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

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

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  • that is NOT either trying to understand/program an agent on their own (against an environment) or as a uniform and completely socialized part of a society

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  • 1. Social Complexity Bruce Edmonds Centre for Policy Modelling, Manchester Metropolitan UniversitySocial 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 ofSuch 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-drivenaltruism• 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 ahorizontal line,center it its tagSettings andvalue, width its tolerance, Parametersheight 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 dominanceinteractions• 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.ukSocial Complexity, Bruce Edmonds, Interdisciplinary Workshop on Complex Systems, Manchester, October 2012, slide 22