6.7 Course Conclusions: The Network Dynamics of Social Behavior

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Network Dynamics of Social Behavior

72 classificações

University of Pennsylvania

Network Dynamics of Social Behavior

72 classificações

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

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Problem Solving in Networks

How can you best organize a team to produce innovative solutions to complex problems? If people on the team can’t communicate, then they can’t share strategies, and won’t learn from each other’s success. But if they communicate too much, they’ll cluster around just a few ideas, and won’t explore the entire problem space. This week introduces an agent-based model of problem solving and shows how network structure can be used to navigate this classic exploration/exploitation trade-off.

6.1 Exploration and Exploitation: Networks of Innovation2:30

6.2 Modeling Complex Problems7:30

6.3 Modeling Problem-Solving in Teams2:41

6.4 Thinking about Models: Problem-Solving in the Real World1:18

6.5 Using NetLogo: Complex Problem Solving in Networks9:51

6.6 Conclusions: The Two Pizza Rule1:56

6.7 Course Conclusions: The Network Dynamics of Social Behavior4:45

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Damon Centola
Associate Professor
Annenberg School for Communication

So there are several take-home lessons from the modules that we've looked at.

We started off with Schelling and looked at the basic dynamics of segregation.

One of the interesting implications there was

individual choices that didn't have any preference for segregation,

nevertheless, could lead to segregation.

So there are sometimes unintended consequences of individual actions,

and it's very hard for any individual to

anticipate ahead of time what those consequences may be.

Furthermore, for policymakers, it's hard to reason from individual choices to

collective outcomes without some way

of studying the aggregation dynamics that produce them.

So building on this, we looked at the small world model,

and what that showed was that when we look at the aggregation dynamics

going from individual behaviors to collective outcomes,

the interaction structure makes a difference in terms of how spreading works.

So we saw there that long distance ties or weak ties in

a network could dramatically accelerate the rate of diffusion.

What's interesting there is that for any given individual network,

it will be undetectable that the structure of the network could change.

Nevertheless, these imperceptible differences and they connect in

the population translate into very large,

very significant effects for the dynamics of diffusion.

Then, looking even deeper into the dynamics of behavior change and

social network structure with the Complex Contagions.

And there, what we saw is that although we had

some very clear intuitions in a small worlds model

and the weak ties theory about how long distance ties could accelerate diffusion,

there's a really big difference between talking about information and

disease diffusion versus behavior diffusion.

We looked at behavior spreading.

In fact, what we saw was that the networks that could accelerate

disease actually slowed down the diffusion of behavior.

And so there's a subtle interplay or interaction between a type of contagion that

spreads and the complex dynamics of aggregation that lead to population change.

Building on this, we look to the Emperor's dilemma.

So this was a situation where people relied on social reinforcement to change behavior,

but the behavior they were changing to was something they explicitly didn't want.

And what we saw was that these local dynamics of reinforcement could

transform a normal citizens in a society into

false enforcers who could create a cascade of

social enforcement that would carry an unpopular norm throughout a population.

What we saw there was that a big lie is easier to spread than a small lie.

The reason for this is that a big lie requires some demonstration of sincerity.

A small lie can be complied with without any additional demonstrations,

but a big lie is harder to believe.

And so in order for people to really demonstrate their compliance with the norm,

they have to go an extra step and enforce the norm,

and this is what generates these large scale cascades.

It also highlights the danger of these kinds of

dynamics for getting really unpredictable and

unlikely behaviors to spread to entire populations.

So when we think about coordination at a population scale,

we can also think about it in terms of the name game.

So that's a situation where people work

interacting locally and trying to coordinate with each other,

normally, any arbitrary convention,

but the space of options was unbounded.

Unlike the Emperor's dilemma where a convention was seeded by a few rogues,

this is a situation where people could choose any option they wanted.

All they wanted to do was to figure out a coordination norm with their neighbors.

Through this process of local interactions,

the network structure of how they interacted mediated

how quickly and how effectively the population would reach convergence.

And in the final module, we looked at problem solving,

and took the idea of rapid coordination and highly efficient,

highly connected networks, and thought about what it meant for

the quality of the coordination on the people achieved.

In particular, when people are trying to solve

difficult problems and they coordinate very effectively,

does that lead to better solutions or worse solutions?

And what we saw is that whereas exploitation can be very effective

for moving populations to coordinate very quickly on a solution,

this is only beneficial if the population is working

on a simple problem where there's one obvious solution.

If the problem space is more complex and it's a difficult to explore,

then actually having less efficient communication networks

can encourage exploration and allow

populations over time to move

towards a much better solution than could have been discovered otherwise.

Overall, when we think about all these different kinds of problems and how they tied

together from the unintended consequences of individuals

interacting locally to the dynamics of norms spreading to

the network structures that accelerate or slow down

the spread of norms to the kinds of solutions,

the quality of the decisions that populations ultimately make,

we can also think about what all of these kinds of models

mean for studying collective behavior empirically.

So one of the things we'll talk about in

a different course is how to think about these things experimentally,

how to study the collective dynamics of behavior change in

large populations using humans instead of agent actors.

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