The impact of social structure on economic outcomes

Some extracts from The impact of social structure on economic outcomes.

4 core principles:

1) Norms and Network Density. ... the denser a network, the more unique paths along which information, ideas and influence can travel between any two nodes. Thus, greater density makes ideas about proper behavior more likely to be encountered repeatedly, discussed and fixed; it also renders deviance from resulting norms harder to hide and, thus, more likely to be punished. ... larger groups will have lower network density because people have cognitive, emotional, spatial and temporal limits on how many social ties they can sustain.

2) The Strength of Weak Ties. More novel information flows to individuals through weak than through strong ties. Because our close friends tend to move in the same circles that we do, the information they receive overlaps considerably with what we already know. ...This is so even though close friends may be more interested than acquaintances in helping us; social structure can dominate motivation. This is one aspect of what I have called “the strength of weak ties” (Granovetter, 1973, 1983). ... if cliques are connected to one another, it is mainly by weak ties. This implies that such ties determine the extent of information diffusion in large-scale social structures. One outcome is that in scientific fields, new information and ideas are more efficiently diffused through weak ties.

3) The Importance of “Structural Holes.” Burt (1992) extended and reformulated the “weak ties” argument by emphasizing that ... the strategic advantage that may be enjoyed by individuals with ties into multiple networks that are largely separated from one another. Insofar as they constitute the only route through which information or other resources may flow from one network sector to another, they can be said to exploit “structural holes” in the network. ... One reason resources may be unconnected is that they reside in separated networks of individuals or transactions. Thus, the actor who sits astride structural holes in networks (as described in Burt, 1992) is well placed to innovate.

Prospective employers and employees prefer to learn about one another from personal sources whose information they trust. This is an example of what has been called “social capital” (Lin, 2001). ... for goods where assessment is difficult, such as used cars, legal advice and home repairs, one-quarter to one-half of purchases in the United States are made through personal networks.

Studies of peasant markets often suggest that “clientelization,” defined as dealing exclusively with known buyers and sellers, raises prices above their competitive level

Social relations are also closely linked to productivity. Economic models attribute productivity to personal traits, modifiable by learning. But one’s position in a social group can also be a central influence on productivity, for several reasons. One is that many tasks cannot be accomplished without serious cooperation from
others; another is that many tasks are too complex and subtle to be done “by the book” (which is why the “rulebook slowdown” is a potent labor weapon) and require the exercise of “tacit knowledge” appropriable only through interaction with knowledgeable others.

“loyalty systems”—attempts to elicit cooperation from workers deriving not only from incentives but also from identification with the firm or with some set of individuals that encourages high standards and productivity.

Is Britney Spears Spam?

From my old blog (21st August 2007):
In the last post, I was raving on about ..., uhm, probably about trust bootstrapping, right? :-) I went from a definition of cold reading to a very personal interpretation of Posner's review of Blink. Now, in the same vein (i.e., keeping on being delirious), I move on this nice paper, which carries out (in a way) not offender profiling but MySpace user profiling.
Title: Is Britney Spears Spam? (pdf)

Problem: In social network websites (e.g., MySpace), to decide whether to accept invitations to connect, users manually examine the senders' profiles. However that may be time consuming!

Existing Solutions: One may automate the acceptance of invitations by having users running trust propagation algorithms.

Complication: The authors write that using current trust propagation algorithms may be less than desirable since trust both decays with the number of hops and is usually one-dimensional.

Proposal: Use machine learning techniques to classify user profiles. The classification describes a profile across two dimensions: sociability and promotin. Based on these dimensions' values for a profile, users then decide whether
to accept the invitation of that profile's user. To come up with a dataset on which to evaluate their algorithm, the authors randomly select and rate by hand MySpace users.
Future: I would:
> Apply a new trust propagation algorithm (pdf) to avoid trust decay and apply TRULLO (pdf) to handle multi-dimensional trust.
> Look at literature on criminal profiling. In UCL's main library, I noticed many books about criminal profiling. I wonder whether those books could inform a (future) paper titled "On profiling (not only criminals but) Web 2.0 users" ;-)
> Look at literature on statistical discrimination (previous post) and on customer profiling (mining customer data).
> Consider Tim Finin's comments:"It would be interesting to see how well various measures of the network structure around false and true profies serve as features. I think this is very similar to the problem of recognizing spam blogs (splogs). In our work, we’ve found that local features work well, but splogs can also be recognized by looking at the network structure as well."

Efficient and Decentralized PageRank Approximation

I read this very well-written paper. The authors set out to design a way to compute (an approximated) pagerank in a distributed and efficient way.

"Starting with the local graph G of a peer, the peer first extends G by adding a special node W, called world node since its role is to represent all pages in the network that do not belong to G. An initial JXP score for local pages and the world node is obtained by running the PR algorithm in the extended local graph G' = G+W.
...
we take all the links from local pages to external pages and make them point to the world node. ... as the peer learns about external links that point to one of the local pages, we assign these links to the world node."


Optimized Merging

"At a peer meeting, instead of merging the graphs and world nodes, we could simply add relevant information received from the other peer into the local world node, and perform the PR computation on the extended local graph and still the JXP scores converge to the global PR scores."