This presentation including joint work with Alberto Franco, was presented at the IFORS (International Federation of Operational Research Societies) conference in Quebec City, QC, Canada. We present two different agent-based models for simulating human behavior.
We use the example of group decision making.
The first model uses a cognitive fitness landscape to model the quality of a decision, where participants compare their decision with their nearest neighbor. The decision is based on an external comparison.
The second model uses an internal comparison of a decision with the next best alternative. The model is based on the psychological concept of hidden profiles, where participants only make the best decision by sharing information with the group.
Eight Mile, epitomized by Eminem in the film of the same name, is a street in Detroit that marks the boundary between the majority white northern suburbs and the majority black neighborhoods closer to the inner city.
But what causes this segregation in the first place?
Hypothesis 1: The Central Planner
In Detroit’s case, as with many cities across the USA, it was, in part, due to the zoning of the city by the federal Home Owners’ Loan Corporation, which zoned the city into areas of risk, meaning that banks were indirectly encouraged to develop outer suburbs while not offering mortgages to inner city properties. This lead to wealthier, generally white, residents moving to the suburbs.
Indeed, physical barriers, such at the Detroit Wall, also known as the Eight Mile Wall, were built to separate majority black and majority white neighborhoods.
The legacy of these zones live on today, as seen in the map below from the 2010 US Census. The dividing line between the green (black) areas and the blue (white) areas is Eight Mile Road.
So, segregation exists, and is caused by a central actor. But is there an alternative explanation?
Alternative Hypothesis: Emergence
In 1971, Thomas Schelling set out to model the phenomenon, not by assuming a central planner, but by modelling the interactions of individuals.
Thomas Schelling’s model was this. Assume individuals are placed in a grid, similar to being located on a chess board. Allow individuals who are in a local minority to move. In the example below, the blue circle is in a minority (with 5 out of its 6 neighbors being a different color), and according to the rules of the model, is unhappy. It could decide to move to the vacant square to the north-west, but it would still be in a local minority (with 4 out of 6 neighbors being a different color) and would remain unhappy. So instead, it chooses the space to the south west where 3 out of its 6 neighbors are of the same color, and not being in a minority, it settles there.
Schelling, perhaps without knowing it, introduced agent-based modelling, where, instead of modelling the system as a whole, the modelling of individual agents enables us to see the emergence of macro-level properties, in this case segregation, via the modelling of micro-level (local) interactions.
We can see the effect of micro-level interactions causing macro-level segregation in the model below (developed by Duncan Robertson after Wilensky after Schelling). Each individual, or agent, decides whether they are unhappy or happy; if they are unhappy, they search until they find a vacant location where they will become happy. This continues until all individuals attain happiness.
So, perhaps segregation is not imposed, but is down to us. Or maybe, in reality, it’s a little bit of both.
Please do get in touch if you would like to discuss building or working with agent-based models.
In 2003, as part of a project to document coastal erosion in California, the following photo was posted on the californiacoastline.org website. It is a picture of a beach, some cliffs, lots of trees, and a rather nice house complete with swimming pool, that turns out to be the home of one Barbara Streisand.
For those not in the know, Barbara Streisand, is, in the words of her lawyer, a ‘renowned singer, actress, movie director, composer, and producer’.
Now, it turns out that Barbara Streisand values her privacy. To be specific, she puts a value of at least $10,000,000 on it. When the claim was filed, six people had downloaded the image. However, when Barbara Streisand issues a claim in the LA courts (in her own name) confirming that she lives in the nice house with the swimming pool, court reporters start twitching their notebooks. And so, it came to pass that once the lawsuit was publicised, everyone wants to know what Barbara Streisand’s house looks like. Nearly half a million in the first month.
The Streisand Effect, as it has been dubbed, is an example of unintended consequences. By planning to do one thing (suppress an image), you do the exact opposite.
The addendum to the story is that Barbara Streisand had a resurgence in popularity, culminating in the nearly 100-million downloaded song Barbara Streisand by the popular beat combo Duck Sauce.
Maybe this was, after all, a masterplan to take advantage of the unintended consequences of unintended consequences.
This presentation from the EURO 2016 conference in Poznan, Poland, and from the GDN conference in Bellingham, WA, USA, joint work with Leroy White of Warwick Business School, shows how combining formal and informal organizational networks enables decisions to flow more freely around organizations, but at a cost, leading to an optimal size of informal organizational networks. If organizations can control these, this leads to implications for optimal information flows in companies.
Behavioral Operational Research: Theory, Methodology and Practice(Martin Kunc, Jonathan Malpass, Leroy Wright, Eds.) was published by Palgrave Macmillan in September 2016. My chapter on Agent-Based Modeling and Behavioral Operational Research shows the great potential of using agent-based simulation within BOR, showing how example models can be applied to the field. More details of the chapter can be found on the Palgrave Macmillan site here (DOI:10.1057/978-1-137-53551-1_7).