This project aims to explore oppression, representation, and power dynamics as the collective output of interpersonal interactions arising from transmission of and response to normative behavior mediated by social networks through agent based simulation.

This project is the programmatic implementation of my (UNM Math 499) independent study research paper. It is inspired by the work of Iris Marion Young; specifically, her essay the "Five Faces of Oppression" (1990). One of her primary ideas is that oppression need not necessarily be conceptualized as adversarial; that the ordinary workings of a liberal society could, in their own way, still produce a social structure that constrains some and disadvantages others along group lines (Young, 2009).

To investigate this, this project constructs a virtual society of agents, all of whom have normative values, behaviors, and social bonds to others through both friendship and family. Every time step, each agent interacts with a select sample of other agents from outside her social network and, through conceptualized interpersonal interactions, gains either acceptance or rejection of the values and behaviors that she currently possesses. Instead of immediately acting upon this feedback, however, she then meets with a select group of people from inside her social circle as a form of comparison. The outcome of these two interactions determines whether or not she will keep or change her behavior for the current round. Some agents (due to simulation parameters) have more effective influence than others; it is part of the goal of this project to investigate how their composition might affect the resulting network structures.

That is the basic idea; a full explanation would be offensively long. Readers interested in simulating similar work might consider Hellbing (1998) for an examination of modeling populations as probabilistic constructs or Schelling (1971) for his well-known application of game theory.

To build this project execute the following:

$ make

and to include debugging symbols:

$ make debug

and to run the included tests:

$ make tests

To run see the Usage section below.

This project operates on directories of experiments in order to facilitate batching. A valid experiment is a single directory that contains the following three files:

• census.csv - A CSV (comma separated values) file that contains demographic information about the statistical household size for an arbitrary population.
• params.cfg - A simple configuration file that contains all of the simulation parameters. See the provided sample cases for what this file should look like.
• values.csv - Another CSV file that contains the mathematical definitions of all possible behaviors and values (attribute sample space).

With the above in place, to run this project execute the following:

$ iris --directory [experiment directory] --run [number of runs]

which will scan the specified directory for the required files, load them, and run a single simulation for each run requested. All data is output to a sub-directory with the current timestamp and hash (to avoid time resolution problems with small simulations).

This project outputs the following six (massive) files:

• comm.csv - The final communication graph; this keeps track of every interaction an agent has with another and what the outcome was.
• original-attributes.csv and final-attributes.csv - The starting and final behvaiors and values for each agent. This project guarantees that each simulation starts with an equal distribution of each value and behavior combination across the entire population; this makes it easy to see how it changes over time. In iGraph these are used as vertex attributes.
• original-network.csv The structure of the randomly generated network graph. This includes the familial ties between agents and however many others that are also linked via friendship. In iGraph this is used to supply information about edges between vertices.
• power.csv - The conversion of the communication graph to a numeric quantity that this project conceptualizes as the amount of "power" one agent has over another, dictated by how many influential actions they have taken divided by the number of times they have interacted. Since this uses the communication graph (that is also serialized), this was mostly done so I didn't need to write an additional R script.
• statistics.csv - Keeps track of the overall population change over time, as given by the change in social group membership (delineated by behavior combinations).

Note: All visuals were made in R with iGraph and ggplot2.

A small population of agents with immediate household networks only using U.S. Census data from 2015:

where each agent has randomly assigned values and starting behaviors, color coded as seen above.

The experiment to generate the data for this figure may be found under household inside the sample_cases directory.

The primary output of this project is a concept of net influence, a quantity that measures the amount of influence an agent has at any point in time compared to everyone else in her society. Because this project conceptualizes power as directional, net influence can be considered a measure of the flux of power through a single vertex in a network that includes all agents in a population regardless of connectedness. A visualization of net influence for a single experiment of 100 agents, in which 5% of them are powerful and all have 4 distinct social groups to which they can belong, is shown in the histogram below:

An example of the cyclic shifts the model produces are also shown below:

and as stated are a product of representation; specifically, the representation of the powerful agents. The more powerful agents are added, the more severe the cycles become until a threshold is reached. This threshold indicates the number of powerful agents necessary to ensure that at least one powerful agent has a unique representation at all times, essentially providing a source of representational competition that works to moderate the potential swings in group membership. This would seem to be an interesting argument for the merits of diversity in a representational democracy.

The experiment to generate the data for this figure may be found under power_test inside the sample_cases directory. A comparison without powerful agents may be found under no_power_test inside the same directory.

Unfortunately, I ran out of time before this project could be all that I wanted and so there are some glaring issues worth noting. In particular, this project is poorly optimized with no parallelism and a massive output footprint. The lack of threading means that this project scales poorly; in particular, after ~10,000 agents signficant slowdown occurs. The massive output footprint means that this project generates a lot of data - as in excessive amounts - and so care should be taken if attempting to run this project in batch mode at high agent counts. Finally, this model is cyclic with low sensitivity; using custom initial graphs (e.g. leadership modeling) is likely pointless.

The usual caveats with toy academic models also apply.

This project was written in C++11 and uses CMake as its build system. The excellent library Catch is required for unit testing.

Please note that all dependencies have been configured as external projects and will be downloaded and built automatically.

This project is released under the Apache 2.0 license as specified in License.txt.

1. Hellbing, D. (1998). A mathematical model for the behavior of individuals in a social field. Journal of Mathematical Sociology, 19(3), 189-219.
2. Schelling, T.C. (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1, 143-186.
3. Young, I.M. (2009). Five faces of oppression. In G.L. Henderson & M. Waterstone (Eds.), Philosophical forum (p270). Routledge.