Debunking Neo-Classical Economics
"Modern capitalism" is based on the “science” of neo-classical economics. I use “science” in quotes, because, as I will elaborate, in my somewhat polemical view, neo-classical economics is a zombie science, even though this “dismal science” is taught in most colleges.
It is dismal as a science because it is based entirely on the largely false assumption of "rational choice", evaluating all options, based on full knowledge, in order to maximize ones "utility". This assumption may have been "reasonable" in the time of Adam Smith, when the view of humans, both mind and body, was very mechanistic, but today there is overwhelming evidence, from behavioral fields and evolutionary studies of brain function, which shows that humans predominantly make decisions on emotional, non-rational and instinctive grounds, and only a small subset of such decisions are made on anything approaching a rational basis (see “Recognizing The Illusion Of 'Homo Economicus’”, by
Wim Hordjik)
It is only with the false "rational choice" axiom in place that the nice smooth demand curves can be derived, not through observation, but through what economists like to refer to as “thought experiments. There is little to no experimental evidence for smoothly declining demand curves. Only when one accepts this hypothesis of smooth demand curves, and combines them with the (also assumed) smoothly increasing supply curves, can one arrive at the “law” that "market economies", left to their own devices, are optimizing and self-correcting. That, in turn, leads to the completely false dogma that "government is the problem".
"Free markets" in general are a complete fiction, because only with governmental authority, rules and regulations can “markets” function. Without government there would be no "market economy"; government ensures the basic rules which allow for markets to function. Furthermore ”Economics" as a discipline makes the self-serving and false assumption that economic activity can be "understood" in isolation. However, the interaction between economic activity, political and social activity, and even ethics are very tight and pervasive, so that “economics” can only be “understood”, in terms of making useful policy predictions, if the tight linkages to sociology and politics are included.
Unfortunately I am not equipped, educationally or intellectually, to join the chorus of other economists who claim to have devised “the unifying theory of economics”, still enamored with the advances in physics, where a unified theory is actually much closer to reality. I have to content myself with a critique of the major, most influential strain of economics, “neoclassical economics”, in the hope that once young people entering the field are no longer brainwashed with the false notion that “we know how the economy works”, and enables them to focus their intellectual energy towards coming up with better theories and models, ones which can actually be verified with experimental, observed data.
Rational Decision-Making and Utility Maximization
Rational Decision-Making and Utility Maximization are “axiomatic” theories, where the investigator puts forth a set of “axioms” - self-evident truths, which cannot be proven - and then proceeds to build a set of models. Mathematics is also an axiomatic discipline, but none of the axioms underlying mathematics have been sown to be untrue, nor is there any observable evidence to cast doubt on these axioms.
Economics is a social science, i.e it deals with the behavior, alone or in a social context, of human beings. It is often useful in social science, where experimental observations are not clear cut, as they are in the physical sciences, to build theories and models on the basis of axioms. But it then is the responsibility of the investigators, researchers and academics who espouse such an axiomatic theory or model, to continually test their axioms against current observable evidence, and results from other social sciences. Intellectual honesty and academic integrity would then demand, that if new evidence casts doubts on the validity of the underlying axioms, these theories or models be modified, or in ultimate consequence, discarded.
Adam Smith, in The Wealth of Nations (1776), was a pioneer of the notion of rational self-interest as a motivating factor in economic decision making. This notion is akin to the view espoused by Thomas Hobbes and his followers, best expressed by Julien Offray de La Mettrie in his “Man a Machine” (1748), which argues that everything about human beings can be completely explained in mechanical terms.
Social Science today generally agrees, that human behavior is not deterministic, i.e. given a set of stimuli, a human being does not react in a deterministic, uniform way, but rather the reactions, or behavior, is distributed in a more or less normal distribution. Thus, for example, on an axis defined by “fully rational” on one end, and “non-rational/emotional/instinctual” on the other end, human decision making would exhibit a frequency distribution with a mean or average somewhere between the two extremes (red).
In this view of human behavior, one would need to see evidence that in economic decision making the distribution of human responses shifts dramatically towards the “rational/full information” end of the axis (green). However, there is very little, if any experimental, observable evidence that would justify such a dramatic shift in the distribution of human behavior towards the “rational/full information” end of the axis, which is required to make the axiomatic underpinning of neoclassical economics believable, or “self-evident”.
There is, however, increasing evidence that the distribution of human responses on a “rational/full-information” to “non-rational/emotional/instinctual” continuum might shift significantly towards the latter end (blue). This evidence comes from behavioral economics, evolutionary brain structure studies, and marketing research.
One example of this research described by Daniel Kahneman in his book “Thinking, Fast and Slow”, which describes results of decades of research leading to the realization, that there are (at least) two modes of thought. One is fast, instinctive and emotional, the other slower, more deliberative, and more logical, with the first mode occurring much more frequently. This would strongly support the shift of the distribution of decision making modes described above towards the “non-rational/emotional/instinctive” end of the scale.
Another path of research looks at the imperatives of evolution and how it affected the development of the human brain (see
https://paidpost.nytimes.com/oppenheimerfunds/does-the-body-reveal-secrets-about-our-decisions.html). This suggests that the bulk of decisions made are fast and instinctive, mandated by the need to survive, and that only a small number of decisions involve and invoke the more deliberative pathways of the brain. This research again points towards a shift of the distribution of the decision-making process towards the “non-rational/emotional/instinctual” end of the scale (blue), further invalidating the axiomatic foundation of neoclassical economics, and its reliance on the axiom of rational, fact-based, utility optimizing decision making.
Finally, Marketing research identifies and very successfully uses the susceptibility of humans to be influenced by non-factual, emotional, non-rational stimuli to influence their economic decisions. Just consciously paying attention to the typical and pervasive ads on TV (and in print media) demonstrates that these do the opposite of encouraging or enabling rational, fully informed purchasing decisions. An excellent and very readable book on this is “Predictably Irrational”, by Dan Ariely. This demonstrates the third strong force pushing the distribution of human decision making towards the “non-rational/emotional/instinctual” end of the scale.
So what? you may ask. Why is a non-economist, with perhaps three to four economics classes to his credit in college, sounding off about this? There are, to be sure, many people much more qualified to present these kinds of competing and conflicting theories in the broader field of Economics. However, I feel very strongly that the “junk science” of the dominant strain of economic thought, embodied in neoclassical economics, must be debunked, because it is causing real harm to our political, social and, yes, economic environment.
The majority of colleges and universities base their economics curriculum on neoclassical economics. Legions of students, including those who study economics as a “minor” subject, some of whom go on to become business leaders and politicians, are indoctrinated, even brain-washed, with the completely false notion that “we” actually know, as in understand, how the economy works, that there are “laws” (“the law of supply and demand”) and “rules” which guide economic behavior (as in “the invisible hand”) to be self-correcting, equilibrium-seeking and optimizing, and worst of all, implicitly and sometimes explicitly stating that government intervention in the “free markets” is detrimental. The full idiocy of this indoctrination was perhaps most vividly on display when arguably one of the most powerful people controlling and guiding economic development, Alan Greenspan, responded to the financial meltdown of 2007/8 with "I made a mistake in presuming that the self-interests of organizations, specifically banks and others, were such as that they were best capable of protecting their own shareholders and their equity in the firms."
The Future - Where do we go with “Economics”
I’m certainly not well qualified to provide a roadmap on where “Economics” as a discipline should go. But having argued for a dismantling of the current dominant form of academic economics, I feel some responsibility to at least make some suggestions.
First, using my background in “Systems Analysis” - the original meaning, before Computer Science co-opted that term for itself - one rule for defining the “system” one wishes to model is that most of the significant interactions should be “internal” and as few as possible of the significant interactions should cross the boundary of the “system”. In my view the interactions between politics, social phenomena and the psychology of decision making, on the one hand, and “economics” are so tight and important that any attempt to model “economics” in isolation, and expect those models to be useful for meaningful real-world predictions, are bound to fail. So my first suggestion would be to expand the boundaries of “economics” to include the very direct and tight interactions with politics (e.g. changing tax rates) and social phenomena (e.g. differing attitudes towards savings vs. spending in different societies, different attitudes towards “freedom” vs “shared responsibility”).
Second, I believe we have to accept that, contrary to economists claiming that “we know how the economy works”, we really know very little about that. Economists may be able to model very limited steady-state, equilibrium phenomena, but they have been unable to make meaningful and consistent policy input to predicting, for example, effects of tax reductions/increases, or predicting and preventing such major economic phenomena as the financial meltdown in 2008. The Greenspan quote cited above seems to be an acknowledgment of the inability of economists to “understand” the larger scale economic phenomena beyond the very short term steady-state, equilibrium phenomena.
Some academic economists have likened their axiomatic models to the continuing search for a “unifying theory of physics”. But one has to keep in mind that models in physics are for the most part based on observable data, or, where they developed from axiomatic theories (E=mc2), physicists are continually testing the predictions of such models against observable data.
If we compare the state of knowledge in economics versus in physics, I would liken it, somewhat tongue-in-cheek, to Newton sitting under the apple tree, observing apples fall to the ground, and speculating about what causes the apples to accelerate as they fall. We have to date no confirmed cause-effect relationships in economics; there are many statistically relevant correlations, but none can be specified as cause-effect relationships, very different from Newton’s theorem regarding gravitational attraction.
Thus, I believe, that we need to enter a phase of intensive collection of data on how decisions are actually made by the various actors in the social/political/economic continuum, both on the demand and the supply side, in order to escape the “deadly embrace” of the false axiom that these decisions are made rationally, with full information and with a “utility optimizing” goal.
In today’s hyper-connected (internet) and hyper-monitored (Facebook, Google, Amazon et.al. monitor your every click) world it seems to me quite feasible to define large samples of real people and collect data on how they actually make decisions, both as consumers and as suppliers. To be useful, such data cannot just be passively collected, like the Nielsen Ratings of the past, but must include active participation by the sample members. For example, it is not sufficient to passively monitor that sample member “12345” purchased an automobile brand a, model X, but rather, the sample member would have to supply (as accurately as possible) the decision process gone through in making that purchase.
Similarly, on the supply side, managers/decision-makers who are part of the sample would be asked to specify in as much subjective and objective detail as possible why/how certain decisions were made. For example, was a merger made because objective data showed it would benefit “the company”, or because the bonus structure of the managers made such a move profitable for the managers; or, did a company decide to expand and hire because of some new tax regulation, or because of some observed or perceived new demand.
Such data collection would have to continue over many years, and new samples of participants would have to be defined regularly (to avoid systematic bias). It is quite possible that such data collection would become an accepted part of our social order for the foreseeable future, as the motivating factors and mechanisms of decision-making are likely the change over time.
The datasets collected in this way would, by necessity, be huge, with many, many dimensions, such as socioeconomic data, detailed data about the transaction or “decision”, much of it possibly un-structured and not well categorizable, and even subjective. But we have today the data storage (“Big Data”) and computational capabilities to store and analyze such huge data sets. Much of the analysis would again result in “statistical correlations”, like much of the empirical research going on today. But the wealth of data thus available would also be a rich source for discovering possible bona-fide cause-effect relationships. Short of that, the wealth of data available would also lend itself to building “simulation models” (as opposed to axiomatic models) which could go a long way to providing tools for real policy making. Today economists seem to be at sea when asked to predict the effect of, for example, a tax cut or rise, or a minimum wage law. But with long-term time-series data sets available, simulation models may become quite good at making such policy assessments. Furthermore, such simulation models, based on massive long-term data sets, might become useful at predicting (and avoiding) such catastrophic “market failures” as we experienced during the financial meltdown of 2008.
