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- 1. System Dynamics – three methodological considerations Rationality, theory/observation link, “3Ps” in modelling Andreas Größler Radboud University Nijmegen, the Netherlands
- 2. Issues discussed in this lecture 1. Rationality: perfect vs. bounded rationality, rationality in the model and the modelling process 2. Theory/observation link: inductive vs. deductive ways to do research, „Wheel of science“ 3. 3Ps in modelling: policy, politics, and polity
- 3. Rationality
- 4. Rationality = 1. Reasonable, based on reason 2. In an economic sense: choice amongst decision alternatives which maximises the utility of the decision-maker (with respect to his/her preferences) Mindless behaviour Perfect rationality
- 5. Optimal decisions are improbable Real decision situations are characterised by complexity and uncertainty • In general, optimal decisions are not possible • absolute (or perfect) rationality changes to bounded, intended rationality
- 6. How is rationality measured? Absolute rationality – Result counts; it is optimal – Decision process is determined by the optimal outcome Bounded rationality – Result is optimal only by chance; in all other cases it is at best satisfying regarding an aspiration level – Important: decision process and decision rules – “procedural rationality”
- 7. Bounded rationality and SD Servo-mechanism theory, but not: system dynamics Advances in decision making, but not: bounded rationality
- 8. Literature review • Morecroft (1983): bounded rationality implicitly embedded in SD models • Morecroft (1985): bounded rationality should be represented in decision models • Sterman (1987): expectation formation is boundedly rational • Sterman (1989): misperception of feedback as one component of bounded rationality • Radzicki (1990): institutional economics should use SD to model bounded rationality • Lane (1994): relevant modelling must include boundedly rational decision- making • Kampman & Sterman (1998): effects of market mechanisms on outcomes from boundedly rational behaviour • Dyner & Franco (2000; 2004): modelling bounded rationality in the energy world
- 9. Three starting-points to examine rationality in system dynamics Rationality ... … when creating a model … in the model‘s structure … when using the model processcontent
- 10. Ideal model development process afterForrester1994 Describe the system Convert description to level and rate equations Simulate the model Design alternative policies and structures Educate and debate Implement changes in policies and structures step 1 step 2 step 3 step 4 step 5 step 6
- 11. Rationality in model development Model development: • Frequently does not follow a formal process • Depends on skills of modeller • Objective: Modelling of real, not optimal systems: • Bounded rational description of bounded rationality (br2) Importance of validation!
- 12. Vicious circle model/reality Bounded rationality in the problem area Difficulty of knowledge elicitation Complexity of modelling processBounded rationality within the modelling process Quality/utility of model – + + + – The utility of “good” models and the difficulty of modelling them Complexity of problem domain + –
- 13. Bounded rationality in the model structure • Boundedly rational behaviour of real actors must be replicated in the model structure/policies (“premise description”) • In particular, in information flows: wrong relations (functions) or missing links between variables • Material flows determined by physical characteristics • No explanation of reasons for bounded rationality goods on stock goods delivered to customershipment customer order rate fulfilment ratio (forgetting) shipment delay remoteness factor … …
- 14. How to handle bounded rationality • Habit, routines, and rules of thumb • Managing attention • Goal formation and satisficing • Problem decomposition and decentralized decision making afterSterman2000 Need to be represented in model
- 15. Filter in policies Level rate 1. Cognitive limitations 2. Operating goals, rewards and incentives 3. Information, measurement and communication systems 4. Organisational and geographical structure 5. Tradition, culture, folklore and leadership 1 2 3 4 5 afterMorecroft1994
- 16. Critical distinction Which issues are necessary abstractions in the model development process? Which issues are simplified in accordance with artefacts of bounded rationality occurring in reality? Which issues are mistakenly simplified by modeller?
- 17. Rationality when using a model • Using a model means simulation experiments = scientific approach • Goal: Improved, more robust policies, i.e. less boundedly rational decisions because simulation should overcome cognitive limitations of humans
- 18. Improvement of policies • Frequently, no structural changes, only variation of parameters = acceptance of bounded rationality • Design of robust policies requires changes in model structure and, hence, in organisational structure
- 19. • Bounded rationality: • Negative influence in the modelling process and on simulation experiments • But: Model structure should represent bounded rationality of real decisions Summary (so far) Formal ModelReal World Problem modelling simulation incorporation of bounded rationality learning to mitigate bounded rationality bounded rationality of model developer bounded rationality of model user
- 20. Bounded rationality in model structure: An example Inventory model, Lyneis 1981 Supplier Production Personnel Customers demand parts ordered parts received desired production capacity personnel shipment
- 21. Supplier Make-to-order Auftragsbestand von Zulieferer bestellrate der teile lieferzeit teile zulieferer rüstverzögerung + durchschnitt produktion des zulieferers startet - gewünschte produktionsrate des zulieferers auftragsbestand zu erledigen + ZEIT UM AUFTRAGSBESTAND ZU ERLEDIGEN - durchschnittliche bestellrate + gewünschte produktionskapazität des zulieferers + + gewünschter auftragsbestand des zulieferers KAPAZITÄT DES ZULIEFERERS anpassung der produktionskapazität des zulieferers ZEIT UM PRODUKTIONSKAPAZITÄT ANZUPASSEN - + + - kapazitätsauslastung des zulieferers + - TABELLE FÜR KAPAZITÄTSAUSLASTUNG DES ZULIEFERERS MIMIMUM ZULIEFERER RÜSTVERZÖGERUNG + - + + produktionsverzögerung des zulieferers + erhaltene lieferzeit teile ZEIT UM LIEFERZEIT TEILE ZU ERHALTEN + Bestellte Teileproduktion des zulieferers startet + + eingangsrate + GLÄTTUNGSZEIT BESTELLRATE DES ZULIEFERERS
- 22. Supplier as ‘homo oeconomicus’ • No capacity restrictions • Infinitely fast reactions • Complete knowledge about future (certainty) • Result: In each period produces exactly the amount of goods that is demanded optimal solution
- 23. Supplier in the Lyneis model • Only one information cue used to decide about capacity and production: order rate of producer • Order rate is smoothed to filter out peaks • This figure serves as a prognosis value for future order rates • Very inexact estimation bounded rationality
- 24. More robust policies for the supplier • Shorten reaction times if possible and useful Change of parameters • Use other processing rules, e.g. investment algorithms instead of permanent capacity adjustment Change of functional relations • Consider more information, e.g. expected order rate at producer, data about business cycles, seasonal effects Change of structural linkages
- 25. Bounded rational policies can be dangerous afterSterman2000 Demand Capacity Utilisation Capacity Price Market Share Competitor Price Industry Demand + + + - + - + Fill the Line Competitor Market Share Competitor Demand Competitor Capacity Utilisation Competitor Capacity + + + + - - + Fill the Line Price War
- 26. “The road to hell is paved with good intentions”
- 27. Local rationality leads to crisis, catastrophe, bankruptcy
- 28. But: can success emerge from boundedly rational behaviour?
- 29. Locally bounded, but globally successful Agent 10 . . . Agent 1N Agent 20 . . . Agent 2M Agent 11 Agent 21 Agent 30 . . . Agent 3L Agent 40 Agent 4K Agent 31 Agent 41 Level 1 rate 1 rate 2 aux 1aux 2 CONST 1 Level 2 rate 3 rate 4 CONST 2 aux 3
- 30. Theory/observation link 30
- 31. Goals of Human Inquiry • Making sense of the world • Common sources: tradition/authority and personal experience (Asch’s experiment) prove that the earth revolves around the sun! • Explain and predict: why? and what? • Prediction without explanation is possible; explanation often leads to prediction • Predict and control ( interventions)
- 32. Shortcomings of Human Inquiry • Inaccurate observations (visual puzzles) • Overgeneralizations (“all...are...”) • Selective perception/observation (looking for confirmation) • Illogical reasoning (“the exception that proves the rule”, gambler’s fallacy)
- 33. Science • Making sense of the world in a specific way • Knowledge in terms of statements about reality • Generation of new knowledge through systematic (scientific) research • Objective: describe and explain ‘reality’ (knowledge) pre- /modern/post-modern • Our knowledge materializes in statements about that reality (laws = observed regularities, not individual exceptions) • Research uses methodology/methods • Management sciences: research and intervention
- 34. The Foundations of Social Science • The Charge of Triviality (Darwin: “fool’s experiment”) • Social Regularities Aggregates, Not Individuals • What About Exceptions? (probabilistic predictions) The collective actions and situations of many individuals. • People Could Interfere (if “irregular” behavior becomes commonplace, new theories are needed) Focus of social science is to explain why aggregated patterns of behavior are regular even when individuals change over time or how the regularities change.
- 35. Social Sciences: Issues • Finding universal laws is problematic • A couple of reasons: • Complexity • Researcher effect • Research changes reality • We often end up with statements like “in general”, “in principle”, “with a high likelihood”, “under this and that condition”, … • discussion in the beer game
- 36. The Links Between Theory and Research • Deductive Model – research is used to test theories. • Inductive Model – theories are developed from analysis of data. • The Traditional Image of Science – The deductive model of scientific inquiry begins with a sometimes vague or general question, which is subjected to a process of specification, resulting in hypotheses that can be tested through empirical observations.
- 37. Social versus natural sciences • Differences in research object – reflexivity/reactivity • Social sciences similar objectives? (interventions?) • Idea of unity of sciences (logical positivism)
- 38. The wheel of science Theories Hypotheses Observations Empirical generalizations Induction Deduction
- 39. Types of theories, types of models Range of theory Goal of theory content structure Explaining… grand theory midrange theory minor theory
- 40. “3Ps” in modelling
- 41. What system dynamics wants to achieve… • Policy design/ Policy making: decision processes that convert information into action (Forrester, 1994). <-> decisions • Policy design is an analytical/cognitive/rational task • However, be aware of a too mechanistic/rationalistic view of organisations • Therefore, consider politics and polity • Politics: games played on the self-interest of people • Polity: institutional structures in which politics/policies take place
- 42. The 3Ps and system dynamics models Problem articulation Dynamic hypotheses Model formulation e.g. Policies Model testing Policy* formulation& evaluation Politics Polity Politics* Polity*
- 43. The 3Ps and the system dynamics modelling process Problem articulation Dynamic hypotheses Model formulation e.g. Policies Model testing Policy formulation& evaluation Politics Polity Policies The 3P‘s The modelling processdetermines scope determines implementation changes due to model results and modelling process
- 44. References Andreas Größler, Peter Milling and Graham Winch (2004): Perspectives on Rationality in System Dynamics – A Workshop Report and Open Research Questions, System Dynamics Review, 20(1), pp. 75–87. Andreas Größler (2008): System Dynamics Modelling as an Inductive and Deductive Endeavour, Systems Research & Behavioral Science, 25(4), pp. 467–470. Andreas Größler (2010): Policies, Politics, and Polity, Systems Research & Behavioral Science, 27(4), pp. 385–389. 45

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