Want a practical complexity heuristic?

Update: 7 May 2016

What I didn’t explain is the hard part is figuring out what the parts are, and how they fit together. But there’s a good example in my upselling solution blog post, on how I figured out what imho was blocking growth at Causeway, with the help of expert ‘sales hacker’ Richard Harris. I guess the exec team at Causeway have found their own way to a solution, with the business transition to SaaS.

There you go, click on the pic for the three tweet answer, thanks.

Beware: this is not ‘top level’ thinking. This is a heuristic.

PS: I came up with all this a day after staring in to the sky whilst waiting for the morning minibus to Sony in Weybridge – and after tweeting about a strange line in the sky – by chance stumbled on the origin of the phrase ‘Occam’s Razor’ which is relevant to the design of heuristics: “One should not increase, beyond what is necessary, the number of entities required to explain anything.”

The answer to my question – ‘Ockham Stack’ (see Q & A below with @CoxeyLoxey) – is named after the village in Surrey where William of Ockham, the guy who coined the phrase Occam’s Razor, came from. So hope that didn’t increase beyond what’s necessary, the # entities required to explain it!

Influential people + influential friends = spread products

Identifying social influence in networks is critical to understanding how behaviors spread. We present a method for identifying influence and susceptibility in networks that avoids biases in traditional estimates of social contagion by leveraging in vivo randomized experimentation. Estimation in a representative sample of 1.3 million Facebook users showed that younger users are more susceptible than older users, men are more influential than women, women influence men more than they influence other women, and married individuals are the least susceptible to influence in the decision to adopt the product we studied. Analysis of influence and susceptibility together with network structure reveals that influential individuals are less susceptible to influence than non-influential individuals and that they cluster in the network, which suggests that influential people with influential friends help spread this product [red text highlighting added].

Identifying Influential and Susceptible Members of Social Networks
Sinan Aral, Dylan Walker

Science http://dx.doi.org/10.1126/science.1215842

Social media have provided plentiful evidence of their capacity for information diffusion. Fads and rumors but also social unrest and riots travel fast and affect large fractions of the population participating in online social networks (OSNs). This has spurred much research regarding the mechanisms that underlie social contagion, and also who (if any) can unleash system-wide information dissemination. Access to real data, both regarding topology—the network of friendships—and dynamics—the actual way in which OSNs users interact, is crucial to decipher how the former facilitates the latter’s success, understood as efficiency in information spreading. With the quantitative analysis that stems from complex network theory, we discuss who (and why) has privileged spreading capabilities when it comes to information diffusion. This is done considering the evolution of an episode of political protest which took place in Spain, spanning one month in 2011

Locating privileged spreaders on an online social network

Javier Borge-Holthoefer, Alejandro Rivero, and Yamir Moreno

Phys. Rev. E 85, 066123 (2012)

http://link.aps.org/doi/10.1103/PhysRevE.85.066123