System Dynamics – three
methodological considerations
Rationality, theory/observation link, “3Ps” in modelling
Andreas Grö...
Issues discussed in this lecture
1. Rationality: perfect vs. bounded rationality, rationality in the model and
the modelli...
Rationality
Rationality =
1. Reasonable, based on reason
2. In an economic sense: choice amongst decision alternatives which
maximises...
Optimal decisions are improbable
Real decision
situations are
characterised by
complexity
and uncertainty
• In general, op...
How is rationality measured?
Absolute rationality
– Result counts; it is optimal
– Decision process is
determined by the o...
Bounded rationality and SD
Servo-mechanism theory,
but not: system dynamics
Advances in decision making,
but not: bounded ...
Literature review
• Morecroft (1983): bounded rationality implicitly embedded in SD models
• Morecroft (1985): bounded rat...
Three starting-points to examine rationality in system
dynamics
Rationality ...
… when creating a model
… in the model‘s s...
Ideal model development process
afterForrester1994
Describe
the
system
Convert
description
to level
and rate
equations
Sim...
Rationality in model development
Model development:
• Frequently does not follow a formal process
• Depends on skills of m...
Vicious circle model/reality
Bounded rationality
in the problem area
Difficulty of
knowledge elicitation
Complexity of
mod...
Bounded rationality in the model structure
• Boundedly rational behaviour of real actors must be replicated in the
model s...
How to handle bounded rationality
• Habit, routines, and rules of thumb
• Managing attention
• Goal formation and satisfic...
Filter in policies
Level
rate 1. Cognitive limitations
2. Operating goals, rewards
and incentives
3. Information,
measurem...
Critical distinction
Which issues are necessary
abstractions in the model
development process? Which issues are simplified...
Rationality when using a model
• Using a model means simulation experiments = scientific approach
• Goal: Improved, more r...
Improvement of policies
• Frequently, no structural changes, only variation of parameters =
acceptance of bounded rational...
• Bounded rationality:
• Negative influence in the modelling process and on simulation
experiments
• But: Model structure ...
Bounded rationality in model structure: An example
Inventory model, Lyneis 1981
Supplier
Production
Personnel
Customers
de...
Supplier
Make-to-order
Auftragsbestand
von Zulieferer
bestellrate der teile
lieferzeit teile
zulieferer
rüstverzögerung
+
...
Supplier as ‘homo oeconomicus’
• No capacity restrictions
• Infinitely fast reactions
• Complete knowledge about future (c...
Supplier in the Lyneis model
• Only one information cue used to decide about capacity and
production: order rate of produc...
More robust policies for the supplier
• Shorten reaction times if possible and useful
 Change of parameters
• Use other p...
Bounded rational policies can be dangerous
afterSterman2000
Demand
Capacity
Utilisation
Capacity
Price
Market Share
Compet...
“The road to hell is paved with good intentions”
Local rationality leads to crisis, catastrophe, bankruptcy
But: can success emerge from boundedly rational behaviour?
Locally bounded, but globally successful
Agent
10
.
.
.
Agent
1N
Agent
20
.
.
.
Agent
2M
Agent
11
Agent
21
Agent
30
.
.
.
...
Theory/observation link
30
Goals of Human Inquiry
• Making sense of the world
• Common sources: tradition/authority and personal experience
(Asch’s e...
Shortcomings of Human Inquiry
• Inaccurate observations (visual puzzles)
• Overgeneralizations (“all...are...”)
• Selectiv...
Science
• Making sense of the world in a specific way
• Knowledge in terms of statements about reality
• Generation of new...
The Foundations of Social Science
• The Charge of Triviality (Darwin: “fool’s experiment”)
• Social Regularities  Aggrega...
Social Sciences: Issues
• Finding universal laws is problematic
• A couple of reasons:
• Complexity
• Researcher effect
• ...
The Links Between Theory and Research
• Deductive Model – research is used to test theories.
• Inductive Model – theories ...
Social versus natural sciences
• Differences in research object – reflexivity/reactivity
• Social sciences similar objecti...
The wheel of science
Theories
Hypotheses
Observations
Empirical
generalizations
Induction
Deduction
Types of theories, types of models
Range of
theory
Goal of
theory
content structure
Explaining…
grand
theory
midrange
theo...
“3Ps” in modelling
What system dynamics wants to achieve…
• Policy design/ Policy making: decision processes that convert
information into ac...
The 3Ps and system dynamics models
Problem
articulation
Dynamic
hypotheses
Model
formulation
e.g. Policies
Model
testing
P...
The 3Ps and the system dynamics modelling process
Problem
articulation
Dynamic
hypotheses
Model
formulation
e.g. Policies
...
References
Andreas Größler, Peter Milling and Graham Winch (2004): Perspectives on
Rationality in System Dynamics – A Work...
Three methodological issues for system dynamics practice
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Three methodological issues for system dynamics practice

  1. 1. System Dynamics – three methodological considerations Rationality, theory/observation link, “3Ps” in modelling Andreas Größler Radboud University Nijmegen, the Netherlands
  2. 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. 3. Rationality
  4. 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. 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. 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. 7. Bounded rationality and SD Servo-mechanism theory, but not: system dynamics Advances in decision making, but not: bounded rationality
  8. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 26. “The road to hell is paved with good intentions”
  27. 27. Local rationality leads to crisis, catastrophe, bankruptcy
  28. 28. But: can success emerge from boundedly rational behaviour?
  29. 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. 30. Theory/observation link 30
  31. 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. 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. 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. 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. 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. 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. 37. Social versus natural sciences • Differences in research object – reflexivity/reactivity • Social sciences similar objectives? (interventions?) • Idea of unity of sciences (logical positivism)
  38. 38. The wheel of science Theories Hypotheses Observations Empirical generalizations Induction Deduction
  39. 39. Types of theories, types of models Range of theory Goal of theory content structure Explaining… grand theory midrange theory minor theory
  40. 40. “3Ps” in modelling
  41. 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. 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. 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. 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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