selectionbias's comments

I don't see how micro-founded macro models equate utility with willingness to pay? Work-horse New Keynesian models begin with a representative agent so willingness to pay of different consumers doesn't even make sense in this context. Moreover, these models are (to my knowledge) seldom used for welfare analysis but rather to examine things like the effects of montetary policy on employment and growth. Models with heterogeneous agents certainly don't assume willingness to pay is the same as utility. I'm an econometrician not a macroeconomist though so perhaps I'm missing something.

As someone has already commented, the definition of an 'efficient market' in no way equates willingness to pay with utility, and in fact economists seldom make this assumption. Pareto efficiency is very deliberately not utilitarian. A Pareto efficient outcome needn't be a 'good' outcome, it is just an outcome such that no other outcome would make everyone better off. If an outcome isn't Pareto efficient then there is room for improvement. It's worth noting that while Pareto efficiency is central to some very neat foundational concepts taught in introductory econ, modern ecenomic research uses a range of welfare measures to quantitatively evaluate policies. This includes utilitarian welfare analysis. These maybe better capture the actual ethical goals we should have when making policy decisions, but they are usually a bit ad hoc and don't lead to such neat results.

My problem with the 'Bayes=rationality' type of argument is that it ignores context and isn't really a case for reporting Bayesian vs frequentist estimates. If I am a researcher publishing results then I have an audience who interpret my results. If my audience is Bayesian and accept my model then all I need to do is report sufficient statistics and they can make their own Bayesian inferences given their priors, or better yet, I can just post my whole dataset. The very reason we need to report things like credible sets or confidence intervals rather than just sufficient statistics is because audiences in the real world want summary stats that they can easily interpret and are transparent. The best approach to inference is one that is the most useful to audiences, and that depends on context and practicalities rather than on some underlying philosophy of subjective vs objective probabilities.

Why wouldn't you just ride on the tugboat?

Both of your assertions are inaccurate. First of all the economics prize, while not an original prize, is recognized by the Nobel foundation, and that 'Swedish bank' is the central bank of Sweden.

Secondly, the idea that modern academic economics is driven by ideology and linear regression is demonstrably false. Take a look at the latest edition of QJE, the highest impact factor economics journal (link: https://academic.oup.com/qje/issue/134/4). Most of those articles are use empirical evidence to answer questions with obvious policy importance (e.g., causes of food inequality, effectiveness of workplace wellness policies). And modern econometric methods do not amount to linear regression, they include things like regression discontinuity design, instrumental variables techniques, panel data methods, that can provide convincing evidence of causal effects when only quasi-random variation is available. The Nobel prize your commenting on was awarded to researchers carrying out RCTs to assess the impact of specific interventions, how is that mere ideology and linear regression?

Conway's Game of Life is Turing complete, so if it is possible to build an artificial intelligence in a regular computer then it is possible to build one within the Game of Life. Also, understanding the basic rules of the game of life is not the same as understanding how an artificial intelligence within it functions. The rules that determine how patterns of pixels change in the game can be written down on a single sheet of paper, a description of an artificial intelligence built within the game would probably be absurdly complex.

In the context of the incompleteness theorem a 'theory' consists of a formal language to describe theorems, some primitive axioms and some rules that can be used to prove theorems from the axioms. Godel's incompleteness theorem states (loosely speaking) that if a theory is rich enough to describe the arithmetic of the natural numbers, is consistent and is 'effectively axiomatized', then there are statements that can be expressed within the theory and that are true, but that cannot be proven using the rules of deduction in the theory. In short, this is a totally different meaning of the word 'theory' to the one you are thinking of.

Suppose we are able to formulate a very neat, parsimonious mathematical model and it happens to extremely accurately describe every physical phenomenon, so accurately that we cannot find even the tiniest violation. Now, that does not mean the model is 'correct', it might be that we have just not measured precisely enough to detect its failures. But there does seem to be a mysterious tendency for very neat mathematical models to very accurately describe physics, and so maybe it is not unreasonable to conclude that this model is at least probably true (i.e., that it is never violated). Would this not then tell us something pretty profound? Even if the way we understand the math (in terms of ideal shapes, in terms of symbols) is inherently human, we would still have possibly discovered a full description of the behavior of the universe, and even if that in itself doesn't tell us why the universe exists or what it is, it would surely help us to answer those questions.

>Why is the implication that this is due primarily to sexism?

Well perhaps because of the persuasive evidence the article cites from peer-reviewed journals. For example, in the paragraph below from the article:

"In one experiment, reported by Corinne A. Moss-Racusin and her coauthors in PNAS, 127 US scientists were asked to hire an undergraduate lab assistant and decide on a salary based on fictional CVs of equally qualified men and women. The scientists were more likely to offer the position to men as well as more hours of mentorship, and gave a lower salary to women, about eighty-eight cents to the dollar."