Love the ideas shared here. The list really huge and it will cover almost everything for any things. Thanks

for the such a lovely post! Again thank you for sharing .

Always tempted to link that. However, this guy hasn’t just met the subject – he just loves applying basic physics and telling everyone that econs never have. A strange critique given how much I’m told by other disciplines that econ over-relies on classical physics techniques!

Most of the time, both sides haven’t actually checked to see what econs actually do …

Math is a useful language to help make our assumptions clear – but the assumptions need to be built on something. I read that as what you are pointing at here, and agree.

Interesting – even if it was shown that entropy maximisation describes the observed distribution, I still find it hard to ignore seemingly relevant covariates, or even more importantly the inherent heterogeniety in the characteristics of individuals in the sample.

The kicker here is two-fold:

1) Measurement problems and the such make it a lot more tenuous to compare even a gigantic sample to the “true” distribution. Something I think economists take into account implicitly when holding back on inference from data.
2) Even if the true distribution can be seemingly described by entropy maximisation, I can’t make the leap to prediction due to the Lucas Critique – with agents making active choices, the underlying parameters of the model won’t be invariant to policy. This part of the “mechanism”, stemming from individual choice, also requires modeling. In this case, the process involved would be random chance – and the policy would be redistribution of income based on it solely being chance. So even if we were to accept this extreme assumption (which is patently too extreme), we are only part of the way towards policy design.

This is how I understand it, but if you have any feedback where you think I’m confused I’d be happy to hear it! Methodology and language, especially when it starts crossing disciplines, is important to discuss.

Fair enough.

If the distribution predicted by entropy maximisation describes the true distribution well (it doesn’t) then I would argue the entropy maximisation “explains” the distribution. Entropy maximisation *is* the mechanism.

But otherwise agree.

My thought process runs description -> explanation -> understanding.

Description outlines the data. Explanation tells us whether something drives something else. Understanding tells us why something drives something else.

Mechanism may have been too strong a term – I think you’ve got a good point there. If their argument is that inequality could occur purely due to chance, then that is a potential explanation – and I suppose my irritation has more to do with them acting like it is “the” explanation 🙂