The End of ‘Casino’ Investment
From caveat emptor to the process approach
With Miroslaw Manicki
Enjoying progressive pots of gold
There has been a longstanding effort to look to a metaphorical pot of gold for providers at the end of the rainbow. This has been the goal for hundreds of years. The glories of enterprise and wish-fulfillment are wonderful to behold.
All has not been rosy and universally-beneficial in the process — certainly not in the introduction of modernity in stark and often harsh ways. Contemporary discoveries of many of the benefits and joys of cooperative living and of traditional, indigenous cultures points to ways that could have been and now could be (Polanyi, 1944/1957/2001; Wallace, 2003, 2004).
Chipping away at the longstanding cultures of mankind over the last several centuries, commerce based on extreme finance has descended further and further into consumer-hostile contrivances. It hasn’t been all bad; many new, convenient, and efficient products and services have been the result — but the commercial model in support of financial capital above all has been largely hostile and one-sided.
This leaves the countries of the world vulnerable to erosion of social fabric, which itself diminishes the value of money. The pot of gold as has been brought together: Can it be preserved as to quantity and effect? Are there further gains to be had? How are such goals to be achieved in a technological environment that shows signs of eroding both society and economic stability?
There are answers. These involve novel uses of technology that support the empowerment of subject matter experts and representative agents of both private and public governance organizations and social networks. Furthermore, such answers support an economic shift from accommodating known and unknown risks to supporting ongoing planning and disbursement of funds.
How the pot of gold and social discontent both appeared
Famously as to the English, they banded together almost five hundred years ago with the Welsh and the Scottish with support from Holland to form the British governmental, military, and commercial juggernaut. At the time, one of the principal commercial objectives was to mount commercial fleets of ships to support sales to and the purchases from people in far-flung corners of the world. Extremes of nature, wars, piracy, unpredictable and incompatible commercial preferences, competition, and many other risks stood in the way of success in each case.
There was no guarantee of success, nor a certain pot of gold at the rainbow’s end. Risks abounded but means of coming to understanding and adapting to realities as they presented themselves ‘pots of gold’ presented themselves with success. As a result of herculean efforts and profound sacrifices, financial success was achieved. New products and services, broader markets, and growing populations of consumers — both domestic consumers and industrial enterprises — the need for more money was accommodated by governments, with Britain in the lead until the U.S. dollar took over.
After hundreds of years of change-based commerce, there now is an enormous ‘pot of gold’. Global investable assets are at $180 trillion levels according to one report (Dodard and Le, 2022), at $245 trillion according to another (Shorrocks and Davies, 2023a). The second report lists total wealth of all kinds at $455 trillion, down by 1.96% in 2022 — the first decrease since 2008.
The $180 trillion figure averages out at $22,200 per person. Of course, wealth is not owned by individuals in this way. Directly and indirectly, 44.5% of financial wealth is owned by the global top 1% of wealth owners (Shorrocks and Davies, 2023b). That is truly bizarre, but this essay is not about that.
It is interesting to review the categories of investment wealth as they exist. Almost half is represented by equity, which involves various kinds of stock, both public and private. As can be seen at the end of the line, with $5.4 trillion in the brown segment, there is a good deal of actual gold in question.
Of course, there are many assets on top of this that are not ‘investible’. There has been a doctrine of tough love at the basis of this; if people are to thrive if not simply survive, they will have to learn to play the game.
And a game it is. The point is to not be fooled. Gainving value for your efforts continues to be risky.
Conjuring up risks
On the commercial side, an important point of the game is to fool — to engage in tomfoolery, if only to create dissatisfaction with the purpose of selling more or of switching to something more profitable to the provider. If marketeers cannot keep consumers buying their products, they can at least entice them to try something new.
The traditional point of marketing is to induce some kind of change. This is done by inducing dissatisfaction. To be sure, dissatisfaction can be attributed to much innovation and development in the modern era. Dissatisfaction can be seen as a stimulus for invention; motivation for achievement; justification for risk-taking.
I should recant that statement as being too harsh. It isn’t necessarily true that marketing equates with deception, but much of it does. In its purest sense, marketing simply connotes trade. You go to market and exchange goods and perhaps currency of some kind.
There are limits here. Some marketing is zero sum: One vendor gains at the direct loss of another. Other marketing aims at increasing the size of the market — creating opportunity through innovation where none existed before. It can be said that there are limits there — such as in limited income and wherewithal of consumers. Selling more brings more opportunity for jobs, which can have positive effects on income, making increased economic activity possible.
From a provider standpoint, it can be difficult to meet the needs of consumers, even when their preferences are clear. An important rule of thumb in an economy — particularly where consumer consumption as opposed to industrial or government consumption is in question — is how much of the consumer segment represents meeting needs and how much entails providing wants. Typically, needs have represented biological requirements of individuals, while wants have represented luxury, vanity, or entertainment items.
Fluidity and leveraging the pot of gold
Below we can see a tree growing out of a pot of gold and apparent reverse in direction of the flow of gold. The tree represents nuance and complexity in terms of process — important means of creating and distributing wealth in a knowledge-driven technological environment.
The point now is no longer to promote high risk, which was a necessary condition colonization and conquest, but a hunt for dependability. A pot of gold on its own does not make any money, nor can it serve to meet needs in direct ways. If currency is to retain its importance, it must be tied to legitimate financial activity. Legitimate financial activity needs to be associated with value given and value received. In this, we are referring to the primary activity of meeting peoples’ needs. This is outlined in a recent book with Miroslaw Manicki, Smart banking: Putting money in its place (Tingey and Manicki, 2023).
Signs of desperation on the part of technologists
There are some ‘Hail Mary’ efforts in finance, both associated with technology. One is crypto currency; the other is artificial intelligence. Neither serves rational goals. They fall into the category of things that can be done technically, but they serve primary human interests only on the periphery.
There are some interesting features of cryptography, but the principal proposition of using intensive computational resources, including energy, for the sole purpose of generating coin, is ill-advised. Blockchain is a good idea — the point of event accounting, which is an important concept that was initiated in the financial accounting literature in 1969, leads to an idea of a general-purpose general journal of all processes that well could be expressed using blockchain concepts. This would bring great benefits to the economy and to society.
Artificial intelligence is neither artificial nor intelligent. AI technology requires substantial human programming under the hood to derive rules and priorities as to how machines derive their outcomes. Dating to the original dual 1937 publications by Alonzo Church and Alan Turing about prospects for machine-based modeling of human thought, efforts to simulate human cognition go back almost a hundred years (Copeland, 2015).
As projects along those lines continued forward, there has never been a meaningful effort to address human knowledge and how it can be leveraged and enjoyed better by means of computing capacity. It is about how knowledge artifacts might be scanned for value without meaningful input from the article’s authors, who are otherwise very much available.
Evidence of evasion
I was an invited observer of a 1999–2002 DARPA artificial intelligence project whose purpose was to extract knowledge from experts with ten times the rapidity of prior efforts. That did not happen. They found that experts had little time and little interest in sitting for “extraction” sessions, which added little to their knowledge store, which were often themselves demeaning and unpleasant, and which constituted a threat to their career prospects — at least potentially.
So, AI offerings provide opaque outputs based on embedded “nudges” by project creators that are populated with data from scans of documents published by research individuals and groups without participation or review by the authors. Purveyors of artificial intelligence systems are insistent that decisions by their products cannot be audited or evaluated in any way. They commonly note that outcomes are “interesting” but possibly “surprising”, and not always in illuminating ways.
There is no method, nor methodology, in this. There is no peer review, nor any other kind of meaningful review.
Often the technologists in question laugh off the fact that they dropped out of college or didn’t finish their research once commercial plans were developed to market their ideas. This has led to the kind of hubris that we can see in the process, that leads them to disregard the entirety of human knowledge production except to purloin it indirectly with no concern for context nor nuanced efforts at creation and curation of knowledge by qualified scientists and philosophers.
The situation screams to the heavens of projection of ignorance: Since they do not know about something — a concept, a methodology, an aspect of science or practice — presumably, it does not exist. In the minds of the technologists, any risks brought onto society — many of which they admit to themselves — are worth the vague and questionable benefits to be achieved. Their logic is that they have been working on a kind of bomb for reasons of their own. It is incumbent on society to use it. The main concern of the technologists is that there needs to be a system to assign responsibility and liability for undesirable outcomes to someone other than themselves.
Self-deception and ignorance — the Dunning-Kruger effect in technology
The following is a description of the kind of self-deception and untrammeled, but unjustified confidence and hubris that is on display with respect to artificial intelligence. The condition is called the Dunning–Kruger effect, introduced into psychological literature in 2009 by Justin Kruger and David Dunning. A telling excerpt from that publication is provided below with apologies for its length.
In 1995, McArthur Wheeler walked into two Pittsburgh banks and robbed them in broad daylight, with no visible attempt at disguise. He was arrested later that night, less than an hour after videotapes of him taken from surveillance cameras were broadcast on the 11 o’clock news. When police later showed him the surveillance tapes, Mr. Wheeler stared in incredulity. “But I wore the juice,” he mumbled. Apparently, Mr. Wheeler was under the impression that rubbing one’s face with lemon juice rendered it invisible to videotape cameras.
We bring up the unfortunate affairs of Mr. Wheeler to make three points. The first two are noncontroversial. First, in many domains in life, success and satisfaction depend on knowledge, wisdom, or savvy in knowing which rules to follow and which strategies to pursue. This is true not only for committing crimes, but also for many tasks in the social and intellectual domains, such as promoting effective leadership, raising children, constructing a solid logical argument, or designing a rigorous psychological study. Second, people differ widely in the knowledge and strategies they apply in these domains, with varying levels of success. Some of the knowledge and theories that people apply to their actions are sound and meet with favorable results. Others, like the lemon juice hypothesis of McArthur Wheeler, are imperfect at best and wrong-headed, incompetent, or dysfunctional at worst.
Perhaps more controversial is the third point, the one that is the focus of this article. We argue that when people are incompetent in the strategies they adopt to achieve success and satisfaction, they suffer a dual burden: Not only do they reach erroneous conclusions and make unfortunate choices, but their incompetence robs them of the ability to realize it. Instead, like Mr. Wheeler, they are left with the mistaken impression that they are doing just fine. As Miller perceptively observed … and as Charles Darwin sagely noted over a century ago, “ignorance more frequently begets confidence than does knowledge”.
In essence, we argue that the skills that engender competence in a particular domain are often the very same skills necessary to evaluate competence in that domain — one’s own or anyone else’s. Because of this, incompetent individuals lack what cognitive psychologists variously term metacognition, metamemory, meta-comprehension, or self-monitoring skills. These terms refer to the ability to know how well one is performing, when one is likely to be accurate in judgment, and when one is likely to be in error. For example, consider the ability to write grammatical English. The skills that enable one to construct a grammatical sentence are the same skills necessary to recognize a grammatical sentence, and thus are the same skills necessary to determine if a grammatical mistake has been made. In short, the same knowledge that underlies the ability to produce correct judgment is also the knowledge that underlies the ability to recognize correct judgment. To lack the former is to be deficient in the latter (Kruger and Dunning, 2009).
AI proponents personify this condition. Many dropped out of college in early stages — some after only weeks in school — or, better-said, at school, because many of them barely attended a few courses. Others did complete courses of study, but in narrow technical and engineering-related courses of study, such as artificial intelligence itself. Having taken required general education courses, the implications of those apparently had minimal effect.
I recently listened to an interview of one of these. He was asked about having dropped out of college very early on. He said that he had benefitted from collaboration with smart colleagues, teenage young men like himself who “were very smart”.
That is not good enough, not for a person that presumes to be a guiding light and a leader in world affairs, if not social and cultural activities generally. He does not know what he does not know. He does not care for what he doesn’t know. He says he reads books. That is not the same thing as participating in the key aspects of education — presentation, dialog, assessment, competitive aspects, and humbling if not humiliating failures that are a part of rigorous undergraduate and advanced study.
Shaming and marginalizing experts
Commercial activity — commercial success — is an entirely different phenomenon. One can succeed commercially via deceptive marketing — by foolery — while coming to conclusions that are pernicious, thoughtless, mean-spirited, and anti-social. Such a person would tend to minimize inputs they didn’t understand or didn’t care for. They can extend beyond persuasion, becoming at the same time narrow-minded, willful, careless, thoughtless, and brutish. All of these can be vastly multiplied by means of money and reputation.
I have personally witnessed badgering by ignorant young technologists of biologists, physicists, safety specialists, marketing managers, genealogists, general managers, and many other competent, senior professionals. Leveraging technological successes in the past, often by entrepreneurs as just described, representatives of their organizations and their movement can tend to work their way through society seemingly as locusts — forcing their incomplete and stilted solutions onto the rest of us with no regard to outcomes, good or bad. Another result is that they browbeat and shame both experts and users into using their incomplete and imperfect systems, having taken away any hope that such people could resolve their issues on their own.
Often this has occurred in the course of database design or in logic design.
Sessions I witnessed were brutal and condescending. The point was that a person with negligible knowledge of nature or society — or of even business and marketing findings — could and would use tools in the two areas to demean and disarm experts to the point of what I would call knowledge shaming. They would reduce them to quiescent silence in that they could not make use of the tools to express their knowledge in ways that were acceptable to the technician.
In both cases, the tools are arcane, non-intuitive, and arbitrary complex to the extent that they require many months, and in many cases, years to understand and to deploy. One problem with experts in such situations is that once they dedicated the time and effort to learn to use the tools, they would have lost their edge in the primary fields. The cognitive burden required in both areas makes it very difficult to stay abreast in either one.
Database design makes use of what is called relational database design. This resulted from interpretation of a 1970s publication by Edgar Codd (1970) on how data might be structured, customized in each case. I make the case that the underlying idea of Codd’s is sound, but is typically extended beyond its utility (Tingey, 2013). That ties in with work on process by accountant George Sorter (1969) that was published at about the same time, after about two decades of computer use, almost all mainframe computer use, which provided groundings for thoughtful concepts and suggestions.
Both database design and process/event accounting implementation were originally intended to empower users, to make systems more relevant and responsive to user needs. Both traditions were turned upside down in subsequent publications and used for the opposite effect. These issues are considered in a book chapter on “Event accounting” in The Angels Are In the Details, a book I published in 2013 (Tingey, 2013).
Logic shaming takes place routinely using a model called Uniform Modeling Language, or UML. I was directly involved in a major project — declared as a “skunkworks” project by its host institution — that involved badgering of genealogy professionals by trying to force them to learn UML. The tool involved an explosion of concepts and symbols that had nothing to do with genealogy. The estimated learning cycle for the genealogists and UML was two years of training and coaching.
The genealogists refused to do so. Immersed in the art, craft, and requirements of their professional work, they recused themselves from the project in spite of negative employment implications within the organization. As dedicated experts, they saw themselves more as professional genealogists than they did as mere employees of the organization.
I offered program leaders other ways of teaching and empowering the genealogists. The technologists went along with my suggestions for several weeks before killing the project in a curious. They would set appointments with me week after week, cancelling each time as I was enroute — in a 2–1/2 hour trip to their locations. After the fifth cancellation the project ended.
That was typical.
How to create an environment for dependable investment
The two issues come together in the effort to engender investment opportunities based on detailed planning, enlisting the direct, hands-on participation by subject matter experts and other principals in the tasks at hand. We will benefit greatly from a global investment environment stimulated by planning and performance more than selective participation in risky ventures and flights of fancy. There is less of a need to create wealth than to effectively deploy it and make use of it to support dependable outcomes. This will make for dependable portfolios.
It isn’t that there shouldn’t be opportunities for investigative activity — for innovation, for new ventures promising new and better products and services. Associated risks are beneficial to society. They provide incentive structures for outsized efforts and vehicles for the efforts of inquiring people.
The well-being of the people should not be dependent on such efforts. One of the great socio-political risks to a healthy, happy society and stability is speculation and risk-taking regarding the food people need, their basic living conditions, their health, basic clothing, education, and preparation for adulthood.
This allows for an opportunity for “blue ocean” activities and enterprises. This provides an extensive platform for deployment of existing wealth in ways that can preserve it and provide prospects for constant returns.
References
Codd, E. F. 1970. A relational model of data for large shared data banks.
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Dodard, F., and Le, A. 2022. Global market portfolio: Value of investable assets touches all-time high. Boston: State Street Global Advisors.
Kruger, J., and Dunning, D. 2009. Unskilled and unaware of it: How difficulties in recognizing one’s own incompetence lead to inflated self-assessments. Psychology, 2009, 1, 30-46. DOI: 10.1037//0022–3514.77.6.1121.
Shorrocks, A., and Davies, J. 2023a. Global wealth levels 2002. In I. Khan and P. Donovan (Eds.), 2023, Global wealth report 2023: Leading perspectives to navigate the future. Zurich: Credit Suisse Research Institute, UBS Group, ,9–19.
Shorrocks, A., and Davies, J. 2023b. Global wealth distribution. 2002. In I. Khan and P. Donovan (Eds.), 2023, Global wealth report 2023: Leading perspectives to navigate the future. Zurich: Credit Suisse Research Institute, UBS Group, 21–36.
Sorter, G. H. 1969, January. An “events” approach to basic accounting theory. The Accounting Review, 44(1), 12–19.
Tingey, K. B. 2014. Chapter 5 — George Sorter’s 1969 “event accounting” idea. In K. B. Tingey, 2013, The angels are in the details: Control and regulation in a good way. Logan, UT: Spendlove Medical Research Institute, 2020 Program for Global Health, Profundities LLC, 110–162. https://www.amazon.com/dp/1492214205.
Tingey, K. B., and Manicki, M. 2023. Smart banking: Putting money in its place. Logan, UT/Piotrków Trybunalski: Profundities LLC. https://www.amazon.com/dp/B0BRLYJY5N
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