Using agent-based simulation for socio-ecological uncertainty analysis

Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 1
Using agent-based simulation for socio-
ecological uncertainty analysis
Bruce Edmonds
Centre for Policy Modelling
Manchester Metropolitan University

Motivation and discussion
Part 1

WWF Living Planet Report 2018
• Published yesterday:
http://worldwildlife.org/pages/living-planet-report-2018
• Reported a world-wide -50% to -67% decrease in
vertebrate abundance since 1970
• Based on monitoring/estimates numbers of 16,700
species around the world

Ecological Modelling and Data
Two approaches:
1. Model-driven: use sparse data (e.g. declared
catches, surveys) with equilibrium-based
mathematical models to estimate diversity etc.
However, does not capture anything like the
complexity of existing ecosystems.
2. Data-driven: Analyse what data there is (or
implied data, e.g. satellite images) and try and
work out what is happening. However, the
complexity of what is happening is difficult to
infer from just data. No good prediction (even
using ML techniques).

Socio-Ecological Systems
• Are the combination of human society embedded
within an ecological system (SES)
• Many social and ecological systems are far too
complex to predict
• Their combination is doubly complex
• E.g. fisheries, deforestation, species extinctions
• Yet we still basically use the 1970s robotics
“predict and plan” approach to these…
• …as if we can plan optimum policies by
estimating/projecting future impact

In this talk I describe…
• …a complex simulation of an ecosystem in which
humans (or human affects) can be included to
illustrate some of how such a risk-analysis
approach could work
• The model does not intend to be predictive…
• But rather to reveal some of the real possibilities –
things that might happen
• It shows how unpredictable its outcomes can be
• And that, in this model, there is no “safe” level of
exploitation, but significant extinction risks at
whatever level

A Socio-Ecological Test Bed
– general description
Part 2a

Individual/Agent-Based Modelling
• Is where each individual is separately represented
• Each can have its own properties or behaviours
• The interactions between these are also explicitly
modelled (as messages between these
programmed to have the required effects)
• The simulation is the run (many times) to see the
range of what ‘unfolds’, sometimes in unexpected
ways (so called ‘emergent’ phenomena)
• These outcomes are then analysed, visualised
• Called ‘agents’ if these individuals can be
interpreted as thinking (learning, reasoning etc.)

Design Criteria
To exhibit emergent:
• detailed entity-entity interactions
• complex food webs between many species
• co-evolutionary development
• spatial complexity (different niches, diffusion
processes, predator waves, etc.)
• all entities embedded within the spatial nutiritional
‘economy’
• possibility of invasive species, extinctions, new
species by mutation etc.

This model…
• …is a dynamic, spatial, individual-based ecological model
that has 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 the impact of humans can be imposed or even
agents representing humans ‘injected’ into the simulation
• The outcomes can be then analysed at a variety of levels
over long time scales, and under different scenarios
• Paper: Edmonds, B. (in press) A Socio-Ecological Test
Bed. Ecology & Complexity.
• Full details and code at: http://openabm.org/model/4204

The Model
• A wrapped 2D grid of
well-mixed patches with:
– energy (transient)
– bit string of characteristics
• Organisms represented
individually with its own
characteristics,
including:
– bit string of characteristics
– energy
– position
– stats recorders
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. until a viable is established, random new individual
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 is randomly paired with a number of
others on the 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
9. Statistics. Various statistics are calculated.

Demonstration of the basic model

First, evolve a rich mixed ecology
Evolve and save a suitable
complex ecology with a
balance of tropic layers
(final state to the left)
Herbivores
Appear
First Successful
Plant
Simulation
“Frozen”
Carnivores
Appear
Trophic Level
Log10(Abundance)

Then explore starting from there
Herbivores
Appear
FirstSuccessful
Plant
Simulation
“Frozen”
Carnivores
Appear
Evolve a complex
ecology and save
this state
Do multiple runs of the
simulation starting
from there for each
condition to test
After, collect statistics or visualisations about what
happened in the runs to understand the possible paths

– applied to impact of simple ‘hunter-
gatherer” humans
Part 2b

An Example of Adding Pretty Simple
“Human” Agents
• The agents representing humans are “injected” (as a
group) into the simulation into a pre-evolved ecology
with complex food webs
• 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 mutation
– might share excess food with others around
– might have different migration rates etc.
• Could have many other learning, reasoning abilities

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

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 rate people
vs migration rate others
proportion of
ecology types
25 simulations
each treatment
red=plant,
blue=mixed,
purple=single
species,
green=non-
viable

Migration (all) vs. food rate (all with humans)

Some observations
• It does not ever get to a ‘steady state’ but is
constantly changing and co-adapting
• So approaches to assessing resilience that assume
this are not easily applicable
• But we can compare with and without “humans” after
a long period of time
• In this model, the way “humans” adapt seems to be
more significant that which particular adaption is
adopted
• This is only a simple kind of society
• Competition among human groups and their general
social evolution is also significant here

– applied to fisheries collapses
Part 2c

North Atlantic Cod Fishery Collapse
• In July 1992 Canada’s fisheries minister
placed a moratorium on all cod fishing off the
NE coast of Newfoundland and Labrador.
That day 30,000 people lost their jobs and
hundreds of years fishing for cod off those
coasts ended.
• Models being used predicted healthy stocks
up until 1989, and hence had made the
problem worse.
• Subsequent Harris report: “…scientists,
lulled by false data signals and, to some
extent, overconfident of the validity of their
predictions, failed to recognize the statistical
inadequacies in their bulk biomass model
and failed to properly acknowledge and
recognize the high risk involved with state-
of-stock advice based on relatively short and
unreliable data series.”

Global Fisheries Collapses
• Not limited to Atlantic Cod
• Complete lack of primary data
• Models do not capture
complex inter-species
interactions
• Let alone the possible
consequences of fishing

In this version of the model
• Plants and higher order entities (fish)
distinguished (no photosynthesizing herbivores!)
• First a rich competing plant ecology is evolved
• Then single fish injected until fish take hold and
evolve until there is an ecology of many fish
species, run for a bit to allow ‘transients’ to go
• This state then frozen and saved
• From this point different ‘fishing’ polices
implemented and the simulations then run
• with the outcomes then analysed

Total Extinction Prob. & Av. Total Harvest
(last 100 ticks) for different catch levels
Catch level (per tick)
ProportionofMaximum

Num Fish (all species, 20 runs) – catch
level 25
0
1000
2000
3000
4000
5000
6000
0
31
62
93
124
155
186
217
248
279
310
341
372
403
434
465
496
527
558
589
620
651
682
713
744
775
806
837
868
899
930
961
992

level 35
0
1000
2000
3000
4000
5000
6000
0
31
62
93
124
155
186
217
248
279
310
341
372
403
434
465
496
527
558
589
620
651
682
713
744
775
806
837
868
899
930
961
992
Catch target=30

level 50
0
1000
2000
3000
4000
5000
6000
0
31
62
93
124
155
186
217
248
279
310
341
372
403
434
465
496
527
558
589
620
651
682
713
744
775
806
837
868
899
930
961
992

Average (over 20 runs) of fish at end of 5000
simulation ticks
0
1000
2000
3000
4000
5000
0 20 40 60 80 100
Number Fish for Different Catch Levels

Average (over 20 runs) of numbers of fish
species at end of 5000 simulation ticks
0
20
40
60
80
100
120
140
0 20 40 60 80 100
Num Fish Species with Catch Level

Average Number of Species vs. Catch
Level (from a different starting ecology)
0
2
4
6
8
10
12
14
0 5 10 15 20 25 30 35 40
Num Species Fish

Average Number of Species, Catch=20
0
5
10
15
20
25
30
35
0 200 400 600 800 1000
AverageNumberofSpecies
Time
"by patches"
"uniform"

0
5
10
15
20
25
30
35
0 200 400 600 800 1000
Time
"by patches"
"uniform"

A risk-analysis approach
1. Give up on estimating future impact or “safe” levels
of exploitation
2. Make simulation models that include more of the
observed complication and complex interactions
3. Run these lots of times with various scenarios to
discover some of the ways in which things can go
surprisingly wrong (or surprisingly right)
4. Put in place sensors/measures that would give us
the earliest possible warning that these might be
occurring in real life
5. React quickly if these warning emerge
6. Continue developing the science, led by some
proper data collection rather than simple models

Conclusions
• Complex systems can not be relied upon to behave in
regular ways – often averages, equilibria etc. are not
very informative
• Simpler models may well make unreliable
assumptions and not be representative
• Rather complex models can be part of a risk-analysis,
especially when data is sparse
• Identifying some of the ways in which things can go
wrong, implement measure to watch these, then be
able to react quickly to these (‘driving policy’)
• This can be used to direct data gathering for direct
monitoring

The End!
These slides: http://slideshare.net/bruceedmonds
Centre for Policy Modelling: http://cfpm.org
A paper about the basic ecological model: http://cfpm.org/???
The basic model (without “humans”) at:
http://comses.net/model/4204

Using agent-based simulation for socio-ecological uncertainty analysis

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