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

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 EdmondsCentre for Policy Modelling,

Manchester Metropolitan University


Outline of the talk

1. Some philosophy of modelling – the consequences of complexity

2. The problem of ecological survival3. Truly integrated socio-ecological modelling4. Towards understanding the sociology of survival


Some PhilosophyPart 1


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


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’


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


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


This talk argues for the following strategy: weakening the generality of our formal models to achieve more validity in the face of the AAAIn 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


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)


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


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)


KISS vs. KIDS as a search strategy

Simplest Possible

More Complex in Aspect 2

etc.

More Complex in Aspect 1

KISS

KIDS


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!


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

Representation

Simplification

DIM

Analytically Solvable Model


The ProblemPart 2


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


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)


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


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!


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


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


Integrated Socio-Ecological ModellingPart 3


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


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


How Dominance is Decided

(Caldarelli, Higgs, and McKane 1998)


Model sequence each simulation tick

1. Input energy equally divided between patches.2. Death. A life tax is subtracted, some die, age incremented3. Initial seeding with random new individual until one is viable4. 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.


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)


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


Longer-Term Trends in Num. Species

Red=many trophic layers, blue=herbivore ecologyNumber unique species (with high mutation rate 1%)


Simulation at (up to) Reference Point

HerbivoresAppear

First SuccessfulPlant

Simulation“Frozen”

CarnivoresAppear


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


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


Effect of humans vs. food input to world

diversity of ecology, blue=with humans, red=without


Effect of humans vs. food input to world

proportion of ecology types, red=plant, blue=mixed, purple=single species, green=non-viable


Migration vs. food rate (all with humans injected)

red=plant, blue=mixed, purple=single species, green=non-viable


Extinction due to Consuming all Others


Waves of (Human) Predator-Prey Patterns


Human migr. rate vs. diversity (all with humans, other entities having 0.1 migration rate)


Some Elements of a “Computing the Sociology of Survival” Project

Part 4


The Double Complexity of Modelling Socio-Ecological Systems

Social Model

Ecological Model

Complex Individual-based Model

SimpleSystem-dynamics

ModelComplex Individual-based Model

SimpleSystem-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)


Coordination Mechanisms

or Games

Ecological Impact

From:

Socially Constrained Decisions

EcologyCultural Elements

evolutionaryprocess

evolutionaryprocess

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


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!


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!


The End

Me: http://bruce.edmonds.nameThe Centre for Policy Modelling:

http://cfpm.orgThese Slides available at: http://slideshare.net/BruceEdmonds