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)

39. The wheel of science
Theories
Hypotheses
Observations
Empirical
generalizations
Induction
Deduction

40. Types of theories, types of models
Range of
theory
Goal of
theory
content structure
Explaining…
grand
theory
midrange
theory
minor
theory

41. “3Ps” in modelling

42. 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

43. The 3Ps and system dynamics models
Problem
articulation
Dynamic
hypotheses
Model
formulation
e.g. Policies
Model
testing
Policy*
formulation&
evaluation
Politics
Polity
Politics*
Polity*

44. 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

45. 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.
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