The free market model for society has its roots in 18th-century notions of natural law: the idea that humans are self-interested and self-commanded and that they relentlessly seek to gain from the exchange of goods, assistance and favors in all social transactions. Open competition for such theoretical individuals is a natural way of life, and if all the costs (e.g., pollution, waste) are taken into account, then the dynamics of open competition can result in an efficient society. 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.
The power of markets to distribute resources efficiently—together with the assumption that humans are relentless competitors—is the bedrock of most modern societies. It works well for stocks and commodities, and reasonably well for wages and housing. The contemporary trend is to apply market thinking to all sectors of society. But does this 18th-century understanding of human nature truly form a good model for all of these sectors of our modern society? I think not.
The Real World Isn’t A Market, It Is An Exchange Network
Perhaps the major flaw in the free-market view of human nature is that people are not simply self-interested, self-commanded individuals. What we are interested in, and our command mechanism itself, is overwhelmingly determined by social norms created by interactions with other people. Modern science now understands that cooperation is just as important and just as prevalent in human society as competition. Our friends watch our backs in sports and business teammates cooperate to win against other teams, and everywhere people support family, children and the elderly. In fact, the main source of competition in society may not be among individuals but rather among cooperating groups of peers
Moreover, recent economics research has shown that the basic assumption within classic market thinking—that there are many sellers and buyers that can be substituted for each other easily—does not apply even to economies such as that of the U.S. Instead, we should think of the economy as an exchange network: a complex web of specific exchange relationships. In fact the idea of a market, in which it is imagined that all the participants can see and compete evenly with everyone else, is almost always an oversimplification. In reality some people have better connections, some people know more than others, and some purchases are more difficult than others, due to distance, timing, or other secondary considerations.
Just as financial markets and the invisible hand are an oversimplification, so is the idea that political competition produces a “market of ideas” that somehow results in good government. Political or economic labels such as “bourgeoisie,” “working class,” “libertarian” are often inaccurate stereotypes of groups of people who actually have widely varying individual characteristics and desires. As a result, reasoning about society in terms of classes or parties is imprecise and can lead to mistaken overgeneralizations. In the real world, a group of people develops deeply similar norms only when they have both strong interactions and they recognize each other as peers.
Modern Natural Law: Exchanges, Not Markets
Why is the real world made up of exchange networks rather than markets? In a word: trust.
Relationships in an exchange network quickly become stable (we go back again and again to the person who gives us the best deal), and with stability comes trust, i.e., the expectation of a continued valuable relationship. This is different than in a typical market, where a buyer may deal with a different seller every day as prices fluctuate. In exchange networks, buyers and sellers can more easily build up the trust that makes society resilient in times of great stress. 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.
This insight comes from what I call social physics: using game theory to mathematically examine the properties of human societies, such as comparing a society based on exchange networks with one based on markets. For instance, the equations from the thesis of my PhD student Ankur Mani show that the dynamics of exchange networks structurally favor fair outcomes, with the surplus generated by the relationship equally divided between the individuals involved. As a consequence of fairness, there is more stability and greater levels of trust. Exchange networks are also more cooperative, robust and resilient to outside shocks. Social physics provides a good recipe for building a society that will survive.
Adam Smith thought that the invisible hand was due to a market mechanism that was constrained by peer pressure within the community. In the succeeding centuries we have tended to emphasize the market mechanism and forgotten the importance of the peer pressure part of his idea. Social physics strongly suggests 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 competition.
How can we move from a market-centric to a human-centric society?
So how does this idea of an exchange society apply to modern life? Today we have mass media to spread information, and our much higher levels of mobility allow us to interact with many more people. Information is so universally available and our social networks are extremely broad. Do these facts mean that we have transitioned from an exchange society to a market society? I think the answer is no.
Even though we now have much greater breadth and rate of interaction, our habits still depend mostly on interactions with a few trusted sources—those people whom we interact with frequently—and for each person the number of such trusted individuals remains quite small. In fact, the evidence is that the number of trusted peers that we have today is pretty much the same as it was tens of thousands of years ago.
This small, relatively stable network of trusted peers still dominates our habits of eating, spending, entertainment, political behavior—and technology adoption. Similarly, face-to-face social ties drive output in companies and accounts for the productivity and creative output of the largest cities. This means that the spread of new behaviors throughout society is still dominated by local, person-to-person exchanges even in the presence of modern digital media and modern transportation. We still live in an exchange society, albeit one with much greater levels of exploration.
How can we use these insights about human nature to design a society better suited to human nature? Economic theory still provides a useful template for shaping our society, but we have to begin with a more accurate notion of human nature. Because we are not just economic creatures, our vision of a human-centric society must include a broader range of human motivations – such as curiosity, trust, and social pressure.
Social physics suggests that the first step is to focus on the flow of ideas rather than on the flow of wealth, since the flow of ideas is the source of both cultural norms and innovation. A focus on improving idea flow, rather than financial flows, will allow individuals to make better decisions and our society to develop more useful behavioral norms. A key insight from social p
sics is that it is critical that the flow and exchange of ideas be inclusive, because insufficiently diverse idea flow leads to rigid and insular societies, and insular communities (including the society of Adam Smith’s time) often inflict terrible damage on weaker communities with whom they share resources.
Idea flow is the spreading of ideas, whether by example or story, through a social network—be it a company, a family, or a city. Being part of this flow of ideas allows people to learn new behaviors without the dangers or risks of individual experimentation. People can also acquire large integrated patterns of behavior without having to form them gradually through laborious experimentation.
In fact, humans rely so much on our ability to learn from the ideas that surround us that some psychologists refer to us as Homo imitans. The collective intelligence of a community comes from idea flow; we learn from the ideas that surround us, and others learn from us. Over time, a community with members who actively engage with each other creates a group with shared, integrated habits and beliefs. Idea flow depends upon social learning, and indeed, this is the basis of social physics: Our behavior can be predicted from our exposure to the example behaviors of other people.
Because “idea flow” takes into account the variables of a social network structure, the strength of social influence between people, and individual susceptibilities to new ideas, it also serves another vital role: It gives a reliable, mathematical prediction of how changing any of these variables will change the performance of all the people in the network. Thus, the mathematical framework of idea flow allows us to tune social networks in order to make better decisions and achieve better results.
For example, what can be done when the flow of ideas becomes either too sparse and slow or too dense and fast? How does the “exploration” process—using social networks to search for ideas and then winnow them down to just a few good ones—result in a harvest of ideas that produces good decisions? Is this just a random recombination of ideas with little contribution from our individual intelligences, or are there strategies that are critical to successful exploration? The mathematics of social physics lets us answer these questions.
How Can We Harvest the Best Ideas?
The exploration process is fundamentally a search for new ideas within one’s social network, so to understand how to find the best ideas I launched two big data studies that contain almost two million hours of interaction data covering everyone within two communities for a total of over two years. These studies allowed me to build quantitative, predictive models of how we humans find and incorporate new ideas into our decisions.
The studies paint a picture of humans as sailors. We all sail in a stream of ideas, ideas that are the examples and stories of the peers who surround us; exposure to this stream shapes our habits and beliefs. We can resist the flow if we try, and even choose to row to another stream, but most of our behavior is shaped by the ideas we are exposed to. The idea flow within these streams binds us together into a sort of collective intelligence, one comprised of the shared learning of our peers.
The continual exploratory behavior of humans is a quick learning process that is guided by apparent popularity among peers. In contrast, adoption of habits and preferences is a slow process that requires repeated exposure and perceptual validation within a community of peers. Our social world consists of the rush and excitement of new ideas harvested through exploration, and then the quieter and slower process of engaging with peers in order to winnow through those ideas, to determine which should be converted into personal habits and social norms.
I think of organizations as a group of people sailing in a stream of ideas. Sometimes they are sailing in swift, clear streams where the ideas are abundant, but sometimes they are in stagnant pools or terrifying whirlpools. At other times, one person’s idea stream forks off, splitting it apart from other people and taking them in a new direction. To me, this is the real story of community and culture; the rest is just surface appearance and illusion.
When the flow of ideas incorporates a constant stream of outside ideas as well, then the individuals in the community make better decisions than they could on their own. To bring new ideas into a work group or community, however, there are three main things to remember:
Social learning is critical. Copying other people’s successes, when combined with individual learning, is dramatically better than individual learning alone. When your individual information is unclear, rely more on social learning; when your individual information is strong, rely less on social learning.
One disturbing implication of these findings is that our hyperconnected 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. 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.)
Contrarians are important. When people are behaving independently of their social learning, it is likely that they have independent information and that they believe in that information enough to fight the effects of social influence. Find as many of these “wise guys” as possible and learn from them.
Such contrarians sometimes have the best ideas, but sometimes they are just oddballs. How can you know which is which? If you can find many such independent thinkers and discover that there is a consensus among a large subset of them, then a really, really good strategy is to follow the “contrarian consensus.”
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. To 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. If the so-called common sense from social learning is just an overconfident version of what isolated people think, then you are likely in a groupthink or echo chamber situation. In this case, a surprisingly good strategy is to bet against the common sense.
But it is also important to diversify by considering more than one strategy at a time because, as our environment changes, the old strategies stop working and new strategies take the lead. Therefore, it is not the strategies that have been most successful that you want to follow; it is really the strategies that will be most successful that you have to find. And since predicting the future is hard, diversification of social learning is important.
In summary, people act like idea-processing machines combining individual thinking and social learning from the experiences of others. Success depends greatly on the quality of your exploration and that, in turn, relies on the diversity and independence of your information and idea sources. By harvesting from the parts of our social network that touch other streams, that is, by crossing what sociologist Ron Burt called the “structural holes” within the fabric of society, we can create innovation. When we choose to dip into a different stream, we bring up new habits and beliefs, and it is these innovations that help us make better decisions, and help our community to thrive.
Alex Pentland directs M.I.T.’s Human Dynamics Laboratory and the
M.I.T. Media Lab Entrepreneurship Program, and co-leads the World Economic Forum Big Data and Personal Data Initiatives. His research group and entrepreneurship program have spun off more than thirty companies to date. In 2012 Forbes named Pentland one of the seven most powerful data scientists in the world.
This chapter is excerpted from Social Physics: How Good Ideas Spread – The Lessons from a New Science (Penguin Press, 2014) with permission.
 Smith, A., Theory of Moral Sentiments (First Edition, 1759; Penguin Classics, 2009).
 Acemoglu, D., V. Carvalho, A. Ozdaglar, and A. Tahbaz-Salehi, “The Network Origins of Aggregate Fluctuations,” Econometrica 80 (5), pp. 1977–2016 (2012).
 Mani, A., A. Pentland, and A. Ozdalgar, “Existence of Stable Exclusive Bilateral Exchanges in Networks” (2010). See http://hd.media.mit.edu/tech-reports/TR-659.pdf.
 Dunbar, R., “Neocortex Size as a Constraint on Group Size in Primates,” Journal of Human Evolution 20(6), pp. 469-493 (1992).