A fine line – descriptive or normative science?
from Joachim H. Spangenberg and Lia Polotzek
Next to the inability to describe long-term developments and to take into account the structural uncertainty of complex systems, there is a more fundamental problem regarding current economic modelling manifesting itself in IAM/DSGE models. It consists of the fact that economic models are presented as being purely descriptive, while they actually carry quite some normative baggage. This becomes particularly relevant as the function of economics in society changed from depicting and explaining the reality of the economic system to serving as a tool to facilitate political decision making processes.
Through the rise of the rational choice paradigm and economics’ development into a science of choice, it has become vague whether its approach to decisions is more of a descriptive or of a normative character. Usually, in neoclassical economics, expected utility theory is claimed to be used as a purely descriptive theory. Yet this claim is false as the idea of rational choice in conjunction with utilitarian assumptions is inherently tied to a specific concept of welfare and its normative assumptions (Muthoo, 1999). These circumstances have made it almost impossible for economists to draw a precise line between a descriptive and a normative approach, although few are aware of this challenge. This is dangerous as it disguises the outcomes of economic models as purely rational, whereas in fact they contain a plethora of underlying normative assumptions representing a specific world view (Spangenberg, 2016).
This has some very real and direct consequences when it comes to climate scenarios and the corresponding IAM/DSGE models: they are built in order to find solutions on how to achieve certain emission reduction targets (Kuhnhenn, 2018). The underlying economic models are optimisation models, which – under given boundary conditions – try to maximise the social utility function, most often represented by the GDP. Thus, in IAM/DSGE models, what is presented as the “optimal” outcome is more wealth in a national economy, in monetary terms (distribution plays no role). In such models, any measures leading to a reduction of GDP growth would not be regarded as an “optimum” as they would be regarded as “expensive” in and by the model. The consequence is that measures, which might lead to less production and consumption either cannot be depicted or are not used (Kuhnhenn, 2018). This is striking as changes of the consumption patterns and levels are a necessary condition for reaching the climate goals, as described above. In summary, it is our very standards of evaluation, which lead to deeply ideologically biased policy recommendations being presented as “objective” scientific insights, which has made economics the favourite legitimation science of neoliberal decision makers in politics and business.
http://www.paecon.net/PAEReview/issue87/SpangenbergPolotzek87.pdf
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One cannot but second this as absolutely correct and refreshing. Thank you.
An account can be technically descriptive while at the same time, in effect, normative. Any given economic situation can be ‘objectively described’ in many different ways, through any of a number of lenses — as a collection of individual consumer preferences, as the culmination of broad historical forces, as the confluence of various instances of the exercise of political power, etc. Each such account can be ‘objective’, correct and ‘descriptive’. But yet, in its effect upon the reader, is importing the ‘normative’ biases and presumptions of the describer. Purely ‘objective’ descriptions therefore don’t really exist.
” the idea of rational choice in conjunction with utilitarian assumptions is inherently tied to a specific concept of welfare and its normative assumptions”: true, but things are even worse.
Rational choice is scientifically meaningless without knowing what rational and what irrational is. The very definition of the rational immediately introduces an ideological bias. Moreover, the concept of rationality tacitly presumes an ontological worldview that might be suitable for physics and chemistry yet certainly not for economics.
A pity the authors felt obliged to end their excellent paper so hopelessly. I agree with Rhonda, though I would put the point rather differently: politicians want to be told what to do so they don’t have to exercise their own judgment (c.f. Brexit) nor be responsible for failures. Again, I agree with Christian, for economic rationality now judges proposals against subjective values despite there being other ways of judging than liking models. The four functions in our brains also compare models with what we see now, models with such world history as we know, and what we see as valuable in that wider survey of history.
In their paper the authors make related points worthy of comment:
“[T]he expected utility framework combined with the assumption of rational agents and market equilibria led to complicated but not complex, rather neat and tidy economic models spanning over longer periods of time.
“Macroeconomic theorising is required to provide a microeconomic justification – exactly the opposite order of what it should be, taking into account the emerging properties on higher system levels.
“Part of adapting its ontology to modern scientific standards would be to overcome the mechanistic world view and replace it by one based on a physical economy of matter and energy flows, and abolish equilibrium thinking and modelling to replace it by evolutionary approaches (Spangenberg, 2018). This would help avoiding what Herman Daly (2000) has called “dumb mistakes”, like for instance considering the collapse of agriculture (Bélanger and Pilling, 2019) as a minor problem, as agriculture only makes up for 2 to 4% of the GDP in affluent countries, without asking about what people will eat in that case”.
It seems to me that unless everybody understands in very simple terms the difference between complication and complexity, we are going to continue to have complicated theorising and confusion of complexity with chaos theory. Complication means “with ties”, as in tangled one-dimensional strings; complexity means “with parts”, which in time means two-dimensional motion: none (unchanging motion), one (the integral of motion over a period of time is a distance: hence position), two (the differential of motion is acceleration) and three ( the force controlling acceleration, which if changed creates a new situation, hence evolution). Thus an electric circuit may be complicated but a cybernetic control system is complex. A ship uses motion, correction of position and correction of course to stay on course; it uses a change of course to avoid obstacles, but this takes the ship off course and will cause chaos if the course is not corrected.
On macro-economic theorising, consider a simple Venn diagram, in which a circle represents a concept and anything inside it objects included in the concept. Let an outer circle represent the world; then its energy and matter form a circle within it, life a circle within that, thinking humanity a circle within that and human social constructs such as the economy, circles within that. It can be shown that all these circles are, like our brains, complex: involving four types of object interacting, the point here being that the monetary control sub-system within our economy is complex, creating chaos with its parasitic aim of money-making.
I find it obvious that the ontology envisaged in my third quotation is generated by the Venn diagram model and its complexity analysis. We are clearly parasitic not only on the world’s other life forms but on its mineral resources.
As I have in another thread, I take issue with the application of the word “science” to economics. Economics is not a science and it cannot be a science. It is an ideology. The policy applications of an ideology may be “science-informed”, or not, as the cases might be, but the discipline itself, economics of any ideological persuasion, is not a science.
Economics properly considered is really political economy. The term “political economy” carries two connotations: one meaning “national economy” and the other literally meaning economics is always political. The attempt to hive off economics from political economy and pretend that economics is not political but somehow an objective discipline has been a grotesque failure academically, socially and we see now environmentally. In turn, a political economy theory, an ideology if you will, always returns and must return to moral philosophy to argue its legitimacy, be those arguments good or bad according to the tenets adopted for judgement.
In moral philosophy it is not possible to elaborate out the full shape of good and how to get there, at least not without being religiously or metaphysically dogmatic. It is possible, usually, to define egregious ills and how to most likely avoid them. Even less so in political economy than in science (see Popper on falsification) is it the case that we can we develop a perfect, eternal and irrefutable theory. Yet current orthodox (neoclassical) economic theory pretends to be exactly that. We can however, refute political economy theories (e.g. ones which posit endless growth) which are in clearly conflict with the observable and highly dependable laws of hard science.
In theory, we can throw out theories which will lead to near-term disaster. Neoclassical economics and conventional economics in general is clearly one of those theory sets. However, given the power formations of the current political economy ideology, internal criticism has not proven effective to date and even democratic opinion and voting have also been rendered relatively inoperative due to oligarchic and corporate power. We are at a very late stage, very close to runaway climate and ecological disasters. At this late stage, forerunner disasters will have to occur (unfortunately), as incontrovertible empirical evidence events directly impacting on and galvanizing the vast majority of the people to demand real change.
Science is a cultural construct. Thus, it is normative through and through. Two examples. One big, one small.
When Galileo looked at a pendulum, he saw a regularity that could be measured. To explain it required a revolutionary way of understanding objects in motion. Galileo’s advantage over the ancient Greeks was not that he had better data. The data was no better, perhaps a bit worse. Galileo saw the regularity because he already had a theory that predicted it. He contended that a pendulum of a given length not only keeps precise time but keeps the same time no matter how wide or narrow the angle of its swing. A wide-swinging pendulum has farther to travel, but it happens to travel just that much faster. In other words, its period remains independent of its amplitude. Galileo phrased his claim in terms of experimentation, but the theory made it convincing—so much so that it is still taught as gospel in most high school physics courses. But it is wrong. The regularity Galileo saw is only an approximation. The changing angle of the bob’s motion creates a slight nonlinearity in the equations. At low amplitudes, the error is almost nonexistent. But it is there, and it is measurable even in an experiment as crude as the one Galileo describes. Small nonlinearities were easy to disregard. People who conduct experiments learn quickly that they live in an imperfect world. In the centuries since Galileo and Newton, the search for regularity in experiment has been fundamental. Any experimentalist looks for quantities that remain the same, or quantities that are zero. But that means disregarding bits of messiness that interfere with a neat picture. This messiness, nonlinearity is everywhere. In every part of physical life and societal life. Yet, for more than three centuries science books and papers, professional and for the layperson said nothing about it. Science lied to us all. Science gave us norms to follow about research and facts but failed to follow them. Sounds normative to me.
Small example. In 1961, President Eisenhower left office with an oft-quoted warning against “the potential for the disastrous rise of misplaced power” by “the military-industrial complex.” Science in the United States today is both the handmaiden of that complex and a complex itself seeking that same power. Science and scientists are today an intricate entanglement of university, government, and big business. Federal research grants over the years have systematically favored a limited group of institutions, which “guaranteed that the rich got richer.” “Two-thirds of research is now financed by companies. And much of this ‘privatized’ science is falling into the hands of ever fewer—and ever bigger—global corporations,” the New Scientist reported in 2002. The corruption of science is not confined to researchers who work directly for corporations. University laboratories and government agencies are equally complicit. So much so that their ties to industry bind them ever more tightly into a single object. “Conflict of interest in science has become the norm of behavior, rather than the exception,” observes Sheldon Krimsky; “The academic research milieu is generally acknowledged to be permanently and unabashedly linked to the private sector.” The privatization of science was greatly enabled by The Bayh-Dole Act of 1980, which allowed universities and small businesses to patent the findings produced by federally funded research. There was little doubt that eventually major corporations would gain the same privileges, which they did in 1987. The boundary lines separating government, industrial, and academic research have increasingly blurred. The upshot is that public dollars pay universities to produce knowledge that becomes the private property of corporations who control both the use of this knowledge and what consumers pay for the products resulting from this use.
Ken,
Certainly, the methods, foci, embedding and entangling of science in culture and political economy are (complex) facts. I do not disagree with you on that. Nevertheless, we have to ask ourselves is hard science (the only discipline which can be properly termed science in my view) different from religion and ideology? It turns out that it is.
Hard science is a cultural construct but its “global” target of observation is not a cultural construct. That is to say, the cosmos is not a cultural construct. The social “sciences” plus the arts, all properly called the Humanities, along with ideology and religion are cultural constructs and have as their object of study cultural constructs and their artefacts. It seems to me that this fundamental difference in the object of investigation sets hard science apart. Science can say more that makes an approach to objective truth than can the Humanities but it (science) can only say this “more” within a far more limited purview.
Can science, like logic and rationality, be misused? Of course, it can. Can science generate unforeseen consequences? Of course, it can. Human science can alter systems on earth. We are wrecking the biosphere, so the answer is obvious. Can science, or even mere conscious observation without some chain of physical (matter, force or field) connection, alter the so-called “fundamental laws of nature”, what Francis Bacon termed “nature working within”. No, it cannot. This is so, not withstanding the pop-science, pop-philosophy nonsense that is written about the double-slit experiment. But getting into arguments about the double slit experiment will take up a lot of time and space.
Certainly, mechanistic, deterministic science is an obsoleted view in terms of the philosophy of science and complex systems. We now have to deal with ideas of emergence and radical novelty.
“Understanding emergence along the lines of self-organization has become so ubiquitous the two terms have just about become synonymous. However, the usual connotations of self-organization result in a misleading account of emergence by downplaying the radical novelty characterizing emergent phenomena. It is this radical novelty which generates the necessary explanatory gap between the antecedent, lower level properties of emergent substrates and the consequent, higher level properties of emergent phenomena. Without this explanatory gap, emergent phenomena are not unpredictable, are not non-deducible, are not irreducible, and thus are not truly emergent. For emergent phenomena to be genuinely emergent, processes of emergence must accomplish the seemingly paradoxical feat of producing an explanatory gap while simultaneously maintaining some degree of continuity with the substrate level.” – Professor Jeffrey A. Goldstein.
But to get back to the substantive point, to correctly assert that science is entangled in culture and political economy is not to assert, nor demonstrate, that the actual observations of science (as opposed to the selected foci of science) are somehow 100% cultural constructs. Hopefully, you are not asserting that.
Scientific models are always approximations as you illustrated and we can over time come up with better approximations. I take a position of priority monism about the cosmos. I won’t expound it here. I also take a position that all human perception and understanding proceed by modelling. The brain models everything it perceives and (attempts to) understand. Again. I won’t elaborate here.
The incompleteness of even hard science is not “just for now”. It is very likely (certain I think) to prove intractable.A proper consideration of Priority Monism, and emergence and evolution in the cosmos demonstrates that mathematical and scientific incompleteness are intrinsic and unavoidable, if the initial thesis for priority monism is correct. Under an assumption of thorough-going priority system monism, an explanation or model of reality, as a set of language statements or mathematical equations, can never be complete. In addition to being abstracted and simplified, an explanation or model of reality (of “all existence”) or of a sub-system thereof, must itself perforce be a smaller sub-set system (an emergent subset generated and mediated by a conscious agent) of monistic reality itself (of “all existence”). In emergence it adds a “radical novelty” to all-existence (the cosmos). The explanation or model however cannot have any existence entirely separate and unconnected from the posited monistic system otherwise the monistic system would not be monistic by definition.
The above must be true under the a priori assumption of priority monism IFF (if and only if) that a priori assumption itself is true. A theory model is (or becomes in the emergent sense) a subset of the monistic system. A sub-system of a system can never fully replicate or model the entire system. The model must always be incomplete.
A clear characteristic of a monistic cosmos system, which manifests characteristics of emergence and evolution, will be that it demonstrates only partial reducibility to modelling and/or explanation. There will unavoidable incompleteness in all mathematical, scientific and philosophical theories. This consideration appears, perhaps, as an extension of Hume’s observation of the infinite regress problem for the explanation of causes; which observation itself implies complex and extensive chaining and interconnection in the form of the system connectedness of causes. As well as the infinite regress problem we might also detect, in the other direction, an “infinite emergence” problem. There is no theoretical upper or final limit to emergent novelties except possibly the heat death of the cosmos as a limit. Although, there may be limits to categories of emergence. We don’t expect the evolution of magical unicorns.
Finally, it must be admitted that by reason of combination of its great instrumental power along with its intractable incompleteness of knowledge, science does have serious and emergent dangers. Human generated climate change is just one clear and significant example of the dangers for applied scientific “instrumental reason” interacting with extant socioeconomics in their joint encounter with immense biosphere system complexity, emergence and “radical novelty”. The scope for unforeseen consequences seems unbounded.
Ikonoclast, hate to disappoint, but the cosmos, using the term you prefer is a cultural construct. How else would people recognize it, talk about it, or use it in their lives?
When you discuss the “fundamental laws of nature” you’re talking about norms, standards of expectations and guidelines. Human norms have been altered thousands of times in human history. Science is no different. The norms of science are altered constantly. I presented two examples. The alterations may be fair, equitable, and democratic. Or they may be unfair, inequitable, and autocratic. But all are alterations in science’s norms. And then you provide your own example in the quote from Jeffrey Goldstein.
You got it. The observations of science are 100% cultural constructs. As are the rules of science, the techniques of science, and scientists themselves. That in no way negates the usefulness of science. In fact, if science fails then at some point culture will try other paths to the same kind of usefulness provided by science. One final comment to make my point. There are multiple sciences in the history of Sapiens. With similarities but also clear differences. For example, Chinese science and the science of India. The Euro-American version of science is dominant in the world today mostly because Euro-American culture has been dominant for over 500 years.
We can never “know” that any model (I prefer imaginings) is complete or certain. Experience, scientific and lay, argues against completeness and certainty. It follows that regression (deterioration) and emergence (development) are delineated for whatever they are by cultural constructions. Cultural constructs.
On the final point we agree, Ikonoclast. The scope for unforeseen consequences of the current version of science in the west is wide, exceptionally wide. And growing wider.