Tag Archives: social science

social interaction in cities

Here’s an interesting article from the University of Bern, Switzerland, on social interaction in cities. The engineer in me likes to see some hard data and theory applied in the social sciences.

Cities and the Structure of Social Interactions: Evidence from Mobile Phone Data

Social interactions are considered pivotal to urban agglomeration forces. This study employs a unique dataset on mobile phone calls to examine how social interactions differ across cities and peripheral areas. We first show that geographical distance is highly detrimental to interpersonal exchange. We then reveal that individuals residing in high-density locations do not benefit from larger social networks, but from a more efficient structure in terms of higher matching quality and lower clustering. These results are derived from two complementary approaches: Based on a link formation model, we examine how geographical distance, network overlap, and sociodemographic (dis)similarities impact the likelihood that two agents interact. We further decompose the effects from individual, location, and time specific determinants on micro-level network measures by exploiting information on mobile phone users who change their place of residence.

And here’s a more touchy-feely article in Vox on how the U.S. suburban development pattern discourages social interaction.

the key ingredient for the formation of friendships is repeated spontaneous contact. That’s why we make friends in school — because we are forced into regular contact with the same people. It is the natural soil out of which friendship grows…

This kind of spontaneous social mixing doesn’t disappear in post-collegiate life. We bond with co-workers, especially in those scrappy early jobs, and the people who share our rented homes and apartments.

But when we marry and start a family, we are pushed, by custom, policy, and expectation, to move into our own houses. And when we have kids, we find ourselves tied to those houses. Many if not most neighborhoods these days are not safe for unsupervised kid frolicking. In lower-income areas there are no sidewalks; in higher-income areas there are wide streets abutted by large garages. In both cases, the neighborhoods are made for cars, not kids. So kids stay inside playing Xbox, and families don’t leave except to drive somewhere.

I buy this about 75%. I am lucky to live and work in a highly walkable urban neighborhood, and I do have a lot of friendly spontaneous interactions with people around the neighborhood. I have a “scrappy” job where I bond with my co-workers, like soldiers in the trenches. I am also a middle-aged family person and somewhat of an introvert. Part of the reason I don’t have a lot of close adult friendships outside of work and family is that between work and family, I have all the human interaction I can really handle. If I have 15 minutes free on a given day, I would rather spend it alone than interacting with yet another person. I suppose this could change when the kids get a little older and/or when I don’t have to work so much, assuming I live long enough for these things to happen. So I’m just saying there are family pressures, financial and career pressures, and personality differences that influence these things alongside urban form.

Back to the first article, it suggests that high school, college, band camp, and even most workplaces might not be the best model of the most fulfilling and productive social interactions that can develop among adults in the best cities. In high school and college we tend to form small, tight-knit groups where most people in the group network only within the group. The first article above, if I am interpreting it correctly, describes a case where not only are individuals interacting frequently within a social network, but relatively open social networks themselves are interacting with each other as individuals within them interact in random and freewheeling ways. It’s wonderful. Now if you’ll excuse me I’m going to sit on my couch for a little while, watch some TV, unwind and recharge so I can handle the social interaction that will be thrown at me tomorrow.

agent-based social system modeling

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One approach to agent-based social system modeling is the Institutional Analysis and Development Framework developed by Elinor and Vincent Ostrom at the Indiana University:

The IAD Framework offers researchers a way to understand the policy process by outlining a systematic approach for analyzing institutions that govern action and outcomes within collective action arrangements (Ostrom, 2007, 44). Institutions are defined within the IAD Framework as a set of prescriptions and constraints that humans use to organize all forms of repetitive and structured interactions (Ostrom, 2005, 3). These prescriptions can include rules, norms, and shared strategies (Crawford and Ostrom 1995; Ostrom 1997). Institutions are further delineated as being formal or informal; the former characterized as rules-in-form and the latter as rules-in-use.

The IAD framework identifies key variables that researchers should use in evaluating the role of institutions in shaping social interactions and decision-making processes. The analytical focus of the IAD is on an “action arena”, where social choices and decisions take place. Three broad categories of variables are identified as influencing the action arena: institutions or rules that govern the action arena, the characteristics of the community or collective unit of interest, and the attributes of the physical environment within which the community acts (Ostrom 1999; Ostrom 2005). Each of these three categories has been further delineated by IAD scholars into relevant variables and conditions that can influence choices in the action arena. For instance, the types of rules that are important in the IAD include entry and exit rules, position rules, scope rules, payoff rules, aggregation rules, authority rules, and information rules. Key characteristics of the community can include factors such as the homogeneity of its members or shared values. Biophysical variables might include factors such as the mobility and flow of resources within an action arena.

The IAD further defines the key features of “action situations” and “actors” that make up the action arena. The action situation has seven key components: 1) the participants in the situation, 2) the participants’ positions, 3) the outcomes of participants’ decisions, 4) the payoffs or costs and benefits associated with outcomes, 5) the linkages between actions and outcomes, 6) the participants’ control in the situation, and 7) information. The variables that are essential to evaluating actors in the action arena are 1) their information processing capabilities, 2) their preferences or values for different actions, 3) their resources, and 4) the processes they use for choosing actions.

Here are a couple papers that describe attempts to operationalize this framework:

MAIA: a Framework for Developing Agent-Based Social Simulations

Modelling socio-ecological systems with MAIA: A biogas infrastructure simulation

swing the election

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Here’s an interesting interactive tool on FiveThirtyEight.com where you can play around with U.S. voter turnout and preferences among various demographic groups.

I ran a few scenarios:

The default scenario is that each demographic group (educated white, uneducated white, black, hispanic/latino, and Asian) votes for the same party in the same proportions as 2012, and turns out at the same rate, but the absolute size of each group is adjusted for changes between 2012 and 2016.
- electoral votes 332-206 in favor of DEMOCRATS
Let’s go back to the default, and all the Asian people stay home.
- 332-206 in favor of DEMOCRATS (just not enough people, and maybe already concentrated in democratic states)
Back to the default, and all the hispanic/latino people stay home.
- 283-255 in favor of DEMOCRATS (perhaps hispanics/latinos are also concentrated in already democratic states?)
Back to the default, and black turnout falls from 66% to 29%
- 286-252 in favor of REPUBLICANS (perhaps this flips some key midwest swing states like Pennsylvania, Ohio, Michigan, Wisconsin, etc.)
Back to the default, and uneducated whites swing strongly to the right, from 62% last time to 69% Republican (maybe a terrorist attack? a major incident with China or Russia? I don’t want to say false flag, this is not one of those conspiracy websites…)
- 282-256 in favor of REPUBLICANS (probably those swing states again)
Stay with the previous scenario, but educated whites swing ever so slightly to the left, from 56% Republican last time to 54% Republican (what would cause this? I don’t know, some crazy right-wing candidate spouting racist nonsense maybe, I’m not naming names…)
- 275-263 in favor of DEMOCRATS

So the bottom line is that the minority groups tend to vote Democrat.The uneducated whites tend to vote Republican. The educated whites are the swing voters who end up being the deciding factor. So it is hard to see how a Republican candidate who appeals strongly to uneducated whites but alienates educated whites could ever stand much of a chance.