Jason Smith — What do equations mean?

Jason Smith comments on J. W. Mason and Arun Jayadev on MMT and conventional economics from the point of view of scientific modeling in macro.

Information Transfer Economics
What do equations mean?
Jason Smith

Lars P. Syll — P-hacking and data dredging

I think there are two separate issues here that depend on intent. "P-hacking" likely implies intent, and that is not necessarily a factor in all cases, and it may well not be in many if not most cases.

In some cases there may be intent to persuade by playing loose, or even to deceive. I recall that How to Lie with Statistics was required reading in the Stat 101 course I took over fifty years ago. But this is not the only issue.

As Richard Feynman famously observed, science is about not fooling ourselves. This applies to each of us individually owing to cognitive bias. Humans are smart, but we mare still primates. 

Nobody is entirely free of cognitive-affective bias. So we have to take steps to counter this tendency. Science was developed as an instrument to address this.

The reason we use rigorous method is to avoid, or at least minimize, our tendency toward being shaped by cognitive biases such as confirmation bias and anchoring. 

Methodology is about using instruments on good data in a rigorous fashion that reduces not only error in application but also bias.

There is no method that completely eliminates error and bias. On one hand, GIGO, and being human, on the other.

A lot of the problems in doing science as well as applying other rigorous instruments lies in measurement. The highest level of formality does nothing to affect errors in measurement. I happened to be thinking about the issues around measurement just prior to reading this post.

And lot of the most interesting things are difficult to measure when humans are involved and psychology enters into the data significantly. History also present issues regarding not only data quality and availability but also changing context that affects the data.

It's good we are having a debate about p-values, since there are issues there than do seem to be influential in a negative way.  And it is not only the stat, but also the data that the method is being applied to.

There are essentially three areas of interest. The first is the method, in this case probability and statistics  as formal method. The second is data and its reliability and precision, along with data collection. The third is data processing and selection. All of these are subject to error and manipulation. This is especially a problem when data sets are proprietary and are not transparent.

But the debate should not stop there. The methodological debate is not over, as some would have it. Science is always tentative on discovery and it is a work in progress. Science is often viewed as a fixed body of true knowledge. That is not a good approach to doing science. The fundamental principle of science is questioning authority, especially that of received belief, intuition and common sense.

Humans are fallible, and it is doubtful that we can ever finally work out all the kinks epistemologically and methodologically. We are a work in progress, too, just as is science.

As a discipline becomes more formalized, there is a greater tendency to emphasize formal rigor at the expense of data and evidence, especially when there are issues around data and evidence. Such tendencies are fertile ground for cognitive-affective bias.

Epistemology, logic, and methodology are foundational to gaining reliable knowledge. We need to keep this in mind.

On one hand, the search for absolute knowledge is a chimera since no criteria can be established as absolute. Criteria are stipulated. This realization should make us humble — and careful.

On the other hand, humans are not lost in a sea of relativity either. History has shown that it is possible to arrive at knowledge that is reliable and practical if intelligence is applied and bias reduced.

Lars P. Syll’s Blog
P-hacking and data dredging
Lars P. Syll | Professor, Malmo University

Philip Ball — Quantum Theory Rebuilt From Simple Physical Principles

Important from the perspective of philosophy of science.

Perhaps the most significant line is the last one:

What is needed is new mathematics that will render these notions scientific,” he said. Then, perhaps, we’ll understand what we’ve been arguing about for so long.

Recall that Newton had to develop the calculus as a new mathematical notation in order to express his discoveries in classical physics.

Quanta Magazine
Quantum Theory Rebuilt From Simple Physical Principles

Philip Ball

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

Phil Price — An obvious (?) fact about constrained systems.

In economics the constraints are restrictive assumptions introduced for tractability and simplification.

The post makes the point that when one constraint is relaxed in a precise way in that can be measured with respect to the system, the response of the system is knowable. This is how economic models are used.

However, if all the constraints imposed by the restrictive assumptions of economic models are removed, as they are in the real world, the behavior of the actual system becomes unknowable from the model, a point that Keynes noticed and pointed out.

This is a reason it is difficult to develop models that work as representational models where human motivation and behavior is involved. Such models are infected with uncertainty. As a financial speculator, Keynes was well aware of this as a person that had written a book on probability theory.

As Richard Feyman said, "Imagine how much harder physics would be if electrons had feelings!"

Statistical Modeling, Causal Inference, and Social Science
An obvious (?) fact about constrained systems.
Phil Price

Jason Smith — The reason for the proliferation of macro models?

The situation Noah [Smith] describes is just baffling to me. You supposedly had some data you were looking at that gave you the idea for the model, right? Or do people just posit "what-if" models in macroeconomics ... and then continue to consider them as .... um, plausible descriptions of how the world works ... um, without testing them???

Information Transfer Economics
The reason for the proliferation of macro models?
Jason Smith

Jason Smith — Qualitative economics done right, part 1

Steve Keen, Wynne Godley, SFC, MMT mentioned.

Information Transfer Economics
Qualitative economics done right, part 1
Jason Smith

Jason Smith — Macro is not like string theory, part III (Equations!)

I thought of another way to drive home the point that DSGE macro is not like string theory. It's essentially another way of representing the Venn diagram in that post, but this time in terms of equations. Basically, string theory is built up from a bunch of very successful pieces of physics in a natural way. A DSGE model is built of a bunch of pieces that haven't been empirically validated or worse appear to be wrong.…

Information Transfer Economics
Macro is not like string theory, part III (Equations!)
Jason Smith

Jason Smith — Post Keynesian blues

Jason Smith comments on Post Keynesianism in light of the debate begun by Noah Smith on "occultism."

Information Transfer Economics
Post Keynesian blues
Jason Smith

Jason Smith — Complexity without purpose: evolution as a framework for economics

Jason Smith responds to David Sloan Wilson.

Information Transfer Economics
Complexity without purpose: evolution as a framework for economics
Jason Smith

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