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 Edmonds
Centre for Policy Modelling,
Manchester Metropolitan University

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

Some Philosophy
Part 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 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

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
Data Evidence
Simple Model
Data Evidence
Simple Model
Complex Model
Representation
Simplification
DIM
Analytically
Solvable Model

The Problem
Part 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 Modelling
Part 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 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.

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 ecology
Number unique species (with high mutation rate 1%)

Simulation at (up to) Reference Point
Herbivores
Appear
First Successful
Plant
Simulation
“Frozen”
Carnivores
Appear

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
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)

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

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.name
The Centre for Policy Modelling:
http://cfpm.org
These Slides available at: http://slideshare.net/BruceEdmonds

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

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