#barabasi

Albert-Laszlo Barabasi’s new book, The Formula (https://amzn.to/2RIEwqK) is an interesting and engaging fact-driven analysis of the way networks are influential in achieving success across many fields of endeavor. 

Central to his arguments are (1) performance and success are different animals, and (2) performance drives success only when performance can be measured accurately, otherwise networks drive success. Barabasi focuses his analysis on areas where performance metrics are poor or nonexistent. 

One particularly interesting insight is that expert judgments of relative quality are sucky where they seem to matter most, specifically in determining the relative rankings of high quality alternatives. In one example he draws from research on wine judging in competitive tastings.

Wines submitted to competitions are in all likelihood samples from the upper tail of the distribution of wine quality. Therefore, differences in quality among these wines will be small, because there is a practical upper bound on wine quality. Practically speaking, the overall quality differences among the wines are too small at the upper tail of quality for reliable ranking. 

The research cited by Barabasi suggests expert judges are very inconsistent in their rankings of these high quality wines.  All the effort expended on ranking yields essentially random rankings among the high quality wines.

Barabasi’s conclusion in this case is that the best strategy for a consumer is to stay away from the low quality wines and to buy the cheapest of the set of highest quality wines, subject to one’s preferences over specific wine characteristics (e.g., red/white, fruity/earthy, etc.).

Let’s apply this insight to the task asset allocators face when deciding how to allocate (rank) among competing hedge fund opportunities.

Performance measures for hedge funds are noisy, in part because common measures don’t distinguish skill from exposure to market beta or luck from chance events. Performance measures for new or recent start-ups are even less reliable, because they lack sufficient data. In these cases, expert judgment trumps performance.

But how good can expert judgment be? 

The set of new or recently launched funds is not a random sample from the universe of individuals with trading experience. HF start-ups will, by and large, represent the upper tail of the distribution of trading talent (at least measured by realized prior performance). HF hopefuls will not market successfully with a pedigree of mediocrity. 

It is possible that expert judgment applied to the allocation decision will be very inconsistent in ranking these funds.  If this is the case, the best decision rule may be as follows: 

The neural roots of friendship, or how brain networks rule social networks: https://t.co/nKxZvhQneS Nice insights, @NAChristakis pic.twitter.com/4zh0HkISZf

Predicting Missing Links: A paradigm changing method developed by Istvan Kovacs, with strong direct experimental support. Available on Biorxiv: https://t.co/5yk7RsHx6F. pic.twitter.com/dsbcFqQkWy

The first ever podcast of the Harvard Club: A wonderful conversation about networks and history with Niall Ferguson. Than you @nfergus and John Cornish for making this happen! https://t.co/Kv7Wrl2LqZ pic.twitter.com/aPKzw1LPxt

Aulas dos dias 21/11/2017 e 28/11/2017

BARABÁSI, A. L. Linked: a nova ciência dos networks.  São Paulo: Leopardo, 2002

The Emergence of Network Science

Documentary unfolding the science behind the idea of six degrees of separation. Originally thought to be an urban myth, it now appears that anyone on the planet can be connected in just a few steps of association. Six degrees of separation is also at the heart of a major scientific breakthrough; that there might be a law which nature uses to organize itself and that now promises to solve some of its deepest mysteries.