Showing posts with label Daniel Kahneman. Show all posts
Showing posts with label Daniel Kahneman. Show all posts

Sunday, July 14, 2019

Gigerenzer: “The Bias Bias in Behavioral Economics,” including discussion of political implications — Andrew Gelman


Gerd Gigerenzer takes aim at Daniel Kahneman, Richard Thaler and Cass Sunstein for being uncritical and going too far. While not endorsing rational choice theory, he stresses that the truth lies between the extremes of rationality and irrationality and claims behavioral economics tends to over emphasize irrationality consequent on cognitive-effective bias. It's neither reason or all bias, either all or mostly, but a combination of rationality and irrationality.

Statistical Modeling, Causal Inference, and Social Science
Gigerenzer: “The Bias Bias in Behavioral Economics,” including discussion of political implications
Andrew Gelman | Professor of Statistics and Political Science and Director of the Applied Statistics Center, Columbia University

See also by Andrew Gelman

The butterfly effect: It’s not what you think it is.

The piranha problem in social psychology / behavioral economics: The “take a pill” model of science eats itself

Monday, January 9, 2017

Branko Milanovic — Pareto, Taleb and the tails of income distributions

I am reading Nassim Taleb’s Antifragile and then I went back to rereading parts of his extraordinary Black Swan. The Black Swan’s blurb by Daniel Kahneman, “The Black Swan changed my view of how the world works” is fully justified. It will remain one of absolutely indispensable books, a huge epistemological advance. Antifragile is, perhaps, an even more ambitious book because it aims to make systems (including people) antifragile, that is thriving in conditions of (what Taleb calls) “opaque randomness”.  So, it is broader in scope and has a prescriptive part that The Black Swan does not. 
Here I would like to address one of the two themes of Taleb’s that find immediate resonance among people who work on income inequality and globalization: the former one. I leave globalization for another post.
Wonkish.

Global Inequality
Pareto, Taleb and the tails of income distributions
Branko Milanovic | Visiting Presidential Professor at City University of New York Graduate Center and senior scholar at the Luxembourg Income Study (LIS), and formerly lead economist in the World Bank's research department and senior associate at Carnegie Endowment for International Peace

Thursday, January 1, 2015

Brad DeLong — Robert Lucas Rejects the “Microfoundational” Project


Humans are not atoms. But Robert Lucas saying it is hugely important for the impact.
We’re not going to build up useful economics… starting from individuals…
WCEG — The Equitablog
Robert Lucas Rejects the “Microfoundational” Project
Brad DeLong

Tuesday, August 6, 2013

Matias Vernengo — What killed theory? What theory?

So Noah Smith and Paul Krugman are again trying their hand at the history of economic ideas to understand what happened with the profession in the last three decades....
Naked Keynesianism
What killed theory? What theory?
Matias Vernengo | Associate Professor of Economics, University of Utah

Scientific models are (supposed to be) general descriptions of some segment of reality. Their connection with reality is buttressed by correct predictions. But theories are never finally confirmed, since general assertions regarding unbounded sets, and future information is unbounded, are always open to disconfirmation. A single solid disconfirmation is sufficient to falsify a hypothesis, whereas theories are not disconfirmed as such. When problems develop, at first ad hoc solutions are devised within the theoretical framework, but when problems mount or a single issue calls the whole way of looking into question, then theorists look for a better instrument for seeing, i.e., a new theory.

Theories are ways of seeing reality from a particular perspective. The closer they are to a general description that is borne out by testing, with major hypotheses surviving attempts at falsification, the more scientific they can claim to be. Regardless of past successes, anomalies often arise that the theory does not explain, and these become the problem set that defines the cutting edge of the discipline.

No model can be representational for other than a simple bounded system, where anomaly is ruled out. As complexity increases, the potential for representational modeling decreases owing to the uncertainty introduced by unknown unknowns that cannot be foreseen prior to their emergence from analysis of the system based on existing information.

The way of seeing embedded in a theory that attempts to generally describe a complex system is scientific to the degree that it enables accurate prediction and unscientific to the degree it doesn't. The latter condition arises owing to two chief reasons. First, emergence, and secondly, assumptions that are not empirically testable or which testing tends to call into question if not disconfirm. Emergence is not controllable, but assumptions are, at least to a degree.

To the degree that a theory is unscientific and the approach is uncritical, it can be described as mythological. And to the degree that a mythological way of seeing is accepted, the field is dominated by magical thinking.

However, a purely empirical approach focusing only on data is unsatisfactory for it offers no way of seeing the entire data set scientifically, i.e., in terms of a general description. It is not possible to have an integrated field of study without presuming some way of seeing, that is organizing raw data into information. Hence, ignoring the operative way of seeing in using a purely empirical approach invites magical thinking as the method and mythology as the POV.

This is a knotty issue in social science, and conventional economics is deeply affected by it. The assumption of the market as a "natural" phenomenon is a myth based on magical thinking, i.e., "the invisible hand," whose basis is "rationality," functioning as deus ex machina. Homo economicus is a mythological creature.


Sunday, June 30, 2013

Daniel Kahneman on correlation, causation and mean regression

It took Francis Galton several years to figure out that correlation and regression are not two concepts – they are different perspectives on the same concept: whenever the correlation between two scores is imperfect, there will be regression to the mean …
Causal explanations will be evoked when regression is detected, but they will be wrong because the truth is that regression to the mean has an explanation but does not have a cause.
Lars P. Syll
Regression to the mean – when causes trump statistics
quoting Daniel Kahneman | Professor Emeritus of Psychology and Public Affairs at Princeton University's Woodrow Wilson School and Nobel Laureate in Economics (2002)