Sandy’s theory is that 50,000-100,000 years ago, humans lacked language, yet still managed to communicate with each other through “honest signals” (ancient primate signaling efforts which developed biologically to communicate our intentions, our trustworthiness, our suitability as a collaborator, whether we were bluffing, etc.). When language was introduced, it didn’t over-write or eliminate these honest signals but evolved to be synergistic with these signals. While we focus much more on language, these signals are measurable (Sandy’s group developed machines to read these signals) and often equally or more effective at predicting various behaviors than language. Sandy’s research aims to shine a light on this powerful channel that we know less about.
Sandy notes that such data from electronic ID badges (sociometers) and specially-programmed smart phones, can give us a “god’s eye” view of how the people in organizations interact, and observe the “rhythms of interaction for everyone in a city”.
What are such behaviors?
Sandy’s group at the MIT Media lab focuses on 4 of them, although there are probably others (laughter, yawning, etc.).
- INTEREST, shown by activity. An autonomic response. For example in children, this is evinced by jumping up and down or in dog’s by barking or wagging tail.
- ATTENTION, by looking at influence. Evidence of thalmic attention. Sandy observes that people actively following in conversations break in faster than they could with normal attention spans. Shows that they are processing the conversation and discussion as it goes along and predicting the right time to break in.
- EMPATHY, as shown by mimicry. This is evinced by mirror neurons, which are observable in infants as young as 3 hours old that can imitate a mother sticking her tongue out. People who evince higher levels of mimicry are seen as more empathic and more trustworthy. For example, they had computerized agents trying to sell an unpopular policy to students; in the cases where the computerized agent mimicked the body movements of the experimental subject with a 4 second delay, the computerized agent was 20% more successful in selling the policy to the experimental subject and the subject was unaware that he/she was being mimicked.
- EXPERTISE, as shown by consistency. This a function of the cerebellar motor. We assume that people who can do things more smoothly are more expert because of the number of actions that need to be simultaneously coordinated.
What do these honest signals predict?
These are only some of the examples:
-Computers attentive to these honest signals (and ignoring the content) were as successful in predicting from pitches by entrepreneurs which business plans would be judged by business school students as successful.
– Effective sales pitches: listening to the first few seconds of a telephone sales pitch (without listening to the language) but listening to tone, timing, etc., the computer could predict with 80% accuracy which would be successful calls.:
-Success in speed dating: monitoring the female’s signals predicted 35% of the variation in which couples exchanged their phone numbers, and this was significantly higher than any other factor researchers could find. Interestingly, the men’s signals were not predictive, but somehow men must have been able to subconsciously pick up on the women’s signals, because in almost all cases the men didn’t ask for phone numbers where it wasn’t reciprocated by women.
– They also found that honest signals predicted depression, predicted who was likely to be successful in negotiating for a pay raise, job interviews, who was bluffing at poker, etc.
Successful individual-level traits: they found that the most successful folks with these “honest signals” were ones who were high in activity, high in influence (others were more likely to mirror their communication styles then they were likely to mirror others’) high in “variable prosody” (their pitch varied and they sounded open to ideas), and high in body language dominance (i.e., they were more likely to directly face another person and others were more likely to not face them square on). They were often far more successful in these “honest signals” than they were aware of.
Sandy notes that unlike an MRI, one can hook up an entire organization to these sociometers and absorb micro-second by micro-second, and the results are highly predictive. But the challenge is that while the people who exhibit these highly successful individual traits are useful to organizations, they are usually in “connector” roles for organizations, with star-shaped patterns of communication, where ideas flow through these individuals. While this speeds up the decision-making process, it actually impairs the brainstorming process. Sandy’s group is experimenting with devices to see if making participants aware of the dynamics of a team can influence their behavior in a positive manner. They have shown with some experiments (Japanese-American teams designing Rube-Goldberg-type projects, and distance teams) that it can change people’s behaviors in a positive manner. The challenge will be to see if the group’s behavior can be more connected at the brainstorming phase and more “star-shaped” at the decision-making stage.
Sandy noted that they have been able to extract many properties of the social networks using smart phones: from a combination of where people are (GPS), when, and communication flows (who they talk to and when). He noted some interesting experiments to observe the flow of nurses in a nursing ward, or the flow of taxis in San Francisco, or communication (e-mail and face-to-face) between departments in a German bank. They are now at the stage of trying to get whole dormitories or parts of the city of Boston using these smart phones to try to track social networks and patterns in these data. (I’ve written about digital traces before.)
How could these flows of people be used:
–Traffic: one could monitor, for example, delivery vans coursing through the road networks and by observing flows slower than typical, spot emerging traffic problems.
–Urban tribes: Sandy noted that by monitoring flows of taxis, you can distill separate patterns of interconnected places. In other words people who live in this neighborhood, work in this area, go to these restaurants, go to these nightclubs. (You are not actually monitoring individual people but patterns of association. This is equivalent to Netflix telling you that people who like “The Firm” also like “Michael Clayton”.) Or one can even find sub-patterns in a neighborhood:e.g., locations from which people regularly are returning from nightclubs at 3 or 4 AM.
-You can then use these patterns to “find people like me”: based on your own patterns (where you work, where you live, etc.), the system could tell you where many people in your neighborhood shop, go to dinner, or hear music.
– Lending: one major bank told Sandy that credit scores are not very good (except at the high end) in predicting repayment rates on loans. Banks would love to use behavioral information (who is at nightclubs late at night, who goes to work early) to predict repayment rates.
– Health insurance: similarly one could imagine rates tied to activity levels (who was jogging or getting enough sleep or…)
– Germs: they want to use these devices to watch the spread of germs through social networks.
The above examples of health insurance and lending make one understand why there are clear privacy implications. Do we want banks or health insurers knowing what we are doing (going to nightclubs) to set our rates? Will this be used to impose behavioral bases for “red lining”, where people in certain areas (like the old red lined areas) don’t get loans because of some behavior of theirs that is correlated with low repayment rates? Does it make any difference if these people can supposedly change their behavior?
-Sandy thinks we should move from company owning the personal data and sharing with no one or only sharing if an individual didn’t say it was confidential to the person owning the data and being able to decide how it gets used and whether the owner gets compensated for such use.
-There are clearly issues here about how the decision is framed? Does the individual truly understand why certain marginal information is so useful to a bank or insurer? And there may be negative externalities for all, even if you don’t choose to share your information with these companies?
Sandy’s research also raises questions about what happens when you start incentivizing people in companies based on these behaviors, or you start teaching people about these hidden “honest signals”. Do people start learning how to display these honest signals and dupe people who are not as aware of this (e.g., mimicking others to increase sales or do better in negotiations). If so, do people start focusing on these behaviors (like mimicry) and consciously teach themselves not to be swayed by this? Do companies find that people who pretend to be connectors (to get a pay raise) are actually less valuable to companies than the people who do it naturally (and are unaware they are doing this)?
See interesting related story in NYT, “You’re Leaving a Digital Trail. Should You Care?” (John Markoff, 11/30/08), mentioning Alex Pentland’s work among others and discussing the SF taxi example.