1. Socio-environmental Agents Thomas E Downing SEI Oxford

2. Choosing methodology Attributes of multi-agent modelling A water example Challenges

3. Modelling approaches Frameworks and observation: –Descriptive: Good as sources & validation, but difficult to generalise from –Sociological Theory: rich, difficult to unambiguously relate to any specific case –Statistical and experimental: Valid but impossible to extend to future Disciplinary(?): –Micro-economic: Puts techniques above problem –Game theory: Only solvable with a small number of discrete choices –Population dynamics: Does not (really) relate to micro behaviour –Physics-derived models: Can be useful for post hoc encapsulation –Descriptive computational simulation: difficult to get enough observations Multi-agent –Robotic experiments: costly and unreliable, experiments take a lot of time and effort –Artificial life computational models: Good on process, can be disconnected –Artificial Intelligence/Machine Learning: Useful techniques but strongly a priori –Agent-based social simulation: emerging integration? From Bruce Edmunds: www.cpm.mmu.ac.uk

4. Choosing meta-methodology Individuals Groups Societies Representing society Integrating nature & society Quantitative Qualitative

5. Attributes of multi-agent systems Software agents… Correspond to real-world actors –sample diversity of populations Embed behaviour –beliefs, norms, goals, plans Interact –environment –each other Environment Internal process ActionPerception

6. Demand for water in southern England EA EA1 WATER WC WC1 WC2 WC3 NEG1 NEG2 NEG3

7. Water demand ABSS Historical climate Individual (30% N) Group (55% N) MH climate CCDeW Project: Edmunds, Moss et al.

8. Challenges Validation: what is modelling for anyway? Scale: what grain of analysis is best? Complexity: simple may not be better? Computational speed: slow! Stakeholder interface: distributed games?