An introduction to Social Complex Systems with two contrasting example simulations.

1. Social Complexity
Bruce Edmonds
Centre for Policy Modelling,
Manchester Metropolitan University
Social Complexity, Bruce Edmonds, Interdisciplinary Workshop on Complex Systems, Manchester, October 2012, slide 1

2. Part 1:
Discussion on Social Complexity
Social Complexity, Bruce Edmonds, Interdisciplinary Workshop on Complex Systems, Manchester, October 2012, slide 2

3. About Complexity
• No complete agreement on what “Complexity” means
• But it is something to do with the fact that emergent
(usually macro) outcomes result from micro-level
interactions… where “emergent” means that it is hard to
derive the outcomes from the initial conditions in a
simple/analytic manner…
• …so it is sensible to understand the outcomes in a
different way from the micro-level, even given that the
macro-level is constrained by the micro-level
• To show this one needs to exhibit systems with simple
parts/interactions that results in some complex
outcomes, but systems with complicated
parts/interactions might still have complex emergent
outcomes (it is just more difficult to tell)
Social Complexity, Bruce Edmonds, Interdisciplinary Workshop on Complex Systems, Manchester, October 2012, slide 3

4. Is Social Complexity Different?
• Social systems are clearly complex since
we experience phenomena that emerge
from the actions and interactions of
individuals (e.g. language)
• However there are ways in which social
phenomena are different in kind due to:
– The complexity (e.g. cognition) of individuals
– “Downward causation” from whole to parts
– Social Embeddedness
– The Existence of a “Naïve” Interpretation
Social Complexity, Bruce Edmonds, Interdisciplinary Workshop on Complex Systems, Manchester, October 2012, slide 4

5. Complexity of Social Parts (us!)
• The parts of social systems are (a) complex
themselves and (b) poorly understood (in
formal terms)
• People have a complex cognition, including:
reasoning, learning, imagining etc.
• They have a memory of past situations
• They act in highly context-dependent ways
• They seems to be wired (by evolution) to
form complicated social alliances etc.
Social Complexity, Bruce Edmonds, Interdisciplinary Workshop on Complex Systems, Manchester, October 2012, slide 5

6. Micro-Macro Link
• Schelling (1978) Micromotives and Macrobehavior
• The behaviour of individuals clearly comes
together to effect (construct) the macro
(society level) outcomes (e.g. in elections)
• But, in social systems, the macro-level
simultaneously constrains the actions of
individuals in many ways (e.g. social norms,
laws, actions of government)
• This “downward causation” (Campbell 1974) is
characteristic of social systems and contrasts
with the case most physical systems
Social Complexity, Bruce Edmonds, Interdisciplinary Workshop on Complex Systems, Manchester, October 2012, slide 6

7. Social Embedding (SE)
• Granovetter (1985) Economic Action and Social
Structure: The Problem of Embeddedness
• Contrasts with the under- and over-socialised
models of behaviour
• That the particular patterns of social
interactions between individuals matter
• In other words, only looking at individual
behaviour or aggregate behaviour misses
crucial aspects of social phenomena
• That the causes of behaviour might be spread
throughout a society – “causal spread”
• Shown clearly in some simulation models
Social Complexity, Bruce Edmonds, Interdisciplinary Workshop on Complex Systems, Manchester, October 2012, slide 7

8. Context-Dependency
• Many aspects of human cognition are known to be
heavily context-sensitive, including: language,
memory, decision making, reasoning, and
perception.
• This enables groups to co-develop sets of habits,
norms, expectations etc. that pertain to particular
kinds of situations
• These can become instituted over time:
– the more recognisable the kind of situation, the more
particular kinds of behaviour can be developed for it;
– the more kinds of behaviour that is special to a kind of
situation, the more it is distinguishable
• As a result, behaviour in one context might be very
different than another, not be general
Social Complexity, Bruce Edmonds, Interdisciplinary Workshop on Complex Systems, Manchester, October 2012, slide 8

9. Existence of a ‘Naïve’ Interpretation
• Human cognition has evolved with strong
social abilities, e.g. it seems:
– We have an ability to imagine what it feels like to
be someone else
– We already have a naïve idea of how it works
• Which allows participants to reason/react
reflexivley on the society they inhabit
• But it also means that
– some things are so obvious we don‟t notice them
– if we have the wrong idea about how society works
this is difficult to shake off (especially if the wrong
idea is accepted by ones peers)
Social Complexity, Bruce Edmonds, Interdisciplinary Workshop on Complex Systems, Manchester, October 2012, slide 9

10. So What Can We Do in the Face of
Such Complexity?
• Mathematical models are either too simple or
not analytically solvable
• Statistical Models often do not show
emergence as is observed and tend to show
weak but significant interactions between most
global variables
• Natural language is rich in meaning but
imprecise and leaves interpretation open
• Empirics are either limited or have no control
cases to allow comparison
• What about Agent-based simulation?
Social Complexity, Bruce Edmonds, Interdisciplinary Workshop on Complex Systems, Manchester, October 2012, slide 10

11. KISS vs. KIDS
• There is no reason to suppose that social
phenomena happens to be simple enough so
that: a model that is adequate for
understanding it, is understandable by us (the
„anti-anthropomorphic‟ principle). There are
reasons to suppose it is not.
• Thus we are faced with a choice:
– Models simple enough to analyse but which are
„distant‟ from the evidence (rigour)
– Models complicated enough to capture sufficient of
the social reality but impossible to completely
analyse (relevance)
Social Complexity, Bruce Edmonds, Interdisciplinary Workshop on Complex Systems, Manchester, October 2012, slide 11

12. Part 2:
Two Simple but Contrasting
Simulations
Social Complexity, Bruce Edmonds, Interdisciplinary Workshop on Complex Systems, Manchester, October 2012, slide 12

13. A simple model of homophily-driven
altruism
• Riolo et al. (2001) Evolution of Cooperation without
Reciprocity
• This model demonstrates how the “birds of a
feather” phenomenon can be used to achieve
cooperation between intrinsically selfish
individuals without explicit recognition of
kinship or reciprocity (memory)
• Each individual
– Has a tag – a characteristic (in this case a number)
that has no “meaning” but is visible to others
– Has a level of tolerance – it will share resources
with others whose tag is close to its own (is within
tolerance of its own tag)
Social Complexity, Bruce Edmonds, Interdisciplinary Workshop on Complex Systems, Manchester, October 2012, slide 13

14. When donations occur (homophily)
Tag value
Tolerance value
Range of tag values
| other‟s tag – my tag | ≤ my tolerance
Social Complexity, Bruce Edmonds, Interdisciplinary Workshop on Complex Systems, Manchester, October 2012, slide 14

15. What Happens in this Model
• Each time click:
– Scores are set to zero
– Each individual is paired with others a set
number of times and then each time:
• If the other‟s tag value is within the tolerance of own
tag value then donate to it (10% gets lost)
– Individuals with a relatively low total score die
– Individuals with a relatively high score
reproduce into next population (with small
probability of mutation of tolerance or new tag)
Social Complexity, Bruce Edmonds, Interdisciplinary Workshop on Complex Systems, Manchester, October 2012, slide 15

16. Each individual
shown as a
horizontal line,
center it its tagSettings and
value, width its
tolerance,
Parameters
height its age,
color indicates
its lineage Some Global
Outcomes
Social Complexity, Bruce Edmonds, Interdisciplinary Workshop on Complex Systems, Manchester, October 2012, slide 16

17. Conclusions from Riolo et al Model
• An attractive and interesting idea
• No direct relationship to any data, rather is an
exploration of an idea that can be interpreted to be
about social systems
• Model (even though fairly simple) was not well
understood by its authors
• Model was brittle to small changes of assumption (e.g.
changing „≤‟ to „<„)
• In fact donation is effectively „forced‟ upon individuals
• But idea can be used to achieve a temporary „vicosity‟ in
population that can allow emergence of global
cooperation under more complex conditions: multiple
groups, able to escape parasites etc. (e.g. Hales)
Social Complexity, Bruce Edmonds, Interdisciplinary Workshop on Complex Systems, Manchester, October 2012, slide 17

18. A simple model of ape dominance
interactions
• Hemelrijk (2000) Self-reinforcing dominance
interactions between virtual males and females
• Basic movement rules:
– Random movement if isolated
– move towards nearby others (attraction off)
– males move towards females (attraction on)
• If very close then pick a fight with probability
related to extent of dominance over other
– If win dominance increases (more if opponent was
more dominant), if lose similarly decreases
– If a fight is lost turn randomly and move fast
– If fight won follow loser (but not so fast)
Social Complexity, Bruce Edmonds, Interdisciplinary Workshop on Complex Systems, Manchester, October 2012, slide 18

19. Each individual
shown as an
arrow, direction
indicates
travel, size is
dominance,
blue males,
red female
(black when
fighting)
Social Complexity, Bruce Edmonds, Interdisciplinary Workshop on Complex Systems, Manchester, October 2012, slide 19

20. Conclusions from Hemelrijk Model
• Micro-level mechanisms are plausible: dominance
mechanism and movement rules have some rooting
in observations of apes
• Model explains several different global aspects that
are observed (change in relative dominance of
females when in heat, spatial distribution of
dominant individuals, amount of violence in different
species of apes, etc.)
• However, exact timing and sequencing of
dominance interactions in model seem to matter, so
some results are brittle (others seem robust)
• A relatively simple target social system
• But now open to further testing and exploration by
being made precise within a simulation
Social Complexity, Bruce Edmonds, Interdisciplinary Workshop on Complex Systems, Manchester, October 2012, slide 20

21. General Conclusions
• Understanding social phenomena is hard!
• ABM provides a way to stage abstraction and explore
social processes „in vitro‟
• But a mixture of approaches and techniques is probably
essential:
– at different levels of abstraction
– for different aspects of the same system
• On their own, simple models
– will not tell us much about what is observed
– more like computational analogies to sort out ideas
• Needs (ultimate) connection to evidence (the „in vivo‟)
and much caution in interpretation
• Stay awake until the last presentation for an example of
a more complex (KIDS-type) simulation model!
Social Complexity, Bruce Edmonds, Interdisciplinary Workshop on Complex Systems, Manchester, October 2012, slide 21

22. The End
Bruce Edmonds
http://bruce.edmonds.name
Centre for Policy Modelling
http://cfpm.org
Manchester Metropolitan University
Business School
http://www.business.mmu.ac.uk
Social Complexity, Bruce Edmonds, Interdisciplinary Workshop on Complex Systems, Manchester, October 2012, slide 22