[vwfwerwetqwerqw]

You probably know more about economics than me (I'm just a mathematician), so I won't argue against you, but if I understand your two posts correctly, it seems like what you're describing is completely useless. If a model doesn't have predictive power then what is it good for?

I understand from your other post that the justification of Austrian "models"/"explanations" comes from their simplicity. Which I guess is some application of Occam's razor? But that makes no sense - the simplicity of a model on it's own is no indication of correctness. You always need some notion of the likelihood of the observed evidence under that given model. Otherwise, you can just say "all people make all decisions completely randomly" and that's the simplest model of all.

A completely non-predictive model has no real-world meaning, because it can't be used to effect reality. I suspect that Austrian models do effectively have some small predictive power - there is an implicit causal analysis that comes from the small element of qualitative evaluation of uncertainty, no doubt used instinctively by practitioners to decide whether an argument sounds plausible.

Anyway, it all sounds completely ridiculous. How is their method of choosing explanations any better than a witchdoctor deciding that thunder is a sign of the gods being angry?

[jacques_chester]

Austrians argue that predictions can't, as a matter of epistemology, be made.

Of course, humans do that all the time. What's missing from the original Austrian concept in Mises is allowing for imprecision -- making accurate predictions with error bars for variability below the threshold of detection.

What von Mises didn't like about this and what Rothbard hated about it is that such predictions are statistical and probablistic in nature. They aren't deontological, they can't be nutted out a priori from first principles.

They have a real bee in their bonnet about it. It's a shadow (ha!) of Platonist/Aristotelian bunfights.

Out here in that's-nice-but-I-have-shit-to-do land, grownups accept that models are wrong. But even if the map is not the territory, most of the time it's still good to have a map.

[vwfwerwetqwerqw]

Non-predictive analysis can't be used to make decisions, and therefore can't have any effect on the real world unless it is being used incorrectly. By their own argument, Austrian economics is a waste of time.

A dislike of stochastic models (however understandable in historical context) should not be taken seriously in the modern world. Complex chaotic systems are modelled through intensive probabilistic simulation all the time.

[jacques_chester]

I think it's a side-effect of the socialist calculation debate that's broken free of its roots.

Austrians make predictions all the time, however. Every new round of monetary expansion is going to end the world.

[danenania]

For me, the major idea that the Austrians bring to the table is that the study of economics is not a science and never can be. Attempts to create and support models by quantitive analysis of data are flawed because human economies are so astronomically complex that it's impossible to isolate variables, and therefore impossible to design valid economic experiments or draw sound conclusions from economic data.

There are many parallels to algorithms analysis. If we want to know how fast an algorithm runs, our approach isn't to try to run it on every processor in existence and compare the results. Instead we break down the algorithm logically to deduce its theoretical running time.

Austrian economics--in its good parts anyway--is an attempt to build a framework for economics that is more like the asymptotic analysis of algorithms. But this very compelling goal is frequently derailed by politics and polemics.

[vwfwerwetqwerqw]

Algorithmic analysis is absolutely scientific. Asymptotic analysis is based on models of computer behaviour that are testable (and people do test them and do improve on them). Moreover, the asymptotic models make predictions and those predictions are testable (and people do test them and do improve on them).

The asymptotic analysis of algorithms would not be useful if it didn't make accurate predictions about real world phenomenon. I've written peer reviewed computer science papers where I have devised an algorithm, predicted its asymptotic behaviour and then validated through empirical testing.

What you're describing sounds completely different. If the major idea is that human economies cannot be scientifically analysed, then surely all analysis is a waste of time? An analysis that isn't based on the scientific method isn't more likely to be correct.

[yxhuvud]

Well, they are certainly correct in that Austrian economics isn't science.

[svv]

Well, pure mathematic models also don't have any real-world meaning, and no predictive power with regard to the real world events. Still, nobody thinks that the analytical proof of the Pythagorean theorem is somehow inferior to the empirical one (involving multiple reproducible experiments that measure sides of the actual triangles drawn on a piece of paper with a ruler). We are content with mathematics being consistent within itself.

However, if we (1) add certain assumptions about, say, physical laws, the structure of the reality, the way we can observe it, (2) propose a reasonable way to map some aspects of the world to our mathematical models, (3) calibrate the result according to the future experiments/observations -- we might get a quite useful natural science like physics or astronomy. The pure math will serve only as a low-level tool. That's how the Austrian economists see the place of the pure economics theory: just an analytical tool to be used along with the other disciplines/sciences for solving real-world problems.

For example: Austrian economists believe that it can be deduced from the first principles/axioms that enforcing minimal wage above existing market wages results in involuntary unemployment (most of the non-Austrian economists also believe this is the case, although their methodological basis is different). Assume there was a policy act that included, among other things, raising the minimal wage. A year passed, and it so happened that there have been changes in the economy indicators, including lower unemployment rate. The act was not the only thing that happened in that time; lots of factors may have contributed to the outcome. An Austrian researcher will definitely not count raise-the-minimal-wage factor as contributing in favor of the lower unemployment rate; the factor will be counted against the lower-unemployment-rate outcome, and will require further explanations, what other factors have outweighed its effect.

Using math (well, formal systems/notations) does not guarantee your whole research about the real world is correct, but it establishes a baseline for consistency and rigorous reasoning. It's the same with basic economics.

(My formal education was in mathematics / CS. I study economics, epistemology etc. only as a hobby.)