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Agent Based Modelling and Scenario Analysis in the Policy Process

Agent Based Modelling and Scenario Analysis in the Policy Process. Lessons from the FIRMA project informed by the first morning of the European Water Scenarios Workshop. Andrea Tilche’s objectives. Model supported consistent scenarios Integrating qualitative and quantitative approaches

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Agent Based Modelling and Scenario Analysis in the Policy Process

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  1. Agent Based Modelling and Scenario Analysis in the Policy Process Lessons from the FIRMA projectinformed by the first morning of the European Water Scenarios Workshop Scott Moss, Centre for Policy Modelling

  2. Andrea Tilche’s objectives • Model supported consistent scenarios • Integrating qualitative and quantitative approaches • Richly expressive analysis • Story lines and modelling approaches • Transparent and well documented • Stakeholder involvement • Scenarios support risk based a risk based approach to decision making Scott Moss, Centre for Policy Modelling

  3. Joe Alcamo’s scenario vision • Qualitative and quantitative scenarios different but complementary • Iterative process between experts, modellers and stakeholders/policy makers to develop scenarios • “Final scenario” necessary for wider use • “We have to be visionary but also practical” Scott Moss, Centre for Policy Modelling

  4. Scenario Development Process Scott Moss, Centre for Policy Modelling

  5. Models from the FIRMA Project • Validation • Integration of qualitative and quantitative • Integration of natural science and social behaviour in single model Scott Moss, Centre for Policy Modelling

  6. Validation • Agents designed to describe real human or organisational actors • Individual behaviour • Social interaction • how • with whom • Rules can be validated by domain experts (eg stakeholders) Scott Moss, Centre for Policy Modelling

  7. Model Structure - Overall Structure PolicyAgent • Ownership • Frequency • Volume Households Ground • Temperature • Rainfall • Sunshine Aggregate Demand Scott Moss, Centre for Policy Modelling

  8. An Example Social Structure - Global Biased- Locally Biased- Self Biased Scott Moss, Centre for Policy Modelling

  9. Statistical validation – model version 2 Scott Moss, Centre for Policy Modelling

  10. Social Behaviour and Climate Change Reference runs MH climate change Individual Social Social influence: individual=33%, social=80%. All runs: 1973=100. Scenarios broadly correspond to EA reference scenarios: individual (alphaand beta); social (gamma and delta). Scott Moss, Centre for Policy Modelling

  11. Model of UK Foresight Scenario • OFV specified by Environment Agency • Water saving devices specified by Agency • Increased volatility turned out to be a bug Scott Moss, Centre for Policy Modelling

  12. Some simulation general results • The more agents influence one another, the less variability across scenarios in aggregate water for demand • Confirmed that agent interaction and threshold behaviour generates heavy-tailed distributions • Any population distribution has undefined variance (and maybe undefined mean). • Conforms to observed domestic water demand Scott Moss, Centre for Policy Modelling

  13. Two approaches Compared Agent based: Volatile Unpredictable • Dynamic simulation: • Smooth scenarios • Predictable through convergence Scott Moss, Centre for Policy Modelling

  14. Risk • Clustered volatility results from inter alia • Threshold behaviour by individuals • Dense patterns of social interaction with neighbours/acquaintances • Implies heavy tailed population distributions or (more plausibly?) no population distribution • Actuarial science based on finite-variance distributions • If distributions change endogenously through social interaction, does this affect prevailing risk/uncertainty assessment techniques? Scott Moss, Centre for Policy Modelling

  15. Environmental Scenario Objectives(paraphrased from Alcamo and Henrichs) • To raise awareness about environmental problems • To take into account large time and spatial scales • To illustrate alternative environmental futures • To explore alternative policy pathways • To assess policy robustness to different conditions • To investigate connections among future problems • To combine qualitative and quantitative information Scott Moss, Centre for Policy Modelling

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