3.6 Conclusion: The Spread of Behavior in a Complex World

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From the course by University of Pennsylvania

Network Dynamics of Social Behavior

55 оценки

University of Pennsylvania

Network Dynamics of Social Behavior

55 оценки

How do revolutions emerge without anyone expecting them? How did social norms about same sex marriage change more rapidly than anyone anticipated? Why do some social innovations take off with relative ease, while others struggle for years without spreading? More generally, what are the forces that control the process of social evolution –from the fashions that we wear, to our beliefs about religious tolerance, to our ideas about the process of scientific discovery and the best ways to manage complex research organizations? The social world is complex and full of surprises. Our experiences and intuitions about the social world as individuals are often quite different from the behaviors that we observe emerging in large societies. Even minor changes to the structure of a social network - changes that are unobservable to individuals within those networks - can lead to radical shifts in the spread of new ideas and behaviors through a population. These “invisible” mathematical properties of social networks have powerful implications for the ways that teams solve problems, the social norms that are likely to emerge, and even the very future of our society. This course condenses the last decade of cutting-edge research on these topics into six modules. Each module provides an in-depth look at a particular research puzzle -with a focus on agent-based models and network theories of social change -and provides an interactive computational model for you try out and to use for making your own explorations! Learning objectives - after this course, students will be able to... - explain how computer models are used to study challenging social problems - describe how networks are used to represent the structure of social relationships - show how individual actions can lead to unintended collective behaviors - provide concrete examples of how social networks can influence social change - discuss how diffusion processes can explain the growth social movements, changes in cultural norms, and the success of team problem solving

From the lesson

Complex Contagions and the Weakness of Long Ties

This week will begin by discussing the limitations of simple disease-like models of social contagion, introducing the idea of “complex contagions” to model people’s frequent need for social reinforcement before spreading a piece of information or behavior. While simple contagions always spread faster as networks get smaller, this week will demonstrate the paradoxical nature of complex contagions, which can spread slower (or not at all!) in the smallest networks.

3.1 Social Contagions: Beyond Information Diffusion1:23

3.2 From Simple to Complex Contagions4:13

3.3 How to Model Complex Contagions4:25

3.4 Threshold Models in Networks9:55

3.5 Using NetLogo: Complex Contagions in Small World Networks2:26

3.6 Conclusion: The Spread of Behavior in a Complex World3:28

Meet the Instructors

Damon Centola
Associate Professor
Annenberg School for Communication

So why does all this matter?

It matters because there are a number of implications of how disease spread that

turn out not to generalize to situations where behaviors spread.

So for instance, when we think about the spread of an innovation.

Innovation spread much more effectively in a small world, or

in a world with weak ties.

When those innovations are simple or

easy to adopt when they're costs less there's not a lot of risk involved.

However, when innovations are risky all of a sudden the networks that would help

the simple innovation spread slow down the spread of a difficult innovation.

More importantly, when we think about collective action or

recruitment to a new kind of social movement.

If it's an easy social movement, something like a neighborhood trash cleanup,

then people just really need to know where to go and when.

So it's just a matter of information diffusion and in that case,

small worlds would be better for organizing collective action.

However, if it's recruitment to something like Freedom Summer where there's a lot of

cost involved and it's extremely risky.

And that's a situation where small world networks will actually

slow down diffusion.

First of all because people need multiple sources of reinforcement to be convinced

to adopt.

But second of all because the riskier the innovation is the more the information

flow is a problem.

Particularly if you're trying to organize a revolution against,

say an oppressive government.

Then you actually don't want a lot of information flow,

you want it to be secret that you're doing this sort of thing.

And so you'd prefer networks that were more closed to a neighborhood effect that

ensure that people would trust the people they talk to.

And that the signals that came in were reliable.

And so these are all situations where complexity changes the diffusion dynamics

of a contagion.

Making it more likely to spread through clustered networks than through small

world networks.

One of the interesting things to think about from a modeling point of view when

we think about all these implications is what happens when the networks become more

realistic.

Or the assumptions move more from a modeling space into the real world.

Well, one of the things we can do in a model is explore some of these

assumptions.

So right now the models we've looked at have had thresholds of one or

thresholds of two.

But we can ask about threshold heterogeneity.

What if there's a distribution of thresholds?

Or what if thresholds are probabilistic so

people can change their decisions over time?

Moreover, instead of just looking at networks where everyone has the same

number of ties and ties are rewired.

What if there are hubs in the network?

Some people who are extremely well connected.

And other people have only a few ties.

Well, hubs are extremely effective for spreading information and

spreading disease.

As soon as a hub gets infected or gets a piece of information everyone the hub is

connected to gets the piece of information or gets infected.

Is it the same for a complex contagion?

Finally, we've been looking at adding ties as rewiring.

But what if you actually added ties to networks?

So increased the density.

There's more and more links in the network, and you introduced weak ties, and

made world small that way.

How would all of these differences affect the dynamics?

One of the useful things about modeling in this way is that we can ask all of these

questions, in fact we did.

It turns out that the threshold heterogeneity actually makes

the results stronger.

It turns out that when you put hubs into the network it actually makes it

harder to spread complex contagions.

And it turns out when you add ties to the network, for much of the same reason as

hubs, it actually also make it harder to spread complex contagions.

So in conclusion we can use the models to explore all these different robustness

assumptions.

And look at the variety of situations where changing the model and

introducing features that make it more realistic don't affect the diffusion

of complex contagions.

Interestingly find the very few cases where it does.

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