Friday, November 13, 2015

Social Physics: How Good Ideas Spread - the Lessons from a New Science

_Social Physics:  How Good Ideas Spend - the Lessons from a New Science_ by Alex Pentland
NY:  The Penguin Press, 2014\ISBN 978-1-59420-565-1

(viii)  What I have learned from these experiences is that many of the traditional ideas we have about ourselves and how society works are wrong.  It is not simply the brightest who have the best ideas;  it is those who are best at harvesting ideas from others.  It is not only the most determined who drive change;  it is those who most fully engage with like-minded people.  And it is not wealth or prestige that best motivates people;  it is respect and help from peers....

(ix)  Most people think about using a framework centered on the individual and the eventual steady-state outcome, whereas I think in terms of social physics:  growth processes within networks.

We live in social networks, not in the classroom or laboratory.

(4)  Social physics is a quantitatve social science that describes reliable, mathematical connections between information and idea flow on the one hand and people's behavior on the other....

The key insights obtained with social physics all have to do with the flow of ideas between people.

(7)  We need to move beyond merely describing social phenomena to buildling a causal theory of social structure.  Progress in developing this represents steps toward what David Marr called a computational theory of behavior:  a mathematical explanation of why society reacts as it does and how these reactions may (or may not) solve human problems.

This sort of computational theory of behavior, which focuses on the human generative process, is what is required to build better social systems....

Almost uniquely among the social sciences, this new social physics framework provides quantitative results at scales ranging from small groups, to companies, to cities, and even to entire societies.  Currently, a social physics framework is in daily use in several commercial deployments, serving tens of millions of people in tasks such as financial investing, health monitoring, marketing, improving company productivity, and boosting creative output.

(8)  Who we actually are is more accurately determined by where we spend our time and which things we buy, not just by what we say or do.

The process of analyzing the patterns within these digital bread crumbs is called reality mining, and through it we can tell an enormous amount about who individuals are.

(11)  Data for Development (D4D) -

(14)  Anonymized data, visualizations, code, documentation, and papers can be found at  These data sets were obtained under U.S. federal human subjects law....

On May 1, 2013, I hosted the public unveiling of Data for Developoment, which is perhaps the world's first true big-data commons:  It describes mobility and call patterns along with economic, census, political, food, poverty, and infrastructure data for the entire African country of Ivory Coast.  These data are now available from

(15) ... two most important concepts in social physics:
Idea flow within social networks, and how it can be separated into exploration (finding new ideas/strategies) and engagement (getting everyone to coordinate their behavior).

Social learning, which is how new ideas become habits, and how learning can be accelerated and shaped by social pressure.

(16)  But rather than study how economic agents work and how economies function, social physics seeks to understand how the flow of ideas turns into behaviors and action.  Put another way, social physics is about how human behavior is driven by the exchange of ideas - how people cooperate to discover, select, and learn strategies and coordinate their actions - rather than how markets are driven by the exchange of money.

(17)  By creating social systems that look beyond aggregates such as markets, classes, and parties, and instead examining the detailed patterns of idea exchanges, I show how we can begin to build a society that is better at avoiding market crashes, ethnic and religious violence, political stalemates, widespread corruption, and dangerous concentrations of power.
NB:  Or better at creating them

..... In short, to achieve the exciting possibilities of a data-driven society, we require what I have called the New Deal on Data:  workable guarantees that the data needed for public goods are readily available while at the same time protecting the citizenry.  Maintaining protection of personal privacy and freedom is critical to the success of any society.

(26)  As Steve Jobs put it:
Creativity is just connecting things.  When you ask creative people how they did something, they feel a little guilty, because they didn't really do it, they just saw something.  It seemed obvious to them after a while.  That's because they were able to connect experiences they've had and synthesize new things.

(29)  It seems that the key to harvesting ideas that lead to great decisions is to learn from the successes and failures of others and to make sure that the opportunities for this sort of social learning are sufficiently diverse.

(33)  What postdoctoral student Erez Shmueli, Yaniv, and I found is that a community of social learners spontaneously forms what is called a scale-free fractal network - one whose connections are systematically more diverse than merely random - and, in addition, that connections in the network change over time in this same scale-free fractal manner.

(34)  It's a lot like the chance of getting the flu during flu season, only ideas usually don't spread as far or as fast as the flu.  In fact, the only way we have reliably been able to trigger rapid cascades of idea flow is through the use of social network incentives, as we will see in the next few chapters.

(35)  What Kelly found was that star producers engage in "preparatory exploration";  that is, they develop dependable two-way streets to experts ahead of time, setting up a relationship that will later help the star producer complete critical tasks.  Moreover, the stars' networks differed from typical workers' networks in two important respoects.  First, they maintained stronger engagement with the people in their networks, so that these people responded more quickly and helpfully.  As a result, the stars rarely spent time spinning their wheels or going down blind alleys.

Second, star performers' networks were also more diverse.  Average performers saw the world only from the viewpoint of their job, and kept pushing the same points,  Stars, on the other hand, had people in their networks with a more diverse set of work roles, so they could adopt the perspectives of customers, competitors, and managers.  Because they could see the situation from a variety of viewpoints, they could develop better solutions to problems.

(39)  By reducing the rate of idea flow to allow greater diversity, we moved the social network back into its sweet spot and raised average performance.  Through managing idea flows, our tuning of the network turned average traders - often the losers in our current financial system - into winners.

(39-40)  Social learning is critical... Increasing your reach and your network's diversity makes it more likely that you can find the best strategies.

Diversity is important:  When everyone is going in the same direction, then it is a good bet that there isn't enough diversity in your information and idea sources, and you should explore further.  A big danger of social learning is groupthink.  How can you avoid groupthink and echo chambers?  You have to compare what the social learning suggests with what isolated individuals (who have only external information sources) are doing....

Contrarians are important:  When people are behaving independently of their social learning, it is likely that they have independent infomration and that they beleive in that information enough to fight the effects of social influence....

(40-41)  If you can find many such independent thinkers and discover that there is a consensus amonbg a large subset of them, then a really, really good trading strategy is to follow the contrarian consensus.

(41)  One disturbing implication of these findings is that our hyper-connected world may be moving toward a state in which there is too much idea flow,  In a world of echo chambers, fads and panics are the norm, and it is much harder to make good decisions.  This suggests that we need to pay much more attention to where our ideas are coming from, and we should actively discount common opinions and keep track of the contrarian ideas.  We can build software tools to help us do this automatically, but to do so we have to keep track of the provenance of ideas.

(45)  Our behavior can be predicted from our exposure to the example behaviors of other people.

(48)  In fact, exposure to the behavior examples that surrounded each individual dominated everything elese we examined in the study.

(49)  Therefore, people seem to pick up at least some habits from exposure to those of peers (and not just friends).  When everyone else takes that second slice of pizza, we probably will also.  The fact that exposure turned out to be more important for driving idea flow than all the other factors combined highlights the overarching importance of automatic social learning in shaping our lives.

(50)  When sifting through these hundreds of gigabytes of data, we found that the amount of exposure to people possessing similar opinions accurately predicted both the students' level of interest in the presidential race and their liberal-conservative balance....

Overheard comments and the observation of other people's behavior are effective drivers of idea flow.
NB:  How did Solidarity start?  By talking loud at the bus stop.

(54)  Mathematical models of learning in complex environments suggest that the best strategy for learning is to spend 90 percent of our efforts on exploration, i.e., finding and copying others who appear to be doing well.  The remaining 10 percent should be spent on individual experimentation and thinking things through.

(57)  Psychological studies have shown that the snap judgments of people are more altruistic and cooperative than the decisions made slowly and thoughtfully.  Examples such as the reactions of spectators at the Boston Marathon bombings, or of neighbors after the recent Oklahoma tornadoes show that this fast-thinking core of human nature plays an important role in building strong communities.

(60)  Using the terminology of economics, in most things we are collectively rational, and only in some areas are we individually rational...

Consider the word "kith," familiar to modern English speakers from the phrase "kith and kin."  Derived from old English and old German words for knowledge, kith refers to a more or less cohesive group with common beliefs and customs.  These are also the roots for "couth," which possessing a high degree of sophistication, as well as its more familiar counterpart, "uncouth."  Thus, our kith is the circle of peers (not just friends) from whom we learn the "correct" habits of action.

(63)  Similar patterns of social decision making are common in many animals and virtually all primates.  The signaling mechanisms vary from vocalization to body posture to head movements, but the structure of the decision-making process remains pretty much the same:  cycles of signaling and recruitment, until a tipping point is reached when everyone in the group accepts that a consensus has been reached.  Some evolutionary theorists think that this type of "social voting" process could be the most common type of decision making in social animals, in part because it is very good at accounting for the cost-benefit trade-offs of everyone in the group.  In addition, this type of consensus process typically avoids extreme decisions, making it more likely that the entire group will follow.....

Average performers thought teamwork meant doing their part on the team.  Star performers, however, saw things differently:  They pushed everyone on the team toward joint ownership of goal setting, group commitments, work activities, schedules, and group accomplishments.  That is, star performers promoted synchronized, uniform idea flow within the team by making everyone feel a part of it, and tried to reach a sufficient consensus so that everyone would willingly go along with new ideas.

(64)  A surprising finding is that when people are working together doing the same thing in synchrony with others - e.g., rowing together, dancing together - our bodies release endorphins, natural opiates that give a pleasant high as a reward for working together.

Similarly, business research has shown that this sort of engagement - repeated cooperative interactions among all members of the team - can improve the social welfare of the group.

(65)  What our grandmothers would have known, though, was that nearly all the social influence occurred between close friends who had a face-to-face relationship.  [Facebook election experiment with a vote message and a vote message including the faces of friends]

....There is growing evidence that the power of engagement - direct, strong, positive interactions between people - is vital to promoting trustworthy, cooperative behavior.

(66)  Further, there is strong evidence that economic incentives don't work very well anyway.  But social physics tells us that there is another way:  by providing incentives aimed at people's social networks rather than economic incentives or information packets that are aimed at changing the behavior of individual people.

(69)  In other words, the number of direct interactions is a very precise measure of the strength of the social pressure exerted between people.  Moreover, the number of interactions also predicted how well people maintained their new, healthier behavior after the experiment ended.

Similarly, the number of times people had direct interactions with each other gave a surprisingly accurate prediction of the trust they expressed in each other.  That is, the amount of direct interaction between two people predicts both the shared level of trust and the effectiveness of peer pressure....

That is, we focus on changing the connections between people rather than focusing on getting people individually to change their behavior....

Social network incentives act by generating social pressure around the problem of finding cooperative behaviors, and so people experiment with new behaviors to find ones that are better.

(71-72)  These results suggested trying an approach based on social physics.  So along with our colleagues at ETH, we next deployed a digital social network as part of the electric utilities' Web pages and gave people small rewards to encourage them to form local buddy groups.  As in the FunFit experiment, this buddy network used social network incentives rather than standard economic incentives:  When a person saved energy, then gift points were given to their buddies.

This social network incentive caused electricity consumption to drop by 17 percent, twice the best result seen in earlier energy conservation campaigns and more than four times more effective than the typical energy reduction campaign.  Just as in the FunFit experiment, behavior change was most effective when it leveraged the strength of the surrounding social ties.

(73)  What we found [after examining over 1000 companies' internal digital social networks] was surprising:  When the digital social network grows in bursts of engagement, the network ends up being far more effective than if it grows gradually. In companies in which people received a flurry of invitations to join the company's digital social network, they were much more likely join and use the network than they were in response to the same number of invitations spread out over time.
NB:  Important organizing idea

(77)  Engagement requires interaction:  If people are to work together efficiently, there needs to be what is called network constraint:  repeated interactions between all of the members of the group - not just between a leader and the members, or between the members and the entire group (as at a group meeting).  The extent to which good network constraint has been achieved can be tested by asking if the people you talk to also talk to each otehr.  If not, get them talking:  We found that the number of direct interactions was a very good measure of the social pressure to adopt cooperative behaviors.  Moreover, the number of interactions also predicted how well people maintained their new, more cooperative behaviors.

(78)  Engagement requires cooperation:  Remember the Bell Stars:  They pushed everyone on the team toward joint ownership of the group, involving everyone in goal setting, work activities, and getting credit for group accomplishments.  These star performers promoted engagement within the team by making everyone feel part of the team, and they tried to reach sufficient consensus so that everyone would willingly go along with new ideas.

Building trust:  Trust, by which I mean the expectation of future fair, cooperative exchanges, is built from the history of exchanges between people...  Social network pioneer Barry Wellman's suggestion that the number of telephone calls between two people is a good measure of their investments in the relationship - an investment often called social capital - seems exactly right.

(88)  The largest factor in predicting group intelligence was the equality of conversational turn taking;   groups where a few people dominated the conversation were less collectively intelligent than those with a more equal distribution of conversational turn talking.  The second most important factor was the social intelligence of a group's members, as measured by their ability to read each other's  social signals.  Women tend to do better at reading social signals, so groups with more women tended to do better...
NB:  Letting everyone have a voice means better outcomes

(89)  Think about it:  Individual intelligence, personality, skill, and everything else together mattered less than the pattern of idea flow....

The characteristics typical of the highest-performing groups included:  1) a large number of ideas:  many very short contributions rather than a few long ones;  2) dense interaction:  a continuous, overlapping cycling between making contributions and very short (less than one second) responsive comments (such as "good," "that's right," "what?" etc.) that serve to validate of invalidate the ideas and build consensus;  and 3)  diversity of ideas:  everyone within a group contributing ideas and reactions, with similar levels of turn taking among the participants.
NB:  short blasts again

(94)  When we analyzed the large data set we collected, we found that the most important factors for predicting productivity were the overall amount of interaction and the level of engagement (the extent to which everyone is in the loop).  Together these two factors predicted almost one third of the variations in dollar producitivity between groups.

(96)  The sociometric data that my research group and I have gathered from many different organizations show that creative output depends strongly on two processes:  idea discovery (exploration) and the integration of those ideas into new behaviors (engagement).

(101)  When we compared the creativity ratings to the interaction patterns, what Wen [Dong] and I found was that the teams that showed more variations in the shapes of their networks also rated themselves as having more creative output.  In other words, greater oscillation between patterns of exploration and engagement within these social networks correlated with creative productivity, at least as judged by the people in the networks (see the Reality Mining appendix). 

(103)  There is considerable evidence in the scientific literature showing that unconscious cognition can be more effective than conscious cognition for solving complex problems.  Our fast thinking seems to work best when our more logical, slow-thinking minds aren't interfering, such as during sleep or when we are turning an idea over in the back of our minds.  Because fast thinking uses associations rather than logic, it can make intuitive leaps more easily by finding creative analogies.  It can take the experience of a new situation, let it soak in for a while, and then by association produce an array of analogous actions.  In contrast, our attentive, slow-thinking mode provides insight into our actions, helping us detect problems and work through alternate plans of actions.

(106)  In studies of more than two dozen organizations I have found that interaction patterns within them typically account for almost _half_ of all the performance variation between high- and low-performing groups.  This makes the pattern of idea flow the single biggest performance factor that can be shaped by leadership, and yet today there isn't a single organization in the world that keep track of both face-to-face and electronic interaction patterns.

(107)  The most useful visualizations convey the levels of engagement and exploration within the organization, since these are the two main patterns that are characteristic of healthy idea flow.  In personal terms, the notion of engagement means that if the people you talk to also talk to each other, then you are in the loop and in good shape.  We have found that engagement levels predict up to half of the variation in group productivity, independent of content, personality, or other factors.  Exploration is how much the members of a group bring in new ideas from outside;  that in turn predicts both innovation and creative output.  Because innovation is the most important driver of long-term performance, it is important that managers encourage exploration for new ideas by helping employees establish diverse connectons between people.

(114)  A key social intelligence problem is to know when there's enough diversity in the ideas that have been harvested.

(115)  The first method [to solving the echo chamber problem] might be called the bookie solution... a scheme that first asks each person what they thought everyone else was going to say.  This "common knowledge" is then discounted, since it is obviously being counted more than once....

In the wise guys method, we look for individual who can accurately predict how other people will act but whose own behavior is different.  The logic is that if a person can predict other people's actions, then they already know the common knowledge,

A third solution for echo chamber problems is one that I came up with:  To estimate the amount of social influence between people, keep track of the dependencies between people's ideas and their behaviors.  For instance, people who regularly have similar opinions probably have similar sources of information, so opinions by such birds of a feather can't count as independent.

(118)  People can teach themselves to be charismatic connectors - they are made, not born.  The trick is to do what creative people do:  they pay attention to any new idea that comes along, and when something is interesting, they bounce it off other people and see what their thoughts are;  they also try to expand their social networks to include many different types of people, so they get as many different types of ideas as possible.  They use the coffee pot or water cooler to talk to the janitor, the sales guy, and the head of another department.
NB:  steps to good connections

(121-122)  [Red Balloon Challenge]  The purpose was to discover the best strategies for how the Internet and social networking can be used to solve time-critical search problems.  Examples include:  search-and-resuce operations in the aftermath of natural disasters;  hunting down outlaws on the run;  reacting to health threats that need instant attenton;  or rallying supporters to vote in a political campaign.

(125)  Rather, the point is that it's possible to get people to build an organizaton that does the work.  That is why we rewarded people both for finding balloons and for recruiting people to help search.  We rewarded people roughly equally for these two tasks, because building the network was just as important as the actual work of searching.
NB:  is there a reason why a successful political campaign organization does not often (or ever?) become a successful political support organization?

(128)  High-stress situations lead to greater engagement levels almost immediately, as people begin to talk to each other in order to figure out what to do, and then begin the task of forging new patterns of interaction that are better adapted to the situation.  Later, changes in the network of interactions act like social network incentives, as the desire to reduce stress drives the development of new patterns of interaction.

(129)  Our Red Balloon Challenge follow-up interviews suggested that people signed up their friends as a favor to the friends.  That is, recruiting a friend was like sharing a free lottery ticket.  You don't necessarily expect to win, but sharing the ticket strengthens the social ties with your friend.  By sharing, you make it more likely that they will share with you or help you out on another occasion;  you are building trust and social capital.

(130)  As the experiments in Chapter 4 (62-87) showed, strong social ties create the conditions in which peer pressure is the most effective mechanism for promoting cooperation....

Because idea flow creates culture, supports productivity, and enables creativity we should place greater value on professions that enhance idea flow:  teachers, nurses, ministers, and policemen, along with doctors and lawyers who work for charities, as public defenders, or for inner-city hospitals.  Better rewards for work that reinforces our social fabric would allow us to find a better, more sustainable blend between individual ambitions and the health of society.

(132-133)  We found that each of the different social roles that psychologists identify, i.e, protagonist, supporter, attacker, or neutral, uses different social signaling and, as a consequence, different patterns of speaking length, interruption of others, frequency of speaking, etc.

The same is true of the information content:  Someone contributing a new idea speaks differently than someone who is orienting the group to return to a previous idea or someone who is neutral.  As a result, each person's pattern of interaction can be used to identify their functional role - follower, orienteer, giver, seeker, and so on - without listening to the words.

(133)  We are all familiar with many of these social signals, but others are more difficult for us to perceive consciously.  A familiar example is mood contagion.  If one member of a group is happy and bubbly, others will tend to become more positive and excited.  Moreover, this signaling-induced effect on mood serves to lower perceptions of risk within groups and to increas bonding.

Similarly, people tend to mimic each other automatically and unconsciously.  Despite being mostly unconscious, this mimicking behavior has an important effect on its participants:  Its increases how much they empathize with and trust each other.  Not surprisingly, negotiations with lots of mimicry tend to be more successful, no matter which party starts copying the other's gestures first.

(142)  In my expereince, these behavior demographics typically provide predictions of consumer preferences, financial risks, and politcal views that are more than four times as accurate as standard geographic demographics based on zip codes.
NB:  demographic tribes

(145)  The typical city bus system gets only around one mile per gallon per person in fuel efficiency, except at rush hour, but we have to keep those huge buses on the street.

(150)  There are three types of interventions that are naturally suggested by the social physics perspective.

Social mobilization:  As used in the Red Balloon Challenge (see Chapter 7), social mobilization is critical for tasks such as searching for missing kids or escaped criminals, and for finding critical supplies after a disaster such as an earthquake or tornado....

I imagine that the primary use of this type of incentive will be to create new organizations rather than to solve short-term crises.

(151)  Tuning the social network:  A second type of intervention involves tuning the network to provide sufficient idea diversity.  In Chapter 2 I showed that people made much better decisions when they could see those of a wide range of other people, and their outcomes.  The exception to this wisdom of the crowd phenomenon was when the social network was so dense that it formed a sort of echo chamber, so that the same ideas circulated around and around.

To solve the problems of both insufficient diversity and echo chambers, we were able to tune the flows of ideas between people by providing small incentives, or nudges, to individuals.  These caused isolated people who were too interconnected to engage less and to explore outside their current contacts.
NB:  Echo chamber nudges which were....?

(152)  We can also create diversity ratings of news blogs and similar civic media, so that no one interest group can drown out everyone else....

Leveraging social engagement:  This third type of network intervention is helpful in addressing tragedy of the commons situations by using social network incentives to increase engagement around the problems within local communities.

(153)  When people saved energy gift points were given to their buddies.  The social pressure this created caused electricity consumption to drop almost 17 percent - twice the improvement seen in earlier energy conservation campaigns.

(158)  The result of this research is a simple mathematical equation that describes how people tend to have lots of social ties to people who live nearby and increasingly fewer ties with people who are farther and farther away.

(162-163)  Another interesting outcome that emerged when Coco [Krumme] analyzed people's purchasing behaviors was that their patterns of exploration have statistics that are similar to the foraging behavior of animals.  We constantly compare among familiar local alternatives to get the best value for our money, of course, but we also go on explorations to find new sources and experiences. These bursts of shopping have the same character as when animals occasionally choose to hunt in a new area, or search for new food sources.

These bursts of exploration - shopping trips, days off that are spent wandering around the city, weekend getaways - seem to be important in growing the local ecology of cities.  If we looked at cities with greater than average rates of exploration in the credit card data, we found that in subsequent years they had a higher GDP, a larger population, and a greater variety of stores and restaurants.  It makes sense that more exploration, which results in a greater number of interactions between current norms and new ideas, would be a driver of innovative behavior.

(164)  In fact, the relationship between the amount of disposable income and amount of [shopping] exploration is very predictable:  For each additional dollar of disposable income we see a small increase in both the diversity of socialization and the diversity of store visits.

(165)  Wealth allows people to invest more in exploration.  Perhaps this is because good financial status makes people feel more confident and secure in exploring new social opportunities.  Exploration appears to be driven by the human need for social contact and novelty rather than by the search for wealth.

(168)  The best size for such a city can even be calculated:  If within each peer group everyone is a friend of a friend, then the math of social physics indicates that we get maximum engageent for populations of up to roughly one hundred thousand people.  This suggests that the best solution is small-to-medium-sized towns in which everyone is within walking distance of a town center, the stores, the schools, and the clinics.

(178)  New Deal on Data - workable guarantees that the data needed for public goods are readily available while at the same time protecting the citizenry....

A key insight that motivates the creation of a New Deal on Data is that our data are worth more when shared, because they can inform improvements in systems such as public health, transportation, and government.

(179)  One way to enhance idea flow is through the creation of a public data commons, e.g., freely available maps and statistics about matters such as employment and crime rates.  Robust data sharing and anonymization technology can create a data commons that respects citizens' privacy, corporations' competitive interests, and, in addition, provides oversight of government.

(180-181)  New Deal on Data
You have the right to possess data about you.  Regardless of what entity collects the data, the data belongs to you, and you can access your data at any time.  Data collectors thus play a role akin to a bank managing the data on behalf of its customers.

You have the right to full control over the use of your data.  The terms of use must be opt-in and clearly explained in plain language.  If you are not happy with the way a company uses your data, you can remove it, ust as you would close your account wiht a bank that is not providing satisfactory service.

You have the right to dispose of or distribute your data.  You have the option to have data about you destroyed or deployed elsewhere.

(182)  ... my research group and I here at MIT, in partnership with the Institute for Data Driven Design (cofounded by John Clippinger and myself), have helped build openPDS (open Personal Data Store), a consumer version of this type of system, and we are now testiing it with a variety of industry and government partners.  Soon sharing personal data could become as safe and secure as transferring money between banks.  [SWIFT system for data]

(185)  We need social physics, so that we can move from systems based on averages and stereotypes to ones based on the analysis of individual interactions.

(187)  Trento, Italy "open data city" living lab:

(190)  Instead, the power of social physics comes from the fact that almost all of our day-to-day actions are habitual, based mostly on what we have learned from observing the behavior of others.  Because most of our actions are habitual and based on physical, obvservable experiences, i.e., stories heard, actions seen, etc., they can be described as repeated patterns....

Unlike apes or bees, however, we know that humans always have an internal, unobservable thought process, and this will occasionally emerge to defeat our best social physics models.  The consequence is that although we can ue social physics to design living spaces, transportation systems, and governments that are tuned for daily routine and typical human behavior, we will always have to leave room for unusual personal choices.  What is surprising is that the data tell us that deviations from our regular social patterns occur only a few percent of the time.  As a consequence, we have to be very careful to provide for these green shoots of individual innovation and not give in to arguments about cost and only support the most common patterns.  (Please see the Fasct, Slow and Free Will appendix for more detail on this subject.)
NB:  And how do we leave open that space for "unusual personal choices"?

(191)  Because markets and classes are averages or stereotypes, reasoning that uses these terms leads inevitably to considering all the people in the market or class to be the same.  Adam Smith's markets end up being as dehumanizing as Karl Marx's classes.
NB:  But at least they're zip code categorized and sociometric monitored.

(194-195)  As Adam Smith explained:
They are led by an invisible hand to make nearly the same distribution of the necessaries of life, which would have been made, had the earth been divided into equal portions among all its inhabitants, and thus without intending it, without knowing it, advance the interest of the society, and afford means to the multiplication of the species.
NB:  Note that the effect of the invisible hand is "equal portions among all its inhabitants."

(195)  As we have seen in previous chapters of this book, people cooperate with each other to establish social norms.  These norms are what we call culture.  In fact, the main source of competition in society may not be among individuals but rather among cooperating groups of peers.  
NB:  Peer group politics and social/cultural organization, voluntary association of Kropotkin

...Classes versus peer groups.  Peer groups with shared norms are different from the traditional idea of class, because they are not defined only by standard features such as income, age, or gender (e.g., traditional demographics), their skills and education (per Max Weber) or their relationship to the means of production (per Karl Marx).  Instead, group members are peers in the context of a particular situation.
NB:  Situationism and the spectacular, detournement as the individual exception

(201)  In markets, one must usually rely on having access to an accurate reputation mechanism that rates all the participants, or to an outside referee to enforce the rules.

As a consequence of greater stability and trust, the equations showed that the dynamics of exchange networks intrinsically cause them to evolve to be fair, and the surplus generated by the relationship is equally divided between the individuals involved.  And as a consequence of more fairness, more stability, and greater levels of trust, exchange networks are also more cooperative, robust, and resilient to outside shocks.  That is a good recipe for building a society that will survive....

Our results strongly suggest that the invisible hand is more due to the trust, cooperation, and robustness properties of the person-to-person network of exchanges than it is due to any magic in the workings of the market.  If we want to have a fair, stable society, we need to look to the network of exchanges between people, and not to market competion.
NB:  Mutual Aid, Kropotkin again

(203)  Social physics suggests that the first step [to design a society better suited to human nature] is to focus on the flow of ideas rather than the flow of wealth, since the flow of ideas is the source of both cultural norms and innovation....

I beleive that there are three design criteria for our emerging hypernetworked societies:  social efficiency, operational efficiency, and resilience...

Social efficiency:  In the language of economics, social efficiency refers to the optimal distribution of resources throughout society - a process that, as Adam Smith famously described, occurs through the workings of an invisible hand.

(206)  We can adapt this trust network technology for everyday, person-to-person interactions and so create an exchange network society instead of having to always resort to open-market mechanisms.  Just as the banks sign up to the SWIFT network so that they can safely interact with other banks, individuals could sign up for trust networks so that they could safely interact with other inidividuals or companies, secure in the knowledge that their personal data would be used only in the ways they agreed to.
NB:  Sounds Utopian to me.  Is SWIFT that secure?  Can we have control over our own personal data when government(s) and corporations already possess it?

(207)  The open market and strong personal control models are but two approaches to social efficiency.  Blends of these two models are also possible.  For instance, we could create a limited data commons that is free and open to the public but yields much a greater benefit when combined with personal, private data.

(208)  One step toward achieving this goal of operational efficiency is to create a public data commons that lets us see the big picture in real time.  Not every piece of data needs to be part of this god's-eye view of the world, however.  The commons generally needs only aggregate anonymous data that are relevant to the tasks at hand.

(209)  In other words, exploration for good ideas would happen in the digital realm, but engagement for consensus would primarily happen face-to-face.  By iterating between exploration and engagement among and between the different groups of buddies, we might be able to scale up the ancient decision-making processes that we see in social species ranging from bees to apes and that are still necessary to win consensus among fast- and slow-thinking humans.

(211)  All of this suggests that in order to maintain the robustness of the entire society, we need a diverse set of competing social systems, each with its own way of doing things, together with fast methods of spreading them when required.  This sort of robustness is exactly what we achieve when we tune a system for the best idea flow....

As a consequence, these organizations are now beginning to train everyone in the system in the principles of distributed leadership.  When decision making falls to those best situated to make the decision rather than those with the highest rank, the resulting organization is far more robust and resistant to disruption.  
NB:  distributed leadership, see Market Basket

(212)  On May 1, 2013, we saw the public unveiling of what is perhaps the world's first true big data commons, with ninety research organizations from around the world reporting hundreds of resuts from their analysis of data describing the mobility and call patterns of the citizens of the entire African country of Ivory Coast.
NB:  Trento, Italy, Ivory Coast, Iceland DNA.... big data aggregations 

(214)  D4D research at

(215)  Throughout this book I have argued that we need to think about society as a network of individual interactions rather than as markets or classes.

MIT Media Lab's City Science initiative

(226)  The framework that PhD and postdoctoral students Yves-Alexandre de Montjoye, Erez Shmueli, Samuel S. Wang, and I have developed, called openPDS, uses the World Economic Forum definition of "ownership" of data that I proposed as the New Deal on Data, i.e., the rights of possession, use, and disposal.  In addition, it follows the policies of the National Strategy for Trusted Identities in Cyberspace (NSTIC), the US Department of Commerce green paper, and the US International Strategy for Cyberspace.  The framework, openPDS, is also strongly aligned with the European Commission's 2013 reform of the data protection rules.  These recommendations, proposed reforms, and regulations all recognize the increasing need for personal data to be under the control of the individual, as he is the one who can best judge the balance between the associated risks and rewards.
NB:  As if the individual he, she, it, them..., can figure out what the risks, rewards, and balance just might be

(229)  Under the openPDS/dynamic privacy mechaism, a piece of code would be installed inside the user's PDS.  The installed code would use the sensitive location and accelerometer data to compute the relevant answer within the safe environment of the PDS.  That answer alone would be sent to the remote server.

Combined with data ownershp, this simple idea allows users to benefit from a personalized experience without having to share raw data, such as raw accelerometer readings or GPS coordinates.  In other words, the code is shared, not the data.

(232)  My observations about how mental health could be assessed by the honest signals of behavior that can be measured by the sensors in mobile phones and about the value of users sharing these signals with their friends inspired the inclusion of the openPDS and funf systems into DARPA's Detection and Computational Analyis of Psychological Signals program (DCAPS).

In DCAPS, smartphones provide a pervasive platform that enables continuous sensing and monitoring in natural settings while minimizing the effort burden placed on veterans.  These devices can record the user's tone of voice, frequency of interactions with other people, general levels of movement and activity, as well as other subtle and honest social signals.  In fact, a sizable proportion of the current DSM-IV symptoms that are used to diagnose various psychological health conditions focus on changes in behavior that are precisely the types of measurements that can be effectively captured by smartphone interactions (DSM-IV, soon to be updated as DSM-V, is the most widely accepted mental health diagnostic manual.

(243)  The influence model is built on an explicit abstract definition of influence:  An entity's state is affected by its network neighbors' states and changes accordingly.  Each entity in the network has a specifically defined strength of influence over every other entity and, equivalently, each relationship can be weighted according to this strength.

Our experimental results in the Friends and Family study, the Social Evolution study, and elsewhere have shown that the amount of experience to peers who have already adopted a particular behavior can provide a good estimate of the probability that an individual will adopt that behavior, at least for the behaviors where the actions and outcomes are visible.  This is why social physics works.  Without these sorts of strong social learning and social pressure effects we would instead have to model the detailed thought patterns of each individual.

(257)  For the slow mode, often a single exposure to a new idea or a new piece of information will be enough to change behavior.  An example of this simple contagion model is the spreading of a new fact (that road is under construction) or a rumor ("she did what!?").  This same model is also typical of the spread of disease through a population.  Infectious ideas, like infectous diseases, travel along social ties.  This is simulated by a cascade of state transitions within the influence model of the social network.

We know, however, that much of our behavior is due to fast-thinking habits.  Here a simple contagion model does not do a good job of capturing changes in many habitual behaviors.  For the fast mode of thinking we usually need exposure to several examples in which someone else successfully used the new behavior before are willign to try it for ourselves.  In these cases, a second, complex contagion model is a better descrition of the adoption of habitual, fast-thinking behaviors.
NB:  Fast-thinking is slow on the uptake?

(260-261)  We proposed a new model for networked societies and provided a new set of mechanisms for policy makers to address the problem of externalities.

These mechanisms are suitable for a networked society in which externalities are global but interactions are local.  Rather than the individual internalizing the externalities via Pigouvian taxation or subsidies, we localize them to one's peers in a social network, thus leveraging the power of peer pressure.  When the externalities are localized, cooperation is achieved locally, and thus global cooperation is also observed.  Therefore, the social mechanisms incentivize peers (via taxation or subsidy to them) to exert pressure (positive or negative) on an individual, thus causing a drop in negative externality (or increase in positive externality).

We show that under certain very general conditions, this approach can yield a socially efficient and better outcome at a lower budget than the one for the Pigouvian subsidies.

Our main insight is that by targeting the individual's peers, peer pressure cna amplify the desired effect of a reward on the target individual.  In contrast with the Pigouvian approach, which focuses on the individual causing the externality, our mechanism focuses on their peers in the social network.  The idea is to incentivize agent A's peers to exert (positive or negative) pressure on A.

By targeting the individual's peers, peer pressure can amplify the desried effect on the target individual.  That is, under certain conditions, the resulting reduction in negative externality can be larger, given an identical subsidy budget.


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