Maven7 managed to create an open atmosphere, attracting a good mixture of business managers, business consultants, and scientists in the well-equipped building of the Central European University. The density of ties and suits was much higher than in a normal physics or computer science conference – which also guaranteed better coffee and cake selections: the caterer was from the famous Gundel restaurant.
The keynote speech was given by Albert-László Barabási who summarized different aspects of the evolution of network analysis, which might be most influential for economy: he took us on a time travel, beaming us through the preferential attachment model and its close cousin the fitness model. He pointed out clear connection to markets: it is not always the first move into a market that will make you big; if you have a genuinely new function to provide, you can still win the market even if you come later. It is known that this model can lead to a Bose-Einstein-condensate if one of the nodes has an extremely high fitness, i.e., even a late-comer can grab a big share of all subsequent customers: In the evolution of such a network this will turn into a constant percentage of edges attached to this one node while in the normal preferential attachment model, the maximal degree will rather have a shrinking relative share of all edges. Barabási showed that essentially the operating system market might be of this fitness type since Microsoft has had a market share of 85% for the last two decades. I’m not sure whether this model holds since this network is fundamentally different from the preferential attachment model: most nodes in the operating system network are normal users, not companies producing and selling operating systems. Thus, a new node is much more likely to be a user and she can only choose from those producers that are already in the market. It would certainly be interesting to test whether the model applies to this slightly different situation. He also briefly mentioned the link community algorithm by Sune Lehmann, which, in my personal view, is one of the best ideas about clustering in networks in years. The link points to the enormous supplemental material presented along with the article.
The next invited speaker was Hamid Benbrahim who made a very interesting point: he introduced the “sigma-delta-disconnect”, namely that we either know much about aggregates (sums, represented by the sigma) or about the difference between things (represented by delta). In essence, he pronounced that there is a gap in our understanding of the macro and the micro level of our complex financial markets. He also pointed out that due to the new technology our markets have now synchronized much more than 10-20 years ago because any small leverage between markets is now easily identified and can be harnessed within milliseconds – virtually at the speed of light. He also showed a case in which two software agents created a small crash on the price of some stock because the first one wanted to sell a huge chunk of it. To not decrease the price, the agent is of course smart enough to offer it in small portions – however, the second agent is programmed in detecting behavior like this and guesses that there will be a large chunk sold and bets against the price. This leads to the predicted decrease of the price until finally the process was stopped. After a five seconds break, other agents realized that the price of the stock was undervalued and started buying it and after around an hour the price was back to normal. This shows how the modern trading system can fall into positive feedback loops that leverage the system out of its equilibrium position.
Alberto Calero made an interesting remark that the Gartner report on Ten Technology Trends 2011 contained three key technologies related to network analysis: Social communication and collaboration, social analytics, and next generation analytics. He also shared his experience on how to convince fellow CEOs that network analysis can help in business decisions: he did not really disclose the details but it became clear that network analysis was an eminent part in advertising the new services made available by mobile phones in the 90s like SMS. He also reported on a new campaign in which customers are rewarded in groups, and he emphasized that attracting groups of people to a mobil phone provider might become much more important than attracting single customers. This was of course also a big topic in the later talks that reported on different churn models and the strategies to prevent it.
Valdis Krebs finally spoke on how to communicate network analysis results. He reported from his experience with various case studies and emphasized that we need new measures and new ways to report them. One of his customers once showed him a simple chart with a curve: the curve goes up – everything is fine. The curve stalls: watch out! The curve goes down – not good at all. Curve goes up again: you’re back into business. He asked whether it was possible to turn network analytic results into something as simple as that. So, a first step in this seems to be to replace all occurrences of ‘social network analysis’ by ‘organization network analysis’. Valdis’ answer to that request is, e.g., not to show networks, but to show a Venn diagram representation of an embedded and clustered network instead since that is more digestible. He also emphasized to develop a standardized procedure, use only a few standardized programs, and allow for the transfer of best practice. In general his advice is: make it simple.
This last point basically was the outcome of many of the talks later as well: do not make a research project out of every case study but communicate your results in a simple and short manner. In the round table discussion, the invited speakers agreed (make that ‘round chair discussion’) that we need to follow a schematized, standardized way of doing a network analysis report on economical networks – be it communication, trust, or inspiration networks. Maven7 is helping in that by promoting their first software tool, aiming at consultants that want to include network analysis to their repertoire. A second main point was that maybe we focus too much on single nodes and their position in a network. Helping a company should not boil down to “try to connect these three people more” but rather in creating an atmosphere in which positive network structures can emerge.
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