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.
The ‘℮’ symbol, or the ‘e-mark’ is a symbol you will see on packaging such as tins or packets in Europe. Millions of us will see this symbol every day, but what does it actually mean?
The raison d’etre for the e-mark comes from the problem of selling goods to the public. We would all like to think that we are getting what we pay for, but does that mean we should always get what we pay for?
Well, if you use the ℮-mark, then no. And yes if you don’t. So you use the ℮-mark. By doing so, some of us are short-changed, but, on average, we shouldn’t be.
The e-mark was introduced in 1976 by the legislation known by the snappy title of ‘Council Directive 76/211/EEC of 20 January 1976 on the approximation of the laws of the Member States relating to the making-up by weight or by volume of certain prepackaged products’.
This sets out a nominal value of a product. This means that, on average, we should not receive less than the value stated before the e-mark. But we would be really annoyed if we received, say, nothing, and someone else received twice the nominal amount. So, the concept of tolerable negative error was introduced at the same time, to set out the minimum legal amount that each packet or tin or container should contain. The idea is that only a few containers can weigh less than the declared value less the tolerable negative error (but none can be twice the tolerable negative error… that would be, well, intolerable).
In packets from 5 grams to 10 kilogrammes, the tolerable negative error varies from 9% (quite a lot) to 1.5% (not such a lot), the rationale being that it is easier to measure larger values with greater accuracy.
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.