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Socio-environmental Agents

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

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Socio-environmental Agents

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  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 Integrating nature & society Quantitative Qualitative Individuals Groups Societies Representing society

  5. Environment Perception Action Internal process 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

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

  7. Water demand ABSS Historical climate MH climate Individual(30% N) Group(55% N) 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?

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