My guess is that there is also a prospect theory story going on here – we value resources that we earn for ourselves a lot more than resources given to us. We WANT to increase consumption without incurring the mental penalty of spending our money. Getting a gift, we can frame it as an unearned gain and consume it more readily, while spending our own money is framed as a loss. The mutual gifting process essentially allows us to get around our biased utility function.

The other components that people have mentioned definitely factor in as well – particularly how cash nets out, and so you’d see it in non-reciprocal relationships more frequently. The story about information and search costs I think is valid as well, and it fits in with my story just fine. You provide a list of things, and the gift-giver can either guarantee you a guaranteed utility gain by buying something on the list, OR they can optionally use your stated preferences and their personal information to present you with a gift that they predict you will like but that you simply did not know existed yet. As I said above, this pleasantly surprises us, since it is a successful forecast of our own preferences.

Hopefully this is not too weak of a social theory!

?!?!?

Isn’t this difficult because in Asian societies, gifts of cash are the norm? In Korea, China, Japan, Taiwan, Singapore, Thailand and even parts of India, it’s perfectly fine to give cash.

Well, yeah, that makes it difficult – but isn’t that precisely the point that Robin’s trying to make? A theory which explains why giving cash is considered tasteless in the US but fails to explain why it’s acceptable in Korea is not a very useful theory at all. We would get much more insight from a general theory of what makes an acceptable gift which explains why a gift which is acceptable in Seattle isn’t acceptable in Seoul, and vice versa.

That seems like a reasonable description. The main exception is those who stake their status on building a kind of ‘guru’ reputation, by building their specific pattern framework at the expense of others, and reinterpreting or selectively rewarding those finding that adhere to his model. Then there is a selection pressure in favour of more sane simplified patterns, albeit a weak one.

Not explicitly, perhaps. Yet it is inevitable that the professional opinions and expert testimony of economists do just this when they give predictions of the economic behaviors of super-intelligent, self improving ems. We’ve seen that over the last several weeks.

The context of my 98% (obviously a made up number, but I think in the right ballpark) wasn’t markets per se, but I’ll try to translate.

In wheat markets, for example, the farmers, the bakers, and the various intermediate transportation, processing and storage operations know a lot more than any wheat market theorist. Otherwise we wouldn’t need a market, we could rely on central planning that uses our models. Surely this is an uncontroversial Austrian point.

If there are wheat market theorists I very much doubt they generate even 2% of the value of all the applied intelligence that goes into wheat related activities.

In financial markets the situation is much more peculiar, since the market participants to a large extent are trying to anticipate and respond effectively to each other, not exogenous phenomena. It isn’t clear how to divide their subject knowledge from their theories about the market, and in fact widely held theories (such as Black-Scholes) tend to have a huge influence on the behavior of the financial markets, for good or ill.

So it isn’t clear how to apply my 98% to the financial markets. However it is pretty clear that these markets failed to predict future facts that are extremely important to them. I would argue this is because the participants follow theories about the markets that are not grounded in enough concrete information.

Jed, in the areas of social science I know best, most academic rewards are for collecting more data, and for building ad hoc models; there is little payoff for the sort of pattern-finding I’m describing. In those areas the strategy I describe gives great intellectual payoffs, if poor academic rewards.

“Since I know you are an advocate of prediction markets, let me point out that this general point applies to them”

Forgive me, Jed, I don’t know how many markets you run. My prediction market is 96% accurate so far. The 4% issue has never been a “small data” or “sampling” issue, if understand your point here.

Rather, that 4% has always been either an information issue or a trust issue. After the first information issue, I figured out a solution to that problem and it hasn’t happened again. The trust issue’s the be-otch.

Because otherwise, causal inference becomes very simple — and it’s not simple in practice.