most effective ways to contribute is to be cross-disciplinary, and to "raid" other areas for useful ideas and procedures. A different perspective can be incredibly valuable. If a particular issue in one field is quite troubling and confusing, looking at it from a different angle can clarify things sometimes. With more experience, it is clear that one person's applied research is someone else's pure research, and vice-versa. Also, what initially appears to be "only" pure research with no conceivable use can eventually lead to all kinds of incredible and powerful technologies. It just might take some time and tinkering and some thought. The opposite can be true as well of course. An exotic practical application can open the gateway to a raft of beautiful pure results. If one stands astride a few fields, and can appreciate both the pure and applied approaches and requirements, then having this depth of knowledge and this vantage point can be rewarding. Arguably, there are probably very few substantial pieces of mathematics or scientific discoveries that cannot be used somehow, somewhere. Usually, we have not investigated them enough and/or we are not yet smart enough to figure out how they might be used. But, one just has to have patience. However, in many quarters in R&D, patience is something that is in markedly short supply. Few seem to want to wait for anything or want to think hard about anything; they think they should not have to. People seem to be under the illusion that everything should unfold like it does in a Hollywood movie or in a television show. But it never ever does, or at least this is very rare. Most think that pure research falls strictly in the long term category, and applied research is synonymous with short term research. Although this is often the case, this impression is not necessarily accurate. It is quite common to have very long term applied research programs, like the work towards controlled nuclear fusion. And pure research can be short term, or long term in nature.[5] A.1.4: Concrete and Abstract Research Another way to categorize and classify research and development efforts is along the lines of "concrete research"[6] and "abstract research". For example, if someone is working in computational number theory in mathematics, using computing devices such as computers, then one would probably say they are doing "concrete research". Computational mathematics can almost be characterized as an empirical investigation in mathematics. In computational math, concrete or actual examples of various mathematical entities are constructed using computers. These examples are tested using computers, which can far exceed human capabilities. On the other hand, abstract research does not always involve explicitly creating exemplars. In mathematics, existence and nonexistence proofs fall into the category of abstract research. Sometimes, the researchers have difficulty constructing explicit examples satisfying their theorems.[7] Most of the empirical sciences are largely explored with concrete research. However, some abstruse areas like quantum field theory in physics have departed from concrete research. In some cases, they have come to resemble abstract mathematics, in recent years. Unfortunately, it is so difficult to create empirical tests in these situations that quantum field theory is frequently not testable, or "falsifiable". It is almost beyond human capabilities, it would seem, at least so far, anyway.[8] A.1.5: Theoretical and Empirical Research Clearly, there are two major components to the scientific method. One is, the construction of models, which scientists also call "theories". This is a different meaning of the word "theory" than what the general population understands.[9] These models or theories are then used to make "predictions", or estimates of the results of various measurements. The "predictions" are not really prognostications, necessarily, as the public might expect.[10] The construction of models is "theoretical" research. The other main component of the scientific method is empirical research. Empirical research includes both laboratory or experimental measurements, and field observations. These results are then compared with the "predictions" of the models produced by the theoretical research. If there is some level of agreement between these measurements and observations,and the model predictions, then the model is judged to have some validity. This is true until better models are created, or different or improved measurements are available. And then iteration occurs, and we hopefully get better and better models of reality. A.1.6: Quantitative and Qualitative Research As a field matures, typically it becomes more quantitative. Most scientific fields start out with qualitative observations. Then, as more is learned, people start to measure things quantitatively.[11] Although statements about the dominance of physics might sound a bit pompous and arrogant, the reason for it is that physics became quantitative far earlier than many other sciences. The chief reason for this is that physics is in some ways easier than the other sciences, so it is more disposed to quantification. However, to facilitate productive research, accommodations must be made for quantitative observations and analyses. And that leads naturally to hiring some with mathematical and statistical and data analysis skills. Of course, one also needs access to data, or those who can provide data, such as those who have some abilities in empirical disciplines, such as laboratory and field sciences, and hardware experts of various flavors. Unless an R&D entity is intended to concentrate on only certain segments of the scientific process, a broad range of talents and facilities will be required. If the intention is that a given R&D entity only fulfills a very narrow role in the STEM pipeline, then there must be some way to connect to others in other organizations who can perform the other necessary roles. Otherwise, the R&D pipeline will not contribute much. A.1.7: Notes [1] It was common during the glory days of Bell Labs, that in the pure long-range, blue sky research area, the so-called "area 11" (which only constituted a couple of percent of Bell Labs), the technical staff would decide for themselves what projects to pursue. A common refrain from managers at Bell Labs in area 11 at that time was, "If I knew what you should be doing, I would already be doing it myself."[2] Even though this was true in principle, however in practice, a large fraction of the work in area 11 at Bell Labs was of a short term and applied nature. [2] Allowing the investigators to select which project to work on is known as the 'Haldane Principle'. It is named after Scottish philosopher, lawyer and politician Richard Burdon Haldane. Haldane championed the idea in various commissions and committees in the early 1900s. This principle was solidified as a goal in 1918, and enacted into law in 2017. However, it had been generally adhered to in the UK for about a century before this. [3] "You can't expect the sheep to respect the best and the brightest. They don't know the difference, really. The vast majority of them [humans] do not possess the ability to judge who is and who isn't a really good scientist. That is the main problem with science, in this century. Science is being judged by people, [and] funding is being done by people, who don't understand it." -- Nobel Prizewinner Kary Mullis [4] Industrial R&D labs https://www.linkedin.com/posts/dansgoldin_a-big-impact-we-can-make-in-this-new-industrialization-activity-7199746551051689984-gX1U/ Post by Dan Goldin, former head of NASA: 'I've been meeting and working with teams who have exciting ideas and designs for the future of the American industrial economy. Industrial R&D Labs like Bell Labs use[d] to work on tough problems like this. Curious, should we bring back the industrial R&D lab?' -Comment by Charles Camarda, Astronaut and Research Engineer: 'We used to have them. They were called NASA Research Centers. When NASA decimated funding to independent applied research we destroyed our research culture and our impact on industry. NASA and WPAFB [Wright-Patterson Air Force Base] matured composite materials analysis and understanding so it could be adopted by commercial aircraft companies. When you place all your funding into SOA [SOA=State of the art, or Service Oriented Architecture] large space projects at the expense of research for 30-40 years this is what happens. You lose your core ideology gradually over time like the proverbial boiling frog. Take a look at what is happening to Boeing. Its very difficult to regain that core culture/ideology once it is lost and it takes time.' [5] One example I have observed that illuminates some of the prevalent attitudes about pure and applied research comes from an Ivy League university. A friend had a tenured position there, and in collaboration with others, they produced a beautiful algorithm. This algorithm is essentially a tool. It potentially could have many uses in both pure and applied research. However, as far as I know, it has not yet been harnessed for any pure or applied R&D projects. My friend left this tenured position because his colleagues at the Ivy League school were incredibly unpleasant towards him and treated him poorly, according to his accounts. Therefore, he accepted two positions further south, one at a large state university and one at DARPA. He was much happier jointly holding these two new positions. I spoke to the people who had reportedly treated my friend poorly at the Ivy League school. They were quite defensive. One, who has a PhD in statistics, has decided to now "put on airs" and to pretend he is currently ONLY doing pure research of a very deep mathematical variety. This is just ridiculous, given his shallow background. It is all purely a matter of conceit, and for bragging purposes. I also spoke to my friend's mentor at the large state university. The mentor had founded an "applied mathematics" institute on the campus. However, although the mentor was nominally an applied mathematician, probably because it was easier to obtain funding with that description, he behaved very oddly when I spoke to him. The mentor literally hyperventilated when I spoke to him about applied R&D. And in fact, when I offered to send him a copy of my patent, he almost passed out. I feared he might suffer a stroke or a heart attack from the stress. You see, he was just pretending to be an applied mathematician. A potential exposure to 'actual' applied mathematics caused him to suffer a panic attack. [6] Concrete research can be characterized by a plethora of other terms. For example, concrete research might also be termed as real, actual, objective, factual, material, and so on. It is a bit less ephemeral and speculative than "abstract" research. Of course, once the "loop is closed" and the abstract work has resulted in accurate predictions, confirmed by empirical efforts, then the abstract work sort of 'solidifies' a bit. It might become "more concrete" in nature. And it could be used as the basis of assorted engineering and technological designs. In fact, it can be such an accurate predictor of reality that it can be used to construct so-called "digital twins". An example of this comes from fluid dynamics. There are very few remaining wind tunnels in the US. Many have been demolished. The inaccuracies of the model predictions are increasingly dwarfed by the errors of the instruments used to measure things in wind tunnels. So people see no further need for wind tunnels in many cases. [7] A joke that was popular in the Bell Labs Mathematics Center during its heyday was, "This research result is so perfectly general that it does not apply to a single known special case." Although this statement might appear ridiculous, it is not that far from reality, in many cases. These sorts of results are the ultimate examples of 'enthymemes', grand sweeping universal claims that leave out particular instances, and in the extreme, possibly all occurrences. [8] As an example of how frustrating this branch of physics has become, consider the recent comments of physicist Sabine Hossenfelder: I was asked to keep this confidential https://www.youtube.com/watch?v=shFUDPqVmTg I want to read you an email that I was asked to keep confidential because I think it explains some of my worries about academia. I knew that physicists would go on to argue I should have tried to solve the problem internally (within the community) before drawing public attention to it. The reason I published this comment was so that I could later demonstrate that I did this. But just by accident and totally unrelated here is another link: https://www.nature.com/articles/nphys4079 [9] To the general public, the word 'theory' is often used to describe a somewhat vague unsubstantiated guess. However, this is not how the term 'theory' is used in science. A theory is a model to a scientist. A theory is more substantial than a hypothesis or a conjecture, but a theory is not necessarily perfect or akin to the "truth" or reality. As statistician G.E.P. Box said, "All models are wrong, but some are useful." [10] "Prediction is difficult, especially about the future" -- Niels Bohr [11] For example, consider the following quote by Irish mathematical physicist and engineer William Thomson (1824-1907), the First Baron Kelvin: “When you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meagre and unsatisfactory kind.” -- Lord Kelvin This attitude leads naturally to this quote by English mathematician and physicist John William Strutt (1842-1919), 3rd Baron Rayleigh: "In the sciences, there is physics and all the rest is stamp collecting." -- Lord Rayleigh

Assorted Topics in Research and Development C: Improving R&D C.2: Redesigned R&D Organizations C.2.1: Introduction to New R&D Entities Is it possible to create novel Research and Development (R&D) organizations which are more efficient and productive than those which currently exist? What features might we expect or would we like these potential new institutions to have? What is more productive in this kind of organization, to focus on applied work, or on pure work, or on some mix of both? How do the time horizons and the type of funding affect the research and development environment? Does it matter which organizations or groups fund the research? Some of these questions should be kept in mind as the reader reviews this document. C.2.2: Potential New R&D Organizations In light of these observations of assorted issues that appear to be quite widespread in R&D in the US currently, can we imagine organizations which might be less vulnerable to these problems? Can R&D be more productive, regaining its previous stature? After all, we have examples of past R&D organizations that appear to have performed much better.[1] It does seem that there is almost a natural tendency for complacency and incompetence to set in, in an R&D entity, after a time. It seems as though it is very difficult to maintain a long term research and development environment over an extended period. Those who are funding the work almost invariably start to believe the efforts are all a waste of resources after some time. Can these propensities be quashed, through statistics, data and studies? It is not clear, but it would seem that just trusting in the good will and good judgement of the funding entities is not a reliable solution over the long haul. In addition, poor or inferior management can be caustic or even toxic to an R&D enterprise. History demonstrates that sooner or later, poor management becomes ascendant in these R&D organizations and wreaks havoc.[2] And once these incompetents are in place, it is very difficult to remove them. There are numerous examples of this, such as Bell Labs, Xerox and Kodak. The same problematic tendencies and markedly poor judgement also seemed to bedevil R&D consortia like Bellcore, Sematech, US Memories and the Swiss Watchmakers' Centre Electronique Horloger (CEH) in Neuchâtel, Switzerland. Safi Bahcall's book ‘Loonshots’ documents many other examples of difficulties that various innovations and advances in science and technology faced over the last few decades or even the last couple of centuries. Two counterexamples would appear to be the 3M Corporation and Apple Computers under Steve Jobs. However, neither of these organizations was involved in long term, blue sky research. Nevertheless, they adopted policies that at least kept R&D and innovation prominent in their institutions. It remains to be seen if Apple's predominance will fade under the current post-Jobs managers or not.[3] Steve Jobs was constantly making efforts to undercut Apple's own products with newer more advanced products, much to the displeasure of his staff and other stakeholders. Similarly, 3M has a firm policy that each division must derive most of its profits from new products created in the last few years. Therefore 3M managers are forced to not rest on their laurels. Unfortunately, neither approach appears to be of much potential use in blue sky research efforts. Can different R&D structures resist the decay which seems to become so prevalent and even predominant after a while? Can we develop organizations with antibodies against this blight and rot? The next section (and later essays) will describe at least one potential format for a decay-resistant R&D organization to experiment with. There are probably other formats that might also be conducive to a stable "blue sky" R&D organization. Any and all ideas in this direction are worth considering. C.2.3: An R&D Organization Design There is not enough space here to describe in full detail novel R&D structures that might avoid some of the ills of the current R&D entities. However, consider some of the following potential salient features of one such enterprise: a. The innovators and technical staff should be chosen on the basis of their ability to produce meaningful, valuable results. Someone who just produces endless amounts of substandard and repetitive publications probably would not qualify. b. The technical staff should "own" the IP they create. They should benefit directly from what they produce. Other organizations or divisions should not expropriate their IP and their resources. c. The technical staff should be treated as valuable assets, not inconveniences. This transition happened decades ago in the case of entertainers and sports figures. They have agents and some measure of control over their careers. This should be true of technical people as well. d. The productive technical staff should have control over the managerial and support staff. Currently, the opposite is true. In academia, only a few decades ago, this was the rule, not the exception. But things have changed drastically. And the quality of academic institutions has declined as this happened. e. Overhead rates should be minimal. f. Meetings should be minimized and as short as possible. g. The technical staff should shoulder most of the initial risks in choosing projects and pursuing innovation.[4] To the greatest extent possible, potential funding entities and customers should be presented with working prototypes to evaluate. Obviously, this reduces the risk exposure of the customers, end users and external funding sources. In addition, the time to deployment can be accelerated with working prototypes in hand. h. Junior technical staff, like research assistants, should not be treated as slave labor. They should be paid a reasonable wage. Constraints on the ratio of remuneration for team leaders and research assistants should be enforced. i. Using endowments and similar tools, the majority of positions for technical team leaders should be hard money positions. j. Any proceeds that result from consulting undertaken by the technical staff should be passed on directly to the technical staff, with no deductions. This is common in academia, and any new R&D entity must compare favorably with other options available to technical staff. l. Where it is feasible, employees and associates will be encouraged to work remotely. Obviously, this is somewhat more problematic for people involved in laboratory work and people involved in R&D apprenticeship and mentorship programs. m. As many positions as possible should be part-time in nature, at least until the endowments grow sufficiently to support full-time employment. n. Intellectual property agreements must clearly define ownership of various projects, particularly when other organizations are involved. o. Support of and accommodations for families and children of staff should be a priority when they are required to be at a work site. p. Problematic staff should be expunged as quickly as can be done reasonably. If they are still productive, they can be isolated by a variety of methods so they do not interfere with other staff members. q. An extensive dispute resolution structure should be available and the staff encouraged to use it when it is appropriate. r. Human resources often does not take the side of the employee, but just enforces the will of the senior management. Organizational psychologists or employee representatives should be available. And there should be multiple avenues available for a staff member who is encountering difficulties to be able to address the issues. C.2.4: Notes [1] Bell Labs Area 11, IBM Research, a few FFRDCs, the NASA long term research areas, Xerox PARC, BBN, the Weizmann Institute, the Institute for Science and Technology in Austria (ISTA) and some other entities have created fertile R&D entities at some point in their history. Some still retain this reputation, and others have now faded. [2] Once incompetent managers are installed, it is very difficult to avoid a sort of negative feedback spiral. Many managers assume or presume that they cannot be dislodged under any circumstances. They operate as though they have infinite power. Some of this works because most technical staff are loath to get into political infighting. They are naive and non combative. They just want to do their creative work. Few are accustomed to an environment where most comments and statements are falsehoods and lies, because they could not perform productive R&D activities in this kind of situation. So they are easy to bamboozle and hoodwink. These managers favor fellow incompetents and get them promoted. Talented people find it easier to just leave rather than fight to correct an ugly situation. So things steadily get worse, and worse. And the organization spirals down into an unproductive, uncreative state. [3] The Weizmann Institute in Israel, the Institute of Science and Technology in Austria (both shaped by Israeli theoretical physicist Haim Harari) and Rockefeller University in the US seem to be counterexamples as well. These have a mix of long term blue sky research and short term research. They are able to exploit their technical capabilities to support themselves and maintain their excellence and even grow in stature. And they have done this over longer periods of time than most. [4] The investigators and innovators probably understand the risks and the potential returns better than almost anyone else. So it makes sense that they should be those who are evaluating them.

Assorted Topics in Research and Development D: Illation to Assorted Topics in Research and Development D.1: Conclusions • R&D is a valuable human activity. • There are numerous ways to categorize R&D; pure and applied, quantitative and qualitative, long term and short term, theoretical and empirical and so on. • There are different funding models for R&D. It is useful to understand these traditional funding models, particularly hard and soft money funding. • Collaboration between experts with different skills is valuable to move an idea forward towards an actual application or implementation. This process can be characterized as an 'R&D pipeline'. • There are a number of problems and deficiencies that can afflict R&D and hamper its effectiveness. Perhaps one of the worst is poor management practices, including micromanagement troubles. • There are potentially some approaches that could resolve or reduce some of these problems with R&D. Perhaps it might be valuable to experiment with different R&D models. D.2: Summary Research and development is an important element of modern human economic[1] and military activities. It is of such value that it is worthwhile knowing something about how it is performed. It is also useful to recognize when our R&D organizations sometimes come up short, and the reasons for this. If we can understand how R&D fails, perhaps we can construct R&D entities that do not suffer the same deficiencies. These might present more productive and efficient ways to do R&D. It is critical for our future that we think about how to do R&D better.[1] D.3: Notes [1] "We are not more innovative because we are richer. We are richer because we are more innovative." -- Konstantin Kisin [2] Here are a couple of thought-provoking quotes by UK commentator and comedian Konstantin Kisin: "We admire Elon Musk because he builds big things and therefore reminds us to reach for the stars." -- Konstantin Kisin "The promise of a better tomorrow is the debt we owe our children." -- Konstantin Kisin

Assorted Topics in Research and Development Introduction to Assorted Topics in R&D Exordium Research and development is described, along with some of its current defects and possible ways to correct these issues. Abstract Research and development (R&D) is an important human activity. Almost all human technological and scientific progress is a direct result of R&D activities. Because R&D is so critical, it is useful to learn a bit about it. In addition, perhaps we should be trying to improve the efficiency and productivity of the R&D process. Therefore, it is valuable to study R&D for these purposes. Therefore, this series of essays examines in depth a few topics associated with R&D. First, a little general background information is presented in Section A. Then in Section B, some problems with R&D are discussed. Finally, in Section C, a few potential ways to improve the efficiency and productivity of R&D are presented. Preface A: R&D General Information In the first section of this document, some of the more important features of research and development are described. A.1: Major Categories of R&D One of the most important pieces of information about a R&D project is how it can be described. There are numerous categories of R&D which can be used to label a given R&D project. For example, R&D can be short term or long term or both, or pursuing pure or applied goals. The scientific method also divides science into theoretical and empirical work. Theoretical science involves the creation of models, and empirical science provides measurements and data to test these models. R&D efforts can be both qualitative, or quantitative, as well as either concrete or abstract. There are also many other ways to classify R&D, but these are probably the most important categories. A.2: Hard and Soft Money Another topic that is valuable to understand concerns the different ways that R&D is funded, or paid for. The two main types of R&D funding are hard money and soft money. Suppose that hard money is associated with an R&D position. In that case, no grants or contracts are required to obtain this "hard money". Soft money, on the other hand, is derived from research and development grants and contracts. Typically the investigators have to apply for grants and contracts with a proposal to obtain these soft money funds. A lot of effort is consumed in this process. A.3: The R&D Pipeline In years past, particularly more than a century ago, R&D participants were generalists. Now however, as the work becomes more sophisticated and time-consuming, the investigators are increasingly specialists. So for example, there are data processing experts and computer experts and hardware experts and algorithm design experts and mathematical and theoretical experts involved. It really is not possible for one person to be at the bleeding edge of all these disciplines simultaneously.[1] If one wants to create hardware and software prototypes for testing in laboratories or in a field environment, there must be a lot of coordination between all these experts. This is readily observable in projects like the design and deployment of space telescopes, for example. Huge R&D teams with many components are involved with these complicated efforts. To move an initial idea or concept forward through this process, a sort of "pipeline" of experts or diverse specialists has to be created. Each stage of the pipeline relies a bit on what is further upstream and came before it. Each component of the pipeline then contributes their efforts, and passes on results and products to the next stage of the pipeline. B: R&D Problems The current landscape of research and development in the US seems to be starting to exhibit some deficiencies and issues. The R&D process might not be functioning as well as it did decades before. Some observations about shortcomings of many R&D organizations are described. C: Improving R&D It might be possible to return the research and development process to a more fertile and efficient state. One way to do this might be by recognizing and reducing some of the current difficulties that seem to afflict many R and D efforts. New organizational structures reassigning risks could be beneficial as well. These and other ideas are discussed in the final section of this document. Notes [1] A few decades ago, many R&D managers demanded that single individuals should fulfill all these different roles. However, it was not that reasonable to expect this decades ago, and it is even less appropriate to demand this now.

Assorted Topics in Research and Development B: R&D Problems Research and Development, or R&D, is not perfect. In some organizations and during some time periods it has performed markedly better than at other instances in other locations. Why is this? The answer probably lies in the design of our R&D institutions. It might be worthwhile to try to identify some of the defects that are common in R&D entities. This subject is so vast that we cannot do an exhaustive study here. But a few examples of problems in R&D might be illustrative. B.1: Some Issues in R&D The ways in which R&D is managed and funded might provide clues about how research and development efforts can go off the rails. Bureaucratic rules and meddling can also be toxic to R&D efforts. Many organizations purposely try to isolate their R&D branches from the rest of the organization's activities for this very reason. Nevertheless, it is a rare R&D entity that is not eventually subject to all kinds of deterioration and rot. B.1.1: How Can Managers and Funding Officers Impede Progress? Why should this be true? Why are the scientists and technical people in funding organizations and in managerial positions often seen as an impediment to R&D productivity?[1] There are probably numerous possible reasons for this. One is that most funding organizations are unable or unwilling to hire people who have demonstrated or experienced personal success with technical innovation. Once someone has excelled in this pursuit, they are unlikely to give it up readily and willingly.[2] Another potential reason is that it is difficult for people who have not themselves innovated to recognize who is potentially productive, and who is less likely to be productive in R and D. Standard metrics of R and D productivity like citations and numbers of publications are more than slightly flawed.[3] The average person cannot really tell who is good, and who is not, and who might have potential. They do not know how to measure or determine the important skills that allow, or could allow, someone to be productive.[4] They also often cannot spot blatant fraud. As an example of how standard and easy metrics of "talent" can be flawed, senior physics faculty report that their best, most productive and successful students in R&D are not always those who scored highest on the standardized tests, like the SAT and the GRE. These exams roughly measure IQ, but as Einstein said, creativity or imagination is more important than knowledge (or the ability to absorb knowledge swiftly, which roughly equates to IQ). In addition, it is not easy to measure creativity, or an even more valuable factor, assiduousness. Since R&D is about failing over and over and over, if a person does not have uncommon drive and persistence, they will not be productive. Another issue is jealousy, resentment, and/or envy. Often the least productive, least capable people are found staffing funding agencies and taking managerial positions, with some notable exceptions. When these incompetents see their colleagues advancing, accomplishing things and garnering recognition and glory, it irks them. It is a very human reaction to lash out at those who they perceive to be more successful than them. In addition, there is a common phenomenon in STEM where people tend to fall in love with their own ideas. And even if these ideas stand very little chance of succeeding, the originator of an idea has a propensity to promote it over all others. This can become a problem when the originators wedded to failing ideas are the funding officers and managers. Many promising lines of investigation can be discarded or whither away while resources are wasted on failing ideas the funding officers and management are overly attached to for various irrational reasons. There are undoubtedly many other reasons why funding officers and supervisors can mismanage the projects under their purview. But this handful of observations suggests that this situation is not as uncommon as one might hope. B.1.2: Influence of Bureaucracies on R&D Most R&D entities, at least of a reasonable size, come along with a fair amount of bureaucracy. This is true of corporate R&D and government R&D. It has become increasingly true of academic R&D. Therefore, it is important that if an R&D organization is to function efficiently, that the bureaucracy not interfere with the R&D activity very much, if at all. For example, meetings are just a waste of time, in most cases. Meetings should be minimized at all costs. There are many other common features of bureaucracies and the attendant embedded and presumed hierarchies that are incompatible with R&D. For example, often the biggest breakthroughs come from the most junior R&D staff. However this upsets the "apple cart" of expectations in a hierarchical bureaucracy. It is commonly assumed that all of the progress and the rewards should be associated with the most senior people in any organization, not the most junior. Even if it is not reality, the bureaucracy frequently tries to dictate this, which can be corrosive. Anyone who is perceived to not be properly respecting the hierarchy (and that includes both official and unofficial hierarchies), can be branded as insubordinate, seditious or worse. For example, imagine that at some R&D organization, a supervisor is about to publish a spurious result. And suppose that this error is pointed out by someone who is not a supervisor. Although this is the proper action to take in a scientific context, that is, to correct mistakes, this can cause no end of trouble. This is because in a standard hierarchy, the boss or supervisor is assumed to be correct, even when they are obviously incorrect. No one is allowed to question this assumption. Exposing this reality can create a firestorm of repercussions. The "supposedly higher status" individuals like bosses and supervisors can be embarrassed and/or surprised.[7] These people are often endowed with effectively infinite power in organizations, or have seized this power for themselves. Contradicting these people can invite tremendous retribution, even when they are wrong, or maybe more accurately, particularly when they are in the wrong. Other problems can arise from an employee showing too much integrity when there is misbehavior the organization and the managers want to cover up. This "challenge to authority" can paint a target on the backs of these overly ethical employees. They are effectively branded as troublemakers, or traitors.[8] Still more difficulties can be due to an employee, who is paid to innovate, producing too much innovation, or the "wrong kind" of innovation. Being too productive, or producing undesirable results can invite trouble.[9] If an R&D "worker bee" "shows up" or overshadows a "favored and protected" colleague, issues can surface.[10] The people who get in trouble are the productive workers, not the unproductive workers, as ridiculous and contradictory as that might sound.[11] In general, a heavy-handed bureaucracy in an R&D organization that taxes its productive talent with all sorts of ridiculous rules and requirements and "busywork" and produces a toxic environment will soon start to experience a productivity decay. This can become a self-reinforcing negative feedback loop, leading to complete stultification and irrelevance. The best people leave in successive waves, leaving behind a residue of self-congratulatory incompetents who only do "R&D theatre" or "cargo cult science". They go through the motions, but nothing of value is ever produced.[12] B.1.3: Decay and Erosion of Quality As one surveys the current condition of research and development in the US, some issues become apparent. For example, former Vice Chairman of the US Department of Defense Joint Chiefs of Staff General John E. Hyten has expressed concerns about the current unproductive state of US military R&D. Also, a recent publication speculated that the apparent ongoing decrease in R&D quality is associated with the incompetence of the people being attracted to the field.[13][14] In addition, there seems to be a contagion of fraudulent activity in R&D in recent years.[15] Woke ideology, including DEI (Diversity, Equity and Inclusion), has not spared R&D. It has been associated with aggressive demands for mediocrity, and the development of anti-meritocratic systems throughout STEM. Many of the most talented are discouraged from pursuing a career in STEM by a variety of woke-related methods. In addition, woke ideology has as one of its prominent features a substantial amount of wasted resources on unproductive nonsensical woke programs and agendas. These are of such poor quality that they appear to be almost intentional self-parodies. Another factor is the emergence of a generally complacent, cavalier, unproductive and counter-competitive set of attitudes. In the last few decades, the Cold War ended. There has also been a gradual deindustrialization of the US during the same period. After these events, it seemed like most technical efforts were devoted towards trivial matters, at best. Excellence and deep thoughts were set aside to refine web pages and add features to browsers. Merit and standards were discarded as "unfair". Dedication and talent and competition were frowned upon in an "everyone gets a trophy" culture. Prestigious positions that were previously awarded based on performance and excellence are given to those who managed to manipulate the system into giving them an undeserved reward. This is almost akin to the results of a corrupt popularity contest. Substance became a secondary or tertiary or quaternary concern. Status was conferred like a prize upon those who had enough brownie points in a kind of "Intersectionality and Victimhood Contest". Social Justice Warriors were everywhere, virtue signaling. Only people who were unable to perform in a specific job were deemed "worthy" of holding that job.[16] B.1.4: Notes [1] Of course, nontechnical people can occupy these positions. It is a rare nontechnical person who can execute the demands of these positions with aplomb, but they do exist. However, in most cases, technical people are chosen for these jobs. It is probably felt that they will be able to understand the R&D activity going on better, and therefore will be able to make better decisions. Unfortunately, this is often not the case. [2] Einstein even opined on this topic. The experience of innovation and contributing to STEM efforts is so heady that the person involved will do almost anything to return to it, again and again. They will forgo salaries and other resources to continue to follow this enticing, exciting, fruitful and personally rewarding path. [3] A notable feature of modern R&D is the creation of immense tomes and volumes of documents that essentially no one reads. In many cases, probably no one can read this material. Most papers are never cited one time, not even by their authors. [4] "You can't expect the sheep to respect the best and the brightest. They don't know the difference, really. The vast majority of them [humans] do not possess the ability to judge who is and who isn't a really good scientist. That is the main problem with science, in this century. Science is being judged by people, [and] funding is being done by people, who don't understand it." -- Nobel Prizewinner Kary Mullis There are many examples in the history of science of this phenomenon.[5] [5] Einstein, one of the most fabled scientists of the last century or so, was misjudged over and over during his life. To start with, Einstein began speaking late. This is such a common trait of high-IQ individuals that it is now called 'the Einstein Syndrome'. This condition was so marked in Einstein's case that his parents feared that little Albert was retarded, according to his younger sister Maja. As another example, Einstein's teachers and professors were not very impressed by him. Einstein was enrolled in a college program towards a teaching certificate in mathematics and physics at ETH in Zurich. There were 11 students in Einstein's class. Of these, only 5 made it to the final year. Einstein's graduating score was 4th out of these 5 students. He obtained the lowest passing grade. That is, Einstein was at the absolute bottom of his graduating class. All the other graduates were offered teaching assistantships, but not Albert because of his poor performance. Here is another evaluation of Einstein's performance in higher education: "If Einstein were reincarnated as a graduate student now, it seems unlikely that he would complete a PhD." -- Dudley Herschbach, 'Einstein as a Student', Chapter 15 in 'Einstein for the 21st Century', P. L. Galison, G. Holton and S. S. Schweber, Princeton University Press, 2008 Still one more telling comment comes from the famous professor Hermann Minkowski who taught Einstein at Zurich's ETH. Commenting on Einstein's contributions, Minkowski opined; “It came as a tremendous surprise, for in his student days Einstein had been a lazy dog…I really would not believe him capable of it.” -- Hermann Minkowski when he learned of Einstein's work on relativity in 1905. When Einstein submitted his 1905 special relativity paper as a potential PhD thesis, the faculty at the University of Zurich rejected it. Later, Einstein struggled for several years to find paid employment after graduation. He did some tutoring, but eventually Einstein worked for Dr. Friedrich Haller at the Swiss Patent Office in Bern. At first Haller rejected Einstein as unqualified for the position, and it took quite a bit of effort from Einstein's friends with political connections to get Einstein reconsidered. Einstein held a lowly third class patent examiner position for years. Einstein's position at the patent office was also temporary for more than 2 years. Others were immediately hired as 2nd class patent examiners and made permanent employees much more rapidly. Seven years later, in 1909, Einstein resigned from his patent office job to take a chaired faculty position as a professor of Theoretical Physics at the University of Zurich. His supervisor at the patent office was disappointed since it was felt that if Einstein just kept working steadily, he might eventually be promoted to be a first class patent examiner. It was mentioned that it was even possible that Einstein could at some point take over the entire patent examining office. This sort of evaluation is so beneath someone like Einstein that it is difficult to know what to say about it.[6] [6] Another striking observation arises from a conversation with several "esteemed" philosophy professors of some note, who will remain nameless here. They opined that it was a tragic shame that Sir Isaac Newton had wasted all that time studying chemistry and gravity and motion and calculus. Newton had shown some promise in philosophy, they thought, in some of his writings. They felt that if Newton had just ignored all that "worthless boring BS" about science and math, that only morons like STEM people were interested in, that Newton might have really made a name for himself. This conversation was so staggeringly ignorant, arrogant and stupid as to be almost beyond belief. [7] Remember the cardinal rule for success in business is that one should never surprise their boss. However, it is probably even more important not to embarrass the boss. Many if not most managers of R&D activity are either unfamiliar with R&D and/or are failed R&D investigators. They often resent the productive staff they are nominally supervising. They might be envious and have feelings of jealousy. They are often confused by what is going on around them, since they are unable to understand the work they think they should be controlling. However, they cannot admit this because of their egos. Many suffer from "imposter syndrome". This kind of situation can lead to toxic environments, very easily. [8] The well-known Richard Feynman story about the Challenger Disaster on January 28th, 1986 provides an interesting data point on this issue. An examination of this situation reveals that the NASA managers silenced the engineers who knew about the O-ring problem and warned about it, repeatedly. However, the managers had effectively infinite power. Therefore, no one dared to point out that the managers had screwed up, including Astronaut Sally Ride and General Donald J. Kutyna and the engineers. But Feynman was old and an emeritus professor and dying from cancer. So it was safe to leak the story to Feynman and have him tell it on camera at a press conference. It would have been too dangerous for anyone else to do it, because of the power of the managers in the NASA bureaucracy. [9] The phrase "a prophet has no honor in his own country" has its origins in the Bible, in Mark 6:4. It is somewhat akin to the aphorism that "faraway fields look greener", a statement often attributed to publisher Robert J. Collier. It is also similar to the well-known proverb, "familiarity breeds contempt." In an R&D context, these sentiments are quite readily observable. Often investigators in a certain organization are treated with disdain, scorn, or indifference. At a very minimum, they are usually underappreciated. [10] In many R&D organizations, there are certain "investigators" who do not do any investigating. Perhaps they are actually unable to do any investigating. However, often the management favors these people, possibly because of personal relationships or because these nonfunctional "nonplayer characters" (NPCs) do not create any "extra work" for the managers since these NPCs are inactive. Or maybe these NPCs are favored since they never challenge the managers in any possible way, so they seem "nicer" and "better". Anything that casts these "RIP" (Retired In Place) people in a negative light can draw the ire of their protectors, the managers. [11] Sometimes buyouts are offered in an organizational downsizing. For the most part, those who take them are the most productive people an objective observer might want to retain. The entity is then left with the 'dregs' who cannot easily get another position. However, the decision makers might not recognize this at the time. This is because they are often unaware of what is going on in their own organizations, or are in denial, as has already been discussed. [12] If things in an R&D organization get to a certain stage, anyone daring to produce something of value might very well be punished for this act of nonconformance or "treason". A motivated self-starter showing initiative can frequently be characterized as disloyal, rebellious or mutinous. This is particularly true in the case of insecure managers who crave control and feel inadequate. [13] Self-Selection and the Diminishing Returns of Research https://econ.ntu.edu.tw/wp-content/uploads/2024/05/macro_1130523.pdf Authors: Lorenz K.F. Ekerdt and Kai-Jie Wu, May 2024 [14] Another recent example of a similar sort of recruiting competence collapse was observed in the US military. When standards were lowered, it suddenly became more and more difficult to meet enlistment quotas. Even the service academies, where admissions had historically been highly sought-after, began to have increasingly more trouble recruiting the next generation of officers. Some of the prestige of these positions is associated with the competition to gain admittance. Once the standards are reduced, the prestige evaporates, and people are less willing to seek entry. And this creates a negative feedback loop, so things get progressively worse. To paraphrase a well-known joke, people only want to belong to selective groups that refuse to admit them. There is a sort of cachet or an aura that surrounds a club, organization or category that is "picky". [15] Why Science Fraud Goes Deeper Than the Stanford Scandal... https://www.youtube.com/watch?v=2mWwXO_guHk Learn about high-profile cases of scientific fraud, its prevalence, and its impact on academia. Discover situational pressures and solutions while exploring the quest for research integrity. It includes coverage of Diederik Stapel in the Netherlands, Woo Suk Hwang in Korea, and Marc Tessier-Lavigne, the president of Stanford University who stepped down in 2023. A must-watch for scientists and curious minds. -'Science Fictions' book, Stuart Ritchie - anonymous surveys show 2% of scientists admit to personal fraud, and 14% say that they know of other fraudulent scientists [16] In many cases, what we have now are a cadre of 'cosplaying' scientists and mathematicians, vacuously going through the motions. They are effectively engaging in a sort of STEM theatre. Paraphrasing a remark of UK commentator and comedian Konstantin Kisin, "We have been imitating the things that got us here, instead of actually doing them, similar to 'cargo cults' in the South Pacific after WWII". There is a famous aphorism by Aristotle: “Those who know, do. Those that understand, teach.” This was altered somewhat by George Bernard Shaw for his 1905 play "Man and Superman" to malign teachers. Historically, the related precept that was followed was "Those who can, do". Now it seems that this apothegm has been replaced by "Those who cannot, pretend". However things seem to be even worse currently. This is because the current situation might be more accurately described as, "Those who pretend are promoted into positions of power. They get to decide who is allowed to make any attempts, or even what is permitted to be attempted".

Assorted Topics in Research and Development A: R&D General Information A.3: The Research and Development Pipeline The public is only vaguely aware (if at all) of the segments or components involved in the process of research and development. In broad terms, there are two main divisions to the process, with plenty of overlap between the two. There is the broad general area of science, where new natural principles are discovered using the scientific method. Once some natural phenomena are understood well enough to have some predictive power, then technologies can be based on them. And so a second major area involving the creation of technologies exploiting these natural phenomena can emerge. In addition, there is also the related field of mathematics. Both scientists and engineers use mathematics as an important tool to describe natural principles and to design the technologies based on these principles. However, mathematics is a discipline in its own right as well, not just a language that is handy for describing quantitative things. Modern mathematics goes well beyond simple numbers and arithmetic. Symbols which stand for much more advanced concepts are manipulated and combined using logical rules to uncover all manner of beautiful and useful relationships in the quantitative realm. And even these seemingly abstract concepts have been found to be extremely useful in all manner of practical applications, including those in the fields of science and engineering. It is probably worthwhile to describe the scientific method itself. Although everyone should be familiar with this, I have found that many are not, or only have a vague understanding of what the scientific method actually is. Sir Francis Bacon (1561-1626) famously wrote an extensive set of precepts or guidelines for supposedly what constitutes the "scientific method" (which I had to memorize in grade school). In spite of that, I later discovered that professional scientists do not really pay attention to those formal "rules" at all. The scientific method is really quite simple. It involves a sort of model or theory, which is guessed to somehow represent nature. And predictions from this model are compared with evidence. The evidence comes from laboratory experiments or field observations. If the predictions of the model fail to be sufficiently close to the empirical data, the model is modified or discarded. One can dress this simple comparison up quite a bit, with hypotheses and double and triple blind procedures and statistics and all kinds of other stuff. But the scientific method really is a sort of trial and error process of comparison of model predictions with actual measurements of some kind. That simplicity appears more complicated in actual practice. The tasks involved in following the scientific method have become so complicated and specialized that expert teams typically are involved in a scientific investigation. For example, one can utilize specialists in a. laboratory experiments b. field data collection c. data processing d. computer equipment and information technology e. equipment design and manufacture f. funding acquisition g. model development h. statistical analyses i. theoretical and mathematical tool creation and numerous other fields to complete a complicated scientific project. Perhaps a few decades ago, a team of one or two or three people could perform most of these tasks. But as time goes on, and the projects become more elaborate, this becomes steadily less feasible. After there are some reasonably well-established natural phenomena revealed by the scientific method, then clever people can sometimes find a way to utilize the principles that govern these natural events. If there is enough predictability discovered, then technologies can on occasion be based on these principles. This is the province of applied science, and its more practical cousin, engineering. Obviously, the technology produced by applied science and engineering can also be put to use to further pure science. And so, these pure and applied disciplines depend on each other in a way, and augment each other. Drawing strict boundaries between pure and applied activities is not always easy or obvious. Probably way too much effort is expended on this. It is usually just driven by egos. However, if money or other remuneration is the object, rather than just fame, glory and beauty, the applied disciplines are the object of far more financial investment than pure science or pure mathematics. All of these activities can be stitched together into an R&D pipeline, of sorts. One organization does not have to perform all the roles necessary for moving something from an idea to a finished product. However, it is valuable that those with the necessary skills be able to work together in concert, either in one organization or a group of collaborating entities. In my own experience, a typical procedure in the R&D pipeline is to take some result, and "pitch it over the wall" to the next team of specialists downstream. There is unfortunately often very little communication between these teams. Misunderstandings develop, because of a lack of communication. It is far better for the experts in one area to sit down and coach those in the later stages of the pipeline in the discoveries and ideas. There is less chance for confusion in this case. Later teams can still tailor their solutions, drawing on their own particular expertise and experience. But things work more smoothly if there is more direction communication and involvement.

Assorted Topics in Research and Development C: Improving R&D C.1: Attributes of a Productive R&D Environment Clearly, being productive in R&D is not just a matter of financial stability.[1] For example, one needs to be trained properly in R&D. In addition, a person probably needs to have a certain level of passion for the pursuit of R&D to do it proper justice. Without intense interest, it is probably unlikely that an investigator will display the necessary determination to succeed. However, people need other things as well to be successful in pursuing R&D. For example, it is useful to have access to a steady flow of the latest results in any given field. Seminars and lectures and conferences and research papers and discussion groups can help people digest what is going on at the forefront. Also, R&D is a complicated mix of collaboration, cooperation and competition. Not everyone in R&D should be expected to have expertise in the numerous fields that a given project might require. I have observed R&D managers with no expertise in anything, demanding that the people working under their direction should be able to perform every single aspect of every project on their own.[2][3] This wastes resources, including time. However, it might make the manager feel better, since they are able to bully those working for them with impunity. And unfortunately, this is not at all uncommon. In any case, more attention should be paid towards creating productive environments for those who can contribute, under the right conditions. Mixing lower qualified employees in with the high performers can destroy whatever productivity and excellence exists. So it should be done sparingly, if at all. A smarter accommodation might be some sort of segmentation, or partitioning. It is also true that some exposure to high quality long term R&D can serve as a sort of stimulant or fertilizer of sorts, for all R&D activities in an organization. But one has to strike some sort of compromise, between various extremes. Too much exposure can obviously destroy the long term R&D, or at least its quality. These are not particularly easy problems to address, because the social science and associated design strategies are not well understood and developed, it would appear. For any given individual attempting to explore and innovate in STEM, there are several important necessary factors. In approximate order of importance, they are as follows. (1) intelligence Some intelligence is valuable, because it is associated with being able to analyze situations and to acquire information. But intelligence is not the main factor in R&D success, it would appear. (2) creativity As Einstein and others have repeatedly noted, imagination and creativity are far more important than knowledge and information. Most of the knowledge we in STEM is wrong in one way or another. It is at best temporary knowledge. As we get better measurements and think of new approaches, almost all of it gets eventually discarded. (3) diligence By far the most important factor of these three is grit, perseverance, conscientiousness, persistence, and related attributes. When one is trying to make progress on a very difficult problem, in engineering or mathematics or science, most of the attempts will result in failure. And the investigator has to be able to pick themselves up and dust themselves off and try again. If they cannot do this, they will never accomplish much or amount to much. Part of the difficulty that investigators face in high pressure, short term R&D situations is that there is not enough time for diligence or persistance. If a first or second attempt fails, they have to move on. Elon Musk's Spacex failed over and over, for many years before achieving some successes. Those at NASA were disdainful of the Spacex success, since they felt they were not allowed to fail at NASA. But that means they were not able to learn from failure. As has often been noted, failure is the best teacher. One needs the freedom to fail. And one needs the freedom to attempt to pursue what appear to be "crazy ideas". Another important issue in R&D is integrity. A STEM investigator without integrity is going to end up fooling themselves. Some even commit deliberate fraud and suffer few if any consequences. They destroy their reputations and waste resources. Part of the problem of many organizations is that there is little-to-no accountability for a lack of integrity and honesty. And if there are no punishments for this type of behavior, then a rot sets in that destroys the organization. Another toxic element to avoid, that can ruin an R&D organization, is the intrusion of endless meetings and paperwork. Managers might like this sort of activity because it gives them the illusion of control. This stuff is usually a waste of time, and can be classified as administrivia or yak-shaving busy work.[4] An additional problem that besets the modern R&D environment is an excessive focus on publications. Many of these are increasingly low quality, and replete with errors. Some of them are just the same stuff, being republished over and over. Some of these publications are plagiarized, which is an intellectual sin which is rarely punished these days. Nevertheless, plagiarism can be incredibly corrosive. What the immense and unreasonable pressures for publication at all costs creates is huge tomes and volumes of documents that no one reads, and probably no one can read. And because the technical publishing enterprise has been corrupted, this is a very expensive proposition for organizations and individuals. Publishing entities reap immense profits from this activity. And the quality is increasingly watered-down. This is an area in STEM R&D that is ripe for reform. A proper R&D environment might do well to follow Carl Friedrich Gauss' aphorism, "pauca sed matura". The English translation of this Latin phrase is roughly "few but ripe". Pushing to prematurely publish endless pointless and repetitive results is not a proper and efficient use of scarce resources. C.1.1: Notes [1] Of course, at one time, almost the only way to be productive in R&D was to find some royal patron, or someone else wealthy to support one's investigations and endeavors. Later, there was a period when gentlemen scientists and mathematicians were able to self-fund, through inherited wealth. Some of the most famous and productive figures in the history of STEM have been upper class ladies and gentlemen who had the resources and leisure to pursue STEM. But most people who inherit wealth do not currently devote themselves to creative activities, particularly in science, engineering and mathematics. Even those who have built profitable technology businesses frequently do not travel down this path. I personally know some who have done this, but they are fairly rare. [2] These individuals from different groups with varying types of training, experience and expertise have to interface with each other. Therefore, it would be helpful if they all understood each other's language and tools and customs to a certain extent. No one can have mastery of everything, but being able to talk with potential collaborators who might help you build something is very valuable. [3] I have also observed a "pitch it over the wall" mentality where a group at some stage in the application pipeline, just hands a project off to the next group without helping them get up to speed. This is also suboptimal, but favored by many middle managers. I am not quite sure why, because it is quite inefficient. [4] One faculty position I encountered required a minimum of 80-100 hours a week of committee attendance for all faculty. This was before any teaching or class preparation or grading or fund raising or publication writing or laboratory work or R&D. And yet, nominally, the position was an "R&D position". One faculty member who had just been tenured whispered to me that the worst thing about the position was the committee work, and that there was no way to avoid it. I asked the chairman about this and he flew into a rage, demanding to know who had tipped me off since he wanted to destroy them. I refused to tell him. He revealed himself to me, in that instant. It was a very unpleasant interaction, but it spoke volumes. Years later, this department at this university has been annihilated. They were poorly managed, did not produce anything, and so they were obliterated. It appears that the department chairman's plan for playing university politics basically failed. The chairman had the illusion of control, but he was ruining his own department with his arrogance and his ego and stupidity.

Assorted Topics in Research and Development B: R&D Problems B.2: More Issues in R&D B.2.1: Unstable Funding One of the problems in Research and Development (i.e., R and D) is that, for the most part, the funding periods are too short term and funding is too uncertain. In other words, the funding of R&D is sometimes too unstable. Also, there is not always an easy way to bridge between two or more time periods of relatively stable funding. The support staff can usually do this, but the technical staff rarely can, so they basically suffer. Sometimes the experts on the research staff or the technical staff are even lost completely from a project because of these funding gaps.[1] The research staff provide most of the funding for their projects and everyone else, but they are the people who are most often "abused" in many organizations.[2] No one would ever do that to the support staff, at least not in my experience. Yet the support staff almost universally resent the presence of the technical staff, at every Science, Technology, Engineering and Mathematics (STEM) R&D organization I have ever worked at. STEM people spend far too much of their time attempting to raise money. And even after they might be lucky enough to garner some resources, most of these resources are often then siphoned off by unproductive people who do not contribute, and many times, cannot contribute. The "principal investigator" who raised the money and is responsible for the project has no recourse in the vast majority of cases. Some organizations even have divisions or departments, expressly devoted and dedicated to hiding this illegal activity from the government and other outsiders, including the media and the public. B.2.2: Decision Makers The people who we pay to make the decisions about things like funding, hiring, firing, staffing, and "tasking" are seldom our best and our brightest.[7][8] Usually the most innovative ideas do not get funded through this procedure. However, even if funding decision mistakes are made in a long term funding framework, there is some chance that the investigators will be able to perform some "off-the-books, pirate investigations" on the side.[9] But if the funding is more short term in nature, then the investigators are on shorter leashes and therefore have less freedom. One would probably find less unofficial, sanctioned activities going on in this kind of a short term funding environment. The managers often prefer these shorter leashes, probably because they appear to crave control. But in general, these arrangements are less productive and less likely to result in innovations. Under a short term funding regime, productivity and radical new ideas and thinking and innovation are more difficult. All anyone has time to do in these circumstances is to just barely survive; that is it. Out-of-the-box thinking is usually not possible under these conditions. The managers do not seem to really care, but the stakeholders, including the ultimate customers, probably do (or would if they were aware of what was happening). But the managers frequently do not prioritize what the customers want or need, in my experience. One might find this very odd, but it probably occurs more often than not.[11][12] Another issue that R&D organizations confront is what is known as the "Inventor's Dilemma". Often new advances make older procedures and knowledge and products obsolete. And managers, supervisors, and supporters of the previous status are loath to abandon it. This is somewhat akin to the well-known "Not Invented Here Syndrome". Some organizations have tried methods to get around these, but they are not in widespread use.[13] B.2.3: Some Examples As an example, consider the Naval Research Lab (NRL) on the grounds of the Naval Yards in Washington DC. NRL was supposed to be modeled on the Edison Labs in New Jersey (NJ). Thomas Edison wanted the NRL to be in NJ so he could have more influence over it. However, the federal government bureaucrats in DC over-ruled Edison, and the NRL was established close to DC. Another thing that the federal bureaucrats won on was supporting NRL with short term funding. It is probably not likely that Edison would have wanted to create a "soft money shop" (described in a previous essay in this series). However, that is what was produced, even with Edison's supposed guidance, suggestions and advice. There is some innovation at NRL. However, it is probably very little compared to the amount of funding and the number of staff involved. However, innovation is not really the goal in these environments, apparently. The point seems to be about "control". The same is more or less true of various 'Applied Physics Labs' around the US. They are soft money environments, from all appearances. And the result of these arrangements is evident. There does not appear to be much long term thinking or R & D in these places. And there is not much to show for all the resource expenditure either, as near as one can tell. Another example is presented by the FFRDCs (i.e., Federally Funded Research and Development Centers). The FFRDCs are supposed to provide long term research and guidance for various government labs, enterprises and organizations. FFRDCs are funded by hard money, for the most part. From the observations of many, almost all the ideas and progress in the technical areas relevant to the associated communities comes out of the FFRDCs. This seems to be true in almost every case. For example, most agree that the excellence of NASA is bolstered considerably by its primary FFRDC, the Jet Propulsion Laboratory, or JPL. Still one more example is the research area (what was previously known as "Area 11") of Bell Labs. The research area of Bell Labs used to be a long term funding, hard money environment. And it produced a tremendous number of substantial advances during that "golden age" period. Now, Bell Labs is just another short term funding, soft money environment.[14] Bell Labs was a beacon of R and D excellence for decades. And then, through mismanagement and other problems, Bell Labs went bankrupt. It was taken over by Lucent, and then by Alcatel, and now is part of Nokia. What remains of Bell Labs is now essentially a soft money shop. There has been effectively no measurable substantial ground-breaking R and D output now from Bell Labs for decades. The atmosphere that made it productive, the "magic", has been broken, dissipated, and/or destroyed.[16] B.2.4: Studies It would be nice to have studies which provide hard evidence for all these "impressions", which are quite common in the R&D profession. Data is always valuable, if we are trying to understand how to best organize ourselves. B.2.5: Notes [1] The funding mechanisms with inflexible rigid short time lines create many headaches. Many times there is no way to "carry over" funds from one period to another. And the projects often have very narrow scopes, and are subject to rigid plans, determined by the funding officers. Of course, the funding providers like this level of control. They would like even more control, partly to assuage their own egos, it would appear. But the truth is, for the most part, these funding providers are usually not the best arbiters of what are potentially useful and fruitful lines of research. This argument is well-documented. One can gather quite a few examples from the published literature and assorted historical accounts, such as the popular book "Loonshots" by Safi Bahcall.[2] [2] The main thesis of Bahcall's book is that only small organizations can innovate, and that as they grow, a "phase change" takes place so they can no longer innovate. The reader should be cautioned that this hypothesis probably does not describe the situation very accurately. There are numerous counterexamples to this proposed precept, so it is unlikely to hold true in many cases. Nevertheless, the numerous examples of scientific and technical advances Bahcall lists are instructive none-the-less. What is particularly interesting is how much struggle was involved, in almost every case, to accomplish anything.[3] [3] “Most of the important things in the world have been accomplished by people who have kept on trying when there seemed to be no hope at all”. -- Dale Carnegie [4] Technical staff are frequently viewed as expendable, transient, easily replaced, and interchangeable with each other. None of these things is actually particularly accurate. But organizations often adopt these attitudes. So someone with multiple graduate degrees and a collection of patents worth billions is frequently viewed as less important by many R&D organizations than a secretary with a high school diploma or less. The support staff, like secretaries, often sense this and this can cause all kinds of friction and problems.[5] [5] The reason for this is probably because the administrative and managerial staff are not familiar with the technical work. Some of them are failed technical staff who wandered into management. Therefore, they often have chips on their shoulders. Frequently, they want to "settle scores" or exact revenge on the successful technical people.[6] [6] During a review of a division of an immense pharmaceutical company, it was revealed that the R&D division was forbidden by the management to innovate. The scientists were plenty frustrated. They were treated with contempt by the rest of the division. This is almost humorous, because pharmaceutical companies are constantly bragging about how much they spend on R&D in public hearings, in front of Congress under oath, and so on, to justify what some characterize as their "obscene price gouging". According to a variety of reports, often the amount spent on R&D at these corporations is less than the executive bonuses, or the public advertising budgets. So, the entire process starts to appear to be a bit of a farce. This was shocking when it became apparent to me decades ago. However, my colleagues and I have seen the same thing play out at a variety of organizations and institutions during our careers. Innovation by the technical staff is often viewed as a form of obstinate noncompliance or insolent rebellion by the managerial class. And it is often punished accordingly. [7] "You can't expect the sheep to respect the best and the brightest. They don't know the difference, really. The vast majority of them [humans] do not possess the ability to judge who is and who isn't a really good scientist. That is the main problem with science, in this century. Science is being judged by people, [and] funding is being done by people, who don't understand it." -- Nobel Prizewinner Kary Mullis [8] A joke about a new manager who takes over an R&D facility is somewhat relevant in this regard. The manager angrily asks his technical staff, "What have you done for me lately?". One member of the technical staff pipes up, "Well, we haven't caused you any trouble, have we?" Humor aside, there is some core truth here. Successful innovators and investigators are frequently viewed as "insubordinate" by their managers. They make the supervisors feel inferior and jealous. The management resents these people, who are able to accomplish things the managers cannot. They also are frequently difficult to control. They do not take direction well. Their discoveries and inventions can create more work for the management, and extra costs and risks. They dress funny and keep strange hours. Often "normal" people cannot understand them, and so on. [9] Examples of these "pirate", off-the-books projects include Bell's Inequality work. Both the original theoretical work done by John Stewart Bell and the subsequent Nobel Prize-winning experimental verifications of Bell's work were done unofficially and in semi-secret. If their supervisors were aware of what these scientists were up to, they would almost certainly have been brutally punished and their careers might very well have been destroyed. Almost all of Einstein's early work (from about 1900-1916 or so) was carried out sort of "under the radar". It would probably have never been supported under normal circumstances.[10] Claude Shannon's most famous work in information theory was all done more or less in secret. It is not clear that Shannon could have performed it any other way. [10] Einstein was unable to secure regular employment for several years after he graduated from his ETH program with the lowest score in the graduating class. Finally through extraordinary efforts and the assistance of some friends, he was able to obtain a temporary position as a patent examiner. Einstein kept to a strict schedule at the patent office, working 8 hours a day there. However, Einstein secretly kept some papers in a desk drawer that he worked on when his supervisor was not around. Einstein could quickly hide these when someone was approaching, and this allowed Einstein to do some technical work of his own choosing while "on duty" at the patent office. It is clear that if Einstein had been caught, the consequences would have been severe, but this never occurred. Einstein devoted a large fraction of the other 16 hours a day to his research and writing pursuits as well. He tried to get 8 hours a day of sleep, but often his writing and thinking interfered with this. [11] A young researcher in the Bell Labs Mathematics Center noticed some odd phenomena. He asked a senior mathematician about his observations. He said, "I notice that the managers seem to repeatedly make decisions that are bad for the customers, the employees, the shareholders and even for their own careers. What is going on?" The senior mathematician said he had witnessed the same behaviors. The senior mathematician had thought about it for a while, and his hypothesis was that power is like an addictive drug, so consciously or unconsciously, these supervisors want to show everyone else that they can make crazy destructive decisions (that even hurt them professionally), just to demonstrate to everyone that they have this level of power and control and that no one can prevent them from doing so. At later employment, this young researcher saw similar scenes play out over and over. Middle managers would throw tantrums over how averse they were to satisfying the needs and desires of the "customers". They would even punish their employees who were being too attentive to the customers. This is a discouraging state of affairs, but not particularly uncommon, unfortunately. [12] A method that has been used to ameliorate the problems with decision makers is to rotate people in and out of these positions. For example, some R&D departments have a department chair, and that position rotates to someone else every 2 or 3 years. This way an investigator does not feel "trapped" in the position, so that their career is damaged. It also reduces the problem of supervisors abusing their power because soon they will step aside and lose their privileged position. Those they might have been abusing will soon irrevocably be supervising them, so they are somewhat restrained from bad behavior. In funding organizations, those that are most successful like the Defense Advanced Research Projects Agency (DARPA, sometimes known as ARPA) rotate the funding officers in and out of their positions. People with permanent academic or other R&D jobs are recruited for these temporary positions. They do not grow stale in these jobs and bring a fresh perspective to the work, compared to funding officers at other agencies. For this reason, there has been some effort to copy this model at other agencies for intelligence R&D (IARPA) and the health sciences (HARPA). [13] The 3M corporation has a rule that supervisors of various divisions and departments have to derive most of their revenue from recent inventions and discoveries. This forces them not to rest on their laurels and previous successes. Under Steve Jobs, Apple Corporation was always creating new product lines that would cannibalize previous product lines, on purpose. The people working on the older projects were not happy with this, but it kept Apple from falling into the trap of the Inventor's Dilemma. Many other companies like Kodak and Panasonic and Xerox have fallen prey to the Inventor's Dilemma. [14] Academia has some soft money components and segments of various forms. But what makes academia slightly different is that there is still some measure of hard money that is available in the system. When the soft money positions are in a strictly academic environment, there is still teaching going on. There are still lots of people with some or all of their funding provided by long term hard money sources. In addition, there are not very firm controls over what activities take place. Therefore, there is enough breathing room so some innovation is still able to take place, in spite of the issues that are associated with soft money support. The funding providers do not have as much control over the activities taking place, and this is a good thing for innovation and creativity.[15] [15] In academia a big difficulty has been the growth of administration and overhead supporting it, and the shrinking number of "regular" faculty positions, as the faculty are replaced by cheaper adjuncts. So the academic model is slowly weakening under these trends. [16] Industrial R&D labs https://www.linkedin.com/posts/dansgoldin_a-big-impact-we-can-make-in-this-new-industrialization-activity-7199746551051689984-gX1U/ Post by Dan Goldin, former head of NASA: "I've been meeting and working with teams who have exciting ideas and designs for the future of the American industrial economy. Industrial R&D Labs like Bell Labs use[d] to work on tough problems like this. Curious, should we bring back the industrial R&D lab?" -Comment by Charles Camarda, Astronaut and Research Engineer: "We used to have them. They were called NASA Research Centers. When NASA decimated funding to independent applied research we destroyed our research culture and our impact on industry. NASA and WPAFB [Wright-Patterson Air Force Base] matured composite materials analysis and understanding so it could be adopted by commercial aircraft companies. When you place all your funding into SOA [SOA=State of the art, or Service Oriented Architecture] large space projects at the expense of research for 30-40 years this is what happens. You lose your core ideology gradually over time like the proverbial boiling frog. Take a look at what is happening to Boeing. It's very difficult to regain that core culture/ideology once it is lost and it takes time."

Assorted Topics in Research and Development C: Improving R&D C.3: Appendix - Government Research and Development If a stable hard money environment is good for innovation, then why are government R and D organizations so seldom high-performing institutions? That is an interesting question. I do not think it is impossible to create very productive environments inside some segment of a government. However, it is apparently not that easy to do. One only has to look at the copious counterexamples that exist for evidence. Governments, being able to literally print money, are in a slightly different situation than other entities. They control the laws and their enforcement. And they also control the currencies to a certain extent. So governments can obviously produce a financially stable environment, if they want to. The biggest problem that government R&D has is the quality of the people the government hires to do R&D. If you are surrounded by incompetent people in an R&D environment, your own productivity probably suffers. It is well known that some individuals in brain-storming sesssions are so toxic that nothing can be accomplished with their involvement. They effectively just impede progress by getting in the way. Also, governments essentially have no way to get rid of incompetent employees, at least under normal circumstances. In fact, many government organizations intentionally attempt to hire as many incompetent people as they can, in the name of "fairness", or something. In this way, these government jobs are a sort of glorified "welfare program". People that could never function in a normal environment are hired by government.[1] C.3.1: Notes [1] This situation is not all bad, because confronting it forces us to address issues that we would rather ignore because they are uncomfortable. As technology advances, many menial jobs that humans could do will be done more cheaply and faster and more efficiently by machines. It is not clear that anyone has any good ideas about how we should reorganize ourselves in these situations which we might have to deal with in the future. We have not given this enough consideration yet, I think. This would be a perfect area for sociological and psychological studies, however. Efforts to implement "universal basic income" have a mixed track record, at best. But that does not mean we should not continue to experiment with programs. People get satisfaction and meaning out of contributing. So we should be thinking very hard about what we are going to do with people who have very low skill levels. In the case of research and development efforts in government or other organizations, low capability people must not be permitted to interfere with the actual mission. Also, the worst thing one can do is to give them power over everyone else, including the mission, decision making functions, hiring, firing, evaluations, an so on. Unfortunately, that is an amazingly common situation. This has really serious consequences for morale and productivity in an R&D environment.

As I sometimes do, I listened carefully to a fairly recent podcast on this topic and took some notes. I include the link to this podcast and my notes below. You can scan my notes and decide it if is worth your time to listen to the podcast. If you do, you already have a rough idea of what is in the podcast, and what is discussed. You have a sort of "guide" to the podcast. If you do not want to bother listening, you can maybe get something out of the notes, at least. This podcast, and some similar podcasts, might be folded into my recent draft essays on R&D which I have posted here on Thinkspot. Science is in trouble and it worries me. https://www.youtube.com/watch?v=QtxjatbVb7M Physicist Sabine Hossenfelder explains in this podcast. Innovation is slowing, research productivity is declining, scientific work is becoming more disruptive. In this video I summarize what we know about the problem and what possible causes have been proposed. I also explain why this matters so much to me. 00:00 Intro 00:33 Numbers 06:33 Causes 10:32 Speculations 16:25 Bullsh1t Research 22:06 Epilogue -Scientific progress is slowing down and most of what is published in academia is bullsh1t. -number of scientists have increased with time, in absolute numbers (now more than 8.8 million) and as a share of the population -number of scientific publications are growing -physics publication volume doubles every 18 years -electrical engineering literature doubles every 9 years -all the effort we put into science has fewer and fewer results -the most obvious measure is productivity, comparing inputs with outputs, economically -total factor productivity TFP is one metric -the input is labor, capital and technologies -the output is goods and services that you can sell -in 2016 a group of economists looked at this in a paper called 'Are Ideas Getting Harder To Find?' by Bloom, Jones, Reenen and Webb from the National Bureau of Economic Research in Cambridge Massachusetts (working paper 23782) http://www.nber.org/papers/w23782 -There has been a steady growth in TFP in the US of about 5% a year in spite of a huge growth in the number of researchers -also new drugs approved is dropping and crop yields are stable in spite of huge and growing number of new researchers -paper 'Combinations of technology in US patents 1926-2009: a weakening base for US innovation' by Matthew S. Clancy, Economics of Innovation and New Technology, 2018, Vol 27, No. 8. 770-785 found that US patents made less novel connections among technological constituents since the 1950s -by this measure, patent novelty has been going down since the 1960s in the US -thee same was reported in another paper 'Invention as a combinatorial process: evidence from US patents' by Youn, Strumsky, Bettencourt and Lobo, 2015, J. R. Soc. Interface 12 20150272 using US patent records from 1790 to 2010, and found narrowness of inventions in the US has increased -'Are 'flow of ideas' and 'research productivity' in secular decline?' by Cauwels and Sornette, published in 2022 in Technological Forecasting & Social Change, found that research productivity (measured by top researchers per community) has steeply decreased since the 1960s -last year a study appeared in Nature analyzing 45 million papers published worldwide; 'Papers and patents are becoming less disruptive over time' by Park, Leahy and Funk, published in 2023. They used the measure of how many papers were made redundant by a given publication and found that the number of disruptive ideas has gone down in many areas of science with time. -in 2005 Jonathon Huebner published 'A possible declining trend for worldwide innovation' in Technological Forecasting & Social Change, 72 (2005), 980-986 found that the rate of innovation peaked in 1873 and is now rapidly declining (defined as number of technological events divided by the number of people) -these studies all find that in research we are making more efforts for less in return -this seems contradictory since things seem to be changing so fast now -But AI, quantum computers, brain implants, nuclear fusion are based on breakthroughs that are now decades old -What are the possible causes? -R&D funding in the US has not decreased since the moon landing and has remained at roughly the same level (although the federal share of R&D funding has dropped), but this is not just an American phenomenon -US government share of US R&D support is roughly 19% now, and has been approximately stable at this level for decades (although it was 67% in 1960). Corporate share of US R&D support has now grown to 74% (up from about 31% in 1960). -hiring more people has partly made up for the lower productivity per researcher -if companies did not get something out of R&D, they wouldn't do R&D -maybe the low hanging fruit has already been "picked" in R&D? Would this explain this phenomenon? -John Horgan in his book 'The End of Science' said there are no big breakthroughs left -Sabine thinks this is unlikely to be true, and the slowdown is probably mostly because of the way we have organized R&D in recent years -in the US and the EU most research is privately funded -most basic research, with a long time horizon (that one expects to deliver the breakthroughs), is publicly funded and is in academia -during WWII and immediately after there was tremendous pressure and a shared sense of purpose -research and long term research was different and better in the US and the EU until about the 1970s or so -there is a growing problem with fraud in STEM R&D, but it is not yet a major problem -we know science is slowing down and it is impeding societal progress -Is the origin of the problem in academia? -venture capitalists notice this too and many wonder if it is too much bureaucracy -Elon Musk has a lot of stories about how bureaucracy stands in the way of progress at his R&D-based companies -Peter Thiel thinks the large projects like Apollo created large lethargic bureaucracies that were or are politicized -The 'publish or perish' precept values quantity over quality -Academic research is becoming risk averse because there is so much competition for funding and jobs; this explains the lack of novelty and disruptiveness in research results because no one wants to take a chance. They want to only work on a "sure thing", that is, low risk mainstream research, because they want to get published and get a job and so on. -European Research Council wants to fund only transformative research or high risk, high payoff research, but then they also fund the same old stuff instead -it has become a mark of pride among some scientists in academia to proclaim that science is just boring -many produce bullsh*t research, like the book 'Bullsh*t Jobs; The Rise of Pointless Work and What We Can Do About It' by David Graeber; the definition of 'bullsh*t jobs' (and bullsh*t research) is, 'stuff we would be better off without' -this erodes the trust in science, but most of it is invisible and the average person never hears or sees anything about it, so it is limited to small circle of people so far, yet -the public will soon start to ask questions -Patrick Collison of Stripe and his collaborators offered "fast grants" to researchers without a lot of bureaucracy during the pandemic -Collison did a survey among those receiving grants from him, and asked them how satisified they were with their research; 78% of those surveyed and funded said that if they could spend their research dollars on anything they wanted, they would change the theme of their research a lot; that is, they would work on something else, if only they could. This was a survey of people doing biomedical research, but Sabine also surveyed physicist about 20 years ago, and got similar results. Most people in R&D would change the topic of their research efforts if they could. -Sabine interprets this to suggest that most people in academia do not do the research they want to do, they do not do what they consider the most worthwhile research, only 'BS' research, and they know it -of course, every so often, something that looks worthless turns out to be good for something; in German one has the expression that every so often, a blind chicken sometimes finds a kernel of grain -How does one find worthwhile research, research that is "worth the money"? Sabine does not know, and this has her worried. -we do not currently have the technological progress we need to protect ourselves from a supervolcano or an asteroid impact or a large solar flare. We need to protect our species. Technological progress depends on scientific progress, and technological progress is stalling because we are ignoring what is going wrong in academia. -Sabine does not want her children to die because the technical publishing company Elsevier keeps publishing junk.

Once again, I have collected a few thoughts into a handful of essays about research and development (R&D). In this case, there are three parts to this collection. The first section, labeled 'A', presents a cursory discussion of some useful background concepts and topics in research and development. The second section, labeled 'B', describes some of the problems that sometimes arise in R&D organizations. The third section, labeled 'C', presents some potential solutions to these problems. There is also a short introductory essay and a concluding essay. I am posting the rough drafts of them here for your perusal and potential comments. Assorted Topics in Research and Development Introduction to Assorted Topics in R&D Abstract Preface A: R&D General Information A.1: Major Categories of R&D A.2: Hard and Soft Money A.3: The R&D Pipeline B: R&D Problems C: Improving R&D Notes A: R&D General Information A.1: Categories of Research and Development A.1.1: Introduction to Categories of R&D A.1.2: Short Term and Long Term Research and Development A.1.3: Pure and Applied Research and Development A.1.4: Concrete and Abstract Research A.1.5: Theoretical and Empirical Research A.1.6: Quantitative and Qualitative Research A.1.7: Notes A.2: Hard and Soft Money A.2.1: Notes A.3: The Research and Development Pipeline B: R&D Problems B.1: Some Issues in R&D B.1.1: How Can Managers and Funding Officers Impede Progress? B.1.2: Influence of Bureaucracies on R&D B.1.3: Decay and Erosion of Quality B.1.4: Notes B.2: More Issues in R&D B.2.1: Unstable Funding B.2.2: Decision Makers B.2.3: Some Examples B.2.4: Studies B.2.5: Notes C: Improving R&D C.1: Attributes of a Productive R&D Environment C.1.1: Notes C.2: Redesigned R&D Organizations C.2.1: Introduction to New R&D Entities C.2.2: Potential New R&D Organizations C.2.3: An R&D Organization Design C.2.4: Notes C.3: Appendix - Government Research and Development C.3.1: Notes D: Illation to Assorted Topics in Research and Development D.1: Conclusions D.2: Summary D.3: Notes