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Using agent-based simulation for socio-ecological uncertainty analysis

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A talk given in the MMU Big Data Centrem, 30th October 2018.

Both social and ecological systems can be highly complex, but the interaction between these two worlds - a socio-ecological system (SES) - can add even greater levels. However, the maintenance of SES are vital to our well being and the health of the planet. We do not know how such systems work in practice and we lack good data about them (especially the ecological side) so predicting the effect of any particular policy is infeasible. Here we present an approach which tries to understand some of the ways in which SES may go wrong, but constructing different complex simulation models and analysing the emergent outcomes. These, in silico, examples can allow for the institution of targeted data gathering instruments that give the earliest possible warning of deleterious outcomes, and thus allow for timely remedial responses. An example of this approach applied to fisheries is described.

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Using agent-based simulation for socio-ecological uncertainty analysis

  1. 1. 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
  2. 2. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 2 Motivation and discussion Part 1
  3. 3. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 3 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
  4. 4. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 4 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).
  5. 5. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 5 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
  6. 6. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 6 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
  7. 7. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 7 A Socio-Ecological Test Bed – general description Part 2a
  8. 8. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 8 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.)
  9. 9. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 9 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.
  10. 10. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 10 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
  11. 11. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 11 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
  12. 12. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 12 How Dominance is Decided (Caldarelli, Higgs, and McKane 1998)
  13. 13. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 13 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.
  14. 14. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 14 Demonstration of the basic model
  15. 15. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 15 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)
  16. 16. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 16 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
  17. 17. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 17 A Socio-Ecological Test Bed – applied to impact of simple ‘hunter- gatherer” humans Part 2b
  18. 18. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 18 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
  19. 19. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 19 Human migr. rate vs. diversity (all with humans, other entities having 0.1 migration rate)
  20. 20. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 20 Effect of humans vs. food input to world diversity of ecology, blue=with humans, red=without
  21. 21. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 21 Effect of humans vs. food input to world proportion of ecology types, red=plant, blue=mixed, purple=single species, green=non-viable
  22. 22. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 22 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
  23. 23. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 23 Migration (all) vs. food rate (all with humans)
  24. 24. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 24 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
  25. 25. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 25 A Socio-Ecological Test Bed – applied to fisheries collapses Part 2c
  26. 26. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 26 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.”
  27. 27. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 27 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
  28. 28. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 28 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
  29. 29. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 29 Total Extinction Prob. & Av. Total Harvest (last 100 ticks) for different catch levels Catch level (per tick) ProportionofMaximum
  30. 30. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 30 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
  31. 31. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 31 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
  32. 32. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 32 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
  33. 33. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 33 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
  34. 34. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 34 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
  35. 35. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 35 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
  36. 36. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 36 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"
  37. 37. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 37 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"
  38. 38. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 38 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"
  39. 39. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 39 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
  40. 40. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 40 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
  41. 41. Using Agent-Based Simulation for socio-ecological uncertainty analysis, Bruce Edmonds, BDC Seminar, MMU, Oct 2018. slide 41 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

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