Lars P. Syll — The ergodicity problem in economics (wonkish)

Less wonkishly, the basic problem here can be viewed in terms of the logical fallacy of hasty generalization. Hasty generalization involves extending one's one's position, or that held by one's group, universally. In philosophy this result in claims of naturalism to humanity as a whole. For example, natural law is often reducible to a particular set of Western values that is generalized. The "laws" of economics are largely of this sort, and homo economicus as a rational agent that carries them out is basically a reflection of the economists that posited them, assuming all to be like them.

This fallacy has been a temptation from ancient times, but it culminated in the scientific age with the discovery of invariant laws of nature, in physics and astronomy in particular, in that these discoveries could be rendered universally using mathematical expressions. Subsequently, this formalism became a criterion of truth that prevailed for formalists above empirical observation. Owing to the success and prestige of the natural sciences, would-be scientists in other fields, and philosophers as well, sought to emulate the formalism of the natural sciences.

However, the great success of the natural sciences in discovering invariant lays in the ergodicity of the subject matter, which is rendered the mathematical expressions and formal models time-invariant. Lacking ergodicity of subject matter, this would not apply strictly. There is a significant difference between a general case and specific cases. In Economics Rules: The Rights and Wrongs of The Dismal Science, Dani Rodrik argues that of the art or craft of economics is being able to discern which model applies in which case. The natural sciences are not concerned with this kind of decision in the same way. There is a clear difference among theoretical science, experimental science, and engineering.

There is an old joke about some engineers and an economist shipwrecked with nothing to eat other than canned food. The engineers set about trying to figure out how to open the cans by applying their theoretical expertise and practical experience. The economist chimed with, "Let's just assume a can opener."

This actually happened in a less dramatic way. In effecting his synthesis of Keynesian and neoclassical thought, Paul Samuelson was confronted with Keynes having posited future uncertainty at the foundation of the "moral sciences," which we now call social science, including economics.

Samuelson solved the difficulty by assuming ergodicity as a methodological convenience for tractability, as had neoclassical economics in assuming equilibrium. This view became orthodox in conventional economics. The follow-up retort to heterodox objections then became, "The methodological debated is already settled." As Paul Krugman asserted, equilibrium and maximization as a framework.

This doesn't mean that economics or the other social sciences are not scientific or cannot be scientific. It just means that they are not the same as natural sciences and that making claims that approach this are unjustified.

Moreover, there is a difference between the meaning of being a science and being scientific. Being scientific just means observing the scientific method. Engineering is scientific in its approach, but this is applied science.

Being a science assumes a framework in terms of which theories can be compete. For example, the framework of physics includes the conservation laws, which are universal and independent. Of course, there is change over time in physics owing to motion and entropy, for example. But these phenomena are explained using models that data supports. The explanation (formula) is time-invariant, even though the data change.

Economics has no such framework, which is why there are competing views of how to approach economics in the first place. "The law of supply and demand" is not the same as the conservation laws in physics, and the assumption of equilibrium is not ergodicity.

Not being ergodic, economics is not a natural science, which is not the same as saying that economics cannot discover universal invariances that data support regardless of time series, economics, like the other social sciences, being historical. Moreover, social systems are complex adaptive system subject to reflexivity (learning from feedback) and emergence (change that is unforeseeable based on priors).

So the next time someone says, "Where's your model," ask them, "Which one?" 😀

Not one in business or finance takes forecasts as the same as or similar to the physics, and no one confuses weather forecasts to astronomical invariances. But economic forecasts and the reasoning which they are based are often treated as dogma in policy circles. That's a problem.

Lars P. Syll’s Blog
The ergodicity problem in economics (wonkish)
Lars P. Syll | Professor, Malmo University

Daniel Little — Modeling the social

Brief review of Scott Page, The Model Thinker: What You Need to Know to Make Data Work for You.

Understanding Society
Modeling the social
Daniel Little | Chancellor of the University of Michigan-Dearborn, Professor of Philosophy at UM-Dearborn and Professor of Sociology at UM-Ann Arbor

Andrew Gelman — The fallacy of the excluded middle — statistical philosophy edition

Some philosophy of statistics. Short read. Not wonkish.

Statistical Modeling, Causal Inference, and Social Science
The fallacy of the excluded middle — statistical philosophy edition
Andrew Gelman | Professor of Statistics and Political Science and Director of the Applied Statistics Center, Columbia University

Lars P. Syll — The main reason why almost all econometric models are wrong

Since econometrics doesn’t content itself with only making optimal predictions, but also aspires to explain things in terms of causes and effects, econometricians need loads of assumptions — most important of these are additivity and linearity. Important, simply because if they are not true, your model is invalid and descriptively incorrect. And when the model is wrong — well, then it’s wrong....

Simplifying assumptions versus oversimplification.

Lars P. Syll’s Blog
The main reason why almost all econometric models are wrong
Lars P. Syll | Professor, Malmo University

Jason Smith — A list of macro meta-narratives

In my macro critique, I mentioned "meta-narratives" — what did I mean by that? Noah Smith has a nice concise description of one of them today in Bloomberg that helps illustrate what I mean: the wage-price spiral. The narrative of the 1960s and 70s was that the government fiscal and monetary policy started pushing unemployment below the "Non-Accelerating Inflation Rate of Unemployment" (NAIRU), causing inflation to explode. The meta-narrative is the wage-price spiral: unemployment that is "too low" causes wages to rise (because of scarce labor), which causes prices to rise (because of scarce goods for all the employed people to buy). In a sense, the meta-narrative is the mechanism behind specific stories (narratives). But given that these stories are often just-so stories, the "mechanism" behind them (despite often being mathematically precise) is frequently a one-off model that doesn't really deserve the moniker "mechanism". That's why I called it a "meta-narrative" (it's the generalization of a just-so story for a specific macro event).

Now just because I call them meta-narratives doesn't mean they are wrong. Eventually some meta-narratives become a true models. In a sense, the "non-equilibrium shock causality" (i.e macro seismographs) is a meta-narrative I've developed to capture the narrative of women entering the workforce and 70s inflation simultaneously with the lack of inflation today.

Below, I will give a (non-exhaustive) list of meta-narratives and example narratives that are instances of them. I will also list some problems with each of them. This is not to say these problems can't be overcome in some way (and usually are via additional just-so story elements). None have yielded a theory that describes macro observables with any degree of empirical accuracy, so that's a common problem I'll just state here at the top.

The difference among just-so stories, handwaving, modeling for effect, and data-based modeling....

Worth looking at for the weekend — and thinking about.

Information Transfer Economics
A list of macro meta-narratives
Jason Smith

Lars P. Syll — On the non-applicability of statistical models

Math is purely formal, involving the relation of signs based on formation and transformation rules. Signs are given significance based on definitions. Math is applicable to the world through science to the degree that the definitions are amenable to measurement and the model assumptions approximate real world conditions (objects in relation to others) and events (patterned changes in these relations). Methodological choices determine the scope and scale of the model, which in turn determines the fitness of formal modeling for explanation of real world conditions and events.

Contemporary science is chiefly about applying formal modeling to theoretical explanation that covers a wide enough range of phenomena worth explaining to be of interest. The scientific project is about designing useful models for explaining phenomena and also designing experiments to test the model against observation. This involves measurement.

A further challenge is identifying parameters that can be measured to produce data and constructing models based on assumptions of how parameters are related with respect to states and how they change over time.

Then, there are also presumptions that are not stated. For example, it is presumed that science is consilient and therefore, any theoretical explanation that violates the conservation laws is ruled out automatically.

Beyond that philosophical foundations relating to metaphysics, epistemology, ethics, social and political philosophy, philosophy of science, the philosophy of the particular discipline, etc., also come into play.

Quite evidently, there is a lot of room for mistake and slip-ups in the process of "doing science."

Formalization and data are not magic wands, and assuming they are leads to magical thinking. Formalization is only rigorous — necessary based on application off rules — with respect to models. How models relate to what is modeled is contingent and depends on data. Data is dependent on observation and measurement.

All this is difficult enough in the natural sciences, but more difficult in the life sciences and much so in the social sciences.

The philosophy of economics, or foundations of economics if one prefers, needs to take all this into consideration and there needs to be lively debate about it. Is there?

Lars P. Syll’s Blog

On the non-applicability of statistical models

Lars P. Syll | Professor, Malmo University

Brian Romanchuk — Science And Economics

I had largely managed to avoid writing about the latest angst in the economics blogosphere regarding mathematics, science, and economics. I am not a fan of mainstream economics, but at the same time, I question some of the broad brush attacks on economics. The quest to pretend that economics can be a science like physics is doomed, and does not take into account the nature of what is being studied.

Bond Economics
Science And Economics
Brian Romanchuk

Peter Turchin — What Economics Models Really Say

A Review of Economics Rules: The Rights and Wrongs of the Dismal Science by Dani Rodrik (Norton, 2015)

Evonomics
What Economics Models Really Say

Peter Turchin | professor of ecology and evolutionary biology at the University of Connecticut

Daniel Little — Social science or social studies?

This list of legitimate objects of empirical study in the social world, resulting in legitimate and evidence-based knowledge and explanation, can certainly be extended. And if being scientific means no more than conducting analysis of empirical phenomena based on observation, evidence, and causal inquiry, then we can reasonably say that it is possible to take a scientific attitude towards empirical problems like these.

But the hard question is whether there is more to social science than a fairly miscellaneous set of results that have emerged through study of questions like these. In particular, the natural sciences have aspired to formulating fundamental general theories that serve to systematize wide ranges of natural phenomena -- the theory of universal gravitation or the theory of evolution through natural selection, for example. The goal is to reduce the heterogeneity and diversity of natural phenomena to a few general theoretical hypotheses about the underlying reality of the natural world.

Are general theories like these possible in the social realm?….

Here is one possible answer to the question posed above, consistent with the points made here. Yes, social science is possible. But what social science consists in is an irreducible and pluralistic family of research methods, observations, explanatory hypotheses, and mid-level theories that permit only limited prediction and that cannot in principle serve to unify the social realm under a single set of theoretical hypotheses. There are no grand unifying theories in the social realm, only an open-ended set of theories of the middle range that can be used to probe and explain the social facts we can uncover through social and historical research.

In fact, to the extent that the ideas of contingency, heterogeneity, plasticity, and conjuncturality play the important role in the social world that I believe they do, it is difficult to avoid the conclusion that there are very narrow limits to the degree to which we can aspire to systematic or theoretical explanation in the social realm. And this in turn suggests that we might better describe social inquiry as a set of discrete and diverse social studies rather than unified "social science". We might think of the domain of social knowledge better in analogy to the contents of a large and diverse tool box than in analogy to an orrery that predicts the "motions" of social structures over time.

Understanding Society
Social science or social studies?
Daniel Little | Chancellor of the University of Michigan-Dearborn, Professor of Philosophy at UM-Dearborn and Professor of Sociology at UM-Ann Arbor

Jason Smith — A good series on macro and microfoundations

This is an interesting post on the philosophy (foundations) of economics based on philosophy (foundations) of science and of social science. It is about criteria. The fundamental criterion that distinguishes science from other forms of speculation is empirical data, making scientific hypotheses testable.

Knowledge is distinguished from belief and opinion through critical thinking that imposes discipline on the thinking process. In science discipline is provided by the requirement for an empirical warrant provided by data rather than only or chiefly a logical pedigree as provided by a theory and model. Unless scientists discipline themselves with data, they risk slipping into baseless speculation and mistaking it for knowledge owing to the elegance of the theory and model (formalism) or normative bias (ideology).

Science is basically the search for regularity in changing phenomena, which requires observation and measurement of phenomena either directly or instrumentally. Otherwise, it is handwaving or persuasion rather than the rigorous application of reasoning to a subject matter.

Information Transfer Economics
A good series on macro and microfoundations
Jason Smith

Jason Smith — What should we expect from an economic theory?

I got into a back and forth with Srinivas T on Twitter after my comment on an Evonomics tweet. As an aside, Noah Smith has a good piece on frequent contributor at Evonomics David Sloan Wilson (my own takes are here, here, here). I'll come back to this later. Anyway, Evonomics put up a tweet quoting an economist saying "we need to behave like scientists" and abandon neoclassical economics.

My comment was that this isn't how scientists actually behave. Newton's laws are wrong, but we don't abandon them. They remain a valid approximation when the system isn't quantum or relativistic. Srinivas took exception, saying surely I don't believe neoclassical economics is wrong in the same way Newton is wrong.

I think this gets at an important issue: what should we expect of an economic theory?….

Information Transfer Economics
What should we expect from an economic theory?
Jason Smith

Jason Smith — Falsifiable statements are not philosophical disagreements

I would address this by saying that science as that which can be substantiated objectively, in contrast to philosophy as that which can't, meet as the margin. This margin is determined logically by available criteria, and also by the practicality of applying criteria.

This margin is blurring the distinction between philosophy and science and philosophers become more empirical and scientists become more speculative and what they deal with becomes less measurable.

This margin is a frontier of knowledge. It's a grey area not amenable to black and white thinking.

Basically, this debate begins with the distinction the ancient Greeks made between chaos (randomness) and cosmos (order). That which is ordered is "rational," that is, subject to logical reasoning (Greek: <i>logos</i>). Aristotle was the first to establish a theory of causality based on four causes (<i>aitia</i>) — formal, material, efficient, and final or teleological — that are both necessary and sufficient to answer the question "why" completely.

This framed the debate in metaphysics, epistemology, and natural philosophy for centuries, until the modern era when Galileo (among others) emphasized the necessity of observation.

Aristotle had also emphasized the observation against Plato's introspection, but Galileo launched the scientific age by emphasizing it and creating instruments for extending observation (telescope) and measurement (thermometer). Galileo also used the mathematics of his day to formalize knowledge.

This is key, because Aristotle based  his method primarily on first causes (aitia), which can also be rendered first principles or reasons. Observation as a rationale in justification is not essential. First principles are typically justified as self-evident or intuitional rather than observational. Now we would say "initial assumptions" rather than "first principles."

Science really took off and became differentiated from science with the advent of advanced mathematics, initially developed by "philosophers." Descartes introduced analytic geometry and Leibniz the calculus, for example.

Descartes had already posited a hard and fast distinction between mind and world. However, it was David Hume took this to its logical conclusion by further positing that reasoning is purely mental and observation deals only with sense data. There is no basis in either logic or sense data capable of justifying a metaphysical principle of causality.

For Hume, the assumption that sense data are caused by something unknown and unknowable external to them is a matter of belief. Causality is based in observation of constant correlation of sense date. Cause and effect is condition and result.

The history of philosophy since then has been largely either an acceptance of Hume or a reaction to him.

In the philosophy of science, the result was abandoning the question "why" and replacing it with "how." In science this is represented theoretically and formally in terms of mathematical functions, that is invariant relationships of inputs and outputs.

If the function is known, then it is a matter of observing and measuring independent variables and resultant dependent variables. If there is no method of arriving at a function relating measured inputs to measured outputs, then stochastic methods must be used to approximate outcomes from populations or random samples.

This is purely operational, dealing how things work rather than explanatory of why they work. We can know something of the laws of nature expressed mathematically, but not why these law actually apply other than their place in a system of invariance that is the basis of ergodicity. As result science is tentative in its conclusions owing to future uncertainty in the absence of metaphysical causality grounded in knowledge of reality instead of knowledge limited to sense data.

In the transition from philosophy to science it was assumed that there are hidden "laws" as observational regularities that can be discovered by applying a rigorous method that involves both theory based on mathematical models and observation of quantities based on measurement.

There are essentially three division of scientific inquiry based on subject matter and methodology — natural sciences, life sciences and social sciences. Psychology is a bridge science between life science and social science. Different fields lend themselves to different methods of investigation and levels of precision.

One aspect of philosophy involves attempting to elucidate how all this works in terms of a whole. This is called "consilience," that is, complementarity of knowledge in a unified and integrated system of information. While there is as yet no comprehensive unified "theory of everything," most agree that different aspects of scientific inquiry need to be compatible and that integration is self-reinforcing, acting as an attractor.

Those working in one area that are unmindful of work in other areas that contradicts their work or calls it into question, or do not addressing this issue, are working at the margin and may be marginalized or dismissed.

While this alone doesn't prove mavericks wrong, if they don't account for discrepancies, they are likely to be ignored for "magical thinking," or being "ivory-tower philosophers" in the pejorative sense of idle speculation based on unsubstantiated assumptions that do not accord with established observation and explanation.

Positivism was a simplistic solution for establishing criteria in terms of Hume's fork, the distinction between logic and observation. The criteria are tautology and contradiction formally, and empirical truth and falsity based on observation and measurement.

But this proved too rigid to account for how scientists actually proceed. There is a spectrum of thought on this from positivism to Paul Feyerabend's Against Method: Outline of an Anarchistic Theory of Knowledge, in which he claims that "science" is what scientists do rather than what they or others think they do. There is no "scientific method," and trying to impose one as a rule would limit creativity and innovation.

The upshot is that applying general principles is often less than satisfactory because most cover only special cases. So each instance must be approached in context to determine what procedure and which criteria apply, and then some degree of rigor has to be exercised in applying this. There is art or craftsmanship involved.

Anyway, Jason's post is interesting in this regard. It's short and not wonkish.

Information Transfer Economics
Falsifiable statements are not philosophical disagreements
Jason Smith

Daniel Little — Predicting, forecasting, and superforecasting

Issues in forecasting social events versus predicting natural events.

Understanding Society
Predicting, forecasting, and superforecasting
Daniel Little | Chancellor of the University of Michigan-Dearborn, Professor of Philosophy at UM-Dearborn and Professor of Sociology at UM-Ann Arbor

Daniel Little — Defining social phenomena

How does a field of phenomena come into focus as a subject of scientific study? When we want to know about weather, we can identify a relatively small number of variables that represent the whole of the topic -- temperature, air pressure, wind velocity, rainfall. And we can pick out the aspects of physics that seem to be causally relevant to the atmospheric dynamics that give rise to variations in these variables. Weather is a closed system, if a complex one.

Deciding what factors are important and amenable to scientific study in the social world is not so easy. Population size or density? Economic product? Inter-group conflict? Public opinion and values? Political systems? Racial and ethnic identities? All of these factors are of interest to the social sciences, to be sure. But none of this looks like anything like a definition of the whole of the social realm. Rather, there are indefinitely many other research questions that can be posed about the social world -- style and fashion, trends of social media, forms of etiquette, sources of power, and on and on.

For that matter, these don't look much like a macro-set of factors that are generated in some straightforward way by the simple actions of individual persons. These social factors aren't really analogous to macro-level weather factors, emerging from the local cells of temperature-pressure-humidity-direction. Rather, these social concepts or constructs are theorized and developed in a complicated back-and-forth by sociologists or political scientists seeking to identify social-level constructs that seem to give some insight into the ordinary and systematic experiences we have of the social world.

Most particularly, there isn't a natural way of mapping these social concepts into an integrated and comprehensive mental model of the whole of the social world. Instead, these high-level social concepts are partial and perspectival. And this is different from the situation of weather or climate. In the latter domains there are finitely many higher level concepts that serve to characterize the whole of the domain of global climate phenomena. Call this "high-level conceptual closure." There are no questions about climate that cannot be phrased in terms of these concepts. But the social world is not amenable to this kind of closure. We lack high-level conceptual closure for the social world.…

Conventional economics assumes closure because its models are closed. Then the question becomes to what extent are they representational of the world. As one would expect they model the factors that figure in their assumptions with varying degrees of success, and they fail to represent what falls outside of their restrictive assumptions.

Concludes with a quotation from Marx.
Understanding Society
Defining social phenomena
Daniel Little | Chancellor of the University of Michigan-Dearborn, Professor of Philosophy at UM-Dearborn and Professor of Sociology at UM-Ann Arbor

Dani Rodrik — Economists vs. Economics

Summary of Rodrik's new book on modeling and the use of models in econ.

Economics is not the kind of science in which there could ever be one true model that works best in all contexts. The point is not “to reach a consensus about which model is right,” as Romer puts it, but to figure out which model applies best in a given setting. And doing that will always remain a craft, not a science, especially when the choice has to be made in real time. 

The social world differs from the physical world because it is man-made and hence almost infinitely malleable. So, unlike the natural sciences, economics advances scientifically not by replacing old models with better ones, but by expanding its library of models, with each shedding light on a different social contingency.

Project Syndicate
Economists vs. Economics

Dani Rodrik | Professor of International Political Economy at Harvard University’s John F. Kennedy School of Government

Noah Smith — A paradigm shift in empirical economics?

Noah Smith on empirical economics becoming more significant in the field than theoretical economics.

The rise of quasi-experimental methods shows that the ground has fundamentally shifted in economics - so much that the whole notion of what "economics" means is undergoing a dramatic change. In the mid-20th century, economics changed from a literary to a mathematical discipline. Now it's changing from a deductive, philosophical field to an inductive, scientific field. The intricacies of how we imagine the world must work are taking a backseat to the evidence about what is actually happening in the world. 

The driver is information technology. This does for econ something similar to what the laboratory did for chemistry - it provides an endless source of data, and it allows (some) controls.

Now, no paradigm gets things completely right, and no set of methods is always and universally the best. In a paper called "Tantalus on the Road to Asymptopia," reknowned skeptic Ed Leamer cautions against careless, lazy application of quasi-experimental methods. And there are some things that quasi-experimental methods just can't do, such as evaluating counterfactuals far away from current conditions. The bolder the predictions you want to make, the more you need a theory of how the world actually works. (To make an analogy, it's useful to catalogue chemical reactions, but it's more generally useful to have a periodic table, a theory of ionic and covalent bonds, etc.)

But just because you want a structural theory doesn't mean you can always produce one. In the mid-80s, Ed Prescott declared that theory was "ahead" of measurement. With the "credibility revolution" of quasi-experimental methods, measurement appears to have retaken the lead.

This trend would make economics more like psychology and sociology, and also more like the case method adopted by business schools.

This would be a recognition that economics is not chiefly a theoretical science that empirical science then tests, like physics, but rather more like social sciences that lack an overarching theoretical basis owing to the subject matter, which is heavily influenced by human action and there is not overarching theory that explains this in a way remotely like the natural sciences account for observed regularities in physical change. Moreover, unlike other sciences that deal with measurement of the real, economics must take into consideration not only measurement of the real (data) but also nominal (price).

The aim of Positivism to develop a unified science as an objective theory of everything that is built on physics is unfulfilled, and there is no use in pretending otherwise. The gaps between natural science, life science and social science remain large.

Noahpinion
A paradigm shift in empirical economics?Noah Smith | Assistant Professor of Finance, Stony Brook University

James Petras — The Myth of ‘Value-Free’ Social Science Or The Value of Political Commitments to Social Science

Introduction: For many decades, mainstream social scientists, mostly conservative, have argued that political commitments and scientific research are incompatible. Against this current of opinion, others, mostly politically engaged social scientists, have argued that scientific research and political commitment are not contradictory.

In this essay I will argue in favor of the latter position by demonstrating that scientific work is embedded in a socio-political universe, which its practioners can deny but cannot avoid. I will further suggest that the social scientist who is not aware of the social determinants of their work, are likely to fall prey to the least rigorous procedures in their work – the unquestioning of their assumptions, which direct the objectives and consequences of their research.

We will proceed by addressing the relationship between social scientific work and political commitment and examining the political-institutional universe in which social scientific research occurs. We will recall the historical experience of social science research centers and, in particular, the relationship between social science and its financial sponsors as well as the beneficiaries of its work.

We will further pursue the positive advantages, which political commitments provide, especially in questioning previously ignored subject matter and established assumptions.
We will start by raising several basic questions about scientific work in a class society: in particular, how the rules of logical analysis and historical and empirical method are applied to the research objectives established by the ruling elites.….

A major purpose of scientific method is reduction of subjectivity in the interest of greater objectivity. However, since meaning is context-dependent, it is not possible logically to achieve complete objectivity by isolating the positive from the normative. It is also empirically suspect in that cognitive science has shown that the rational and non-rational are deeply entangled in brain functioning. Since it is not possible to stand outside of point of view, it is also impossible to be sure that one has identified all hidden assumptions. The proper course of rational enquiry is to identify and acknowledge the most significant assumptions that are involved in a model, whether it be conceptual or mathematical.

In economics, for instance, there are varying approaches based on different methodological assumptions. The proper course is to articulate the assumptions as completely as possible instead of assuming ideologically that a particular approach is superior in every way, superseding other approaches.

Nor is the assumption that there is one "correct" model that alone is "value-free" merely coincidental.

After World War II, wealthy business elites and capitalist governments in the United States and Western Europe established and funded numerous research foundations carefully selecting the functionaries to lead them. They chose intellectuals who shared their perspectives and could be counted on to promote studies and academics compatible with their imperial and class interests. As a result of the interlocking of business and state interests, these foundations and academic research centers published books , articles and journals and held conferences and seminars, which justified US overseas military and economic expansion while ignoring the destructive consequences of these policies on targeted countries and people. Thousands of publications, funded by millions of dollars in research grants, argued that ‘the West was a bastion of pluralistic democracy’, while failing to acknowledge, let alone document, the growth of a world-wide hierarchical imperialist order.

An army of scholars and researchers invented euphemistic language to disguise imperialism. For example, leading social scientists spoke and wrote of ‘world leadership’, a concept implying consensual acceptance based on persuasion, instead of describing the reality of ‘imperial dominance’, which more accurately defines the universal use of force, violence and exploitation of national wealth. The term, ‘free markets’, served to mask the historical tendency toward the concentration and monopolization of financial power. The ‘free world” obfuscated the aggressive and oppressive authoritarian regimes allied with Euro-US powers. Numerous other euphemistic concepts, designed to justify imperial expansion, were elevated to scientific status and considered ‘value free’.….

The result was "weaponization" of knowledge.

The transformation of social science into an ideological weapon of the ruling class reflected the institutional basis and political commitments of the researchers. The ‘benign behavior’ of post-World War 2 US empire-building, became the operating assumption guiding scientific research. Moreover, leading academics became gatekeepers and watchdogs enforcing the new political orthodoxy by claiming that critical research, which spoke for non-elite constituencies, was non-scientific, ideological and politicized. However, academics, who consulted with the Pentagon or were involved in revolving-door relationships with multi-national corporations, were exempted from any similar scholarly opprobrium: they were simply viewed as ‘consultants’ whose ‘normal’ extracurricular activities were divorced from their scientific academic work.

In contrast, scholars whose research was directed at documenting the structure of power and to guiding political action by social movements were condemned as ‘biased’, ‘political’ and unsuitable for any academic career.….

In other words, academic authorities replicated the social repression of the ruling class in society, within the walls of academia. Their principle ideological weapon was to counterpose ‘objectivity’ to ‘values’. More specifically, they would argue that ‘true social science’ is ‘value free’ even as their published research was largely directed at furthering the power, profits and privileges of the incumbent power holders.….

You can see where this is going. Neoliberalism.

Twenty-five years ago, the concept ‘reform’ referred to progressive changes: less inequality, greater social welfare, increased popular participation and more limitations on capitalist exploitation of labor. Since then, contemporary social scientists (especially economists) use the term, ‘reform’, to describe regressive changes, such as deregulation of capital, especially the privatization of public enterprises, health and educational institutions. In other words, mainstream academics transformed the concept of ‘reform’ into a private profitmaking business. ‘Reform’ has come to mean the reversal of all the working-class advances won over the previous century of popular struggle. ‘Reform’ is promoted by neo-liberal ideologues, preaching the virtues of unregulated capitalism. Their claim that ‘efficiency’ requires lowering ‘costs’, in fact means the elimination of any regulation over consumer quality, work safety and labor rights.

Their notion of ‘efficiency’ fails to recognize that economies, which minimizeworkplace safety, or lower the quality of consumer goods (especially food) and depress wages, are inefficient from the point of view of maximizing the general welfare of the country. ‘Efficiency’ is confined by orthodox economists to the narrow class needs and profit interests of a thin layer of the population. They ignore the historical fact that the original assumption of classical economics was to provide the greatest benefit to the greatest number.…

Once their political commitments define the research ‘problem’ to be studied and establish the conceptual framework, they apply ‘empirical’, historical and mathematical methods to collect and organize the data. They then apply logical procedures to ‘reach their conclusions’. On this flawed basis they present their work as ‘value-free’ social science. The only ‘accepted criticism’ is confined to those who operate within the conceptual parameters and assumptions of the mainstream academics.

"Efficiency" is the neoliberal weapon of choice, as if efficiency were synonymous with effectiveness. It is not.

The antidote? Organized opposition.

Lots more in the post.

The Official James Petras Website
The Myth of ‘Value-Free’ Social Science Or The Value of Political Commitments to Social Science

James Petras | Professor (Emeritus) of Sociology at Binghamton University in Binghamton, New York and adjunct professor at Saint Mary's University, Halifax, Nova Scotia

Jason Smith — All models are wrong, but some are tedious

All models are wrong is properly taken as a rallying cry against tedium. Macroeconomists should not be adding variables and complications to their models because there simply isn't enough data to warrant doing so. Read Nate Silver on overfitting -- there are only about 200 quarterly observations of economic data in the post-war US economy where data is relatively good, which implies that a model should at most have about 10 parameters (some DSGE models have 40 parameters or more!). Noah Smith likes to say that macro data is uninformative. Really what that means is that economists have ignored Box: they shouldn't have so much overparameterization. With fewer parameters, the data isn't uninformative ... if you just have two parameters, the data is actually completely informative.

Information Transfer Economics
All models are wrong, but some are tedious

Jason Smith

Bill Mitchell — The superiority of economists!

Its the Friday lay day blog and today I briefly discuss economists. What a topic! There is an interesting article just published in the Journal of Economic Perspectives that examines the way economists think of themselves and other social science disciplines. It is a horror story really. Having been immersed in the profession for many years now, I sometimes forget how bad it is. Here is what the study found. The title is a deliberate double entendre. It is more about the way economists think they are superior rather than any absolute finding of superiority....

Smackdown follows.

Bill Mitchell – billy blog
The superiority of economists!
Bill Mitchell | Professor in Economics and Director of the Centre of Full Employment and Equity (CofFEE), at the Charles Darwin University, Northern Territory, Australia

Mark Buchanan — Explaining How Economists Explain

…Itzhak Gilboa and a group of economists … recently tried to understand why their profession operates so differently from most sciences. Academic economists, they say, use the term "explanation" in a way that other scientists never would. Instead of developing realistic and testable theories like those in biology or physics, they often aim only to develop "theoretical cases" -- imaginary mathematical worlds with their own rules of cause and effect.

Formalism versus realism, rationalism versus empiricism. Mainstream economists venture into the ivory tower of the mind much further than most philosophers have been willing to tread for fear of becoming lost in their own musings. Talk about being out of touch. It's full-on bonkers as far as explanation is concerned and is purely syntactical without semantic import, that is, mathematical. I guess they know how to develop cool model of possible worlds though. Just not this one.

Bloomberg View
Explaining How Economists Explain

Mark Buchanan
ht Mark Thoma at Economist's View