Flip over at InfoChimps has put together a massive scrape of twitter. While trying to figure out how to process it all, I’ve drawn one day’s scrape* (20-Dec-08). Many thanks to Flip and InfoChimps for the wow work, and doubly so for making publicly available.
If you ever find yourself needing to generate random graphs in Perl (quite the ice breaker, I can tell you), I recommend checking out Matt Spear’s Graph::Maker, which has generators for everything from Erdos-Renyi and Watts-Strogatz to Lollipop graphs. The only downside is the use of Graph which is s-l-o-w for graphs of even moderate size, so makes using it directly for simulations of Social Networks out.
Knowing someone who is happy makes you 15.3% more likely to be happy yourself, the study found. A happy friend of a friend increases your odds of happiness by 9.8%, and even your neighbor’s sister’s friend can give you a 5.6% boost.
“Your emotional state depends not just on actions and choices that you make, but also on actions and choices of other people, many of which you don’t even know,” said Dr. Nicholas A. Christakis, a physician and medical sociologist at Harvard who co-wrote the study.
…quoth the LA Times; and there’s more coverage over at the NY Times (including a full size of that great image).
During one Saturday in the beginning of November, I took part in a multi-party negotiation, which had some surprising results. Out of curiosity, I mapped who wanted what from whom, and ran a basic network analysis. The second surprise of the day was the analysis was really close to the observed results. I hope this description of my network analysis of a common game theory problem spurs discussion about how use network analysis and game theory in combination.
Swift Trust, much like it sounds, is the concept of rapidly coming to intra-team trust. A doctor friend of mine who introduced me to the term, explained it with the context of the ad hoc team of MDs and nurses responding to a cardiac arrest, a code blue.
I have been thinking about this throughout a book I am reading now, Honest Signals, by Alex (Sandy) Pentland from the Human Dynamics group of the MIT Media Lab. In it, Prof. Pentland discusses physiological social signaling, and one point particular to swift trust stood out: with great accuracy, one can predict behavioral outcomes using a “thin slice” of observation. One study was able to predict six-year marital success based on just the first three minutes of a marital conflict. There are many more studies showing similar success including job interviews, therapist competency ratings, and courtroom judges’ expectations of trial outcomes. My guess is there are things about the crash cart scenario which take advantage of this.
Some thoughts about this applied to code blue teams:
the roles are well defined, so the amount of politicking is reduced
time pressure pushes you to trust your colleagues, as there is little other choice
the desired outcome is constrained, so you are only asked to trust in this specific situation
trust develops rapidly with success
trust develops when you don’t have a choice about the team over the long term. (time frame is short, so don’t know if this comes into play).
If these are right, here are a few predictions about the crash response process:
there are a number of quick steps taken as a group before administering to the patient. That would help establish some trust right at the beginning.
the team members know each other at least by reputation, that goes a long way to giving the benefit of the doubt.
the outcome is critical, so everyone is pushed to excel. This works in the the trust/success feedback loop.
team members talk about crashes with their non-team colleagues. this helps the reputation feedback.
Are there any MD’s or RN’s out there who care comment? I have only the most cursory knowledge about the way the team is conducted, not to mention the actions team members take. Does this fly?
I am big fan of looking to outside fields for ideas and expertise. Case-in-point: I recently came across a reference to a great study about contract law – when people rely on the contract for enforcement during the course of business, and when they don’t. Hint: they usually don’t; they rely on the relationship.
Translating the findings to social network analysis, we come up with six great pieces of advice for all aspiring master networkers:
Established relationships provide more value than new ones.
Your reputation is critical to creating new relationships.
The more your peer gets out of a relationship, the more you will get out of it: deliver excellence.
If you are stuck together for the foreseeable future, you will both get more out of the relationship. This could be from getting forced to trust each other or pushing harder to get more out of the relationship.
New relationships are easier through introductions as the introducer can punish the introducee, through reputation or otherwise, if he does not deliver.
Your network is your asset and yours alone, no one is invested the way you are to maintain your relationships.
Oct. 15 (Bloomberg) — Federal Reserve Chairman Ben S. Bernanke said the central bank will consider discarding its long- standing aversion to interfering with asset-price bubbles and warned that the banking business may be concentrated in too few companies.
Individual power increases network risk. When the power goes, so does the network.
But, this risk can also mobilize everyone else to buoy up the network by supporting the powerful (AIG rescue) or group cooperation (bailout lobbying). When a power fails, there will be painful redistribution of wealth (Lehman Brothers) and the market as new relationships are established. The market redistribution remains to be seen, but JPMorgan’s buying up relationships (the network) left and right.
Recommendations to the survivors. It’s easier to buy existing relationships through M&A than to create them from scratch, so think specific geographies and buy local; that’s where you’ll find the majority of relationships. For all those new customers you acquire reach out to them early and often. Build the relationships that kept them with your acquiring company.
Network risk is inherent to trading, and traders will never willingly open their books. The bulk of a trader’s value is in his judgment, not the actual trading. If you knew what they were buying and selling, you could duplicate their portfolio without paying their fees. But, there’s an opportunity for a new Moody’s: grade trading funds on network risk.
First is an important matter of language. There is a large difference between authority and an authority. Authority is power formally granted by a position or role. An authority is some who has power or influence; it’s a matter of trust by others for a given context. E.g. I trust my doctor to diagnose an illness, but have no reason to trust him on gardening.
There are many reasons we (dis-) trust others, including: shared opinions; length, frequency, and consistency of interactions; and how our peers feel about the individual the given context. These are all correlated, but frequency and peer opinion have the biggest impact on transferability of trust.
Frequency can be no surprise, it’s the underpinning of blanket marketing. Familiarity bred through repetition. Ever wonder why you stop on a TV show you dislike while flipping through the channels? Of course, that could be me making excuses for lousy taste.
Peer consideration is the tricky bit. There is a lot of research going into this, but there are some great seminal works that are written for non-academics covering the spread of innovation and adoption of scientific principles.
When our peers already have some experience or opinion on a topic or an authority, then our own opinions are strongly colored by these existing opinions. In fact, as odd as it may sound, given a pattern of relationships where the opinion of all but one individual is known, we can predictably estimate both what the opinion is, and how strong the opinion is, of the unknown person.
But how about when we something brand new to us and our peers? Frequency plays a big role here too. If lots of people, even people we know nothing about, say XYZ is a good idea, we’re likely to give it the benefit of the doubt, trusting the wisdom of the crowd.
So, strictly speaking, is (dis-) trust transferable? Depends. If trust already exists in our network of peers, yes, and predictably so. If the context is brand new to you and your peers, there is no trust to transfer, but we use frequency as a proxy in our decisions.
So what does this all mean? Let’s look at an example: you’re trying to decide whether you agree (trust) what I have written.
I am new to writing about this, and I’m not particularly active in social media, so chances are we don’t have peers in common. You can google for what others have said about me, but you’re not going to find much relevant to trusting me in this context. Ultimately, because I don’t have a track record (frequency & consistency) for participation (peers) in this context, I am at the mercy of how similar our outlooks are, and any opinions that may develop in comments.
I have been thinking about this throughout a book I am reading now, Honest Signals, by Alex (Sandy) Pentland from the Human Dynamics group of the MIT Media Lab. In it, Prof. Pentland discusses physiological social signaling, and one point particular to swift trust stood out: with great accuracy, one can predict behavioral outcomes using a “thin slice” of observation. One study was able to predict six-year marital success based on just the first three minutes of a marital conflict. There are many more studies showing similar success including job interviews, therapist competency ratings, and courtroom judges’ expectations of trial outcomes. My guess is there are things about the crash cart scenario which take advantage of this.