Dear System Dynamicists,
I am a Ph.D. student at the university of Bergen, and I am currently
working on a short paper in the theory of science. In this paper, I try
to identify a research programme for system dynamics according to
Lakatos concepts.
While Thomas Kuhn used the term "paradigm" to characterise an exemplaric
research effort, shared by the members of a scientific community;
Lakatos used the term "research programme" in his theory.
According to Lakatos every theory is based on a set of basic
assumptions. This set of basic assumptions is called the hard core of
the programme. One is unwilling to give up this hard core even if one
meets resistance in the form of empirical counter-evidence and
scientific arguments.
Surrounding this hard core, there is a protecting belt of other
assumptions, auxiliary hypotheses, etc. If we meet resistance, we are
willing to give up or make changes to the assumptions made in this
protecting belt.
So I would like to open a discussion to collect the different points of
view about:
- The hard core assumptions of SD
- The protecting belt assumptions of SD
- Research directions of SD
Best Regards,
Mohamed Saleh
--
MOHAMED MOSTAFA SALEH
University of Bergen, Information Science Dept.
EMAIL:mohameds@ifi.uib.no FAX:47 55584107
PHONE:47 55276866 CELLULAR PHONE:91884489
MAIL: H-181 Fantoft Studentby; 5036 Fantoft; Bergen, Norway
The Hard Core of SD
-
George Richardson
- Senior Member
- Posts: 68
- Joined: Fri Mar 29, 2002 3:39 am
The Hard Core of SD
One version of some of these would be found in the definition of system
dynamics in the Encyclopedia of Operations Research and Management
Science, which I wrote in an effort to be inclusive but it no doubt
represents one view of the characterization of the field.
Another attempt at a statement of the core is the introduction and Part I
of Modelling for Management: Simulation in Support of Systems Thinking,
published by the International Library of Management, Dartmouth Publishing
Co. (1996). Part I is focused on modeling perspectives and contains
excellent examples of what might be thought of as "field-defining"
articles, including Stermans "Learning in and about Complex Systems,"
Richmonds "Systems Thinking: Critical Thinking Skills for the 1990s and
Beyond," and Forresters "14 Obvious Truths" (1960).
And a third source that I am responsible for is the attempt in chapter 6
of Feedback Thought in Social Science and Systems Theory to sketch the
defining characteristics of two threads of feedback thought, one of which
contains system dynamics. [I apologize for these references to my own
writings and editings, but it is true that what I was trying to do in all
these was in some sense getting at the core...]
And of course, the beginnings of all core statements are well put forward
in Jays Industrial Dynamics. No statement of the core or its "protecting
belt of assumptions" would be complete without beginning there. I would
also say most of Jays papers written in the 1960s lay out aspects of the
core, and much of what he has been writing recently aimed at k-12
education is focusing necessarily on the core.
Best wishes--
...GPR
-----------------------------------------------------------------------
George P. Richardson G.P.Richardson@Albany.edu
Rockefeller College of Public Affairs and Policy Phone: 518-442-3859
University at Albany - SUNY, Albany, NY 12222 Fax: 518-442-3398
http://cnsvax.albany.edu/~gr383/
-----------------------------------------------------------------------
dynamics in the Encyclopedia of Operations Research and Management
Science, which I wrote in an effort to be inclusive but it no doubt
represents one view of the characterization of the field.
Another attempt at a statement of the core is the introduction and Part I
of Modelling for Management: Simulation in Support of Systems Thinking,
published by the International Library of Management, Dartmouth Publishing
Co. (1996). Part I is focused on modeling perspectives and contains
excellent examples of what might be thought of as "field-defining"
articles, including Stermans "Learning in and about Complex Systems,"
Richmonds "Systems Thinking: Critical Thinking Skills for the 1990s and
Beyond," and Forresters "14 Obvious Truths" (1960).
And a third source that I am responsible for is the attempt in chapter 6
of Feedback Thought in Social Science and Systems Theory to sketch the
defining characteristics of two threads of feedback thought, one of which
contains system dynamics. [I apologize for these references to my own
writings and editings, but it is true that what I was trying to do in all
these was in some sense getting at the core...]
And of course, the beginnings of all core statements are well put forward
in Jays Industrial Dynamics. No statement of the core or its "protecting
belt of assumptions" would be complete without beginning there. I would
also say most of Jays papers written in the 1960s lay out aspects of the
core, and much of what he has been writing recently aimed at k-12
education is focusing necessarily on the core.
Best wishes--
...GPR
-----------------------------------------------------------------------
George P. Richardson G.P.Richardson@Albany.edu
Rockefeller College of Public Affairs and Policy Phone: 518-442-3859
University at Albany - SUNY, Albany, NY 12222 Fax: 518-442-3398
http://cnsvax.albany.edu/~gr383/
-----------------------------------------------------------------------
The Hard Core of SD
I was very interested to read the query by Mohamed Saleh with its
Lakatosian view of the field and the subsequent contributions. (Sorry to
join this conversation late but I have been away)
I have been thinking about this question along with a colleague in the
Dept of Philosophy, Logic and Scientific Method here at LSE and have also
had a useful exchange with Greg Scholl on the matter. Theres a Working
paper in press at the moment that deal with the question and as of one week
ago I submitted a version to System Dynamics Review.
I was looking at things from a social theory perspective and was
considering the systems theory of Talcott Parsons and the criticisms made of
this Grand Theory approach by C. Wright Mills. The issue that I was dealing
with was that system dynamics is criticized as deterministic. Heres my
twopennyworth (Ive made bold the sections most closely relevant to the
Lakatos question):
Crude appeal to grand theory?
The word determinism is also used to describe the position that cause and
effect are related by laws which exist outside of human subjectivity and
which together form a timeless grand theory. Relevant in this context is
Maxwells principle of determinism, that "The same causes will always
produce the same effect" and that this is so because the laws underlying the
principle describe the operation of invariant relations.
The pursuit of grand theory via causal laws is based on the Humean
tradition that science implies explanation and that explanation involves
relating phenomena to be explained to other phenomena via such causal laws.
The value of the approach is that a set of antecedent conditions may be
combined with relevant laws to make a prediction about an event. Even in
natural science the logical status of grand theory is not a closed issue
since the exact formulation of causal laws is complex and the philosophical
weaknesses in the different formulations of causality would apply to any
field seeking to employ the concept. However, a point made very clearly by
Popper is that determinism via the principle of causality is quite different
from determinism as prophecy and the latter is alive and healthy as a
central concept in natural science.
This form of the determinism criticism is therefore that system dynamics
posits a grand theory about the existence of objective causal laws in social
systems. It has been observed that an assumption of the field is that,
"well-defined laws govern behaviour" (1, p.33) and that, "Forrester talks
about fundamental laws of nature and the social sciences" (2, p.13).
System dynamics has a ranges of theories which appear deterministic in
this sense. For example, models are said to be causal theories and Forrester
writes of a "general systems theory" (3, p.135) and lays out Principles of
Systems (4). There is also the grand claim that underlying models is, "a set
of principles..., incomplete as they may be, that I believe do represent the
actual nature of physical and social reality" (5, p.250).
On the nature of the theories in system dynamics System dynamics offers
theories at four different levels. At the highest level is the claim that
the time evolutionary behaviour of social systems is explainable in terms of
feedback loops and state variables. This very crude statement would need to
be elaborated to establish the plausibility of the claim.
(A slightly less crude statement of the position might be:
Social systems frequently behave in ways which are counter to the intuition
of actors implementing policies aimed at influencing their behaviour.
Because actors who are part of social systems collect information about
their environment and, in light of that information, take action to
influence aspects of the state of the system, the aggregate behaviour over
time of such systems can useful be explained using the concepts of feedback
loops and stocks. The feedback loops are a plausible representation of the
collect/influence/collect cycle of activities and the stocks are a plausible
representation of the system state variables. Models based on these concepts
offer a representation of the causal links between the variables which
allows the rigorous deduction of the consequences of those links. Such
models are therefore theories of the structural source of particular
aggregate behaviours and can be used to make deductions about the mode of
behaviour that will flow from implementing a given influencing policy.)
The important point here is the nature of this claim. This is not a grand
content theory. There are no specific variables or conceptual categories.
For example, it is not suggested that socialisation or class
stratification are meaningful concepts which are necessary to explain the
evolution of all societies. In contrast, this grand claim of system dynamics
is a structural theory - it makes a grand methodological claim about how
certain types of phenomena might be explained.
Moving down to the next theory, we find the principle of how the concepts
of feedback loop and stock should be used to construct models (4). This is
not a grand theory. This is a representation theory, or scheme, proposing a
way of implementing the above grand methodological theory.
A more specific theory is that, unassisted, humans cannot infer the
behaviour of systems represented in the above fashion in a way which is
logically consistent; computers are needed to deduce the behaviour. This is
an empirical claim. As such it is well supported by data (6). Models
therefore allow the application of logic to reveal essentially tautological
but, in practical terms, hidden results (7).
Finally, we come to the idea that each model is a theory. These are
clearly minor content theories, albeit valuable ones. A model is a
concatenation of causal laws offering a plausible representation and hence
explanation for a behaviour mode.
With this clarification it is clear that none of the above theories
proposes an invariant causal law and that there is no crude grand (content)
theory. The only universal law/theory on offer is a grand methodological, or
structural theory, associated with a representation scheme. System dynamics
offers a new structure for thinking about causality but it does not specify
the content of that structure. The laws are structural, or representational,
they have no specific content. This position can still be criticised but it
does not attract the determinism-related criticisms attached to grand theory
in the sense of Parsons and Mills.
Position clarified, but Case open
A suitable note on which to end is that the grand structural theory of
system dynamics, along with the theories of logical deduction and
representation stand or fall not as separate elements to be criticised but
as a coherent research program (8). They should be considered in terms of
the plausibility and coherence of its grand structural claim, the
reasonableness of its representative scheme and its empirical success in
providing explanations for a range of novel phenomena. Viewed in these
Lakatosian terms, the various theories of system dynamics form the hard
core of the field and the healthy flow of diverse applications of the
approach indicates that the research program is a progressive one. Whilst
the issues raised here are still open and the clarified position will be
objectionable to some, system dynamicists are happy to justify their
position in terms of their ability successfully to illuminate the phenomena
to which the field applies. It is on those terms that any of the theories
and laws of system dynamics should be judged.
References
1 Flood, RL and Jackson, MC (1991). Creative Problem Solving: Total
Systems Intervention. Wiley: Chichester.
2 Bloomfield, B (1982). Cosmology, Knowledge and Social Structure: The
case of Forrester and system dynamics. Journal of Applied Systems Analysis
9: 3-15.
3 Forrester, JW (1968) Industrial Dynamics - After the first decade.
In: Collected Papers of Jay W. Forrester (1975 collection). Wright-Allen
Press: Cambridge, MA, pp 133-150.
4 Forrester, JW (1968). Principles of Systems. MIT Press: Cambridge,
MA.
5 Forrester, JW (1994). System dynamics, Systems thinking, and Soft
OR. System Dynamics Review 10: 245-256.
6 Sterman, JD (1994). Learning in and about complex systems. System
Dynamics Review 10: 291-330.
7 Simon, HA (1969) Understanding the Natural and the Artificial
Worlds. In: The Sciences of the Artificial. MIT Press: London, pp 3-29.
8 Lakatos, I (1974) Falsification and the Methodology of Scientific
Research Programmes. In: Lakatos, I and Musgrave, A (eds). Criticism and the
Growth of Knowledge. CUP: Cambridge, pp 91-196.
Regards,
David
____________________________________________________________________________
________________
Dr. David C. Lane
Operational Research Department
London School of Economics and Political Science
Houghton Street Tel.
(44) (0) 171-955-7336
London WC2A 2AE Fax. (44)
(0) 171-955-6885
United Kingdom
e-mail: d.c.lane@lse.ac.uk
Lakatosian view of the field and the subsequent contributions. (Sorry to
join this conversation late but I have been away)
I have been thinking about this question along with a colleague in the
Dept of Philosophy, Logic and Scientific Method here at LSE and have also
had a useful exchange with Greg Scholl on the matter. Theres a Working
paper in press at the moment that deal with the question and as of one week
ago I submitted a version to System Dynamics Review.
I was looking at things from a social theory perspective and was
considering the systems theory of Talcott Parsons and the criticisms made of
this Grand Theory approach by C. Wright Mills. The issue that I was dealing
with was that system dynamics is criticized as deterministic. Heres my
twopennyworth (Ive made bold the sections most closely relevant to the
Lakatos question):
Crude appeal to grand theory?
The word determinism is also used to describe the position that cause and
effect are related by laws which exist outside of human subjectivity and
which together form a timeless grand theory. Relevant in this context is
Maxwells principle of determinism, that "The same causes will always
produce the same effect" and that this is so because the laws underlying the
principle describe the operation of invariant relations.
The pursuit of grand theory via causal laws is based on the Humean
tradition that science implies explanation and that explanation involves
relating phenomena to be explained to other phenomena via such causal laws.
The value of the approach is that a set of antecedent conditions may be
combined with relevant laws to make a prediction about an event. Even in
natural science the logical status of grand theory is not a closed issue
since the exact formulation of causal laws is complex and the philosophical
weaknesses in the different formulations of causality would apply to any
field seeking to employ the concept. However, a point made very clearly by
Popper is that determinism via the principle of causality is quite different
from determinism as prophecy and the latter is alive and healthy as a
central concept in natural science.
This form of the determinism criticism is therefore that system dynamics
posits a grand theory about the existence of objective causal laws in social
systems. It has been observed that an assumption of the field is that,
"well-defined laws govern behaviour" (1, p.33) and that, "Forrester talks
about fundamental laws of nature and the social sciences" (2, p.13).
System dynamics has a ranges of theories which appear deterministic in
this sense. For example, models are said to be causal theories and Forrester
writes of a "general systems theory" (3, p.135) and lays out Principles of
Systems (4). There is also the grand claim that underlying models is, "a set
of principles..., incomplete as they may be, that I believe do represent the
actual nature of physical and social reality" (5, p.250).
On the nature of the theories in system dynamics System dynamics offers
theories at four different levels. At the highest level is the claim that
the time evolutionary behaviour of social systems is explainable in terms of
feedback loops and state variables. This very crude statement would need to
be elaborated to establish the plausibility of the claim.
(A slightly less crude statement of the position might be:
Social systems frequently behave in ways which are counter to the intuition
of actors implementing policies aimed at influencing their behaviour.
Because actors who are part of social systems collect information about
their environment and, in light of that information, take action to
influence aspects of the state of the system, the aggregate behaviour over
time of such systems can useful be explained using the concepts of feedback
loops and stocks. The feedback loops are a plausible representation of the
collect/influence/collect cycle of activities and the stocks are a plausible
representation of the system state variables. Models based on these concepts
offer a representation of the causal links between the variables which
allows the rigorous deduction of the consequences of those links. Such
models are therefore theories of the structural source of particular
aggregate behaviours and can be used to make deductions about the mode of
behaviour that will flow from implementing a given influencing policy.)
The important point here is the nature of this claim. This is not a grand
content theory. There are no specific variables or conceptual categories.
For example, it is not suggested that socialisation or class
stratification are meaningful concepts which are necessary to explain the
evolution of all societies. In contrast, this grand claim of system dynamics
is a structural theory - it makes a grand methodological claim about how
certain types of phenomena might be explained.
Moving down to the next theory, we find the principle of how the concepts
of feedback loop and stock should be used to construct models (4). This is
not a grand theory. This is a representation theory, or scheme, proposing a
way of implementing the above grand methodological theory.
A more specific theory is that, unassisted, humans cannot infer the
behaviour of systems represented in the above fashion in a way which is
logically consistent; computers are needed to deduce the behaviour. This is
an empirical claim. As such it is well supported by data (6). Models
therefore allow the application of logic to reveal essentially tautological
but, in practical terms, hidden results (7).
Finally, we come to the idea that each model is a theory. These are
clearly minor content theories, albeit valuable ones. A model is a
concatenation of causal laws offering a plausible representation and hence
explanation for a behaviour mode.
With this clarification it is clear that none of the above theories
proposes an invariant causal law and that there is no crude grand (content)
theory. The only universal law/theory on offer is a grand methodological, or
structural theory, associated with a representation scheme. System dynamics
offers a new structure for thinking about causality but it does not specify
the content of that structure. The laws are structural, or representational,
they have no specific content. This position can still be criticised but it
does not attract the determinism-related criticisms attached to grand theory
in the sense of Parsons and Mills.
Position clarified, but Case open
A suitable note on which to end is that the grand structural theory of
system dynamics, along with the theories of logical deduction and
representation stand or fall not as separate elements to be criticised but
as a coherent research program (8). They should be considered in terms of
the plausibility and coherence of its grand structural claim, the
reasonableness of its representative scheme and its empirical success in
providing explanations for a range of novel phenomena. Viewed in these
Lakatosian terms, the various theories of system dynamics form the hard
core of the field and the healthy flow of diverse applications of the
approach indicates that the research program is a progressive one. Whilst
the issues raised here are still open and the clarified position will be
objectionable to some, system dynamicists are happy to justify their
position in terms of their ability successfully to illuminate the phenomena
to which the field applies. It is on those terms that any of the theories
and laws of system dynamics should be judged.
References
1 Flood, RL and Jackson, MC (1991). Creative Problem Solving: Total
Systems Intervention. Wiley: Chichester.
2 Bloomfield, B (1982). Cosmology, Knowledge and Social Structure: The
case of Forrester and system dynamics. Journal of Applied Systems Analysis
9: 3-15.
3 Forrester, JW (1968) Industrial Dynamics - After the first decade.
In: Collected Papers of Jay W. Forrester (1975 collection). Wright-Allen
Press: Cambridge, MA, pp 133-150.
4 Forrester, JW (1968). Principles of Systems. MIT Press: Cambridge,
MA.
5 Forrester, JW (1994). System dynamics, Systems thinking, and Soft
OR. System Dynamics Review 10: 245-256.
6 Sterman, JD (1994). Learning in and about complex systems. System
Dynamics Review 10: 291-330.
7 Simon, HA (1969) Understanding the Natural and the Artificial
Worlds. In: The Sciences of the Artificial. MIT Press: London, pp 3-29.
8 Lakatos, I (1974) Falsification and the Methodology of Scientific
Research Programmes. In: Lakatos, I and Musgrave, A (eds). Criticism and the
Growth of Knowledge. CUP: Cambridge, pp 91-196.
Regards,
David
____________________________________________________________________________
________________
Dr. David C. Lane
Operational Research Department
London School of Economics and Political Science
Houghton Street Tel.
(44) (0) 171-955-7336
London WC2A 2AE Fax. (44)
(0) 171-955-6885
United Kingdom
e-mail: d.c.lane@lse.ac.uk
-
"Richard G. Dudley"
- Junior Member
- Posts: 19
- Joined: Fri Mar 29, 2002 3:39 am
The Hard Core of SD
As a new-comer to the field I sense that it is sometimes promoted as
more than it is. I dont think I would view it as a new field of pure
science, or a method of philosophy, but rather as a method of analysis.
As statistics is a method for analyzing data and for making inferences
about those data, so system dynamics is a tool for analyzing systems and
making inferences about them. This leads to "systems thinking" just as
statistics allows us the think more critically in terms of the "average
outcome", and that something has a "good chance" of happening.
As with statistics, systems methods can be applied improperly. Thus one
goal of the field of systems dynamics should be to develop and promote
better techniques for "correctly" applying this analysis method (to real
world problems).
Such goals might include development of such things as: 1)means of
selecting the correct system model (e.g. testing how well a model
system fits the real system), 2)better approaches for analyzing system
behavior. 3)more precise, clear, meaningful descriptions for various
types of systems, and their components, means of gauging the accuracy of
model predictions.
--
Richard G. Dudley
rdudley@indo.net.id
http://home.indo.net.id/~rdudley
http://ourworld.compuserve.com/homepages/drrdudley
more than it is. I dont think I would view it as a new field of pure
science, or a method of philosophy, but rather as a method of analysis.
As statistics is a method for analyzing data and for making inferences
about those data, so system dynamics is a tool for analyzing systems and
making inferences about them. This leads to "systems thinking" just as
statistics allows us the think more critically in terms of the "average
outcome", and that something has a "good chance" of happening.
As with statistics, systems methods can be applied improperly. Thus one
goal of the field of systems dynamics should be to develop and promote
better techniques for "correctly" applying this analysis method (to real
world problems).
Such goals might include development of such things as: 1)means of
selecting the correct system model (e.g. testing how well a model
system fits the real system), 2)better approaches for analyzing system
behavior. 3)more precise, clear, meaningful descriptions for various
types of systems, and their components, means of gauging the accuracy of
model predictions.
--
Richard G. Dudley
rdudley@indo.net.id
http://home.indo.net.id/~rdudley
http://ourworld.compuserve.com/homepages/drrdudley
-
John.Barton@BusEco.monash.edu.au
- Junior Member
- Posts: 3
- Joined: Fri Mar 29, 2002 3:39 am
The Hard Core of SD
Like the discussionabout SD in an elevator some time back, questions like that
posed by Mohamed Saleh raise issues regarding our understanding of the
field, compared with explanation. As Angyl pointed out in 1941, and Ackoff
reminds us more recently (although he referred to knowledge instead of
explanation), understanding requires putting things into context
(synthesis), while explanation requires analysis.
To better explain SD it is useful to attempt to place it in the context
other approaches to systems thinking, as, for example, David Lane has done on
a number of occasions. A simple pattern emerges- approaches to systems inquiry
are characterised by three inter-related aspects. These are:
(1) How the system of interest is defined. In SD this is effectively achieved
using reference modes (whether based on hard or soft data) to define the
problem and its boundaries. In a broader context this process relates to
establishing the system principle, ie, system purpose, and hence the
characteristic of the whole that is present in each of the parts, and which
provides the organising principle for the parts.
(2) What form the systems logic takes. This relates to how we describe
wholes. In SD we use the idea of a structure expressed in servomechanism
terms to describe wholes. This contrasts with, eg, the world views of the
biologists, although, as Richardson points out in his History of Feedback
Thought, perhaps not by as much as we might think in the first place.
Furthermore, again as David Lane suggests in SD1712, the use of the
servomechanism structure can be considered as essentially a means of
developing causal hypotheses.Simulation then contributes to the testing of
these hypotheses, and their subsequent application to policy analysis and
learning.
(3) How we apply the systems logic to the system of interest, ie, what
learning structure is employed. In SD a variety of approaches have evolved.
>From the earliest times, Forrester emphasised the role of teams and learning
(eg, see Industrial Dynamics), Richardson and Pugh describe an SD modelling
approach (eg Fig 1.11 in Intro to SD Modeling with DYNAMO) and Lyneis
describes a feedback/learning structure for corporate planning (MIT notes).
More recently of course, we have the team model building work of Richardson,
Vennix and Andersen etc, not to menetion the enormous importance of Senge
linkage of SD to the learning structures developed by Argyris and Schon.
Unfortunately, most comparisons of SD with other approaches, ignore the
presence of these learning structures with the result that only the SD is
perceived as being just the model.
Clearly the first two aspects relate to ontogolical concerns, while the third
relates to epistemological issues. Various approaches to systems inquiry
differ in the world views adopted. Epistemological implications follow.
These three aspects are not new. Examples include Talcott Parsons
sociological framework as mentioned by David Lane, and Checklands later
descriptions of his approach to action learning (He discusses the Framework
of ideas, the Method to be applied, and Area of application).
Recognising the need for a complete approach to systems inquiry to include the
three aspects outlined above, strengthens the methodological basis for SD. It
emphasises the importance of, eg, using reference modes, not only as part of
the model validation process, but also as being an even more fundamental part
of the SD process, ie, defining the systems principle.
In more general terms, this framework reminds us that ontological and
epistemological aspects need to be kept in balance, and made transparent, and
helps us to better understand the strengths and weaknesses of a particular
approach, and possibly its applicability.
John Barton, Room N 7.39,
Department of Management, Monash University,
PO Box 197, Caulfield East, Victoria, Australia, 3145.
From: John.Barton@BusEco.monash.edu.au
posed by Mohamed Saleh raise issues regarding our understanding of the
field, compared with explanation. As Angyl pointed out in 1941, and Ackoff
reminds us more recently (although he referred to knowledge instead of
explanation), understanding requires putting things into context
(synthesis), while explanation requires analysis.
To better explain SD it is useful to attempt to place it in the context
other approaches to systems thinking, as, for example, David Lane has done on
a number of occasions. A simple pattern emerges- approaches to systems inquiry
are characterised by three inter-related aspects. These are:
(1) How the system of interest is defined. In SD this is effectively achieved
using reference modes (whether based on hard or soft data) to define the
problem and its boundaries. In a broader context this process relates to
establishing the system principle, ie, system purpose, and hence the
characteristic of the whole that is present in each of the parts, and which
provides the organising principle for the parts.
(2) What form the systems logic takes. This relates to how we describe
wholes. In SD we use the idea of a structure expressed in servomechanism
terms to describe wholes. This contrasts with, eg, the world views of the
biologists, although, as Richardson points out in his History of Feedback
Thought, perhaps not by as much as we might think in the first place.
Furthermore, again as David Lane suggests in SD1712, the use of the
servomechanism structure can be considered as essentially a means of
developing causal hypotheses.Simulation then contributes to the testing of
these hypotheses, and their subsequent application to policy analysis and
learning.
(3) How we apply the systems logic to the system of interest, ie, what
learning structure is employed. In SD a variety of approaches have evolved.
>From the earliest times, Forrester emphasised the role of teams and learning
(eg, see Industrial Dynamics), Richardson and Pugh describe an SD modelling
approach (eg Fig 1.11 in Intro to SD Modeling with DYNAMO) and Lyneis
describes a feedback/learning structure for corporate planning (MIT notes).
More recently of course, we have the team model building work of Richardson,
Vennix and Andersen etc, not to menetion the enormous importance of Senge
linkage of SD to the learning structures developed by Argyris and Schon.
Unfortunately, most comparisons of SD with other approaches, ignore the
presence of these learning structures with the result that only the SD is
perceived as being just the model.
Clearly the first two aspects relate to ontogolical concerns, while the third
relates to epistemological issues. Various approaches to systems inquiry
differ in the world views adopted. Epistemological implications follow.
These three aspects are not new. Examples include Talcott Parsons
sociological framework as mentioned by David Lane, and Checklands later
descriptions of his approach to action learning (He discusses the Framework
of ideas, the Method to be applied, and Area of application).
Recognising the need for a complete approach to systems inquiry to include the
three aspects outlined above, strengthens the methodological basis for SD. It
emphasises the importance of, eg, using reference modes, not only as part of
the model validation process, but also as being an even more fundamental part
of the SD process, ie, defining the systems principle.
In more general terms, this framework reminds us that ontological and
epistemological aspects need to be kept in balance, and made transparent, and
helps us to better understand the strengths and weaknesses of a particular
approach, and possibly its applicability.
John Barton, Room N 7.39,
Department of Management, Monash University,
PO Box 197, Caulfield East, Victoria, Australia, 3145.
From: John.Barton@BusEco.monash.edu.au
-
"Drew Christie"
- Newbie
- Posts: 1
- Joined: Fri Mar 29, 2002 3:39 am
The Hard Core of SD
I think SD is more a craft like carpentry, engineering and medicine than
the relatively unified sciences that Kuhn and Lakatos were talking about
(e.g., quantum mechanics). The SD toolbox includes an emphasis on
feedback, structure instead of events, delays, identifying stocks and
flows, trigger points, computer simulation, etc. The tools are useful
across a wide range of disciplines and problems. I dont think SD has core
assumptions (or consequences) analogous to nothing going faster than light
in General Relativity.
Drew Christie
University of New Hampshire
From: "Drew Christie" <drewc@cisunix.unh.edu>
the relatively unified sciences that Kuhn and Lakatos were talking about
(e.g., quantum mechanics). The SD toolbox includes an emphasis on
feedback, structure instead of events, delays, identifying stocks and
flows, trigger points, computer simulation, etc. The tools are useful
across a wide range of disciplines and problems. I dont think SD has core
assumptions (or consequences) analogous to nothing going faster than light
in General Relativity.
Drew Christie
University of New Hampshire
From: "Drew Christie" <drewc@cisunix.unh.edu>
The Hard Core of SD
Concerning George Richardsons reply:
Well, gosh, George, tell us what you SAID in all of those places.
Jim
From: Jim Hines <jimhines@interserv.com>
Well, gosh, George, tell us what you SAID in all of those places.
Jim
From: Jim Hines <jimhines@interserv.com>
-
"John P. Davulis"
- Junior Member
- Posts: 2
- Joined: Fri Mar 29, 2002 3:39 am
The Hard Core of SD
I agree with Drew Christie and believe that an appropriate metaphor for
SD could be Richard Rortys suggestion for the "method" of philosophy:
"The method is to redescribe lots and lots of things in new ways, until
you have created a pattern of linguistic behavior which will tempt the
rising generation to adopt it, thereby causing them to look for
appropriate new forms of nonlinguistic behavior, for example, the
adoption of new scientific equpiment or new social institutions. This
sort of philosophy ... works holistically and pragmatically. It says
things like try thinking of it this way ...." (Contingency, irony, and
solidary, Cambridge Univ. Press, 1989, p. 9)
SD offers us a powerful language to redesribe the world and tools which
we can successfully utilize to address a large number of diverse
problems. There is no need for practitioners to get too metaphysical.
--
John P. Davulis e-mail: jpdavulis@cmpco.com
Manager, Economic & Load Forecasting phone: 207-623-3521 x2248
Central Maine Power Company fax: 207-626-9588
83 Edison Drive Augusta, Maine 04336
SD could be Richard Rortys suggestion for the "method" of philosophy:
"The method is to redescribe lots and lots of things in new ways, until
you have created a pattern of linguistic behavior which will tempt the
rising generation to adopt it, thereby causing them to look for
appropriate new forms of nonlinguistic behavior, for example, the
adoption of new scientific equpiment or new social institutions. This
sort of philosophy ... works holistically and pragmatically. It says
things like try thinking of it this way ...." (Contingency, irony, and
solidary, Cambridge Univ. Press, 1989, p. 9)
SD offers us a powerful language to redesribe the world and tools which
we can successfully utilize to address a large number of diverse
problems. There is no need for practitioners to get too metaphysical.
--
John P. Davulis e-mail: jpdavulis@cmpco.com
Manager, Economic & Load Forecasting phone: 207-623-3521 x2248
Central Maine Power Company fax: 207-626-9588
83 Edison Drive Augusta, Maine 04336