p.vii Effective decision making and learning in a complex world
of growing dynamic complexity requires us to become systems thinkers - to expand the boundaries of our mental
models and develop tools to understand how the structure of complex systems creates their behavior... System dynamics
is a perspective and set of conceptual tools that enable us to understand the structure and dynamics of complex systems.
p.4 Many advocate the development of systems thinking - the ability to see the world as a complex
system, in which we understand that "you can't just do one thing" and that "everything is connected to everything else." ...
System dynamics is a method to enhance learning in complex systems.
p.12 Much of the art of system dynamics modeling is discovering and representing the feedback process,
which... determine the dynamics of a system.
p.13 All systems, no matter how complex, consist of networks of positive and negative
feedbacks, and all dynamics arise from the interaction of these loops with one another.
p.28 A fundamental principle of system dynamics states that the structure of the system gives rise to its
behavior.
p.37 Simulation is the only practical way to test these [mental] models.
p.38-39 systems thinking techniques, including system dynamics and qualitative methods such as soft systems
analysis, can enhance our intuition about complex situations... System dynamics is a powerful method to gain useful
insight into situations of dynamic complexity and policy resistance.
p.41-42 System dynamics can be applied to any dynamic system, with any time and spatial
scale. [JLJ - what about a machine playing a game?]
p.79 Develop a model to solve a particular problem, not to model the system. A model must
have a clear purpose and that purpose must be to solve the problem of concern to the client.
p.80 Constantly ask, How will the model help the client make decisions? Use the model to
set priorities and determine the sequence of policy implementation. Use the model to ask the question, How do we get there
from here?
p.85 In practice, as a modeler you are first brought into an organization by a contact
who thinks you or your modeling tools might be helpful. Your first step is to find out what the real problem is and
who the real client is.
p.87 There is no cookbook recipe for successful modeling, no procedure you can follow to
guarantee a useful model. Modeling is inherently creative.
p.90 The time horizon [of the process we are trying to model using system dynamics] ... should extend far
enough into the future to capture the delayed and indirect effects of potential policies. Most people dramatically underestimate
the length of time delays and select time horizons that are far too short. A principal deficiency in our mental models is
our tendency to think of cause and effect as local and immediate. But in dynamically complex systems, cause and effect
are distant in time and space. Most of the unintended effects of decisions leading to policy resistance involve feedbacks
with long delays, far removed from the point of decision or the problem symptom.
p.94-95 Once the problem has been identified and characterized over an appropriate time horizon, modelers
must begin to develop a theory, called a dynamic hypothesis, to account for the problematic behavior...
Many times the purpose of the model is to solve a critically important problem that has persisted for years and generated
great conflict and not a little animosity among members of the client team. All will tenaciously advocate their positions
while deriding the views of others in the group.
p.107 Like all systems, the complex system is an interlocking structure of feedback loops... This
loop structure surrounds all decisions public or private, conscious or unconscious. The processes of man
and nature, or psychology and physics, of medicine and engineering all fall within this structure. - Jay
W. Forrester, Urban Dynamics (1969), p.107
The behavior of a system arises from its structure. That structure consists of
the feedback loops, stocks, flows, and nonlinearities created by the interaction of the physical and institutional structure
of the system with the decision-making processes of the agents acting within it.
p.133 The principle that the structure of a system generates its behavior leads to a useful heuristic to
help modelers discover the feedback loop structure of a system.
p.191-193 Developing facility in identifying, mapping, and interpreting the stock and flow networks of a
system is a critical skill for any modern systems modeler... Stocks and flows, along with feedback, are the two central
concepts of dynamic systems theory.
Stocks are accumulations. They characterize the state of the system and generate
the information upon which decisions and actions are based... The stock and flow diagramming conventions (originated by Forrester
1961) were based on a hydraulic metaphor - the flow of water into and out of reservoirs. Indeed, it is helpful to think of
stocks as bathtubs of water.
p.199 Stocks characterize the state of the system. To identify key stocks in a system, imagine freezing
the scene with a snapshot. Stocks would be those things you could count or measure in the picture
p.208 In all cases, make sure your modeling software and method can include the feedback processes you consider
important.
p.217 As a rule of thumb, clients generally want to see more detail in a model than the modeler thinks is
needed... Roberts (1977/1978) estimated that clients often require twice as much detail as the modeler feels is needed
to feel comfortable with and accept a model as a basis for action, and in my experience this is often an underestimate...
"You must provide the level of detail that causes [the client] to be persuaded that you have properly taken into account his
issues, his questions, his level of concerns. Otherwise he will not believe the model you have built, he will not accept it,
and he will not use it."
p.229 Stocks accumulate their inflows less their outflows. Stocks are the states of the system upon which
decisions and actions are based
p.262 The ability to relate stocks and flows intuitively is essential for all modelers... because most realistic
models have no analytical solutions.
p.263 A mathematical theory is not to be considered complete until you have made it so clear that
you can explain it to the first man whom you meet on the street. -David Hilbert
p.330 by the time sufficient observations have developed for reliable estimation, it is too late to use
the estimates for forecasting purposes.
p.331 The ability of a model to replicate historical data does not, by itself, indicate that the model is
useful.
p.347 Only models that capture the causal structure of the system will respond accurately as conditions
change and policies are implemented.
p.394 How should attractiveness be specified? Attractiveness depends on a wide range of variables
p.426 All beliefs, expectations, forecasts, and projections are based on information available to the decision
maker at the time
p.460 Consistent with the experience of others, the modeling team found that abstract description and conceptual
models did not change the thinking or behavior of key decision makers in the organization. Rather, the mental models and behavior
of the managers responsible for production planning and capacity acquisition changed only when they actively worked with the
model to address important issues. The modeling team worked hard to involve current and future line managers in the development
and testing of the model.
p.461 the model (and all models) can never be considered finished. Models are always a work in progress,
and the model users must constantly ask whether the assumptions of the model are still reasonable as conditions change. Sustained
implementation success depends on creating an ongoing process of modeling rather than a single model, no matter how accurate
or comprehensive.
p.513 Flows of information are continuously converted into decisions and actions.
p.514 To be useful, simulation models must mimic the behavior of the real decision makers so that
they respond appropriately, not only for conditions observed in the past but also for circumstances never
yet encountered. You must specify a robust, realistic decision rule at every decision point in the model...
Decision rules are the policies and protocols specifying how the decision maker processes available information.
Decisions are the outcome of this process.
p.515 Every decision rule can be thought of as an information processing procedure
p.519 To be useful, the decision rules in models must behave plausibly in all circumstances,
not only those for which there are historical records.
p.600 people tend to focus their attention and effort on cues that are readily available, salient, and concrete.
We focus on cues we believe to be relatively certain... Our mental models affect which of the many cues in an environment
we think are important and useful, directing attention to those cues at the expense of others.
p.631 Expectations are fundamental to decision making. Modelers must portray the way the agents represented
in their models form forecasts and update expectations... We constantly form expectations about what is likely to happen,
and these expectations guide our actions.
p.632 Models of the forecasting process must capture the way people form expectations... The model must
capture the cues used in the forecasting process and the way in which the cues are combined to yield the forecast.
p.655-656 First, most forecasts are not very good... improving forecast accuracy is difficult... focus on
the development of decision rules and strategies that are robust to the inevitable forecast errors. The real value of modeling
is not to anticipate and react to problems in the environment but to eliminate the problems by changing the underlying structure
of the system.
p.850 The goal of modeling, and of scientific endeavor more generally, is to build shared understanding
that provides insight into the world and helps solve important problems. Modeling is therefore inevitably a process of communication
and persuasion among modelers, clients, and other affected parties. Each person ultimately judges the quality and appropriateness
of any model using his or her own criteria.
p.850 Your responsibility is to use the best model available for the purpose at hand despite its inevitable
limitations.
p.890 The word validation should be struck from the vocabulary of modelers. All models are wrong,
so no models are valid or verifiable in the sense of establishing their truth. The question facing clients and modelers is
never whether a model is true but whether it is useful. The choice is never whether to use a model. The only choice
is which model to use. Selecting the most appropriate model is always a value judgment to be made by reference to the purpose.
Without a clear understanding of the purpose for which the model is to be used, it is impossible to determine whether you
should use it as a basis for action.
p.890 Models fail because more basic questions about the suitability of the model to the purpose
aren't asked...
