Lecture Notes on Model Thinking IV

Some lecture notes/ scribble on Model Thinking by Scott E. Page. Lecture “Segregation and Peer Effects”, Part 2: “Schelling’s Segregation Model”:

Schelling built a model and executed it in order to analyse the empirical phenomenon of segregation:

Problem: people tend to live in the neighbourhood of people similar to them self. How can this be explained?
Approach: every person has a threshold t saying: “I want to have at least a percentage of t people similar to me my neighbourhood”. Schelling allocated the thresholds and then ran the model.
Result: roughly speaking, already a ‘low’ threshold lead to a ‘high’ degree of segregation.

|=: So Schelling found a surprising effect by running a model. Now, from the point of computational thinking I’d like to ask:

does running a model provide understanding?

Personally, I don’t think so: execution to me is more like a discovery, an expedition into the ‘jungle of knowledge’, that reveals some effects that then can be analysed. Thus ‘running’ in the above case provides ‘just’ an effect. The nice thing is, that we can rely on this result, ‘it does the job’. Nevertheless, we cannot tell, why it does so. Thus, imho ‘running’ is a way to obtain results, bypassing understanding.

Have fun

Seems to be a typical example of what epistemologists call emergence, i.e. macro-level effect from micro level-causes, here as ’emergence by execution’.

About modelpractice

Modeling Theory and Abstraction Awareness in strive for scientific rigour and relevance to information systems engineering.
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4 Responses to Lecture Notes on Model Thinking IV

  1. Hi,
    I guess the discussion is also depending on WHO is the target of understanding. If it’s yourself (the guy that is designing the models), I agree that execution does not add much insights, actually it leads to solution or reality, not to understanding.
    If instead your objective is making OTHER PEOPLE understand your points, I strongly believe that execution adds up a huge value in terms of understanding. People miss several details when looking at models, while they get things more straight when looking at a running application.
    (We have proven this with quantitative data in some of our experiences on BPM projects with WebRatio, BPMN models, and quick prototypes automatically generated).


  2. Hi Marco,

    So, what to do if understanding is too hard? Make it easier by using prototypes!

    In this sense, I fully agree. Although I prefer thinking of it as bypassing the too heavy bits of the understanding, but this may be just a question of wording. I think the human ability/ willingness of understanding (in a way that one can really >commit< to it!) is often overestimated, this is why the value of prototypes is often underestimated.


  3. TooM says:

    understanding can fail due to:
    * complexity of the problem
    * illogicality of the world
    In software projects the latter is in the majority

  4. Hi T
    Yes, make the language more expressive vs keep it simple, is a typical modelling trade-off. The question is where the complexity ends and the illogicality starts.

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