Social Complexity

Social Complexity, Bruce Edmonds, Interdisciplinary Workshop on Complex Systems, Manchester, October 2012, slide 1

Social Complexity

Bruce Edmonds

Centre for Policy Modelling,Manchester Metropolitan University


Discussion on Social ComplexityPart 1:


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)


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


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.


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 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


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


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)


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?


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)


Two Simple but Contrasting Simulations

Part 2:


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)


| other’s tag – my tag | ≤ my tolerance

When donations occur (homophily)

Range of tag values

Tag value

Tolerance value


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)


Settings and Parameters

Some Global Outcomes

Each individual shown as a

horizontal line, center it its tag value, width its

tolerance, height its age, color indicates

its lineage


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)


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)


Each individual shown as an

arrow, direction indicates

travel, size is dominance, blue males, red female

(black when fighting)


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


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!


The End

Bruce Edmondshttp://bruce.edmonds.nameCentre for Policy Modelling

http://cfpm.orgManchester Metropolitan University

Business Schoolhttp://www.business.mmu.ac.uk

Education

Social Complexity