5.9: Diffusion Summary

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來自 Stanford University 的課程

Social and Economic Networks: Models and Analysis

341 個評分

Stanford University

Social and Economic Networks: Models and Analysis

341 個評分

Learn how to model social and economic networks and their impact on human behavior. How do networks form, why do they exhibit certain patterns, and how does their structure impact diffusion, learning, and other behaviors? We will bring together models and techniques from economics, sociology, math, physics, statistics and computer science to answer these questions. The course begins with some empirical background on social and economic networks, and an overview of concepts used to describe and measure networks. Next, we will cover a set of models of how networks form, including random network models as well as strategic formation models, and some hybrids. We will then discuss a series of models of how networks impact behavior, including contagion, diffusion, learning, and peer influences. You can find a more detailed syllabus here: http://web.stanford.edu/~jacksonm/Networks-Online-Syllabus.pdf You can find a short introductory videao here: http://web.stanford.edu/~jacksonm/Intro_Networks.mp4

從本節課中

Diffusion on Networks

Empirical Background, The Bass Model, Random Network Models of Contagion, The SIS model, Fitting a Simulated Model to Data.

5.1: Diffusion12:57

5.2: Bass Model12:21

5.3: Diffusion on Random Networks 9:08

5.4: Giant Component Poisson Case 15:09

5.5: SIS Model17:50

5.6: Solving the SIS Model 9:17

5.7: Solving the SIS Model - Ordering (Optional/Advanced 24:16)24:16

5.8a: Fitting a Diffusion Model to Data (Optional/Advanced 22:47)22:45

5.8b: Application: Financial Contagions (Optional/Advanced 12:47)12:48

5.8c: Application: Financial Contagions - Simulations (Optional/Advanced 13:41)13:42

5.9: Diffusion Summary 3:45

5.10: Week 5 Wrap4:25

與講師見面

Matthew O. Jackson
Professor
Economics

Hi, folks. So, let's just wrap up our discussion and

summarize what we've learned from our diffusion models.

So, in terms of you know, basic lessons, the first thing we saw is that there are

phase transitions, sort of, that can be very abrupt in terms of whether or not

diffusion occurs or not. And so, you know, when we end up with

very low densities of interactions we basically get no contagion.

And we move to middle density and we begin to get a lot of diffusion and very

rapidly in, in the model we looked at when we were computing things for

something like the diffusion model of, on an Erdos–Renyi random graph or as a

simple one word and component sized calculations going from an average degree

of just one to three led us to almost no diffusion to almost complete diffusion.

And then once we get to these higher densities we get sure infection, and, and

essentially all that are susceptible to being infected.

And decree, degree correlates with who ends up being infected both, you know,

in, in most of the models you've looked at having higher rates of interaction are

going to lead to higher infection rates. So, we've seen these kind of, of basic

points. And, you know, more generally, in terms

of the modeling diffusion modeling's going to be important.

It's, it's going to be important in a lot of applications that go beyond just

something like the flu, to model both information and pure effects.

We saw that in the last analysis we just did.

so you want to keep, you know, track of things that are not just simply a infe,

infection models but people communicate, and that distinguishes such models from

epidemiology. we need a lot more models and studies

that identify the fine deals, details of what matters in interactions.

So when we think about diffusion, am I, am I just processing information?

Am I learning from other people? do I care about how they, what, what

they're doing? Do is there peer pressure?

So, you know, if we're looking at something even, even something as simple

as the flu there's decisions. Do I get vaccinated?

Does it depend on how many of my friends are getting vaccinated.

do I not take a trip if there's an epidemic of flu in the city that I might

be going to. There's all kinds of things that really

enrich the setting and make the diffusion models much more complicated.

And these are areas where epidemiologists are starting to do studies as we just

saw. I mean there's studies in ecnomics.

there's studies in different areas which are, which, you know, are enriching these

things. And a lot of that's going to be done by,

once we complicate these models, we can do these things by simulation.

Take the simulated diffusion models and try and match them to data and actually

learn quite a bit that way. So, network structure matters.

sometimes it's retractable, otherwise simulations go a long ways towards making

predictions. we can experiment with, with changes in

network structure and begin to see, you know, how these things affect overall

behavior. So, in terms of, of outlines, what we've

done is we've gone through a lot of the course.

we've talked about networks influencing behavior.

And now, we're getting to sort of the home stretch where we're going to talk

about enriching things. So, diffusion so far, we've treated until

the last, video just a, we just talked about we were treating the process of

diffusion as being fairly mechanical. And now we'll more explicitly try and

model learning and model effects. And then we'll come to modeling games on

network where we actually have strategic interactions between individuals, which

will sort of be an enrichment of the last application we saw and trying to

understand those. So, next up is learning and then we'll go

to Games on Networks and understand interactions and behavior.

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