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Finding out what could go wrong before it does – Modelling Risk and Uncertainty

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Talk given as part of Studienstiftung Summer School for German PhD students

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Finding out what could go wrong before it does – Modelling Risk and Uncertainty

  1. 1. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 1 Finding out what could go wrong before it does – Modelling Risk and Uncertainty Bruce Edmonds Centre for Policy Modelling Manchester Metropolitan University
  2. 2. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 2 Classic Policy Modelling Essential steps: 1. Decide on KPIs of policy success 2. List candidate policies 3. Predict impact of policies: cost and KPIs 4. Choose best policy Sometimes this is embedded within a repeated cycle of: a) Decide on a policy (using steps 2-4 above) b) Implement it c) Evaluate the policy
  3. 3. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 3 Statistical Models Approach: 1. Regress KPIs on known outputs 2. Choose inputs that maximise KPIs 3. Hence choose the policy that might most closely implement those inputs • Assumes generic fixed relationship – average success • Straightforward to do • Requires enough data between KPIs and inputs • Candidate policies and regressed inputs may not be obviously relatable • Not customisable to particular situations
  4. 4. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 4 Micro-Simulation Models Approach: 1. Divide up population in to areas/groups 2. Choose simple statistical or other model for reaction 3. For each area/group regress/adjust model for their own data 4. Maybe add some flows between areas/groups 5. Aggregate over areas/groups for overall assessment • Requires details data for each area/group • Good for heterogeneity of groups/areas • Does not work so well when lots of interaction between groups
  5. 5. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 5 Computable General Equilibrium Models Approach: 1. Construct a simplified economic model of situation with and without chosen policy 2. Calculate equilibrium without policy 3. Calculate equilibrium with policy 4. Compare the two equilibria and see if this represents an improvement and how much of one • Only simple models are calculable • Uses strong economic assumptions • The equilibrium is only one restricted and long- term aspect of the outcomes • Does not have a good predictive record
  6. 6. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 6 System Dynamic Models Approach: 1. Build relationship between key variables using flow and storage approach (maybe in a participatory way) 2. Add in equations and delays 3. Run simulated system with probably inputs 4. Evaluate the results somehow • Good for dynamics with delayed feedback • Does not deal with heterogeneity of actors • ‘Touchy-feely’ judgment of outcomes • Can look more real that evidence proves • Not good at predicting outcome values
  7. 7. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 7 Simulation Models Approach: 1. Build a simulation reflecting how parts of system relate 2. Adjust parameters reflecting particular situation/data 3. Check simulation by running for known situation where outcomes and data is known (validation) 4. Produce different variations of simulation to reflect each policy to be tested 5. Run each variation many times and measure the outcomes • Simulation only as strong as knowledge of system • Might have many unknown parameters • Never enough data to sufficiently validate • Policies can be directly implemented • Outcomes assessed in many different ways
  8. 8. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 8 Some modelling tensions precision (model not vague) generality of scope (works for different cases) Lack of error (accuracy of results) realism (reflects knowledge of processes) Economic Models Scenarios Agent-based models Stats/regression models Reality Wanted for policy decisions
  9. 9. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 9 Problem 1: System Complexity • There is no guarantee that a simple model will be adequate to representing complex social/ecological/economic/technical systems • How the parts and actors interact and react might be crucial to the outcomes (e.g. financial markets) • We may not know which parts of the system are crucial to the outcomes • We may not fully understand how the parts interact and react • System and model are both too complex to fully explore and understand in time available
  10. 10. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 10 Problems 2&3: Error and Uncertainty • The values of many key parameters might be unknown or only approximately known • Data might be patchy and of poor quality • Tiny changes in key factors or parameters might have huge consequences for outcomes (the ‘butterfly effect’) • Levels of error may be amplified by the system (as in automated trading in financial markets) • There may be processes that we do not even know are important to the outcomes
  11. 11. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 11 Problem 4: Structural Change • System evolves due to internal dynamics • For example, innovations might occur • System might have several very different behavioural ‘phases’ (e.g. bull and bear markets) which it shifts between • The rules of the system might change rapidly… • ...and well before any equilibrium is reached • Rule-change might be linked to system state • Different parts of the system might change in different ways
  12. 12. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 12 Prediction • Given all these difficulties for many situations, prediction is not only infeasible… • ...but suggesting you can predict is dishonest • and may give false comfort (e.g. Newfoundland Cod Fisheries Collapse or 2007/8 financial crash) • Most techniques only work in two cases, where: 1. There is lots of experience/data over many previous episodes/cases 2. Nothing much changes (tomorrow similar to today) • Often even approximate or probabilistic prediction is infeasible and unhelpful
  13. 13. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 13 The key question…. How does one manage a system or situation that is too complex to predict?
  14. 14. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 14 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
  15. 15. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 15 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.
  16. 16. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 16 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
  17. 17. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 17 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 • Including naturally occurring levels • Although an increase in radiation might not seem to affect many people, it did result in more illnesses in some
  18. 18. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 18 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
  19. 19. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 19 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
  20. 20. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 20 A Cautionary Tale • On the 2nd July 1992 Canada’s fisheries minister, placed a moratorium on all cod fishing off Newfoundland. That day 30,000 people lost their jobs. • Scientists and the fisheries department throughout much of the 1980s estimated a 15% annual rate of growth in the stock – (figures that were consistently disputed by inshore fishermen). • The subsequent Harris Report (1992) said (among many other things) that: “..scientists, lulled by false data signals and… overconfident of the validity of their predictions, failed to recognize the statistical inadequacies in … [their] model[s] and failed to … recognize the high risk involved with state-of-stock advice based on … unreliable data series.”
  21. 21. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 21 What had gone wrong? • “… the idea of a strongly rebuilding Northern cod stock that was so powerful that it …[was]... read back… through analytical models built upon necessary but hypothetical assumptions about population and ecosystem dynamics. Further, those models required considerable subjective judgement as to the choice of weighting of the input variables” (Finlayson 1994, p.13) • Finlayson concluded that the social dynamics between scientists and managers were at play • Scientists adapting to the wishes and worldview of managers, managers gaining confidence in their approach from the apparent support of science
  22. 22. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 22 Example 1: Fishing! • …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
  23. 23. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 23 In this version • 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
  24. 24. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 24 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 A well-mixed patch Each individual represented separately Slow random rate of migration between patches
  25. 25. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 25 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.
  26. 26. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 26 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
  27. 27. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 27 This version designed to test possible outcomes of fishing policies • Complex aquatic plant ecology evolved • Herbivore fish injected into ecology and whole system further evolved • Once a complex ecology with higher-order predators then system is fixed as starting point • Different extraction (i.e. fishing) policies can be enacted on top of this system: – How much fish is extracted each time (either absolute numbers or as a proportion of existing numbers) – Where uniformly at random or patch-by-patch – How many ‘reserves’ are kept – Is there a minimum stock level below which no fishing
  28. 28. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 28 Demonstration of the basic model
  29. 29. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 29 Typical Harvest Shape (last 100 ticks) for different catch levels over 20 different runs Catch level (per tick) ProportionofMaximum
  30. 30. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 30 Decide in your groups 1. Amount of fish extraction (quota) per tick, either: • Absolute number (0-200) • Percentage of existing stock (0-100%) 2. The way fish is extracted, either: • Randomly over whole grid • Random patch chosen and fished, then next until quota for tick is reached 3. How many patches will be kept as reserves (not fished) 4. When to start fishing (0-999 ticks)
  31. 31. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 31 Total Extinction Prob. & Av. Total Harvest (last 100 ticks) for different catch levels Catch level (per tick) ProportionofMaximum
  32. 32. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 32 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
  33. 33. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 33 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
  34. 34. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 34 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
  35. 35. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 35 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
  36. 36. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 36 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
  37. 37. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 37 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
  38. 38. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 38 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"
  39. 39. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 39 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"
  40. 40. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 40 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"
  41. 41. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 41 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
  42. 42. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 42 Example 2: Social Influence and Domestic Water Demand • Produced for the Environment Agency/DEFRA • Part of a bigger project to predict future domestic water demand in the UK given some different future politico-economic scenarios and climate change • The rest of the project were detailed statistical models to do the prediction • This model was to examine the assumptions and look at the envelope of possibilities • Joint work with Olivier Barthelemy and Scott Moss
  43. 43. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 43 Monthly Water Consumption REL_CHNG .88 .75 .63 .50 .38 .25 .13 0.00 -.13 -.25 -.38 -.50 20 10 0 Std.Dev = .17 Mean = .01 N = 81.00
  44. 44. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 44 Relative Change in Monthly Consumption Date FEB 2001 SEP 2000 APR 2000 N O V 1999 JU N 1999 JAN 1999 AU G 1998 M AR 1998 O C T 1997 M AY 1997 D EC 1996 JU L 1996 FEB 1996 SEP 1995 APR 1995 N O V 1994 JU N 1994 REL_CHNG 1.0 .8 .6 .4 .2 -.0 -.2 -.4 -.6
  45. 45. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 45 Purpose of the SI&DWD Model • Not long-term prediction • But to begin to understand the relationship of socially-influenced consumer behaviour to patterns of water demand • By producing a representational agent model amenable to fine-grained criticism • And hence to suggest possible interactions • So that these can be investigated/confirmed • And this loop iterated
  46. 46. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 46 Model Structure - Overall Structure •Activity •Frequency •Volume Households Policy Agent •Temperature •Rainfall •Sunshine Ground Aggregate Demand
  47. 47. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 47 Model Structure - Microcomponents • Each household has a variable number of micro- components (power showers etc.): bath other_garden_watering shower hand_dishwashing washing_machine sprinkler clothes_hand_washing hand_dishwashing toilets sprinkler power_shower • Actions are expressed by the frequency and volume of use of each microcomponent • AVF distribution in model calibrated by data from the Three Valleys
  48. 48. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 48 Model Structure - Household Distribution • Households distributed randomly on a grid • Each household can copy from a set of neighbours (currently those up to 4 units up, down left and right from them) • They decide which is the neighbour most similar to themselves – this is the one they are most likely to copy • Depending on their evaluation of actions they might adopt that neighbour’s actions
  49. 49. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 49 An Example Social Structure - Global Biased - Locally Biased - Self Biased
  50. 50. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 50 Household Behaviour - Endorsements • Action Endorsements: recentAction neighbourhoodSourced selfSourced globallySourced newAppliance bestEndorsedNeighbourSourced • 3 Weights moderate effective strengths of neighbourhoodSourced selfSourced globallySourced endorsements and hence the bias of households • Can be characterised as 3 types of households influenced in different ways: global-; neighbourhood-; and self-sourced depending on the dominant weight
  51. 51. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 51 History of a particular action from one agent’s point of view Month 1: used, endorsed as self sourced Month 2: endorsed as recent (from personal use) and neighbour sourced (used by agent 27) and self sourced (remembered) Month 3: endorsed as recent (from personal use) and neighbour sourced (agent 27 in month 2). Month 4: endorsed as neighbour sourced twice, used by agents 26 and 27 in month 3, also recent Month 5: endorsed as neighbour sourced (agent 26 in month 4), also recent Month 6: endorsed as neighbour sourced (agent 26 in month 5) Month 7: replaced by action 8472 (appeared in month 5 as neighbour sourced, now endorsed 4 times, including by the most alike neighbour – agent 50)
  52. 52. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 52 Policy Agent - Behaviour • After the first month of dry conditions, suggests AFV actions to all households • These actions are then included in the list of those considered by the households • If the household’s weights predispose it, it may decide to adopt these actions • Some other neighbours might imitate these actions etc.
  53. 53. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 53 Number of consecutive dry months in historical scenario 0 1 2 3 4 5 6 7 8 9 J-73 J-74 J-75 J-76 J-77 J-78 J-79 J-80 J-81 J-82 J-83 J-84 J-85 J-86 J-87 J-88 J-89 J-90 J-91 J-92 J-93 J-94 J-95 J-96 J-97 Simulation Date Numberofconsequativedrymonths
  54. 54. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 54 Simulated Monthly Water Consumption REL_CHNG .075 .063 .050 .037 .025 .012 -.000 -.013 -.025 -.038 -.050 120 100 80 60 40 20 0 Std. Dev = .01 Mean= -.000 N = 325.00
  55. 55. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 55 Monthly Water Consumption (again) REL_CHNG .88 .75 .63 .50 .38 .25 .13 0.00 -.13 -.25 -.38 -.50 20 10 0 Std.Dev = .17 Mean = .01 N = 81.00
  56. 56. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 56 Simulated Change in Monthly Consumption Date SEP 1997 APR 1996 N O V 1994 JU N 1993 JAN 1992 AU G 1990 M AR 1989 O C T 1987 M AY 1986 D EC 1984 JU L 1983 FE B 1982 SEP 1980 APR 1979 N O V 1977 JU N 1976 JAN 1975 AU G 1973 M AR 1972 O C T 1970 REL_CHNG .10 .08 .06 .04 .02 0.00 -.02 -.04 -.06
  57. 57. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 57 Relative Change in Monthly Consumption (again) Date FEB 2001 SEP 2000 APR 2000 N O V 1999 JU N 1999 JAN 1999 AU G 1998 M AR 1998 O C T 1997 M AY 1997 D EC 1996 JU L 1996 FEB 1996 SEP 1995 APR 1995 N O V 1994 JU N 1994 REL_CHNG 1.0 .8 .6 .4 .2 -.0 -.2 -.4 -.6
  58. 58. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 58 30% Neigh. biased, historical scenario, historical innov. datesAggregate demand series scaled so 1973=100 0 20 40 60 80 100 120 140 160 180 200 J- 73 J- 74 J- 75 J- 76 J- 77 J- 78 J- 79 J- 80 J- 81 J- 82 J- 83 J- 84 J- 85 J- 86 J- 87 J- 88 J- 89 J- 90 J- 91 J- 92 J- 93 J- 94 J- 95 J- 96 J- 97 Simulation Date RelativeDemand
  59. 59. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 59 80% Neigh. biased, historical scenario, historical innov. datesAggregate demand series scaled so 1973=100 0 20 40 60 80 100 120 140 160 180 200 J- 73 J- 74 J- 75 J- 76 J- 77 J- 78 J- 79 J- 80 J- 81 J- 82 J- 83 J- 84 J- 85 J- 86 J- 87 J- 88 J- 89 J- 90 J- 91 J- 92 J- 93 J- 94 J- 95 J- 96 J- 97 Simulation Date RelativeDemand
  60. 60. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 60 80% Neigh. biased, medium-high scenario, historical innov. DatesAggregate demand series scaled so 1973=100 0 20 40 60 80 100 120 140 160 180 200 Jan- 73 Jan- 74 Jan- 75 Jan- 76 Jan- 77 Jan- 78 Jan- 79 Jan- 80 Jan- 81 Jan- 82 Jan- 83 Jan- 84 Jan- 85 Jan- 86 Jan- 87 Jan- 88 Jan- 89 Jan- 90 Jan- 91 Jan- 92 Jan- 93 Jan- 94 Jan- 95 Jan- 96 Jan- 97 Simulation Date RelativeDemand
  61. 61. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 61 What did the model tell us? • That it is possible that social processes: – can cause a high and unpredictable variety in patterns of demand – can ‘lock-in’ behavioural patterns and partially ‘insulate’ them from outside influence (droughts only occasionally had a permenant affect on patterns of consumption) • and that the availability of new products could dominate effects from changing consumptions habits
  62. 62. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 62 Conclusions of Example 2 • ABM can be used to construct fairly-rich computational descriptions of socially-related phenomena which can be used – to replicate systems analytic techniques can’t deal with – to explore some of the possibilities • especially those unpredictable but non-random possibilities caused to human behaviour – as part of an iterative cycle of detailed criticism • validatable by both data and expert opinion – to inform be informed by good observation
  63. 63. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 63 A central dilemma – what to trust? Intuitions A complex simulation A policy maker
  64. 64. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 64 But Modeller to Policy Actor Interface is not easy • Analysts/modellers and policy actors have different: goals, language, methods, habits… • Policy Actors will often want predictions – certainty – even if the analysts know this is infeasible • Analysts will know how difficult the situation is to understand and how much is unknown, and will want to communicate their caveats (which often get lost in the policy process) • So discussion between them does not necessarily go easily
  65. 65. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 65 Many views of a model (I) - due to syntactic complexity • Computational ‘distance’ between specification and outcomes means that • There are (at least) two very different views of a simulation (consequences of complexity) Simulation Representation of OutcomesSpecification
  66. 66. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 66 Representation of Outcomes (II) Many views of a model (II) - understanding the simulation (consequences of complexity) Simulation Representation of Outcomes (I)Specification Analogy 1 Analogy 2 Theory 1 Theory 2 Summary 1 Summary 2
  67. 67. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 67 Four Meanings (of the PM) Research World 1. The researcher’s idea/intention for the PM 2. The fit of the PM with the evidence/data The ideavalidation relation extensively discussed within research world Policy World 3. The usefulness of the PM for decisions 4. The communicable story of the PM The goalinterpretation relation extensively discussed within policy world
  68. 68. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 68 Two Worlds Research • Ultimate Goal is Agreement with Observed (Truth) • Modeller also has an idea of what the model is and how it works Policy • Ultimate Goal is in Final Outcomes (Usefulness) • Decisions justified by a communicable causal story Policy Model • Labels/Documentation may be different from all of the above! Modeller Policy Advisor
  69. 69. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 69 Joining the Two Worlds Empirical • Ultimate Goal is Agreement with Observed (Truth) • Modeller also has an idea of what the model is and how it works Instrumental • Ultimate Goal is in Final Outcomes (Usefulness) • Decisions justified by a communicable causal story Model • Labels/Documentation may be different from all of the above! Tighter loop (e.g. via participatory modelling)
  70. 70. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 70 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’) • A tight measure-react loop can be essential for driving policy – modelling might help in this – but this is hard!
  71. 71. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 71 The End! Bruce Edmonds: http://bruce.edmonds.name These Slides: http://slideshare.net/bruceedmonds Centre for Policy Modelling: http://cfpm.org
  72. 72. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 72 Some Pitfalls in Model Construction Pitfalls Part 1
  73. 73. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 73 Modelling Assumptions • All models are built on assumptions, but… • They have different origins and reliability, e.g.: – Empirical evidence – Other well-defined theory – Expert Opinion – Common-sense – Tradition – Stuff we had to assume to make the model possible • Choosing assumptions is part of the art of simulation but which assumptions are used should be transparent and one should be honest about their reliability – plausibility is not enough!
  74. 74. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 74 Theoretical Spectacles • Our conceptions and models constrain how we 1. look for evidence (e.g. where and what kinds) 2. what kind of models we develop 3. how we evaluate any results • This is Kuhn’s “Theoretical Spectacles” (1962) – e.g. continental drift • This is MUCH stronger for a complex simulation we have immersed ourselves in • Try to remember that just because it is useful to think of the world through our model, this does not make them valid or reliable
  75. 75. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 75 Over-Simplified Models • Although simple models have many pragmatic advantages (easier to check, understand etc.)… • If we have missed out key elements of what is being modelled it might be completely wrong! • Playing with simple models to inform formal and intuitive understanding is an OK scientific practice • …but it can be dangerous when informing policy • Simple does not mean it is roughly correct, or more general or gives us useful intuitions • Need to accept that many modelling tasks requested of us by policy makers are not wise to do with restricted amounts of time/data/resources
  76. 76. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 76 Underestimating model limitations • All models have limitations • They are only good for certain things: a model that explains well might not predict well • The may well fail when applied in a different context than the one they were developed in • Policy actors often do not want to know about limitations and caveats • Not only do we have to be 100% honest about these limitations, but we also have to ensure that these limitations are communicated with the model
  77. 77. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 77 Not checking & testing a model thoroughly • Doh! • Sometimes there is not a clear demarcation between an exploratory phase of model development and its application to serious questions (whose answers will impact on others) • Sometimes an answer is demanded before thorough testing and checking can be done – “Its OK, I just want an approximate answer” :-/ • Sometimes researchers are not honest • Depends on the potential harm if the model is relied on (at all) and turns out to be wrong
  78. 78. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 78 Some Pitfalls in Model Application Pitfalls Part 2
  79. 79. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 79 Insufficiently Validated Models • One can not rely on a model until it has been rigorously checked and tested against reality • Plausibility is nowhere NEAR enough • This needs to be on more than one case • Its better if this is done independently • You can not validate a model using one set of settings/cases then rely on it in another • Validation usually takes a long time • Iterated development and validation over many cycles is better than one-off models (for policy)
  80. 80. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 80 Promising too much • Modellers are in a position to see the potential of their work, and so can tantalise others by suggesting possible/future uses (e.g. in the conclusions of papers or grant applications) • They are tempted to suggest they can ‘predict’, ‘evaluate the impact of alternative polices’ etc. • Especially with complex situations (that ABM is useful for) this is simply deceptive • ‘Giving a prediction to a policy maker is like giving a sharp knife to a child’
  81. 81. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 81 The inherent plausibility of ABMs • Due to the way ABMs map onto reality in a common-sense manner (e.g. peopleagents)… • …visualisations of what is happening can be readily interpretted by non-modellers • and hence given much greater credence than they warrant (i.e. the extent of their validation) • It is thus relatively easy to persuade using a good ABM and visualisation • Only we know how fragile they are, and need to be especially careful about suggesting otherwise
  82. 82. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 82 Model Spread • On of the big advantages of formal models is that they can be passed around to be checked, played with, extended, used etc. • However once a model is out there, it might get used for different purposes than intended • e.g. the Black-Scholes model of derivative pricing • Try to ensure a released model is packaged with documentation that warns of its uses and limitations
  83. 83. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 83 Narrowing the evidential base • The case of the Newfoundland cod, indicates how models can work to constrain the evidence base, therefore limiting decision making • If a model is considered authoritative, then the data it uses and produces can sideline other sources of evidence • Using a model rather than measuring lots of stuff is cheap, but with obvious dangers • Try to ensure models are used to widen the possibilities considered, rather than limit them
  84. 84. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 84 Other/General Pitfalls Pitfalls Part 3
  85. 85. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 85 Confusion over model purpose • A model is not a picture of reality, but a tool • A tool has a particular purpose • A tool good for one purpose is probably not good for another • These include: prediction, explanation, as an analogy, an illustration, a description, for theory exploration, or for mediating between people • Modellers should be 100% clear under which purpose their model is to be judged • Models need to be justified for each purpose separately
  86. 86. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 86 When models are used out of the context they were designed for • Context matters! • In each context there will be many conditions/assumptions we are not even aware of • A model designed in one context may fail for subtle reasons in another (e.g. different ontology) • Models generally need re-testing, re-validating and often re-developing in new contexts
  87. 87. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 87 What models cannot reasonably do • Many questions are beyond the realm of models and modellers but are essentially – ethical – political – social – semantic – symbolic • Applying models to these (outside the walls of our academic asylum) can confuse and distract
  88. 88. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 88 The uncertainty is too great • Required reliability of outcome values is too low for purpose • Can be due to data or model reasons • Radical uncertainty is when its not a question of degree but the situation might fundamentally change or be different from the model • Error estimation is only valid in absence of radical uncertainly (which is not the case in almost all ecological, technical or social simulations) • Just got to be honest about this and not only present ‘best case’ results
  89. 89. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 89 A false sense of security • If the outcomes of a model give a false sense of certainly about outcomes then a model can be worse than useless; positively damaging to policy • Better to err on the side of caution and say there is not good model in this case • Even if you are optimistic for a particular model • Distinction here between probabilistic and possibilistic views
  90. 90. Finding out what could go wrong before it does, Bruce Edmonds, Cambridge, Sept. 2018. slide 90 Not more facts, but values! • Sometimes it is not facts and projections that are the issue but values • However good models are, the ‘engineering’ approach to policy (enumerate policies, predict impact of each, choose best policy) might be inappropriate • Modellers caught on the wrong side of history may be blamed even though they were just doing the technical parts

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