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Simulation and Complexity - how they might relate, Oxford 2003, slide-1 Simulation and Complexity - how they might relate Bruce Edmonds Centre for Policy Modelling Manchester Metropolitan University Business School

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Simulation and Complexity - how they might relate, Oxford 2003, slide-2 Outline of Talk 1.A Simple Model of Modelling 2.What Really Happens 3.Consequences of Modelling Complex Phenomena 4.Constraining Our Models 5.Giving Our Models Meaning 6.Example Simulations (Outline)

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Simulation and Complexity - how they might relate, Oxford 2003, slide-3 Some Problems Models that are plausible but with little relation to reality, used as conceptual or formal exploration but then projected upon reality Types of models are confused in terms of use and judgement Programming is much more accessible than doing mathematics - everyone can build a model and discover something

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Simulation and Complexity - how they might relate, Oxford 2003, slide-4 1. A Simple Model of Modelling

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Simulation and Complexity - how they might relate, Oxford 2003, slide-5 Modelling parts and relations (Model of Modelling) Object System knownunknown Model input (parameters, initial conditions etc.) output (results) encoding (measurement) decoding (interpretation)

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Simulation and Complexity - how they might relate, Oxford 2003, slide-6 Some uses of simulation models Entertainment Art Illustration Mathematics Mediation Design Science –I.e. helping to understand phenomena

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Simulation and Complexity - how they might relate, Oxford 2003, slide-7 Some scientific uses of modelling Prediction –Provide information about a current unknown by inference from known information Explanation –Provide an explanation why and how an outcome resulted from some conditions Analogy –Provide a framework for (or a way of) thinking about a poorly understood or complex system (Model of Modelling)

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Simulation and Complexity - how they might relate, Oxford 2003, slide-8 Some criteria for judging models Soundness of design –w.r.t. knowledge of how the object works –w.r.t. tradition in a field Accuracy (lack of error) Simplicity (ease in communication, construction, comprehension etc.) Generality (when you can safely use it) Sensitivity (relates to goals and object) Plausibility (of design, process and results) Cost (time, space etc.) (Model of Modelling)

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Simulation and Complexity - how they might relate, Oxford 2003, slide-9 Some modelling trade-offs simplicity generality Lack of error (accuracy of results) realism (design reflects observations) (Model of Modelling)

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Simulation and Complexity - how they might relate, Oxford 2003, slide What Really Happens (even in the hard sciences)

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Simulation and Complexity - how they might relate, Oxford 2003, slide-11 A possible layering of models (by abstraction) (What really happens) the phenomena data modelphenomenological modelexplanatory modelgeneral laws and theories

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Simulation and Complexity - how they might relate, Oxford 2003, slide-12 A possible layering of models (by granularity and abstraction) (What really happens) the chemical measurementssimulation of many moleculesmodel of molecule interactionatomic and chemical laws

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Simulation and Complexity - how they might relate, Oxford 2003, slide-13 Multiple models Parallel models –e.g. different models gained by different approaches and simplifications, whose results are compared (e.g. Lasers) Context-specific models –e.g. quantum models in micro-world and relativistic models in macro-world Clusters of models –e.g. use of analogical models alongside formal models in atomic physics (What really happens)

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Simulation and Complexity - how they might relate, Oxford 2003, slide Consequences of modelling complex phenomena

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Simulation and Complexity - how they might relate, Oxford 2003, slide-15 More complex models Formal models that are too complex for analytic inference to be feasible –simulation models Complexity and chaos means that the detailed interactions of parts can make a significant difference to results –compound models What is required is not aggregate results but the detail of processes as they occur –detailed descriptive models (consequences of complexity)

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Simulation and Complexity - how they might relate, Oxford 2003, slide-16 Many views of a model (I) - due to syntactic complexity Computational distance between specification and outcomes means that There are (at least) two very different views of a simulation (consequences of complexity) Simulation Representation of Outcomes Specification

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Simulation and Complexity - how they might relate, Oxford 2003, slide-17 Representation of Outcomes (II) Many views of a model (II) - understanding the simulation (consequences of complexity) Simulation Representation of Outcomes (I) Specification Analogy 1 Analogy 2 Theory 1 Theory 2 Summary 1Summary 2

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Simulation and Complexity - how they might relate, Oxford 2003, slide-18 Models are less general Each model is of more limited applicability (e.g. a model of this kind of social influence in this situation) Each model abstracts less from the phenomena (it is more descriptive in nature) Different models for different purposes (rather than using a single model for all) (consequences of complexity)

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Simulation and Complexity - how they might relate, Oxford 2003, slide-19 Many more models Models at different levels of abstraction Models at different levels of granularity Parallel models to check results Models derived from different views Complementary models covering different situations or contexts Descriptive models of different instances Analogical models Different summaries of collections (consequences of complexity)

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Simulation and Complexity - how they might relate, Oxford 2003, slide-20 Example with multiple models (consequences of complexity)

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Simulation and Complexity - how they might relate, Oxford 2003, slide Constraining Our Models

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Simulation and Complexity - how they might relate, Oxford 2003, slide-22 A priori constraints on models By what is feasible in terms of cost and time: simplicity (e.g. computer simulation) By the traditions of academic fields (e.g. utility optimising equilibrium models) By already validated theoretical frameworks (e.g. atomic interaction, Newtonian physics) By expert and stakeholder opinion Observation of phenomena (including anecdotal evidence) (constraining models)

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Simulation and Complexity - how they might relate, Oxford 2003, slide-23 Post hoc constraints Accuracy in terms of low error Consistency and coherence with other models and observations Of: –Aggregate outcomes –Unfolding of simulation process (detail over time) –Behaviour of component parts (detail over model structure) (constraining models)

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Simulation and Complexity - how they might relate, Oxford 2003, slide-24 Constraints on scope Each layer of the abstraction modelling layer will only be able to safely abstract to a limited extent Obligation to sketch out the conditions of applicability of simulation models Abstracting out of the original context risks loosing the meaning of the model Danger of the use of a model as an interactive analogy due to theoretical spectacles effect (constraining models)

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Simulation and Complexity - how they might relate, Oxford 2003, slide Giving Our Models Meaning

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Simulation and Complexity - how they might relate, Oxford 2003, slide-26 Context It is impossible to include all relevant causes in any one model (causal spread) Constant or irrelevant factors can be omitted as long as the conditions under which the model works can be reliably recognised later so it can be applied Set of all excluded factors can be abstracted to a (modelling) context Meaning is bootstrapped from reference inside a specific (real) context (Meaning)

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Simulation and Complexity - how they might relate, Oxford 2003, slide-27 Semantic complexity The difficulty of interpreting a rich meaningful domain and descriptions into an impoverished formal model Establishment of symbol meaning by: –Importing symbols from natural language –Use of symbols in context –Cycle of interaction and learning about symbols (Meaning)

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Simulation and Complexity - how they might relate, Oxford 2003, slide-28 The token processing view That an off-line computation can be viewed as a manipulation of tokens meaningful to humans (by its design) This contrasts with mapping to world via data models (and measurement) Model needs to be embedded in interaction with participants in adaptive cycles All simulation models are somewhat in both worlds (Meaning)

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Simulation and Complexity - how they might relate, Oxford 2003, slide-29 Meaning from intermediate abstraction (often implicit) Object Systemconceptual model Model (Meaning)

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Simulation and Complexity - how they might relate, Oxford 2003, slide Example Simulations

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Simulation and Complexity - how they might relate, Oxford 2003, slide-31 Example 1: a model of social influence and water demand Investigate the possible impact of social influence between households on patterns of water consumption Design and detailed behaviour from simulation validated against expert and stakeholder opinion at each stage Some of the inputs are real data Characteristics of resulting aggregate time series validated against similar real data (Examples)

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Simulation and Complexity - how they might relate, Oxford 2003, slide-32 Example 1: simulation structure (Examples)

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Simulation and Complexity - how they might relate, Oxford 2003, slide-33 Example 1: some of the household influence structure (Examples)

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Simulation and Complexity - how they might relate, Oxford 2003, slide-34 Example 1: example results (Examples)

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Simulation and Complexity - how they might relate, Oxford 2003, slide-35 Example 1: Conclusions The use of a concrete descriptive simulation model allowed the detailed criticism and, hence, improvement of the model The inclusion of social influence resulted in aggregate water demand patterns with many of the characteristics of observed demand patterns The model established how it was possible that processes of mutual social influence could result in widely differing patterns of consumption that were self-reinforcing (Examples)

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Simulation and Complexity - how they might relate, Oxford 2003, slide-36 Example 2: integrating domain expertise and aggregate data Meta-model (or abstract framework) relating a class of consumer preference models to aggregate price and demand time series Within this marketing practitioner sets, focus brand, key characteristics, values of characteristic for brands (market context) Within context practitioner investigates the relationship between particular consumer preference models and aggregate results (Examples)

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Simulation and Complexity - how they might relate, Oxford 2003, slide-37 Example 2: abstract structure (Examples)

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Simulation and Complexity - how they might relate, Oxford 2003, slide-38 Example 2: development cycles (Examples)

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Simulation and Complexity - how they might relate, Oxford 2003, slide-39 Example 2: inference and induction of preference models (Examples)

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Simulation and Complexity - how they might relate, Oxford 2003, slide-40 Example 2: practitioner or expert specifies: A list of labels for each of the brands to be considered A list of labels for each of the relevant product characteristics that are judged to be used by consumers to distinguish between these brands For each product: –For each characteristic: A number representing the perceived intensity of that characteristic associated with that brand (Examples)

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Simulation and Complexity - how they might relate, Oxford 2003, slide-41 Example 2: a UK market for liquor (Examples)

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Simulation and Complexity - how they might relate, Oxford 2003, slide-42 Example 2: preference model Cluster Relative Price Expensive ness Size Specialn ess Uniquen ess A (21%) B (49%) C (29%) (Examples)

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Simulation and Complexity - how they might relate, Oxford 2003, slide-43 Example 2: Conclusions Meta-model designed to be consistent with observations of how people purchased Iteratively tested on several different markets for alcoholic drink in different countries Preference models in terms meaningful to practitioner, because: –They set the market context meaningfully –They interacted with the model within this (Examples)

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Simulation and Complexity - how they might relate, Oxford 2003, slide-44 Conclusions Danger of confusing: Explanatory and predictive models (e.g. economics) Semantic and syntactic views of a model (e.g. unwarranted imputing meaning on suggestive animations of model results) Descriptive and generative models (e.g. analytical summaries of collections of data with generative models) (Conclusions)

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Simulation and Complexity - how they might relate, Oxford 2003, slide-45 Conclusions Some uses of simulations: Making calculation and inference where analytic solutions are not possible Exploring possibilities Establishing counter-examples Informing (and being informed by) good observation of phenomena Making dynamic formal descriptions (staging abstraction) (Conclusions)

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Simulation and Complexity - how they might relate, Oxford 2003, slide-46 Model 2 Model workshop Considering how simulation models might be related to each other Particularly with respect to modelling social phenomena To be held at CNRS, Marseilles, 31st March and 1st April 2003 Deadline for submissions is past but attendance is free, (but tell us you are coming, there may even be free meals)

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Simulation and Complexity - how they might relate, Oxford 2003, slide-47 The End Bruce Edmonds bruce.edmonds.name Centre for Policy Modelling cfpm.org

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