Social Dynamics Flashcards
the 2 different agendas when it comes to macro-micro-macro model
- Hartmut Esser’s Explanatory Sociology: Theoretical integration based on a unified theory of action So for them it is really important to have a good understanding of why people behave the way they do ( action theories)
- Peter Hedström’s Analytical Sociology: Models of social dynamics under varying structural conditions Core conceptual scheme to understand why people behave the way they do, BUT researchers tend to over-invest in explaining behavior, one should rather focus on the third step where sociology has really something unique to offer (also not in psychology)
Granovetter threshold model (1979)
What is being modelled?
- The model is known as a model of collective action because Granovetter takes “riots” as the working example throughout the paper but Granovetter himself said he just uses riots as an example
- In fact, the model is much more general -> fundamentally, models binary interdependent decisions + your decision depends on how many others have already adopted this behavior/decision
- Suggested applications by Granovetter: “diffusion of innovations”, “rumors and diseases”, “strikes”, “voting”, “educational attainment”, “leaving social occasions” or “migration”
Granovetter threshold model (1979)
Concept of threshold
- Threshold: How many other participating individuals does it need before I join in the same behavior? -> how many others need to adopt behavior before I do it
- Can be expressed in absolute numbers but also as a fraction - the latter slightly more convenient
- Example: Theatre play
It was fantastic! 0 (= I applaud even if I am the only one)
It was good: 0.2 (= I applaud if at least 20% do so)
hated it: 0.99 (= I applaud only if at least 99% do so)
Granovetter threshold model (1979)
Core elements vs what is actually modelled
- Actors are assumed to be rational;
- they have to make a binary choice only thing(s) that are actually modelled in Granovetter’s model (everything else, the whole RC, u don’t have to buy into it)
- the choice implies both costs and benefits -> depends on how many others are doing it, i.e.
1) downloading app: the more people download it, the more useful it might be & the greater the cost of missing out
2) migration: pull: already a network that I can rely on + more fulfilling, reduce feelings of uncertainty (picture on social media) // push: if best friend no longer there, your life gets worse, motivates u to go somewhere else chain migration = self-feeding dynamic - actors’ evaluations of costs and benefits are contingent on the choice of other actors;
- actors have a threshold, i.e. “the proportion of the group he would have to see join before he would do so (p. 1422; « proportion » or « absolute number », p. 1424, fn 3);
- actors’ threshold “is simply the point where the perceived benefits to an individual of doing the thing in question (here, joining the riot) exceed the perceived costs” (p. 1422)
Where was average evaluation higher?
Audience Example
First example, bc average lower threshold which means they found it better BUT still fewer people applauded example of emergence: If only looked at individual level (like a psychologist would do), inference to macro level could be problematic bc you disregard a social process/social dynamic that may lead to a different outcome - u need to know how people interact/influence each other
Granovetter threshold model (1979)
Explanatory goal
- Say something about the macro-level consequences of this choice interdependency between actors
- In particular, what are the consequences of different distributions of actors’ threshold in a population
Granovetter threshold model (1979)
explain
- Mobilized actors (t+1) = Actors with threshold lower or equal to active actors at time t (so, at a particular time whose threshold got reached)
- Example: (20%) = t+1(Actors with threshold <= 20%)
- Model doesn’t tell us how 20% got reached but how things will precede from a starting point
1. If it with 20% decrease to 10% (and further downwards trend, really interesting for i.e. autocratic regime)
2. Wherever we start that does not reach diagonal line (x=y), we will have an upwards trend
3. If at 60% increase to ca. 65% (and further upwards trend really interesting for leader of social movement)
4. If at 100% - also not sustainable, below diagonal line - Equilibria: Point in the middle = not a sustainable equilibrium, it is a critical juncture, the other two are stable equilibria
Peter Hedström’s vision for analytical sociology
Defense of Granovetter
- intentional explanations of behavior, combined with the lack of information on the individuals’ intentions –> led to a proliferation of just-so stories (not good data to back up theories, for example cost vs benefit of RC)
- I suggest that we should avoid intentional explanations in all but the rare cases where we have access to reliable information on the mental states of the acting individuals. Instead we should concentrate our explanatory efforts on interaction structures and the macro patterns they give rise to.” (p. 490)
–> Granovetter really silent why people engage in behavior, might not be satisfying for psychologist, but Hedström says we can be so abstract bc not our job, our job is to look at social dynamics that give rise to macro patterns
Influence-response function by Hedström
connection to Granovetter
- “(…)I took my departure in Granovetter’s (1978) threshold model and used influence-response ideas to extend it so that it also could consider the networks in which the individuals were embedded.” (Hedström 2021: 501)
- “Influence-response functions show how an individual’s behavior is affected by the behavior of others and, thereby indirectly, how the structure of social interaction is likely to affect the macro outcomes the individuals bring about.”
Influence-response function by Hedström
How does it work?
- Column vector B (n x 1) which identifies those who had adopted the behavior in question at the previous point in time (binary part: 1 if joined, otherwise 0)
- Social interaction matrix where networks come in: Matrix S (n x n) describes the structure of social interaction between n individuals.
1. Entries: the social network the individuals are embedded in, or to the extent to which individuals pay attention to other individuals’ behavior (e.g., due to spatial closeness) the higher the number, the more connected people are (ranging from 0-1)
2. Example: P1 strongly influenced by 2 & 3, etc. - Product of the social-interaction matrix and the adoption vector yields the influence-response function I (a n x 1 vector): I = (Summe) S×B –> gives you the exposure part of the influence-response function (exposed to people who adopted behavior + your connectedness to them)
- this then will affect the behavior at the next point in time –> Bt+1 = f(I) -> How individuals respond to the influences emanating from others, how actors change their behavior may depend on threshold, etc.
Excursus: Covid 19
Pro Analytical
“If we were to explain why the pandemic developed as it did in a certain area, a simplistic causal-effect approach relating some attributes of the area in question to the state of the pandemic would not suffice”
–> Only summary of individual data but no idea of interaction
Instead, we would have to follow the scheme outlined and clearly specify the core ingredients of the dynamic process through which the virus spreads in the population. Although the virus as such is the key to the epidemic, how the epidemic develops over time to a large extent is the result of a social process. The virus would have died in its infancy had it not been for individuals interacting with one another, and the size of the epidemic and its development over time is, to a large extent, driven by the interaction patterns between the individuals.” (p. 492)
Excursus: Covid 19
Contra Analytical
“…we should avoid intentional explanations in all but the rare cases where we have access to reliable information on the mental states of the acting individuals. Instead we should concentrate our explanatory efforts on interaction structures and the macro patterns they give rise to.” (Hedström 2021: 490)
- Really? For example, is this all that analytical sociology is able to say about the drivers of the COVID-19 pandemic?
- Intentional explanations (action theories) can be helpful in explaining WHY people behave as they do, understand the individual level better
1. Impulsivity / need for social contexts
2. Cost & benefits of wearing masks
3. Framing used to get people to conform to rules
Empirical research on online activity around riots and protests is scarce
- González-Bailón et al. 2011
4 main findings of previous literature (in Granovetters’s footsteps)
- the shape of the threshold distribution, i.e. the variance in the propensity to join intrinsic to people, determines the global reach of cascades.
- individual thresholds interact with the size of local networks: two actors with the same propensity might be recruited at different times if one is connected to a larger number of people (if I’m exposed to more people, my threshold will be activated earlier)
- attaining a critical mass depends on being able to activate a sufficiently large number of low threshold actors that are also well connected in the overall network structure (positive feedback)
- “Complex contagion:” the exposure to multiple sources can be more important than multiple exposures: unlike epidemics, the social contagion of behavior often requires reinforcement from multiple people if I’m always exposed by my Covid-positive wife, I will get it, too, eventually, BUT to take over her opinion, multiple people/sources are important)
Empirical research on online activity around riots and protests is scarce
- González-Bailón et al. 2011
Case
Protests that took place in Spain in May 2011 in reaction to the political response to the financial crisis broad demands for new forms of democratic representation
Empirical research on online activity around riots and protests is scarce
- González-Bailón et al. 2011
What did they do empirically?
- We analyze Twitter activity around those protests
- data set follows the posting behavior of 87,569 users and tracks a total of 581,750 protest messages (identified using a list of 70 #hashtags)
- split data set
1) network: asymmetric network, so even if follow is not reciprocated
2) network data set: only retains reciprocated and therefore stronger connections
-> Contrasting recruitment patterns in both the asymmetric and symmetric networks allows us to test whether the dynamics of mobilization depend on weak, broadcasting links or on stronger connections, based on mutual recognition - threshold measure: Calculated, for each user, the proportion of neighbors being followed that had been active at the time of recruitment (ka/kin ) -> who has already spoken out in regard to this topic = reveals my threshold (big assumption that my behavior is a response to those others so there will be measurement error)
Empirical research on online activity around riots and protests is scarce
- González-Bailón et al. 2011
Results
- Peak in the beginning and peak around 0,5 so when half of their network has already said/tweeted something but beginning also significant peak, otherwise quite uniform
- Big difference in the beginning between symmetric/asymmetric: When broadcasters (i.e. users with a high number of followers who do not reciprocate connections) are eliminated, the number of early participants with ka/kin = 0 increases by an order of magnitude, which suggests that broadcasters are influential at recruiting low-threshold individuals
2 branches
-> Understanding of theory very similar but emphasis different:
