“Causal Processes in Psychology Are Heterogeneous” — Andrew Gelman

A key difficulty here is that, even though interactions are clearly all over the place, they’re hard to estimate. Remember, you need 16 times the sample size to estimate an interaction than to estimate a main effect. So, along with accepting the importance of interactions, we also have to accept inevitable uncertainty in their estimation. We have to move away from the idea that a statistical analysis will give us effective certainty for the things we care about.

"Representative agents" are homogenous. They serve as a "methodological convenience." This implies that the scope and scale are limited. Model implications cannot be extended beyond the boundaries of the assumptions and data.

This doesn't imply that representative agent models are necessarily useless. But the temptation to overextend them must be avoided to prevent conclusions from falling into fallacy — due to hasty generalization, for instance.

What this means is that it is very difficult to get a binary causal connection of data to result through a linear function where human agents are involved owing to heterogenous causal factors. Causality involves a constellation of causal factors, perhaps including catalysts, whose distribution varies with respect to time and conditions. 

Identifying the constellation of factors and estimating their relative weights in a causal process in which the assumptions also require identification is challenging. Therefore, simple static models are usually overly simplistic regarding events of any degree of complexity, as are most design problems involving social agents affected by systemic relationships.

Getting the "science" right in social science is more difficult in the life sciences than the natural sciences, and much more difficult in the social sciences, in most questions that matter anyway.

Statistical Modeling, Causal Inference, and Social Science
“Causal Processes in Psychology Are Heterogeneous”
Andrew Gelman | Professor of Statistics and Political Science and Director of the Applied Statistics Center, Columbia University

My Journey from Theory to Reality — Asad Zaman

Over the twenty years that I have been pursuing an Islamic approach — focusing on the production of USEFUL knowledge, I have managed to heal all three of these divides. This happens naturally, when you focus on solution of real world problems. You automatically need to combine information coming from many different specialization areas. You need to use reasoning and also intuition. You also need to use both theory and its applications to the real world experiences. This leads to substantial changes in the subject matter itself. I have applied this approach with great success to Econometrics, Statistics, Microeconomics, Macroeconomics, Experimental Economics, and even Mathematics itself. I am in process of creating textbooks and teaching materials in all of these areas. Because my work is most advanced in the area of Statistics, I am working on putting it all together in a new course on Real Statistics: An Islamic Approach. There is a large amount of pre-existing material – lectures, texts, exercises, references – that I have created over the past decade on working on this course. However, as I progress, I keep learning new things, and this time I want to put together a polished new version of this course for public use. My primary target audience is teachers of statistics — I would like to persuade them to use this new approach to teach statistics. Those who would like to follow my progress as I construct a new website on a lecture by lecture basis gradually are encourged to fill in the following Registration form. I will use emails to notify them when I complete a new lecture, and also invite feedback on what is there, so that we can build it up with clarity and consensus....

All thinking, since humans think in language, is based on context, meaning being determined by context. The shaper of context is the worldview in which the group is functioning. In the West, the contemporary worldview was shaped by the Western history, chiefly Greek thought, Judaeo-Christian religion, Roman law, and modern science. Its intellectual products were shaped by the Western intellectual tradition that culminated most recently in the rise of science, which new supervenes over what preceded it. The basic assumption of the Western scientific world view is methodological naturalism, which many if not most of the foremost exponents equate with metaphysical materialism.

This is taking place in the overarching worldview of Western liberalism that was developed in the 18th century as an antidote to theological dogmatism. Scientific naturalism and the ideal of unified scientific explanation, or consilience, replaced the great chain of being, as the dominant paradigm of explanation.

Regarding social, political and economic thought, many if not most of the foremost authorities equate economic liberalism with Western capitalism  as the dominant mode of production and also view political liberalism in the form of representative democracy being determined by capitalism as economic liberalism. Initially, economic liberalism implied laissez-faire and sought to replace government by the market. Subsequently, when it become clear that government was needed for institutional structure, classical economic liberalism shifted to neoliberalism, which is the view that economic and financial interest should control government and direct institutional arrangements and operations toward furthering economic interests.

While the West is still the most influential bloc worldwide, that is beginning to change. The rest of the world, which had accepted the assumptions on which this worldview is based owing to the success of the West. Now many are beginning to question whether these assumptions are as robust as they seemed as problems arise and the paradoxes of liberalism manifest.

Consequently, some of those that had accepted the Western stance previously and were also educated in it are beginning to rethink their positions in light of the traditional worldviews that prevail in their societies. Many of the traditional worldviews are embedded in a religious contexts that have become cultural. Even in secular China, President Xi is resurrecting Confucius as a cultural icon, and in the supposedly secular US, dominant religious groups are asserting influence more openly, with science itself subject to challenge when it is perceived to conflict with tradition.

Asad Zaman's post is good example of this rising trend, as well as what a highly educated person asking such questions might do about it. This process is an iteration of the historical dialectic as liberal and traditionalism interact to forge a complementary Zeitgeist that moves history forward a step.

What should a "good" liberal think about this? Freedom of thought and expression are fundamental to liberalism this implies tolerance. So the answer is given by none other than Mao Tse-Tung, "Let a hundred flowers bloom."

Asad Zaman makes one other point worth sharing for those that may not choose to read his post in full.

Sometime during this process of switching from teaching theory to teaching how to solve real world problems, I came across the “Statistics” textbook of David Freedman. This textbook actually implemented exactly this idea that I had come to believe in — do statistics in context of solving real world problems. One amazing characteristic of this textbook is that it has no mathematical formula – ZERO. Freedman explained that students use formulae as crutches to prevent them from thinking. So he explains all concepts in words only, exactly the same insight that I had learnt on my own. Formulas teach you techniques for calculation. We don’t need these techniques — leave them to the computer. We need to UNDERSTAND what these calculations mean. That is a VERY DIFFERENT process. I got involved in an email correspondence with David Freedman, who had very similar experience to mine. He had started out as a very heavily mathematically oriented researchers. His early papers are all very heavy mathematically. Later, when he got involved in doing some testimony in real world court cases, he realized that all of the theory he had learnt was useless in the real world. This is because the assumptions we make in theory are almost always false in the real world. Then he had to learn how to do real world statistics, exactly as I have had to do. Since most fancy assumptions we make in statistics and econometrics are wrong, we need to learn how to do simple and basic inferences, which actually makes life much easier for students of the subject — we need to teach them basic and intuitive things, not complex models and math....  

An Islamic Worldview
My Journey from Theory to Reality
Asad Zaman | Vice Chancellor, Pakistan Institute of Development Economics and former Director General, International Institute of Islamic Economics, International Islamic University Islamabad

Andrew Gelman — Our hypotheses are not just falsifiable; they’re actually false.

On the practical side of philosophy of science. Adding nuance to Karl Popper on falsification.

Further argument for the view that theories are useful but not "true." This may seem to contradict the realist view that theories are general descriptions of causal relationships. But I don't think that this is what is is implied. Rather, useful theories can be viewed as fitting the data because they reveal underlying structures that are not observed directly but only indirectly. 

There is a often a tendency to transfer simple analogies too complicated and complex situations and events. Some causal relationship are observable, as it a hammer driving a nail, with the physical theory explaining it in terms of simple variables related in a function. 

But most interesting issues are much more complicated and nuanced and may be complex, e.g., subject to emergence owing to synergy. There may a constellation of factors involved, and this may be difficult to order in a hierarchy. Some factors may be catalysts that are necessary for an operation but do not themselves enter into it. These may be presumptions that are hidden assumptions.

In addition, statistics is by definition "inexact" in that it deals with probabilities, unlike deterministic functions in which the variables are all known and measurable, and are expressible in terms of a simple function.

While physics is mostly tractable other than at the edges, life sciences are less so, and social sciences and psychology even less. Economics combines social science and psychology, especially macroeconomics and political economy. Economic sociology and economic anthropology take this into account, global economic history also demonstrates it.

This is coming to the fore now as some critics of MMT, the Green New Deal, and "socialism" demand to see data-based model that "prove" proposed solutions have worked in the past. Of course, the record is important, but the demand for "proof" requires a degree of stringency that is not applied in social science and psychology because it is unattainable. Nor is this standard applied to conventional economics either, its econometric approaching being based on formalism rather than being empirically based.

Another important point that Andrew Gelman makes is the futility of pitting theories against each other. That is a recipe for disagreement in that the party that determines the framing wins. Whose assumptions are going to set the criteria? Why?

And, no, I don’t think it’s in general a good idea to pit theories against each other in competing hypothesis tests. Instead I’d prefer to embed the two theories into a larger model that includes both of them.

This is a good suggestion but it is general. Often, the disagreement is over fundamental criteria that determine a frame of reference. This should be obvious in the different approaches to economic theory and economic practice., e.g., econometric and institutional, static and dynamic, simple and complex, natural and historical.

Obviously, a short post like this can only suggest matters that need deeper reflection, open inquiry and sincere debate aimed at solutions to pressing design problems. This is no long just "theoretical." Humanity has to get this right to survive, let alone prosper. We have seemingly dug ourselves into a hole based on policy that is has turned out to impractical in the extreme, such as socializing negative externalities that have led to environmental degradation and threaten ecological collapse if not addressed successfully in a timely fashion. So, let's get with it.

Statistical Modeling, Causal Inference, and Social Science
Our hypotheses are not just falsifiable; they’re actually false.
Andrew Gelman | Professor of Statistics and Political Science and Director of the Applied Statistics Center, Columbia 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

Timothy Taylor — I Don't Know So Well What I Think Until I See What I Say

I've known writers who have the essay almost fully formed in their mind, and it just pours out on to the page. It's happened for me a few times. But most writing for me, and I suspect for others, starts from a place of less clarity. There's an idea, to be sure, and some support for the idea. But as you try to put the ideas into concrete words, you become aware of a lack of precision in what you are saying, of a failure to capture what you really mean to say, of holes and inconsistencies in the argument, of places where the argument is not persuasive or connected or fluent. I sometimes find this hard to convey to students: Writing isn't (usually) about transcribing thoughts, but instead is intertwined with a process of developing insights that are more accurate and complete.…

This is the process of applying critical thinking to creative thinking, of which Ludwig Wittgenstein said, "The purpose of philosophy is the logical clarification of thoughts. Philosophy is not a theory but an activity... Philosophy should clarify and sharply bound thoughts that would otherwise be cloudy and blurry, as it were. " (Tractatus 4.112)

Rigorous thinking need not be formalized, since that is not always possible. For example, this may occur when quality predominates over quantity, or quantity is not sufficiently measurable for the degree of precision needed. or the number of variable and parameters involved makes formalized models intractable.

It's also a reason for using math where measurement is possible and quantity is a factor. For example, a lot of apparently promising entrepreneurial ideas are deflated when one puts numbers on it.

But logic is always applicable is some form, and logic is one pillar of critical thinking. The other pillar is substantiation, e.g., through evidence or authority such as expert testimony or documentaion.

 Another aspect of critical thinking is tacit knowledge. This is a reason that experts in a field are more reliable on matters in that field that non-experts. Their tacit knowledge provides the necessary background upon which to draw.

Critical thinking combines the categorical and dialectical, the dialectical aspect considering possible errors and objections by playing the devil's advocate.

Ideas of any degree of complication or complexity should be discussed in a team that brings may inputs to bear in a dialectic process. RAND Corporation invented the Delphi method for this purpose, for instance. Academics do this by passing their papers around among colleagues before releasing them. Working papers are also used to solicit feedback.

The Internet both helps and hinders this process. Obviously, I think it helps more than hinders.

It helps by forcing clarity, brevity and precision in thinking and expression, as well as training in drawing on and distilling tacit knowledge as background.

It hinders owing to the limited scope of the media, e.g., blogs and social media posts, and the breadth of scale, which necessitate a certain degree of "dumbing down" to reach the broad non-expert audience. As a result the output may be somewhat superficial and even be misunderstood, which poses a reputational risk that some are not willing to accept. There is also the risk of appearing foolish if one exceeds the bounds of one's field of expertise and makes errors, revealing lack of discrimination.

Conversable Economist

"I Don't Know So Well What I Think Until I See What I Say"

Timothy Taylor | Managing editor of the Journal of Economic Perspectives, based at Macalester College in St. Paul, Minnesota

Tyler Cowen — Which happiness results are robust?

Tyler Cowen comments on a paper, and Barkley Rosser, who is not an author of the paper but is interested in the issues, responds in the comments. (Ignore the trolls in the comments.)

I agree with Barkley Rosser. Social phenomena are difficult to measure and subjectivity greatly complicates this. However, it doesn't invalidate all models any more than similar issues invalidate modeling in economics.

Where problems arise lies not so much in modeling and modeling decisions as in misinterpreting the implications of a model owing to unstated presumptions and hidden assumptions, or exceeding scope and confusing scale. Of course, choice of parameters, operational definition, etc., although there are many issues involved that can invalidate a model. Inadequate measurement protocol also can do so. 

Formal modeling complements conceptual modeling, which tend to be fuzzier. But conceptual modeling is also needed to encompass the full scope and deal with issues of scale, for example, by avoiding fallacies of composition. Moreover, formal modeling focuses on quantity, while conceptual modeling is also able to handle quality. Quality is often difficult to quantity.

Cognitive-affective bias and ideology also have to be considered, as some point out in the comments.

This discussion is particularly important from the economic point of view in that economics has traditionally been about "utility," which is a code word for satisfaction, with satisfaction related to happiness. Bentham's Utilitarianism is about not only individual good (micro scale), but also "the greater good of the greatest number" (macro scale).

Marginal Revolution
Which happiness results are robust?
Tyler Cowen | Holbert C. Harris Chair of Economics at George Mason University and serves as chairman and general director of the Mercatus Center

Chris Dillow — Progress in economics

Financial economics. e.g., EMH and CAPM.

Stumbling and Mumbling
Progress in economics
Chris Dillow | Investors Chronicle

Peter Radford — 1937

Hayek, Coase and uncertainty.

In any case I find it fascinating that the two, Hayek and Coase, both in their own way, brought the impact of uncertainty to the fore in the same year.

It’s a shame that economics has never fully embraced, nor realized, the full richness of their ideas. Neither author was willing to step into the world that they clearly understood existed. Hayek was right about universal central planning: it is an impossibility. He was wrong to assert that this implied anything about the market place or prices. By his own argument we simply cannot know whether something is optimal. Uncertainty makes such a thing inscrutable too us. And Coase was equally correct when he saw the need for local central planning: it is the only way we can organize production adequately in the face of uncertainty. But his focus on transactions was a legacy of the classical emphasis on exchange. It ignored the need for active coordination. He missed the requirement for management. He should have talked about “management cost” not “transaction cost”. They’re different animals.

So: an interesting question is this: what happens to Coase’s “institutional structure of production” when information, and by association knowledge, is less clumpy in the economic landscape? Does something like the Internet, which is a vector for information and knowledge, obviate the need for such structure? Does it smooth that landscape out sufficiently for firms not to exist?

We need to think about that.

We need a new version of the discussion that ought to have taken place in 1937.

The Radford Free Press
1937
Peter Radford

Joseph Hickey — How Societies Form and Change

You might find this of interest. As a bonus, it explains some fundamental principles of mathematical modeling.

Hierarchy appears to be an inescapable feature of animal, including human, societies. There are dominants and subordinates, bosses and employees, rulers and subjects, and an individual’s position in the social hierarchy to a large degree determines fundamental aspects of his or her life, including one’s health, access to resources, and influence in society. From the individual’s point of view, the hierarchy provides security, but also reduces one’s freedom. What determines how oppressive society is toward the individual? Would more freedom be better for society? How bad does societal inequality have to become before things start getting better? Are there any fundamental laws or rules that control how social hierarchy forms and evolves in a complex society of many interacting individuals and groups?

I am doing my PhD research in physics on these questions. Something I’m often asked is how can the study of social hierarchy be considered “physics”? The answer is “simple” – physicists try to construct simple models to reveal essential or underlying features of complex natural phenomena. Such a model has to be as simple as possible, yet it must be cleverly constructed so that, in its simplicity, the model captures essential features of reality. If this is achieved, the model can provide insights about underlying rules that may control or influence the real phenomenon.

I have constructed a simple physics model of the formation and evolution of social hierarchies, based on interactions between the individual members of the society. Computer simulations of the model produce societies that resemble real-world societies, and we can study how the simulated societies are formed and how they change in time. A scientific article presenting the model has been submitted to a journal and can be read online at ResearchGate.1 In the following, I outline how the model works for a general (non-specialist) reader and briefly discuss what its results might mean in terms of understanding social hierarchy in the real world.

Dissident Voice
How Societies Form and Change
Joseph Hickey | Executive Director of the Ontario Civil Liberties Association(OCLA)

Andrew Cockburn — Vladimir Putin: Computer Genius?

The title is snark. The post is actually about modeling behavior and its pitfalls and shortcomings. Short and worthwhile.

Truthdig
Vladimir Putin: Computer Genius?

Andrew Cockburn

Lars P. Syll — Time to abandon statistical significance

As shown over and over again when significance tests are applied, people have a tendency to read ‘not disconfirmed’ as ‘probably confirmed.’ Standard scientific methodology tells us that when there is only say a 10 % probability that pure sampling error could account for the observed difference between the data and the null hypothesis, it would be more ‘reasonable’ to conclude that we have a case of disconfirmation. Especially if we perform many independent tests of our hypothesis and they all give about the same 10 % result as our reported one, I guess most researchers would count the hypothesis as even more disconfirmed.

We should never forget that the underlying parameters we use when performing significance tests are model constructions. Our p-values mean nothing if the model is wrong. And most importantly — statistical significance tests DO NOT validate models!

Lars P. Syll’s Blog
Time to abandon statistical significance
Lars P. Syll | Professor, Malmo University

Noah Smith — "Theory vs. Data" in statistics too

Important.

I think Noah has this right. Fit the tool to the job, rather than the job to the tool.

Aristotle defined speculative knowledge in terms of causal explanation. This definition stuck although Aristotle's analysis of causality did not.

In the Posterior Analytics, Aristotle places the following crucial condition on proper knowledge: we think we have knowledge of a thing only when we have grasped its cause (APost. 71 b 9–11. Cf. APost. 94 a 20). That proper knowledge is knowledge of the cause is repeated in the Physics: we think we do not have knowledge of a thing until we have grasped its why, that is to say, its cause (Phys. 194 b 17–20). Since Aristotle obviously conceives of a causal investigation as the search for an answer to the question “why?”, and a why-question is a request for an explanation, it can be useful to think of a cause as a certain type of explanation. (My hesitation is ultimately due to the fact that not all why-questions are requests for an explanation that identifies a cause, let alone a cause in the particular sense envisioned by Aristotle.) — Stanford Encyclopedia of Philosophy

There is a distinction between reasons and causes. Some types of explanation seek only reasons, while other seek causes. Causation subsequently came to be viewed in terms of articulating mechanisms or lines of transmission (models) that are substantiated in evidence.

Explanation by reasons is different since the strict criterion of articulating mechanisms or lines of transmission that can be checked against evidence is not required.

Explanation by reasons rather than strictly by establishing causation is based on the principle of sufficient reason, which is usually credited to Spinoza and Leibnitz.

In philosophical logic, two negative criteria are foundational. Valid reasoning is vitiated by 1) arguing in a circle and 2) infinite regress.

Without recourse to checking against evidence there is no stopping point in assigning causes other than stipulation, e.g. of a first cause.

However, there may be a reason for a stopping point that doesn't involve causality based on evidence from observation or only stipulation, for example, principles that are "self-evident" based on intuition such as Aristotle's conception of intellectual intuition, or Kant's synthetic a priori propositions as articulated in the Critique of Pure Reason. 

On the other hand, Hume argued that causality is merely over-interpretation of constant correlation, there being no knowledge of the world other than that based on sense data. There is no observable causal link.

Cutting to the chase, scientific explanation based on causality is grounded in models that articulate causal mechanisms or lines of transmission that show how things change invariantly, which is the basis for deterministic functions. Where this is not possible, then there are two other avenues. The first is explanation by giving reasons, which is the domain of speculative philosophy. The second is employing statistics to explore patters of correlation. The question then is to what degree causal models can be gained from statistical methods, or whether it is possible at all. 

This is the issue that Noah Smith's post is getting at.

Noahpinion
"Theory vs. Data" in statistics too
Noah Smith | Bloomberg View columnist

Andrew Gelman — Why they aren’t behavioral economists: Three sociologists give their take on “mental accounting”

The other thing—and this is important—is that the perspectives coming from these three academic disciplines are not competing; they’re complementary. It’s important that money in different bank accounts is liquid—or, to be more precise, it can be liquid for those people who choose to let it be so. It’s important that people often seem to behave as if there are walls between the accounts, restricting their transactions and “freezing” the money, as it were. And it’s important to understand the social context of these behaviors.

Analogously, in section 5.2 of our paper on rational-choice models of voting, Edlin, Kaplan, and I discuss how the rational model is complementary with a psychological understanding of voters. It’s my impression that Bandelj, Wherry, and Zelizer are in agreement with me on this general point, that patterns of human behavior can be usefully understood in different theoretical frameworks. There’s no “right” or “wrong” framework (although one can come to correct or incorrect conclusions within any framework), rather, each framework gives us a way of thinking about the behavior, and entry points into studying it further.

I talk more about frameworks, and how they differ from theories, here.*

Statistical Modeling, Causal Inference, and Social Science
Why they aren’t behavioral economists: Three sociologists give their take on “mental accounting”
Andrew Gelman | Professor of Statistics and Political Science and Director of the Applied Statistics Center, Columbia University

* Philosopher of science Karl Popper and others have criticized such theories as being nonscientific because they are non-refutable, but I prefer to think of them as frameworks for doing science. As such, Freudianism or Marxism or rational choice or racism are not theories that make falsifiable predictions but rather approaches to scientific inquiry. Taking some poetic license, one might make an analogy where these frameworks are operating systems, while scientific theories are programs. That’s why I wrote that I can’t say that Wade is wrong, just that I don’t find his stories convincing.…

I respect the effort to push such theories as far as they can go, but I find them generally less convincing as they move farther from their home base. Similarly with economists’ models: they can make a lot of sense for prices in a fluid market, they can work OK to model negotiation, they seem like a joke when they start trying to model addiction, suicide, etc.

All-encompassing frameworks are different from scientific theories. Both are valuable — frameworks motivate theories and help us interpret scientific results — but I also think it’s important to be clear on the distinction.

Andrew Gelman — The role of models and empirical work in political science

I’m more and more becoming convinced of Dan Kahan’s idea that the paradigmatic task of empirical science is not the testing of hypotheses but the gathering of data in order to distinguish between competing models of the world.

Statistical Modeling, Causal Inference, and Social Science
The role of models and empirical work in political science
Andrew Gelman | Professor of Statistics and Political Science and Director of the Applied Statistics Center, Columbia University

Mark Thoma — If the Modifications Needed to Accommodate New Observations Become Too Baroque ...

This is important if only because Stephen Hawking gets it wrong.

Hawking: So which is real, the Ptolemaic or Copernican system? Although it is not uncommon for people to say that Copernicus proved Ptolemy wrong, that is not true..., our observations of the heavens can be explained by assuming either the earth or the sun to be at rest. Despite its role in philosophical debates over the nature of our universe, the real advantage of the Copernican system is simply that the equations of motion are much simpler in the frame of reference in which the sun is at rest.

Sorry. Not so. The logic does not hold.

The models are equal as models but not as causal models of reality, as Newton's theory of gravitation proves beyond doubt. Scientific explanation is assumed to provide causal accounts, not just models that fit. Many models can fit a set of conditions. The challenge for science is distinguishing which are correct and this is accomplished by causal accounts.

The planets orbit the sun rather than the earth and while Copernicus did not know why, we do now. So, strictly speaking, Hawking is correct within the time frame. The causal account was only provided later. But it completely resolves the issue causally without appeal to formal elegance.

Newton's account of planetary motion shows not only how (descriptively), but also why (causally) in terms of the gravitational force — Newton's law of universal gravitation and his laws of planetary motion. Case closed. Ptolemy is a historical footnote. Copernicus, Bruno, Galieo, etc. are vindicated.

The Ptolemaic model is not just more inelegant and cumbersome, it is actually wrong about reality in that there is nothing causing the sun to orbit the earth and there is a well-explained cause of the planets orbiting the sun and the moon orbiting the earth.

Even though Ptolemaic models can be adjusted to fit, it's not just a matter of economy of explanation. There is no comparison between the force of the heliocentric causal account of modern science based on math and empirics, and the ancient account based on the assumed importance of the earth establishing it as the center of the universe and jiggling with the data to make it seem so.

This is no small matter. Copernicus was careful in sufficing it, Giordano Bruno was't and got burned at the stake over for asserting heliocentrism (among other similar matters), while Galileo Galilei had to recant to save himself. This was not about models. It was about causality. The authorities of the time saw heliocentrism as religious heresy that undermined the centrality of the Incarnation, for example.

Similarly, economic models are either adequate causal models as representations of reality or not. Potentially an unlimited number of models can be devised to fit and adjusted as conditions require. As supposedly scientific theories, the degree of elegance they display may be suggestive, but it is inconclusive without causal explanation that is supported by observation (data).

Moreover, simple models may appear elegant but they are actually simplistic when matched against reality. Economy of expression cannot be a deciding criterion in science, although it may be in math or philosophy.

Samuelson's insistence on ergodicity in order to make economics a science more like natural science rather than social science was a necessary condition and not a sufficient one. That must be borne out by facts supporting causal effects through hypothesis generation and testing.

Ergodicity is needed for generality in change. Ergodicity ensures that the future will resemble the past in that there is no difference between an ensemble and a time series. Timeless laws become possible, whereas they are not in a non-ergodic environment in which the future is uncertain in the sense of the well-known disclaimer, "past performance is no guarantee of future performance."

General case theories don't allow for exceptions at the margin. If there are exceptions, then the explanations are of special cases.

For example, Say's "law" does not describe a general case but only a special case of full employment. Assuming full employment "in the long run" is meaningless scientifically without specifying parameters. As Keynes joked, "In the long run we are all dead."

"In the long run" gives no useful information even though it as the gravity of a philosophical principle. It actually conceals a petitio principii or vicious circle.

Nor is there a law of supply and demand that governs markets as a market force, or "invisible hand" that left to itself spontaneously generates natural order in which all available resources are employed optimally (efficiently). Erroneously attributed to Adam Smith, the invisible hand as a metaphor for market forces is largely a Paul Samuelson invention promulgated widely in his 1948 textbook and picked up by the echo chamber.

Nor do rational agent optimize economically; they take all of life into consideration motivationally and not just economic interests, nor do all necessarily prioritize economic interests. That is to say, preferences are both economic and non-economic, and some non-economic preferences (personal, social, political) are prioritized over economic preferences rationally, that is, taking all aspects of purpose into account in "optimizing utility" as well-being. The economic "basket" is a limited case in this. economists need to talk to motivational psychologists about this, as well as sociologists and other life and social scientists.

Assuming equilibrium involves assuming the cause, when the task is to explain the causality. The question is whether general equilibrium prevails as a general or special case and if so, what is the cause. There seems to be no data supporting general equilibrium either as an assumption or as an outcome of market action in a monetary production economy in open economies with government. assuming a closed economy without government is not very instructive. Aiming at this as a goal is insane.

Given chronic unemployment, markets are out of equilibrium more than in it, unless unemployment is defined to fit the assumption of general equilibrium, which is the logical fallacy of begging the question, which is a type of circular reasoning. The arguments based on natural rates (interest and employment) are philosophical rather than scientific. In science, "natural" means observed regularity, whereas neither the natural rate of interest or the natural rate of unemployment are observables. They are theoretical terms. Calling them "natural" is bogus science.

No matter how elegant a model based on such assumptions may be, formal consistency doesn't prove anything about the fitness of the model as a causal representation of reality. The model first must fit, and secondly it has to account for why it fits based on causal transmission in a way that allows for generation of testable hypothesis. Such hypotheses can only be extended generally in ergodic systems, and there are no good reasons to believe that economics is any more ergodic than other social sciences because markets and prices and "economic laws" governing them.

The whole contraption is a Rube Goldberg machine logically because it assumes economic priorities as fundamental and exclusive. Talk about inelegance. The only thing elegant about it is the formal consistency, which alone is meaningless. An elaborate tautology. Impressive tour de force, but empty of content.

We see this sort of faux logic presently in the argument over Brexit, where the (imagined) economic consequence are being used to stoke fear, so as to confuse the actual issues, which are the supremacy of national sovereignty and democracy in a liberal order in opposition to the tyranny of technocracy under unelected and unaccountable bureaucrats. The real issues are not only or even chiefly economic.

The issues are social and political — nationalism and democracy versus internationalism and bureaucracy. Plus, the economics is dodgy. See Remain’s models are built on poor foundations.

Economist’s View
If the Modifications Needed to Accommodate New Observations Become Too Baroque ...
Mark Thoma | Professor of Economics, University of Oregon

Jason Smith — Modeling in physics versus modeling in economics

Jason Smith comments on Paul Pfleiderer's Chameleons: The Misuse of Theoretical Models in Finance and Economics with respect to the difference between modeling in physics and modeling in economics and finance.

Information Transfer Economics
Modeling in physics versus modeling in economics
Jason Smith

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

Ben Goertzel — Life Is Complicated

On "complexification" (complexity + complication) and modeling systems.

So why has the success of complexity science been so, well, complicated?

Some would say it’s because the core ideas of complexity, emergence, self-organization and so forth just aren’t the right ones to be looking at.

But I don’t think it’s that. These are critical, important ideas.

Rather, I think the correct message is a subtler one: Real-world systems aren’t just complex, in the Santa Fe Institute sense of displaying emergent properties and behaviors that self-organize from the large-scale simple interactions of many simple elements.

Rather, real-world systems are what I’ll -- a bit goofily, I acknowledge -- call “complexicated”.

That is: They are complex (in the Santa Fe Institute sense) AND complicated (in the sense of just having lots of different parts that are architected or evolved to have specific structures and properties, which play specific roles in the whole system).

The Multiverse According to Ben
Life Is Complicated

Ben Goertzel | Chief Scientist of robotics firm Hanson Robotics and financial prediction firm Aidyia Holdings; Chairman of AI software company Novamente LLC and bioinformatics company Biomind LLC; Chairman of the Artificial General Intelligence Society and the OpenCog Foundation; Vice Chairman of futurist nonprofit Humanity+; Scientific Advisor of biopharma firm Genescient Corp.; Advisor to the Singularity University and Singularity Institute; Research Professor in the Fujian Key Lab for Brain-Like Intelligent Systems at Xiamen University, China; and general Chair of the Artificial General Intelligence conference series.

ht Tyler Cowen at Marginal Revolution

Nick Johnson — Philosophy of mind and ideas in economics

The human mind necessarily uses conceptual models in order to represent and understand reality. These models are logical and linguistic constructs using language, either ordinary or formal. Understanding how such models are constructed (structure) and how they work (function) has emerged as a central theme in philosophy and logic in the late 19th and early 20th centuries, and it became dominant theme in the mid to late 20th century.

This post is good summary of the basic idea of the way the mind-brain models reality in order to reduce complication and complexity to simpler and more tractable terms through abstraction, similar to the way attention focuses on the important and essential in perception and relegates the rest of the "blooming, buzzing confusion" (William James) to the background (distinguishes signal from noise).

Short and worth a read if you are not trained in philosophy.

The Political Economy of Development

Philosophy of mind and ideas in economics

Nick Johnson

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