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A review by bob_muller
Cuando los físicos asaltaron los mercados: La historia de cómo se trató de predecir lo impredecible by James Owen Weatherall
5.0
This book is well written, engaging, thoughtful, and in the end, very scary. Weatherall makes an excellent case for why economics and its love-child financial economics are failing and for a broader interdisciplinary approach to finance that would take it beyond "simple" economics using the kind of sophisticated mathematics you see in physics modeling.
My only criticism is purely personal--I would have liked to see more math. That would, of course, doom the book for popular readers. And yet, isn't that the problem? If you are in finance, and you have to "turn over the business to the quants" because you don't understand the math, then you're part of the problem.
Rant on.
I must also say that this problem is not limited to economics. I went to MIT in 1976 to study mathematical modeling with the most sophisticated professor out there doing it, and he published virtually nothing useful. The rest of the department there, while excellent at history and sociology, were limited to the more standard "statistical" models, which are usually simpler than those found in economics. I got my PhD and abandoned the field to go into software development, which was much more rewarding in many different ways. I recently had occasion to do some personal research that took me to the library shelf holding the books on mathematical modeling in Political Science. There was one book that I had not read by 1980, and that was by the guy I met on the first day I was at MIT, who got into agent-based modeling and made something of it.
There is an even more serious problem that Weatherall simply doesn't mention: data. I spent the last 11 years working with reference genome data--the structural and functional data around the "standard" genomic representations for various species (human, mouse, fruit fly, zebrafish, arabidopsis plants). I was a founder at a company which has the mission of developing a sustainable business model for such scientific data. What happens when the government stops funding a big database project? The data disappears because nobody will pay for its maintenance and development. Just before I joined this project, I worked for a year in a political science department. I found almost no large databases worth anything, and virtually no recognition by the professors doing data-oriented research that having large, ongoing data maintenance efforts was a worthwhile endeavor--mainly because no one would pay for it. The best data effort I've found in social science to date is the Piketty data on economic inequality, and it remains to be seen how much development that data set will see. Finance has reams of data, but it is very limited in scope and availability, and beyond the basic price data, the quality is deeply questionable (unemployement? inflation? Sure.). Political science and other social sciences have almost no real data of any sort, which pretty much eliminates any real chance of effective mathematical modeling in those "sciences."
Rant off.
My only criticism is purely personal--I would have liked to see more math. That would, of course, doom the book for popular readers. And yet, isn't that the problem? If you are in finance, and you have to "turn over the business to the quants" because you don't understand the math, then you're part of the problem.
Rant on.
I must also say that this problem is not limited to economics. I went to MIT in 1976 to study mathematical modeling with the most sophisticated professor out there doing it, and he published virtually nothing useful. The rest of the department there, while excellent at history and sociology, were limited to the more standard "statistical" models, which are usually simpler than those found in economics. I got my PhD and abandoned the field to go into software development, which was much more rewarding in many different ways. I recently had occasion to do some personal research that took me to the library shelf holding the books on mathematical modeling in Political Science. There was one book that I had not read by 1980, and that was by the guy I met on the first day I was at MIT, who got into agent-based modeling and made something of it.
There is an even more serious problem that Weatherall simply doesn't mention: data. I spent the last 11 years working with reference genome data--the structural and functional data around the "standard" genomic representations for various species (human, mouse, fruit fly, zebrafish, arabidopsis plants). I was a founder at a company which has the mission of developing a sustainable business model for such scientific data. What happens when the government stops funding a big database project? The data disappears because nobody will pay for its maintenance and development. Just before I joined this project, I worked for a year in a political science department. I found almost no large databases worth anything, and virtually no recognition by the professors doing data-oriented research that having large, ongoing data maintenance efforts was a worthwhile endeavor--mainly because no one would pay for it. The best data effort I've found in social science to date is the Piketty data on economic inequality, and it remains to be seen how much development that data set will see. Finance has reams of data, but it is very limited in scope and availability, and beyond the basic price data, the quality is deeply questionable (unemployement? inflation? Sure.). Political science and other social sciences have almost no real data of any sort, which pretty much eliminates any real chance of effective mathematical modeling in those "sciences."
Rant off.