Patterns of leadership

 

Prof. Pentland started the talk with the discussion of kahneman’s work, which generalize two approaches of human behavior learning based on the learning process. One approach is Attention learning, which is slow, serial, controlled, and rule-based learning. The other one is habitual learning, which is fast, parallel, automated, and associated learning. The second one is used all the time according kahnemen’s work.

 

After the discussion, Prof. Pentland introduced his research in last couple of years. Recently, researchers, his team included, have been studying on what they named as Honest Signals. He recognized Honest Signals as a biological basis for understanding interactions, while it is not an affect or cognition, it is largely unconscious signals and responses. Some of their research has been focus on the how signals shape conversations. They have recorded 2300 hours experiments with 800 people on the signal changing. Some experiments include monitoring the signaling change in real time hiring, dating, sales and salary negotiation. They reached a conclusion that one good presentation like pitching your vision or business plan largely does not matter with what you say but how you say with 79% accuracy. That is to say, if the presenter seems excited and to know something, it’s going to be a good presentation with a large chance. Further, he explained how signaling shapes communication patterns. Three types of signaling shape the conversion, including energy, namely means highly active signaling, engagement (influence of each person in the communication), and exploration (variable prosody). By combining the three signaling, they could predict the leader in a group with 80% accuracy.

 

What make the communication pattern important are its influential contributions to determine group performance. They had conducted a study on improving the productivity of a calling center. A common sense is that the more minutes workers talk to each other, the less minutes they are talking with customers, which leads to less productivities. So it seems obvious rule in such company that only allowing worker to have coffee break sequentially, one after another. But in their experiment, they showed increasing the exploration and engagement among workers, however, raises the productivity of their work. Based on this experiment, they suggested changing coffee break and increasing the conversation between workers thus reached a $15M/year saving for that calling center. One point Prof. Pentland stressed all the time was that “we are not so smart”, and mostly we learn by going around, looking things that seem to work and copying that. So one possible reason to explain the calling center problem was that people were gaining collective practicing, which ultimately contributed to group productivity.

 

By comparing creative group with group of bees, which has the same pattern of star-shaped network (exploration) and cohesive network (engagement), he proposed a hypothesis that group performance can be improved by shaping communication patterns. They had run a seven-day experiment within a group in an international company, where one Japanese and seven Germans worked together. On the first day, by recording the signaling in the group, there were a lot of explorations and engagement between Germans, however, Japanese worker seemed being isolated with very few communications and thus resulted low group productivity. After informing the group and attempting to change the communication patterns, on the seventh day, however, they reached a better working performance, where signaling showed communications were more equally distributed in the group.

 

Prof. Pentland revisited the point at the end that “we are not so smart” by explaining social learning accounts for 90% of things learned, while trying by own only contributes 10%. In one study, his team convinced that the patterns of buying and the patterns of health was shaped by the demographical distribution in San Francisco Bay Area.

One interesting finding was that they convinced that for the pattern of apps on their mobile phone, based on their data, being close friends doesn’t mean shaping the same pattern of apps, however people meet more together tends to have more similar pattern of apps. This reminds me online-recommendation mechanism like social recommendation; one could receive recommendation from their ‘friend’. I am really interested how it goes when compared to the recommendation from people you work with or often meet.