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Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 1
Computing the Sociology of Survival
– h...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 2
Outline of the talk
1. Some philosophy ...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 3
Some Philosophy
Part 1
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 4
The Anti-Anthropocentric
Assumption
• T...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 5
Versions of this assumption
• Whilst ot...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 6
Living with the AAA
• Accepting that th...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 7
Possible modelling trade-offs
• Some de...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 8
This talk argues for the following stra...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 9
An argument for simple models I:
The “S...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 10
An argument for simple models II:
anal...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 11
A Dilemma
• KISS: Models that are simp...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 12
KISS vs. KIDS as a search strategy
Sim...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 13
Consequences of a ‘KIDS’ approach
• We...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 14
As done in the ‘SCID’ Project
• A Data...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 15
The Problem
Part 2
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 16
Social Intelligence Hypothesis
• Kumme...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 17
An Evolutionary Perspective
Social int...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 18
The Tethered Goat Analogy
In terms of ...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 19
An Evolutionary Perspective
Social int...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 20
Our Predictament
• With globalisation,...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 21
Practical Consequences
(Among many oth...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 22
Integrated Socio-Ecological Modelling
...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 23
A Combined Socio-Ecological Model
• He...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 24
The Model
• A wrapped 2D grid of
well-...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 25
How Dominance is Decided
(Caldarelli, ...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 26
Model sequence each simulation tick
1....
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 27
One outcome: an Ecology with
multiple ...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 28
Partial validation, case of: neutral p...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 29
Longer-Term Trends in Num.
Species
Red...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 30
Simulation at (up to) Reference Point
...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 31
We then add ‘humans’ into the mix
• Th...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 32
Example Dynamics
• The arrival of huma...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 33
Effect of humans vs. food input to wor...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 34
Effect of humans vs. food input to wor...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 35
Migration vs. food rate (all with huma...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 36
Extinction due to Consuming all Others
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 37
Waves of (Human) Predator-Prey Patterns
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 38
Human migr. rate vs. diversity (all wi...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 39
Some Elements of a “Computing the
Soci...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 40
The Double Complexity of Modelling
Soc...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 41
Coordination
Mechanisms
or Games
Ecolo...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 42
From solving the current ecological
di...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 43
Sociology
of Culture
Complex
Ecologica...
Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 44
The End
Me: http://bruce.edmonds.name
...
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Computing the Sociology of Survival – how to use simulations to understand complex socio-ecological systems and maybe save the world

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An invited talk about WUR, Wageningen, June 2016

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Computing the Sociology of Survival – how to use simulations to understand complex socio-ecological systems and maybe save the world

  1. 1. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 1 Computing the Sociology of Survival – how to use simulations to understand complex socio-ecological systems and maybe save the world Bruce Edmonds Centre for Policy Modelling, Manchester Metropolitan University
  2. 2. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 2 Outline of the talk 1. Some philosophy of modelling – the consequences of complexity 2. The problem of ecological survival 3. Truly integrated socio-ecological modelling 4. Towards understanding the sociology of survival
  3. 3. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 3 Some Philosophy Part 1
  4. 4. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 4 The Anti-Anthropocentric Assumption • That the universe is not arranged for our benefit (as academics) • e.g. that assumptions such as the following are likely to be wrong: – Our planet is the centre of the universe – Planetary orbits are circles – Risky events follow a normal distribution – Humans act as if they followed a simple utility optimisation algorithm • The one that I am particularly arguing against here is that our brains happen to have evolved so as to be able to understand models adequate to the phenomena we observe
  5. 5. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 5 Versions of this assumption • Whilst other animals have severe limitations and biases in their cognition, we don’t • That our tools (writing, computers etc.) allow us to escape our limitations and biases to achieve general intelligence • That simplicity (that which is easier for us to analyse) is any guide to truth (other things being equal etc.) • If your model is not simple enough to analytically solve, you are: (1) not clever enough, (2) lazy (have not worked hard enough), (3) premature (don’t yet have the formal tools to crack it) or (4) mistaken • That simpler models are more ‘scientific’
  6. 6. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 6 Living with the AAA • Accepting that that much of the world around us is fundamentally beyond modeling that is both adequate and sufficiently simple and general for us to completely understand • Acknowledging our (brain+tools) biases and limitations and so considering how we might extend our scientific understanding as much as possible • Phenomena that are simple enough for us to scientifically understand are the exception – the exception to be sought and struggled for • Simplicity is the exception – a science of non-simple systems makes no more sense than a science of non-red things
  7. 7. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 7 Possible modelling trade-offs • Some desiderata for models: validity, formality, simplicity and generality • these are difficult to obtain simultaneously (for complex systems) • there is some sort of complicated trade-off between them (for each modelling exercise) simplicity generality validity formality Analogy Abstract Simulation Data What Policy Makers Want
  8. 8. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 8 This talk argues for the following strategy: weakening the generality of our formal models to achieve more validity in the face of the AAA In particular I am arguing against weakening validity (e.g. to analogy) or abandoning formality to preserve (the illusion of) generality or simplicity What is Essential to an Empirical Science? • Validity: agreement of models to what we observe (the evidence), not science otherwise • Formality: formal models (maths, simulation) are precise and replicable – essential to being able to build knowledge within a community of researchers • Simplicity: ability to analyse/understand our models, good to have but unattainable in general (AAA) • Generality: the extent of the applicability/scope of a single model, there needs to be some small generality to apply models in places other than where developed, but wide generality not necessary
  9. 9. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 9 An argument for simple models I: The “Simple is more General” Fallacy • If one has a general model one can make it more specific (less general) by adding more processes/aspects… • …in which case it can become more complex • However, the reverse is not true… • If one simplifies/abstracts then you don’t get a more general model (well almost never)! – there may be no simpler model that is good enough for your purpose – But, even if there is, you don’t know which aspects can be safely omitted – if you remove an essential aspect if will be wrong everywhere (no generality)
  10. 10. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 10 An argument for simple models II: analogies only appear to have generality • Humans are good at using analogies, relating an idea or example from one context to another • They build the mapping from the analogy to the a context “on the fly” largely unconsciously • The mappings are different each time an analogy is applied, thus not a reliable source of knowledge and each person might build a different mapping but can yield new insights and can guide research direction • Many simple models do not have an explicit mapping to a domain, but are used as analogy • This is sometimes hidden, so when a simulation (or analytic model) models an idea which applies as an analogy to a domain and not directly, given a spurious impression of generality
  11. 11. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 11 A Dilemma • KISS: Models that are simple enough to understand and check (rigour) are difficult to directly relate to both macro data and micro evidence (lack of relevance) • KIDS: Models that capture the critical aspects of social interaction (relevance) will be too complex and slow to understand and thoroughly check (lack of rigour) • But we need both rigour and relevance • Mature science connects empirical fit and explanation from micro-level (explanatory and phenomenological models)
  12. 12. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 12 KISS vs. KIDS as a search strategy Simplest Possible More Complex in Aspect 2 etc. More Complex in Aspect 1 KISS KIDS
  13. 13. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 13 Consequences of a ‘KIDS’ approach • We will have to deal with complicated models that we do not fully understand • We will then have to analyse these models, making simpler models of the complicated models • …maybe forming chains of models/analyses • This ‘stages’ abstraction more gracefully and can separate the processes of representation and simplification • Each one is a kind of check on the next • Reference is preserved in each model!
  14. 14. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 14 As done in the ‘SCID’ Project • A Data Integration Model was formed that brought together the available evidence • Then this is simplified by progressive modelling stages Data Evidence Simple Model Data Evidence Simple Model Complex Model Representation Simplification DIM Analytically Solvable Model
  15. 15. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 15 The Problem Part 2
  16. 16. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 16 Social Intelligence Hypothesis • Kummer, H., Daston, L., Gigerenzer, G. and Silk, J. (1997) • The crucial evolutionary advantages that human intelligence gives are due to the social abilities it allows • Social intelligence is not a result of general intelligence, but at the core of human intelligence, “general” intelligence is a side-effect of social intelligence • Explains specific abilities such as imitation, language, social norm instinct, lying, alliances, gossip, politics etc. • Individuals do not need to be extremely smart, but equipped to learn the group practices and culture and develop this a little
  17. 17. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 17 An Evolutionary Perspective Social intelligence implies that: • Groups of humans can develop their own (sub)cultures of technologies, etc. (Boyd and Richerson 1985) • These allow the group with their culture to inhabit a variety of ecological niches (e.g. the Kalahari, Polynesia) (Reader 1980) • Thus humans, as a species, are able to survive catastrophes that effect different niches in different ways (specialisation)
  18. 18. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 18 The Tethered Goat Analogy In terms of ideas and assumptions, people are like a tethered goat, they can wander a little way from what they were taught but not very far
  19. 19. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 19 An Evolutionary Perspective Social intelligence implies that: • Groups of humans can develop their own (sub)cultures of technologies, etc. (Boyd and Richerson 1985) • These allow the group with their culture to inhabit a variety of ecological niches (e.g. the Kalahari, Polynesia) (Reader 1980) • Thus humans, as a species, are able to survive catastrophes that effect different niches in different ways (specialisation) • Culture is part of our collective toolkit for how to survive… or not!
  20. 20. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 20 Our Predictament • With globalisation, we are developing a universal toolkit and associated culture, so a future catastrophe might wipe us all out together • So the social evolutionary process whereby some cultures in some niches survive is no longer true • and the impact of humankind is such that it is taking too much ecological space and squeezing out a large amount of biological diversity • Thus instead of relying on how we are used to relating to our surrounding environment we now have to manage this deliberately… • collectively understanding and managing how we interact with our environment for our own and others’ survival • In particular, to understand how our culture affects our decision making which affects our environment
  21. 21. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 21 Practical Consequences (Among many other things) we need to: • Accept and seek to understand the full complexity of a complex social system embedded within a complex ecological system • If we just use simple models we will miss some of the more subtle dangers in this complexity • This means quite complex models over much longer time periods • Much longer and collective development of models • And analysing these complex models with all the tools at our disposal
  22. 22. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 22 Integrated Socio-Ecological Modelling Part 3
  23. 23. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 23 A Combined Socio-Ecological Model • Here I present a dynamic, spatial, individual-based ecological model that displays some of the complexity, adaptability and fragility of observed ecological systems with emergent outcomes • It evolves complex, local food webs, endogenous shocks from invasive species, is adaptive but unpredictable as to the eventual outcomes • Into this ecological model, agents representing humans can be “injected” with different societal structures/characteristics and the outcomes analysed • This may help us understand how we might have to structure out society, if we (as a species) are to survive and minimise our degradation of other species
  24. 24. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 24 The Model • A wrapped 2D grid of well-mixed patches with: – an energy economy (transient) – (relatively short) bit string of characteristics of the patch • Organisms represented individually with its own characteristics, including: – (longer) bit string of characteristics (geneome) – energy – position A well-mixed patch Each individual represented separately Slow random rate of migration between patches
  25. 25. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 25 How Dominance is Decided (Caldarelli, Higgs, and McKane 1998)
  26. 26. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 26 Model sequence each simulation tick 1. Input energy equally divided between patches. 2. Death. A life tax is subtracted, some die, age incremented 3. Initial seeding with random new individual until one is viable 4. Energy extraction from patch. Energy divided among the individuals there with positive score when its bit-string is evaluated against patch 5. Predation. Each individual randomly paired with a number of others on patch, if dominate them, get a % of their energy, other removed 6. Maximum Store. Energy above a maximum level is discarded. 7. Birth. Those with energy > “reproduce-level” gives birth to a new entity with the same bit-string as itself, with a probability of mutation, Child has an energy of 1, taken from the parent. 8. Migration. Randomly, individuals move to one of 4 neighbours with a given probability 9. Statistics. Various statistics are calculated.
  27. 27. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 27 One outcome: an Ecology with multiple trophic layers • Here new species are continually developing and spread out in waves, but a mix of trophic levels are maintained (but this varies over time) The world state (left) Number of Species (centre) Log (1 + Number of Individuals at each trophic level) (right)
  28. 28. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 28 Partial validation, case of: neutral patches, random migration, plants only, no humans • Broadly consistent with Hubble’s “Neutral Theory” • “skewed s-shaped” relative species abundance curve • “Multinomial distribution” of log2 species distribution • Except, species- area scatter chart might only reflect small scales
  29. 29. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 29 Longer-Term Trends in Num. Species Red=many trophic layers, blue=herbivore ecology Number unique species (with high mutation rate 1%)
  30. 30. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 30 Simulation at (up to) Reference Point Herbivores Appear First Successful Plant Simulation “Frozen” Carnivores Appear
  31. 31. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 31 We then add ‘humans’ into the mix • The agents representing humans are “injected” (as a group) into the simulation once an ecology of other species has had time to evolve • The state of the ecology is then evaluated some time later or over a period of time • These agents are the same as other individuals in most respects, including predation but “humans”: – can change their bit-string of skills by imitating others on the same patch (who are doing better than them) – might have a higher “innovation” rate than genetic mutation – might share excess food with others around – might have different migration rates etc. • Could have many other learning, reasoning abilities
  32. 32. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 32 Example Dynamics • The arrival of humans (when they don’t die out) has an immediate impact on the ecosystem, in terms of both population and species diversity • Typically they become the top predator and wipe out other higher predators • But also the diversity of human variety can “displace” species variety by inhabiting many niches
  33. 33. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 33 Effect of humans vs. food input to world diversity of ecology, blue=with humans, red=without
  34. 34. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 34 Effect of humans vs. food input to world proportion of ecology types, red=plant, blue=mixed, purple=single species, green=non-viable
  35. 35. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 35 Migration vs. food rate (all with humans injected) red=plant, blue=mixed, purple=single species, green=non-viable
  36. 36. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 36 Extinction due to Consuming all Others
  37. 37. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 37 Waves of (Human) Predator-Prey Patterns
  38. 38. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 38 Human migr. rate vs. diversity (all with humans, other entities having 0.1 migration rate)
  39. 39. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 39 Some Elements of a “Computing the Sociology of Survival” Project Part 4
  40. 40. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 40 The Double Complexity of Modelling Socio-Ecological Systems Social Model Ecological Model Complex Individual-based Model Simple System-dynamics ModelComplex Individual-based Model Simple System-dynamics Model Integrated Complex Model Socio-Ecological Model “…The more serious shortcomings of existing modelling techniques, however, are of a structural nature: the failure to adequately capture nonlinear feedbacks within resource and environmental systems and between human societies and these systems.” (Deffuant et al, 2012, p. 523)
  41. 41. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 41 Coordination Mechanisms or Games Ecological Impact From: Socially Constrained Decisions Ecology Cultural Elements evolutionary process evolutionary process To: From simple collective decision making to include culture To move towards how the various elements that are passed down the generations frame and bias the decision making that, in turn, affects the other species we share ecological space with
  42. 42. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 42 From solving the current ecological disaster to anticipating future ones • From simple problems of coordination that we already know about • To understanding some of the subtle longer-term problems that are a result of our current habits and practices • A sort of sociological risk analysis… • ...identifying the various ways in which how we live can go wrong • Hence put in place monitoring for their emergence • ...and so be in a better position to deal with them • ...before it is too late!
  43. 43. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 43 Sociology of Culture Complex Ecological Models Integrated Socio-Ecological Simulations Ontology/Systems Analysis of Models Data Mining on SES outcomes Comparison/Mapping of Data-Mining with Analysis of Outcomes Solution ‘patterns’ Early-warning indicators The Plan!
  44. 44. Computing the Sociology of Survival, Bruce Edmonds, Wageningen, June 2016. slide 44 The End Me: http://bruce.edmonds.name The Centre for Policy Modelling: http://cfpm.org These Slides available at: http://slideshare.net/BruceEdmonds

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