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Socio-Ecological Simulation - a risk-assessment approach

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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.

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Socio-Ecological Simulation - a risk-assessment approach

  1. 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. 2. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 2 Acknowledgements
  3. 3. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 3 Motivation and discussion Part 1
  4. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 20. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 20 Demonstration of the basic model
  21. 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. 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. 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. 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. 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. 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. 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. 28. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 28 Migration (all) vs. food rate (all with humans)
  29. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 46. Socio-Ecological Simulation - a risk-assessment approach, Bruce Edmonds, Tromsoe, June 2018. slide 46 Concluding Discussion Part 3
  47. 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. 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. 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

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