Paper presented at Simulating Society V: Frontiers in Social Sciences Simulations, Kazimierz Dolny, Poland, September Friday 21- Monday 24, 2001.

1. A New Approach to Social Mobility
Models: Simulation as “Reverse
Engineering”
Edmund Chattoe and Anthony Heath
Department of Sociology
University of Oxford
3 George Street Mews
Oxford OX1 2AA
http://www.sociology.ox.ac.uk
edmund.chattoe@sociology.ox.ac.uk

2. Plan of the Presentation
• Why are sociologists so hard to convince
about simulation?
• What is social mobility?
• A description of MOBSIM
• Initialising populations with structure: a
simulation puzzle

3. Sociology and Simulation
• Fundamental split: “qualitative” versus
“quantitative” approaches
• Qualitative sociologists don’t like
simulation because they don’t like any
modelling
• What about quantitative sociology?
• That shares a view of regularities as social
phenomena not “laws”.

4. “Real Problems”
• Quantitative sociologists want to study
“real” problems:
– Lots of data, direct and surrounding
– Social institutions (schools, factories) and
social structure (family relations)
– Problems set by academic consensus
(tradition) rather than choice

5. Two Kinds of “Theory”
MACRO MACRO MACRO
Statistical Model World Simulation
MICRO MICRO MICRO
?
?

6. Problems with Statistical Models
• Curve fitting (rather than cross checking at
different levels) is not real testing
• “Theories” of micro behaviour compatible
with statistical approaches must either be
very “tidy” (RCT) or run the risk that the
macro behaviour emerging from it not
what was anticipated

7. Reverse Engineering
• “Pick apart” dependent variables and set in
order: the problem of “scope”
• Do “normal simulation stuff”:
experiments, validation, exploring theory
lacunae
• Generate original patterns (and ideally
others) thus producing a simulation that is
both “relevant” and falsifiable

8. The Scope of Models
Labour Markets
Demography
Education
?
?

9. What is Social Mobility?
– Movement between classes over time:
• Inter and intra generational
• Relative and absolute mobility
– Motivations for study:
• Efficiency
• Social Stability
• Morality
– Findings
• Meritocracy and myth
• Comprehensive schools
• Considerable constancy of ASM

10. MOBSIM
• A microsimulation with behaviour
• Initialised at “1901”
• Key Features
– Identities
– Classes (equated with jobs)
– Education (“epoints”)
– Status
– “Institutions” and “Biology”
– Labour market

11. Constructive Surprise Example
• Models of more than minimal
complexity are falsifiable
• People with less education can hold
better jobs
• This occurs because even though hiring
is meritocratic, firing is probabilistic
• Observers might attribute this to
discrimination
• It also suggests new research

12. Distribution of Education Points By Class
0
0.5
1
1.5
2
2.5
0 200 400 600 800 1000 1200
Education Points
Series1

13. Developing the Model
• Endogenising norms
• Disaggregating the school sector
• Dealing with anachronisms
• Disaggregating the labour market
• Removing “arbitrary” parameters
• Adding material resources
• Adding spatial location
• Adding social networks

14. Possible Experiments I: Cultural Shifts
• Homogamy
• Women’s labour market participation
and higher education
• Divorce
• Birth control, demography and genetics
• Deliberate policy
• Ascription and meritocracy

15. Possible Experiments II
• Validating Statistical Assumptions
– Class Aggregates
– Time at Measurement
– Transmission through the father
• “Exogenous” Factors
– War
– Sectoral Change and International Adjustment

16. The Problem of Structured Populations I
• Three kinds of structure
– No structure: agents with separable attributes
initialised randomly
– Some structure: microsimulations in which
attributes like “number of children” matter
– Detailed structure: social mobility, attributes
like “my father” matter
• Again an unremarked feature of these
models

17. The Problem of Structured Populations II
Pseudo Histories Pseudo Parents
TWO POSSIBLE SOLUTIONS

18. The Problem of Structured Populations III
• Pseudo Histories
– Consistency of pseudo history distributions
with actual run distributions
– Problem of “linking” pseudo histories to each
other
• Pseudo Parents
– Hard to generate stable populations
– Large “overhead” of lead time and boom and
slump in initial pseudo population

19. The Problem of Structured Populations IV
• Should we worry that this problem has
never come up before?
• Just how much structure is plausible for
real social systems?

20. What is Microsimulation?
• Agents and attributes
• Environment and different updating rules
• Initialisation