Paper presented at Natural Computing Applications Forum Summer Meeting, Wadham College, Oxford, 3-4 July 2001.

1. 1
Emergence in Social Behaviour:
Blessing or Curse?
Edmund Chattoe
Department of Sociology
University of Oxford
3, George Street Mews, OX1 2AA
edmund.chattoe@sociology.ox.ac.uk
http://www.sociology.ox.ac.uk/chattoe.html

2. 2
Defining
Emergence
• “Refers to a property of a collection of simple
sub units that comes about through the
interactions of the sub units and is not a
property of any single sub unit. For example,
the organisation of an ant colony is said to
“emerge” from the interactions of the lower-
level behaviours of the ants, and not from any
single ant. Usually, the emergent behaviour is
unanticipated and cannot be directly
deduced from the lower-level behaviours.
Complex systems are usually emergent.”
(Flake 1998)

3. 3
Emergence: Physics
Example
• Emergent property: Pressure.
• Interaction: Collision.
• Sub units: Gas molecules.
• Unanticipated: No.
• Not directly deducible: No.

4. 4
Emergence: Sociology
Example
• Emergent property: Riot.
• Interaction: Shouting, watching,
throwing things, colliding.
• Sub units: Demonstrators.
• Unanticipated: Yes.
• Not directly deducible: Yes.
• Additional problem with
macroscopic measurement.

5. 5
Differences Between
Examples
• Small set of responses.
• Fixed set of responses.
• Local interaction only.
• Deterministic interaction.

6. 6
Tasks of Natural Computing
Applications
Physical
System
Social
System
Regularity
Detection
Prediction

7. 7
What Makes a System
Social?
• It doesn’t take much.
• Stock market behaviour.
• Patient monitoring behaviour.
• Where data comes from
determines what it means.
• Failure to find stable
regularities may not be a
technical problem.

8. 8
Emergent
Simulation
• If you can’t jump the gap
between levels, fill it in.
• Budgeting behaviour example.
• Start with interview data.
• Capture key features of
interaction.
• Result is emergent lifestyle
clustering.
• Emergence for falsification.

9. 9
Conclusion
s
• “Sociality” is a neglected
dimension of NCA success
independent of “technical”
issues.
• Explicit simulation can act as a
complementary technique.
• In social systems, emergence is
a curse for simple data mining
but may be a blessing for testing
theories of social action.

10. 10
Adaptation of Budgetary
Plans
Both individual and social
Individual only
Social only
None
Time steps x 100
70.0060.0050.0040.0030.0020.0010.00.00
15.0
14.5
14.0
13.5
13.0
12.5
12.0
11.5
Mean
Fitness

11. 11
Titl
e
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D i m e n s i o n 2
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1 . 5
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D i m e n s i o n 3D i m e n s i o n 1
1 0 . 0
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a 1 9
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- .50
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S t r e s s - 1 = .1 5 7 R S q = .8 9