Not sure what Haskell has to do with it. I've written algorithms that take in 2D images and produce depth maps with live visualization in OpenGL using Haskell. It's incredibly practical once you learn it.
But I also disagree from the point that if physics did what you suggested we'd be no where at all. If they had to start with reality before producing useful models then we would of skipped pretty much all of modern physics today.
All models are wrong, but some are useful as they say.
"once you learn it"...yes, everything is incredibly practical once you remove all the disadvantages. The point is: some people prefer complexity for complexity's sake, and this doesn't work well in a team environment (where the "once you learn it" part becomes quite relevant, one person who prefers complexity for complexity's sake will take down the whole group, not understanding when something should be simple is an indication of ignorance...finance professors rarely have any understanding of actual finance, their ignorance on this is total).
These models aren't useful. Also, the saying is wrong. The reason why is that close to 100% of finance professors will quote that saying (srs, I think I have heard this 20-30 times now) because they use models that are wrong and not useful but this model seems to give them an intellectual reason for doing so: any "wrong" model could actually be good, according to this idea. But wrongness is neither nor there because wrongness for a model is utility, they are identical. The only point is utility. And the reason why these models aren't useful, as I have said already, is that they aren't used outside of academia. Their only utility is giving finance professors something fun to teach. And again, the solution is to build models from the way the world actually is (and btw, these are numerous...almost every successful investor, fundamental or quant, has a systematic process...but these models aren't fun to teach).
Are you implying software isn't complex? Or that imperative languages have low complexity? Haskell takes the complexity of software and provides useful constructs to generalize and abstract some of these complexities. Does it take time to learn? Absolutely. Is it easy for newbies to understand? Definitely not, because it's hard to appreciate their value until you have encountered these issues time and again in software. But it's most definitely not complexity for complexity sake. It can vastly simplify software in practice by restricting the domain in which you are working with a very powerful type system. That is the entire point of it all after all.
I'm not sure which models you are talking about - but models such as Modern Portfolio Theory, or Black Scholes, while inherently flawed have been massively useful in the real world. Claiming they aren't useful is simply not true. But again, you don't mention any specific models so it's hard to even know what you are talking about.
I mean all of them. Black-Scholes was used in industry before academia, and is only used in a heavily adjusted form (for example, option MMs have never used it as the only pricing input). MPT isn't useful: volatility doesn't describe risk to any degree (possibly as you move to the limit of retirement age...but then, not really), the empirical relationship is actually the inverse of that predicted by MPT (i.e. the model is not only wrong, it is misleading and will cause you to lose money), and it is easy to construct superior models that beat MPT models in every way (and even those aren't very good because they often use the same theoretical underpinning...again, most of these models exist because the subject needs to be taught in universities and needs to build on stuff learned earlier...the practical use is zero, which is why no-one really uses these theories...the only place I have seen them used at scale is in investment consultancies, and most of these places are clueless).
> I'm not sure which models you are talking about - but models such as Modern Portfolio Theory, or Black Scholes, while inherently flawed have been massively useful in the real world.
Many finance professors strike me as the kind of people who critique the design of a hammer without having ever built anything themselves. Every tool has perks and limitations, and the challenge of using that tool is to figure out what those things are and get them to bend to your favor. BSM is the lingua franca of the options market and can be tweaked in practice to accommodate many limitations (skew, event volatility, etc).
The point is to make money. If the tool helps you do that, then it's a good tool.
But I also disagree from the point that if physics did what you suggested we'd be no where at all. If they had to start with reality before producing useful models then we would of skipped pretty much all of modern physics today.
All models are wrong, but some are useful as they say.