Lars P. Syll — On the applicability of statistics in social sciences

David Salsburg quote.

Lars P. Syll’s Blog
On the applicability of statistics in social sciences

Lars P. Syll | Professor, Malmo University

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

Lars P. Syll — Truth and probability

Keynes and the fundamentals of probability.

Lars P. Syll’s Blog
Truth and probability
Lars P. Syll | Professor, Malmo University

G.A. Barnard: The “catch-all” factor: probability vs likelihood — Debate between G. A. Barnard and Leonard Jimmie Savage

Similar to there Bayesian versus frequentist debate in statistical reasoning.

Likelihood Principle

My epistemological view on this is that the border between them is fuzzy and needs to be approached on a case by case basis, along with acknowledging a cognitive bias toward greater certainty than is attainable from the given and the reasoning about it.

Humans don't like uncertainty and have a strong bias toward minimizing it at the risk of fooling themselves. Even statisticians.

Error Statistics

G.A. Barnard: The “catch-all” factor: probability vs likelihood

Debate between G. A. Barnard and Leonard Jimmie Savage
Posted by Deborah Mayo, professor in the Department of Philosophy at Virginia Tech and visiting professor at the Center for the Philosophy of Natural and Social Science of the London School of Economics.

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

Steven Weinberg — The Trouble with Quantum Mechanics

Determinism and probability in physics.

Probability was not unfamiliar to the physicists of the 1920s, but it had generally been thought to reflect an imperfect knowledge of whatever was under study, not an indeterminism in the underlying physical laws. Newton’s theories of motion and gravitation had set the standard of deterministic laws. When we have reasonably precise knowledge of the location and velocity of each body in the solar system at a given moment, Newton’s laws tell us with good accuracy where they will all be for a long time in the future. 

Probability enters Newtonian physics only when our knowledge is imperfect, as for example when we do not have precise knowledge of how a pair of dice is thrown. But with the new quantum mechanics, the moment-to-moment determinism of the laws of physics themselves seemed to be lost.... 

In quantum mechanics the state of a system is not described by giving the position and velocity of every particle and the values and rates of change of various fields, as in classical physics. Instead, the state of any system at any moment is described by a wave function, essentially a list of numbers, one number for every possible configuration of the system.6 If the system is a single particle, then there is a number for every possible position in space that the particle may occupy. This is something like the description of a sound wave in classical physics, except that for a sound wave a number for each position in space gives the pressure of the air at that point, while for a particle in quantum mechanics the wave function’s number for a given position reflects the probability that the particle is at that position. What is so terrible about that? ... 

Even so, I’m not as sure as I once was about the future of quantum mechanics. It is a bad sign that those physicists today who are most comfortable with quantum mechanics do not agree with one another about what it all means. The dispute arises chiefly regarding the nature of measurement in quantum mechanics.… 

The introduction of probability into the principles of physics was disturbing to past physicists, but the trouble with quantum mechanics is not that it involves probabilities. We can live with that. The trouble is that in quantum mechanics the way that wave functions change with time is governed by an equation, the Schrödinger equation, that does not involve probabilities. It is just as deterministic as Newton’s equations of motion and gravitation. That is, given the wave function at any moment, the Schrödinger equation will tell you precisely what the wave function will be at any future time. There is not even the possibility of chaos, the extreme sensitivity to initial conditions that is possible in Newtonian mechanics. So if we regard the whole process of measurement as being governed by the equations of quantum mechanics, and these equations are perfectly deterministic, how do probabilities get into quantum mechanics?… 

Today there are two widely followed approaches to quantum mechanics, the “realist” and “instrumentalist” approaches, which view the origin of probability in measurement in two very different ways. For reasons I will explain, neither approach seems to me quite satisfactory.…

The New York Review of Books
The Trouble with Quantum Mechanics
Steven Weinberg | Jack S. Josey-Welch Foundation Chair in Science and Regental Professor; Director, Theory Research Group,, University of Texas, and Nobel Prize (Physics) recipient, 1979

Lord Keynes — Gillies’ Philosophical Theories of Probability, Chapter by Chapter Summaries

Reference.

Social Democracy For The 21St Century: A Post Keynesian Perspective
Gillies’ Philosophical Theories of Probability, Chapter by Chapter Summaries
Lord Keynes

Justin Fox — Instinct Can Beat Analytical Thinking

Absolute must-read. 

I had realized this forty years ago when invited to teach decision theory in a military setting characterized by complexity and radical uncertainty in contexts where speed of response is crucial.

Harvard Business Review — HBR Blog Network
Instinct Can Beat Analytical Thinking

Justin Fox

Lars P. Syll — Hendry and Mizon on the limited value of DSGE models

Lars comments approvingly on H&M.

A great article, confirming much of Keynes’s critique of econometrics and underlining that to understand real world ”non-routine” decisions and unforeseeable changes in behaviour, stationary probability distributions are of no avail. In a world full of genuine uncertainty – where real historical time rules the roost – the probabilities that ruled the past are not those that will rule the future.

Hendry and Mizon on the limited value of DSGE models
Lars P. Syll | Professor, Malmo University

Related: Ricardian equivalence — total horseshit


Take that, Robert Barro.

Lars Syll — Why economic forecasting is such a worthless waste of time

In a discussion on uncertainty and the hopelessness of accurately modeling what will happen in the real world – in M. Szenberg’s Eminent Economists: Their Life Philosophies – Nobel laureate Kenneth Arrow comes up with what is probably the most plausible reason:

It is my view that most individuals underestimate the uncertainty of the world. This is almost as true of economists and other specialists as it is of the lay public. To me our knowledge of the way things work, in society or in nature, comes trailing clouds of vagueness … Experience during World War II as a weather forecaster added the news that the natural world as also unpredictable.

An incident illustrates both uncer-tainty and the unwilling-ness to entertain it. Some of my colleagues had the responsi-bility of preparing long-range weather forecasts, i.e., for the following month. The statisticians among us subjected these forecasts to verification and found they differed in no way from chance. The forecasters themselves were convinced and requested that the forecasts be discontinued. The reply read approximately like this: ‘The Commanding General is well aware that the forecasts are no good. However, he needs them for planning purposes.’

Why economic forecasting is such a worthless waste of time
Lars P. Syll | Professor, Malmo University

Tim Johnson — The rational man, the average man and the replacement of deliberation by will

Weekend reading.

Magic, Maths, and Money — The Relationship between Science and Finance
The rational man, the average man and the replacement of deliberation by will
Tim Johnson | Lecturer in Financial Mathematics at Heriot-Watt University, Edinburgh

Lars P. Syll — Mainstream macroeconomics — a massive intellectual mistake [GIGO]

...the root of our problem goes much deeper. It ultimately goes back to how we look upon the data we are handling. In “modern” macroeconomics – dynamic stochastic general equilibrium, new synthesis, new-classical and new-Keynesian – variables are treated as if drawn from a known “data-generating process” that unfolds over time and on which we therefore have access to heaps of historical time-series. If we do not assume that we know the “data-generating process” – if we do not have the “true” model – the whole edifice collapses. And of course it has to. I mean, who really honestly believes that we should have access to this mythical Holy Grail, the data-generating process?...

...as Keynes convincingly argued in his monumental Treatise on Probability (1921), this is not always possible. Often we simply do not know. We cannot always put exact numbers on the assessments we make. There are no given probability distributions we can appeal to.

In the end this is what it all boils down to. We all know that many activities, relations, processes and events are genuinely uncertain. The data do not unequivocally single out one decision as the only “rational” one. Neither the economist, nor the deciding individual, can fully pre-specify how people will decide when facing uncertainties and ambiguities that are ontological facts of the way the world works.

GIGO.

This is a key problem with formalism. According to the scientific method, an explanation is true if and only if it fits all the facts it purports to explain. In magical thinking, an explanation is presumed true if it fits selected facts that are chosen based on methodological convenience or ideological assumptions. That is to say, the assumptions serve to prove the explanation, which is the fallacy of circular reasoning.

Mainstream macroeconomics — a massive intellectual mistake
Lars P. Syll | Professor of Civics, Faculty of Education and Society, Malmö University

Daniel Little — Ian Hacking on chance as worldview

Ian Hacking was one of the more innovative and adventurous philosophers to take up the philosophy of science as their field of inquiry. The Taming of Chance (1990) is a genuinely fascinating treatment of the subject of the emergence of the idea of populations of events rather than discrete individuals. Together with The Emergence of Probability: A Philosophical Study of Early Ideas about Probability, Induction and Statistical Inference (1975; 2nd ed. 2006), the two books represent a very original contribution to an important aspect of modern ways of thinking: the ways in which the human sciences and the public came to think differently about the nature of social and biological reality.

Hacking's contributions to the history of statistical and probabilistic thinking are particularly valuable for the light they shed light on a crucial moment during which fundamental change in the largest gauge intellectual framework took place -- the shift away from deterministic causation to the idea that phenomena present themselves with a distribution of characteristics.

Understanding Society
Ian Hacking on chance as worldview
Daniel Little | Chancellor, University of Michigan at Dearborn

This is a big deal because at key transition points in history the character of human thinking shifted radically, which led to a new way of seeing the world and therefore a new cultural worldview as it spread. We see this today in the digital divide that separates generations that grew in an analog world and the generations now growing up in a digital world.

Lars Syll — Why assuming ergodicity makes economics totally irrelevant

...the assumption of the equality of these different averages — technically known as the assumption of “ergodicity” — is considered a given by most of contemporary economics. It makes the mathematics easier in the financial portfolio theory that influences countless investors and in frameworks for designing regulations to keep financial risks at acceptable levels. Unfortunately, this error systematically underestimates prevailing risks.

It also may encourage overly optimistic ideas about the ability of an economy to recover from a crisis. For example, those who support policies of fiscal austerity believe that companies, in seeking to maximize their profits, will naturally drive an economy back to steady growth. The economy will spring back if companies and individuals have confidence that their investments will pay off. If that’s the case, why aren’t businesses investing globally when interest rates are at historic lows. What’s holding them back?

The fairly obvious answer is serious downside risk, which makes the reticence entirely sensible — if you live in the real world where time matters.

Lars P. Syll's Blog
Why assuming ergodicity makes economics totally irrelevant
quoting Mark Buchanan

Lord Keynes — Lars P. Syll on Probability and Economics

Lord Keynes provides a list of links to Lars Syll's posts on probability and econ. Lars is fast becoming a go-to guy in methodology.

Social Democracy For The 21St Century: A Post Keynesian Perspective
Lars P. Syll on Probability and Economics
Lord Keynes

Lars P. Syll — Probabilistic reasoning and its limits (wonkish)

Keynes.

Lars P. Syll's Blog
Probabilistic reasoning and its limits (wonkish)
Lars P. Syll | Professor of Social Studies and Associate Professor Economic History, Malmo University

Lars Syll — How do we attach probabilities to the world?

There are no such things as free-standing probabilities – simply because probabilities are strictly seen only defined relative to chance set-ups – probabilistic nomological machines like flipping coins or roulette-wheels. And even these machines can be tricky to handle. Although prob(fair coin lands heads|I toss it) = prob(fair coin lands head & I toss it)|prob(fair coin lands heads) may be well-defined, it’s not certain we can use it, since we cannot define the probability that I will toss the coin given the fact that I am not a nomological machine producing coin tosses.
No nomological machine – no probability.

Lars P. Syll's Blog
How do we attach probabilities to the world?
Lars P. Syll | Professor, Malmo University

Similar to transmission mechanism in causal argument. No mechanism and the argument is just handwaving.

Lars Syll — Probability and economics

Modern neoclassical economics relies to a large degree on the notion of probability.

To at all be amenable to applied economic analysis, economic observations allegedly have to be conceived as random events that are analyzable within a probabilistic framework.

But is it really necessary to model the economic system as a system where randomness can only be analyzed and understood when based on an a priori notion of probability?

Lars P. Syll's Blog
Probability and economics
Lars P. Syll | Professor, Malmo University

Follows up on previously posted Probability and Economics.

Lars Syll — Keynes and Knight on uncertainty – ontology vs. epistemology

Report of a conversation between Lars and Paul Davidson on the philosophical underpinning of probability and its relevance in economics and finance.

Keynes and Knight both asserted uncertainty but their concept of it were different. Keynes asserted ontological non-ergodicity, whereas Knight assumed ontological ergodicity and asserted only epistemological non-ergodicity.

Lars and Paul Davidson explore the implications of this distinctions and come down on the side of Keynes.

These distinctions are key in understanding the fundamental difference between the mainstream and Post Keynesianism.

Read it at Lars P. Syll's Blog
Keynes and Knight on uncertainty – ontology vs. epistemology
Lars P. Syll