An invited talk in Tromsoe, 5 June 2018. Both social and ecological systems are complex, but when they combine (as when human societies farm/hunt) there is a double complexity. This complexity means it is infeasible to predict the outcome of their interaction and unwise to rely on any prediction. An alternative approach is to use complex simulations to try and discover some possible ways that such systems can go wrong. This can reveal risks that other approaches might miss, due to the fact that more of the complexity is included within the model. Once a risk is identified then measures to monitor its emergence can be implemented, allowing the earliest possible warning of this. An example of this approach applied to a fisheries ecosystem is described.

1. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 1
Socio-Ecological Simulation
– a risk-assessment approach
Bruce Edmonds
Centre for Policy Modelling
Manchester Metropolitan University

2. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 2
Acknowledgements

3. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 3
Motivation and discussion
Part 1

4. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 4
The key question….
How does one manage a system or
situation that is too complex to predict?

5. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 5
Lessons from robotics: Part I
Robotics in the 70s and 80s tried to (iteratively):
1. build a map of its situation (i.e. a predictive
model)
2. use this model to plan its best action
3. then try to do this action
4. check it was doing OK go back to (1)
But this did not work in any realistic situation:
• It was far too slow to react to its world
• to make useable predictions it had to make too
many dodgy assumptions about its world

6. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 6
Lessons from robotics: Part II
Rodney Brooks (1991) Intelligence without
representation. Artificial Intelligence, 47:139–160
A different approach:
1. Sense the world in rich fast ways
2. React to it quickly
3. Use a variety of levels of reaction
a. low simple reactive strategies
b. switched by progressively higher ones
Do not try to predict the world, but react to it quickly
This worked much better.

7. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 7
Lessons from Weather Forecasting
• Taking measurements at a few places and trying to
predict what will happen based on simple models
based on averages does not work well
• Understanding the weather improved with
very detailed simulations fed by rich and
comprehensive sensing of the system
• Even then they recognize that there are
more than one possibilities concerning the
outcomes (using ensembles of specific
outcomes)
• If these indicate a risk of severe weather they issue a
warning so mitigating measures can be taken

8. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 8
Lessons from Radiation Levels
• The human body is a very complex system
• It has long been known that too much radiation can
cause severe illness or death in humans
• In the 30s & 40s it was assumed there was a “safe”
level of radiation
• However it was later discovered that any level of
radiation carried a risk of illness (non-threshold
models fitted the data better)
• Including naturally occurring levels
• Although an increase in radiation might not seem to
affect many people, it caused more illnesses in some

9. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 9
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

10. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 10
Why simple models won’t work
• Simpler models do not necessarily get things
“roughly” right
• Simpler models are not more general
• They can also be very deceptive – especially with
regards to complex ways things can go wrong
• In complex systems the detailed interactions can
take outcomes ‘far from equilibrium’ and far from
average behaviour
• Sometimes, with complex systems, a simple
model that relies on strong assumptions can be
far worse than having no models at all

11. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 11
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

12. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 12
In this talk I describe…
• …a complex simulation of an ecosystem in which
humans can be included to illustrate some of how
such a risk-analysis approach could work
• The model does not intend to be approximately
right and give any indication of what will happen
• 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 of fishing that occurs

13. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 13
A Socio-Ecological Test Bed
– general description
Part 2a

14. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 14
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.)

15. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 15
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 embedded within one nutritional ‘economy’
• possibility of invasive species, extinctions, new
species by mutation etc.

16. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 16
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

17. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 17
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

18. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 18
How Dominance is Decided
(Caldarelli, Higgs, and McKane 1998)

19. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 19
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.

20. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 20
Demonstration of the basic model

21. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 21
First, evolve a rich mixed ecology
Evolve and save a suitable
complex ecology with a
balance of tropic layers
(final state to the left with
log population scale)
Herbivores
Appear
First Successful
Plant
Simulation
“Frozen”
Carnivores
Appear

22. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 22
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

23. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 23
A Socio-Ecological Test Bed
– applied to impact of simple ‘hunter-
gatherer” humans
Part 2b

24. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 24
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

25. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 25
Human migr. rate vs. diversity (all with humans,
other entities having 0.1 migration rate)

26. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 26
Effect of humans vs. food input to world
diversity of ecology, blue=with humans, red=without

27. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 27
Effect of humans vs. food input to world
proportion of ecology types, red=plant, blue=mixed,
purple=single species, green=non-viable

28. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 28
Migration (all) vs. food rate (all with humans)

29. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 29
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

30. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 30
A Socio-Ecological Test Bed
– applied to fisheries collapses
Part 2c

31. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 31
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

32. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 32
1000 ticks from a very complex
ecology with mutation
Part2c-1

33. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 33
Total Extinction Prob. & Av. Total Harvest
(last 100 ticks) for different catch levels
Catch level (per tick)
ProportionofMaximum

34. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 34
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

35. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 35
Num Fish (all species, 20 runs) – catch
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

36. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 36
Num Fish (all species, 20 runs) – catch
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

37. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 37
5000 ticks from a somewhat complex
ecology without mutation active
Part2c-2

38. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 38
Average (over 10 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

39. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 39
Average (over 10 runs) of numbers of fish
species at end of 5000 simulation ticks
0
2
4
6
8
10
12
0 10 20 30 40 50 60
NumberofSpecies
Catch Level

40. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 40
Ave. Number of Species vs. Catch Level
(from a different starting ecology, 25 runs)
0
2
4
6
8
10
12
14
0 5 10 15 20 25 30 35 40
Num Species Fish

41. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 41
5000 ticks from a complex ecology
with mutation comparing two different
fishing regimes
Part2c-2

42. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 42
Comparing two ways of harvesting
Two methods of collecting fish:
1. A series of patches (chosen at random) are
cleared out of fish until the catch level is reached
(more like large nets) – “by patches”
2. Fish are randomly selected from across the
whole space until the catch level is reached –
“uniform”
(There are also a fixed number of “reserves” where
no fish are caught from)
20 runs for each condition, over 5000 ticks, starting
from the same starting point.

43. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 43
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"

44. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 44
Average Number of Species, Catch=30
0
5
10
15
20
25
30
35
0 200 400 600 800 1000
AverageNumberofSpecies
Time
"by patches"
"uniform"

45. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 45
Average Number of Species, Catch=40
0
5
10
15
20
25
30
35
0 200 400 600 800 1000
AverageNumberofSpecies
Time
"by patches"
"uniform"

46. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 46
Concluding Discussion
Part 3

47. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 47
Conclusions
• Complex systems can not be relied upon to behave in
regular ways
• Often averages, equilibria etc. are not very
informative
• Future levels can not meaningfully be predicted
• Simpler models may well make unreliable
assumptions and not be representative
• Rather complex models can be part of a risk-analysis
• 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’)

48. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 48
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!

49. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 49
The End!
Bruce Edmonds:
http://bruce.edmonds.name
These Slides: http://slideshare.net/bruceedmonds
Centre for Policy Modelling: http://cfpm.org
The basic model (without “humans”) is available at:
http://openabm.org/model/4204