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Socio-environmental Agents Thomas E Downing SEI Oxford • • • • Choosing methodology Attributes of multi-agent modelling A water example Challenges 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 Integrating nature & society Qualitative Quantitative Choosing meta-methodology Individuals Groups Representing society Societies Attributes of multi-agent systems Software agents… • Correspond to real-world actors – sample diversity of populations • Embed behaviour – beliefs, norms, goals, plans • Interact Environment – environment – each other Perception Action Internal process Demand for water in southern England WATER EA WC EA1 NEG1 NEG2 NEG3 WC1 WC2 WC3 Water demand ABSS Historical climate MH climate Individual (30% N) Group (55% N) CCDeW Project: Edmunds, Moss et al. 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?