1. 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
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. Centre for Policy Modelling
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
Policy Agent
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 (alpha and 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. Centre for Policy Modelling
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. Centre for Policy Modelling
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