12.4

Loading...

Do curso por University of Pennsylvania

Social Norms, Social Change II

142 classificações

University of Pennsylvania

Social Norms, Social Change II

142 classificações

This course is Part 2 of the Social Norms, Social Change series. In this course, we will examine social change, the tools we may use to enact change, and put into practice all we have learned in Part 1. See Social Norms, Social Change Part I at this link: https://coursera.org/learn/norms This course covers scripts and schemas, the cognitive structures in which social expectations are embedded, and their relationship with social norms. The course then examines the essentials of norm abandonment, including the relations between personal beliefs and social expectations. We will also evaluate existing intervention strategies, including legal reforms, information campaigns, economic incentives, and group deliberations. Finally, we look at a variety of tools policy makers may use to effect change, highlight the role of trendsetters in social change, and explore the conditions under which they can be successful. The course is a joint Penn-UNICEF project." Please see the following link for a 30% discount on the book that accompanies this course: https://global.oup.com/academic/product/9780190622053/?cc=us&lang=en&promocode=AAFLYG6

Na lição

Trendsetters and Social Change

This module covers trendsetters and their relations to social change. Who are trendsetters? What are their characteristics? How can we identify them? And how can we use them to bring about positive social change. This module also discusses the role of soap operas and edutainment in bringing about social change, how fictional characters and groups can act as trendsetters, and comparative advantages of edutainment interventions over traditional interventions.

Introduction1:18

Conheça os instrutores

Cristina Bicchieri
S. J. Patterson Harvie Professor of Social Thought and Comparative Ethics
Department of Philosophy

[MUSIC]

In the previous video, I examined how the combination of norm sensitivity,

risk sensitivity, and risk perception may lead people to deviate from a social norm.

Now, I want to look more closely at how

all of the important elements interact to jointly produce a sight of change.

Transactors live and act within a network of social relations and

the structure of these networks may determine

whether their deviant behavior will or will not produce social change.

In every population there are different types of individuals

with different psychological traits.

Specifically, individuals will have differing levels of risk sensitivity,

which is a stable trait.

Norm sensitivity and risk perception.

And these levels will likely vary for the same individual from one norm to another.

A highly autonomous, sophistications, risk insensitive

individual may not be a transactor with respect to particular norm,

due to a high norm of sensitivity or high risk perception.

Someone who's a trend setter in abandoned in tri marriage may not be one

in combating corruption.

It would therefore be wrong to take generically of norm entrepreneurs.

It is only with respect to specific norm

that we can distinguish a conformist from a first mover.

Depending on the distribution of risk sensitivity, risk perception,

and norm sensitivity in a population, change may or may not occur.

People will have different thresholds for

change given their unique combination of characteristics or types.

Observing 10% of people in one reference network join a protest may be enough for

one individual to follow suit whereas a more conservative type will wait and see.

Joining only when 80% of the population is participating.

These individuals have very different thresholds, that is,

the point at which they will change behaviors.

We have already discussed thresholds, and

how trendsetters have a very low even a zero threshold.

Because people have different thresholds for change,

the existence of trendsetters does not guarantee the abandonment of a norm.

Suppose we have five trendsetters who are willing to abandon a standing norm,

when none of their peers ever abandoned it.

And the rest of the relevant population is only willing to abandon

when at least ten of their peers have abandoned.

If there is a big gap between the trendsetter threshold and

the other people thresholds, no meaningful change is likely to occur.

Look at this figure and the little bump.

In this case, there is a gap in thresholds, and

there are not enough trendsetters to meet the majority threshold.

So the new behavior dies out.

This is a case of failed diffusion.

Remember, the sit in staged by the black college students and

working adults I mentioned earlier.

Many sit ins were staged, prior to the successful one in Greensboro,

North Carolina, that fails to incite the same rebellious fever.

An important reason was the low number of black college students prior to the 60s.

Even if all the students have a very low threshold for

protest, there was a huge gap between them and

the rest of the black population who had much more to lose.

And that's probably had a much higher threshold.

A gap in threshold may be filled if people are assigned different weights or

degrees of value to the behavior of those around them.

The friends and families of the initial transactors will value their actions more

than strangers will.

In this way, the action of a few are multiplied for

those socially close to the first movers.

The friends and family who follow them will fill the gap.

This filling of the gap, if widespread,

can make a discontinuous distribution functionally continuous.

If we presuppose a normal distribution of types, and therefore of thresholds,

a model of change could assume that these progressive adoption of new behavior would

reach a tipping point, where the adoption of the behavior becomes self sustaining.

The figure here modeling this change as an S shaped curve

has been successfully used to describe the spread of innovations.

In this model,

adoption of the new behavior may slowly increase until it hits a dipping point.

Upon which, adoption rates massively increase and

later taper off due to saturation.

This model, however, is not a good representation of how social norms change.

In contrast to this move function, thresholds for

norm abandonment in a population are not continuous.

The actions of few individual may influence those close by, but

rarely an entire reference network.

Threshold gaps maybe significant when most people thresholds are very high.

Consider that norm sensitivity may not be normally distributed

whether the distribution is skewed to the right.

Which means most people are very sensitive to the norm or

the left, which means most people are not very sensitive to the norm.

There will be no change unless individuals are reasonably sure

that they are not deviating alone.

In the first case, sensitivity to the norm may be so

high that people are very reluctant to deviate from it.

That is, most people will have a very high threshold.

In the second case, people's sensitivity to their own may be low.

But if there is a mismatch between objective and perceived consensus,

which is typical of pluralistic ignorance, reached perception may be extremely high.

As most people believe that the norma is strongly endorsed by the population,

and again, the threshold will be very high.

In such cases, behavioral change is not gradual and continuous.

And definitely does not take the shape of an S curve.

I also previously argued the change in social norms, especially when the norm

is well entrenched, will involve acquiring reasons for abandoning it.

Though a change in personal beliefs may take the form of an s shaped curve,

behavior will take a very different form, as I will show in the next video.

Explore nosso catálogo

Registre-se gratuitamente e obtenha recomendações, atualizações e ofertas personalizadas.

O Coursera proporciona acesso universal à melhor educação do mundo fazendo parcerias com as melhores universidades e organizações para oferecer cursos on-line.

© 2018 Coursera Inc. Todos os direitos reservados.

Baixar na App Store

Baixar no Google Play