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System Dynamics Interest Meeting Life Science Community Initiative

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Title: System Dynamics Interest Meeting Life Science Community Initiative


1
System Dynamics Interest Meeting Life Science
Community Initiative
  • 18 October 2007

2
System Dynamics Modeling Interest Meeting Agenda
  • Importance of Mental Models
  • Describing Modeling To Non-Modelers
  • Steps of the Modeling Process
  • Causal Loop Diagramming Fundamentals
  • Representative Academic Programs
  • Principles for Successful Use of System Dynamics
  • Validation and Model Testing

3
  • Importance of Mental Models

4
System Dynamics Modeling Interest Meeting Where
does majority of information exist?
System Dynamics and the Lessons of 35 Years By
Jay W. Forrester
5
System Dynamics Modeling Interest Meeting
Motivation Net Benefits?
  • Mental Models .
  • Hold critical project information
  • Difficult to surface, share, challenge, and
    improve


Risk
6
  • Describing Modeling To Non-Modelers

Dan Goldner, Ventana Systems Inc
7
Questions about simulation
What is it?
  • How will it help me?
  • What will it cost?
  • How long will it take?
  • How far can I trust it?

8
Business School in America
9
  • Steps of the Modeling Process

Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
10
System Dynamics Modeling Interest Meeting Steps
of the Modeling Process
11
System Dynamics Modeling Interest Meeting Steps
of the Modeling Process
12
System Dynamics Modeling Interest Meeting Steps
of the Modeling Process
Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
  • 1. Problem Articulation (Boundary Selection)
  • Theme selection What is the problem? Why is
    it a problem?
  • Key variables What are the key variables and
    concepts we must consider?
  • Time horizon How far in the future should we
    consider? How far back in the past lie the roots
    of the problem?
  • Dynamic problem definition (reference modes)
    What is the historical behavior of the key
    concepts and variables? What might their
    behavior be in the future.

13
System Dynamics Modeling Interest Meeting Steps
of the Modeling Process
Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
  • 2. Formulation of Dynamic Hypothesis
  • Initial hypothesis generation What are
    current theories of the problematic behavior?
  • Endogenous focus Formulate a dynamic
    hypothesis that explains the dynamics as
    endogenous consequences of the feedback
    structure.
  • Mapping Develop maps of causal structure
    based on initial hypotheses, key variables,
    reference modes, and other available data, using
    tools such as
  • Model boundary diagrams,
  • Subsystem diagrams,
  • Causal loop diagrams,
  • Stock and flow maps,
  • Policy structure diagrams,
  • Other facilitation tools.

14
System Dynamics Modeling Interest Meeting Steps
of the Modeling Process
Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
  • 3. Formulation of a Simulation Model
  • Specification of structure, decision rules.
  • Estimation of parameters, behavioral
    relationships, and initial conditions.
  • Tests for consistency with the purpose and
    boundary.

15
System Dynamics Modeling Interest Meeting Steps
of the Modeling Process
Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
  • 4. Testing
  • Comparison to reference modes Does the model
    reproduce the prob-lem behavior adequately for
    your purpose?
  • Robustness under extreme conditions Does the
    model behave realis-tically when stressed by
    extreme conditions?
  • Sensitivity How does the model behave given
    uncertainty in parame-ters, initial conditions,
    model boundary, and aggregation?
  • . . . Many other tests (see chapter 21).

16
System Dynamics Modeling Interest Meeting Steps
of the Modeling Process
Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
  • 5. Policy Design and Evaluation
  • Scenario specification What environmental
    conditions might arise?
  • Policy design What new decision rules,
    strategies, and structures might be tried in the
    real world? How can they be represented in the
    model?
  • "What if . . ." analysis What are the effects
    of the policies?
  • Sensitivity analysis How robust are the
    policy recommendations under different scenarios
    and given uncertainties?
  • Interactions of policies Do the policies
    interact? Are there synergies or compensatory
    responses?

17
  • Causal Loop Diagramming Fundamentals

18
Causal Loop Diagramming Fundamentals
19
Causal Loop Diagramming Fundamentals
20
Causal Loop Diagramming Fundamentals
time
remaining
Work
Remaining

schedule
pressure
21
Causal Loop Diagramming Fundamentals
22
Causal Loop Diagramming Fundamentals
23
Causal Loop Diagramming Fundamentals
-
24
Causal Loop Diagramming Fundamentals
25
Causal Loop Diagramming Fundamentals
task
-
26
Causal Loop Diagramming Fundamentals
27
Causal Loop Diagramming Fundamentals
28
Causal Loop Diagramming Fundamentals
29
Causal Loop Diagramming Fundamentals
30
Causal Loop Diagramming Fundamentals
31
Causal Loop Diagramming Fundamentals
32
Causal Loop Diagramming Fundamentals
33
Causal Loop Diagramming Fundamentals
34
Causal Loop Diagramming Fundamentals
35
Causal Loop Diagramming Fundamentals
36
Causal Loop Diagramming Fundamentals
37
Causal Loop Diagramming Fundamentals
38
Causal Loop Diagramming
39
  • Principles for Successful Use of System Dynamics

Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
40
System Dynamics Modeling Interest
Meeting Principles for Successful Use of System
Dynamics
Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
  • 1. 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. Modelers must exclude all factors
    not relevant to the problem to ensure the project
    scope is feasible and the results timely. The
    goal is to improve the performance of the system
    as defined by the client. Focus on results.

41
System Dynamics Modeling Interest
Meeting Principles for Successful Use of System
Dynamics
Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
  • 2. Modeling should be integrated into a project
    from the beginning.
  • The value of the modeling process begins early
    on, in the problem definition phase. The
    modeling process helps focus diagnosis on the
    structure of the system rather than blaming
    problems on the people making decisions in that
    structure.

42
System Dynamics Modeling Interest
Meeting Principles for Successful Use of System
Dynamics
Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
  • 3. Be skeptical about the value of modeling and
    force the "why do we need it" discussion at the
    start of the project.
  • There are many problems for which system dynamics
    is not useful. Carefully consider whether system
    dynamics is the right technique for the problem.
    Modelers should welcome difficult questions from
    the clients about how the process works and how
    it might help them with their problem. The
    earlier these issues are discussed, the better.

43
System Dynamics Modeling Interest
Meeting Principles for Successful Use of System
Dynamics
Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
  • 4. System dynamics does not stand alone.
  • Use other tools and methods as appropriate. Most
    modeling projects are part of a larger effort
    involving traditional strategic and operational
    analysis, including benchmarking, statistical
    work, market research, etc. Effective modeling
    rests on a strong base of data and understanding
    of the issues. Modeling works best as a
    complement to other tools, not as a substitute.

44
System Dynamics Modeling Interest
Meeting Principles for Successful Use of System
Dynamics
Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
  • 5. Focus on implementation from the start of the
    project.
  • Implementation must start on the first day of the
    project. 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 answer the
    question, How do we get there from here?
    Carefully consider the real world issues involved
    in pulling various policy levers. Quantify the
    full range of costs and benefits of policies, not
    only those already reported by existing
    accounting systems.

45
System Dynamics Modeling Interest
Meeting Principles for Successful Use of System
Dynamics
Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
  • 6. Modeling works best as an iterative process of
    joint inquiry between client and consultant.
  • Modeling is a process of discovery. The goal is
    to reach new understanding of how the problem
    arises and then use that understanding to design
    high leverage policies for improvement. Modeling
    should not be used as a tool for advocacy. Don't
    build a client's prior opinion about what should
    be done into a model. Use workshops where the
    clients can test the model themselves, in real
    time.

46
System Dynamics Modeling Interest
Meeting Principles for Successful Use of System
Dynamics
Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
  • 7. Avoid black box modeling.
  • Models built out of the sight of the client will
    never lead to change in deeply held mental models
    and therefore will not change client behavior.
    Involve the clients as early and as deeply as
    possible. Show them the model. Encourage them
    to suggest and run their own tests and to
    criticize the model. Work with them to resolve
    their criticisms to their satisfaction.

47
System Dynamics Modeling Interest
Meeting Principles for Successful Use of System
Dynamics
Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
  • 8. Validation is a continuous process of testing
    and building confidence in the model.
  • Models are not validated after they are completed
    nor by any one test such as their ability to fit
    historical data. Clients (and modelers) build
    confidence in the utility of a model gradually,
    by constantly confronting the model with data and
    expert opinion-their own and others'. Through
    this process both model and expert opinions will
    change and deepen. Seek out opportunities to
    challenge the model's ability to replicate a
    diverse range of historical experiences.

48
System Dynamics Modeling Interest
Meeting Principles for Successful Use of System
Dynamics
Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
  • 9. Get a preliminary model working as soon as
    possible. Add detail only as necessary.
  • Develop a working simulation model as soon as
    possible. Don't try to develop a comprehensive
    conceptual model prior to the development of a
    simulation model. Conceptual models are only
    hypotheses and must be tested. Formalization and
    simulation often uncover flaws in conceptual maps
    and lead to improved understanding. The results
    of simulation experiments inform conceptual
    understanding and help build confidence in the
    results. Early results provide immediate value
    to clients and justify continued investment of
    their time.

49
System Dynamics Modeling Interest
Meeting Principles for Successful Use of System
Dynamics
Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
  • 10. A broad model boundary is more important than
    a great deal of detail.
  • Models must strike a balance between a useful,
    operational representation of the structures and
    policy levers available to the clients while
    capturing the feedbacks generally unaccounted for
    in their mental models. In general, the dynamics
    of a system emerge from the interactions of the
    components in the system-capturing those
    feedbacks is more important than a lot of detail
    in representing the components themselves.

50
System Dynamics Modeling Interest
Meeting Principles for Successful Use of System
Dynamics
Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
  • 11. Use expert modelers, not novices.
  • While the software available for modeling is
    easily mastered by a high school student or CEO,
    modeling is not computer programming. You cannot
    develop a qualitative diagram and then hand it
    off to a programmer for coding into a simulation
    model. Modeling requires a disciplined approach
    and an understanding of business, skills
    developed through study and experience. Get the
    expert assistance you need. Use the project as
    an opportunity to develop the skills of others on
    the team and in the client organization.

51
System Dynamics Modeling Interest
Meeting Principles for Successful Use of System
Dynamics
Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
  • 12. Implementation does not end with a single
    project.
  • Modeling work continues to have impact long after
    the initial project is over. Models can be
    applied to similar issues in other settings.
    Modelers develop expertise they can apply to
    related problems in other pars of the
    organization. They may move into new positions
    and new organizations, taking the insights they
    gained and, sometimes, a new way of thinking,
    with them. Implementation is a long-term process
    of personal, organizational, and social change.

52
  • Validation and Model Testing

Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
53
System Dynamics Modeling Interest
Meeting Validation and Model Testing
Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
  • 1. Boundary Adequacy
  • Purpose
  • Are the important concepts for addressing the
    problem endogenous to the model?
  • Does the behavior of the model change
    significantly when boundary assumptions are
    relaxed?
  • Do the policy recommendations change when the
    model boundary is extended?
  • Tools and Procedures See text.

54
System Dynamics Modeling Interest
Meeting Validation and Model Testing
Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
  • 2. Structure Assessment
  • Purpose
  • Is the model structure consistent with relevant
    descriptive knowledge of the system?
  • Is the level of aggregation appropriate?
  • Does the model conform to basic physical laws
    such as conversation laws?
  • Do the decision rules capture the behavior of the
    actors in the system?
  • Tools and Procedures See text.

55
System Dynamics Modeling Interest
Meeting Validation and Model Testing
Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
  • 3. Dimensional Consistency
  • Purpose
  • Is each equation dimensionally consistent without
    the use of parameters having no real world
    meaning?
  • Tools and Procedures See text.

56
System Dynamics Modeling Interest
Meeting Validation and Model Testing
Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
  • 4. Parameter Assessment
  • Purpose
  • Are the parameter values consistent with relevant
    descriptive and numberical knowledge of the
    system?
  • Do all parameters have real world counterparts?
  • Tools and Procedures See text.

57
System Dynamics Modeling Interest
Meeting Validation and Model Testing
Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
  • 5. Extreme Conditions
  • Purpose
  • Does each equation make sense even when its
    inputs take on extreme values?
  • Does the model respond plausibly when subjected
    to extreme policies, shocks, and parameters?
  • Tools and Procedures See text.

58
System Dynamics Modeling Interest
Meeting Validation and Model Testing
Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
  • 6. Integration Error
  • Purpose
  • Are the results sensitive to the choice of time
    step or numberical integration method?
  • Tools and Procedures See text.

59
System Dynamics Modeling Interest
Meeting Validation and Model Testing
Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
  • 7. Behavior Reproduction
  • Purpose
  • Does the model reproduce the behavior of interest
    in the system (qualitatively and quantitatively)?
  • Does it endogenously generate the sysmptoms of
    difficulty motivating the study?
  • Does the model generate the various modes of
    behavior observed in the real system?
  • Do the frequencies and phase relationships among
    the variables match the date?
  • Tools and Procedures See text.

60
System Dynamics Modeling Interest
Meeting Validation and Model Testing
Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
  • 8. Behavior Anomaly
  • Purpose
  • Do anomalous behaviors result when assumptions of
    the model are changed or deleted?
  • Tools and Procedures See text.

61
System Dynamics Modeling Interest
Meeting Validation and Model Testing
Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
  • 9. Family Member
  • Purpose
  • Can the model generate the behavior observed in
    other instances of the same system?
  • Tools and Procedures See text.

62
System Dynamics Modeling Interest
Meeting Validation and Model Testing
Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
  • 10. Surprise Behavior
  • Purpose
  • Does the model generate previously unobserved or
    unrecognized behavior?
  • Does the model successfully anticipate the
    response of the system to novel conditions?
  • Tools and Procedures See text.

63
System Dynamics Modeling Interest
Meeting Validation and Model Testing
Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
  • 11. Sensitivity Analysis
  • Purpose
  • Numerical sensitivity Do the numberical values
    change significantly ..
  • Behavioral sensistivity Do the modes of
    behavior generated by the model change
    significantly ..
  • Policy sensistivity Do the policy implications
    change significantly ..
  • . when assumptions about parameters, boundary,
    and aggregation are varied over the plausible
    range of uncertainty?
  • Tools and Procedures See text.

64
System Dynamics Modeling Interest
Meeting Validation and Model Testing
Business Dynamics Systems Thinking and Modeling
for a Complex World, John Sterman
  • 12. System Improvement
  • Purpose
  • Did the modeling process help change the system
    for the better?

65
  • Representative Academic Programs

66
System Dynamics Modeling Interest Meeting The
System Dynamics Group Bergen, Norway
67
System Dynamics Modeling Interest Meeting The
System Dynamics Group Bergen, Norway
What is System Dynamics? In a few words .The
purpose of System Dynamics is to help people make
better decisions when confronted with complex,
dynamic systems. The field provides a philosophy
and tools to model and analyze dynamic systems.
Equally important, the field provides techniques
and tools to investigate current decision making
and to help decision makers learn. The modeling
language is intuitive and it is common for all
kinds of applications be they in medicine,
economics, management etc. This makes System
Dynamics an ideal tool for interdisciplinary
work, and it makes learning more efficient
because basic system structures tend to repeat
themselves from one field of application to
another.
68
System Dynamics Modeling Interest Meeting Zackery
Dept. of Civil Engineering, TAM
69
System Dynamics Modeling Interest Meeting Timothy
R B Taylor, Ph.D. Candidate, TAM
  • Influence of Expert Domain Knowledge on Public
  • Policy and Natural Systems 1
  • Dissertation Proposal
  • DRAFT 6/27/07
  • Timothy R.B. Taylor
  • Ph.D. Candidate
  • Texas AM University
  • Zachry Department of Civil Engineering
  • Construction Engineering and Management Program
  • Advisor David N. Ford

70
System Dynamics Modeling Interest Meeting Timothy
R B Taylor, Ph.D. Candidate, TAM
  • Abstract
  • Interactions between society and natural systems
    can be beneficial or harmful to society. Society
    benefits from natural systems by being provided
    with the basic necessities of life (air, water,
    and food). However, recent events such as
    Hurricanes Rita and Katrina and the Asian Tsunami
    demonstrate the harmful impacts natural systems
    can have on society. Domain knowledge is
    developed from observation of natural and
    societal systems. Domain knowledge is contained
    within scientific knowledge and engineering
    knowledge. Scientific knowledge is gained through
    observation of natural and societal systems.
    Engineering knowledge is developed from
    scientific knowledge and is used to manipulate
    natural and societal systems. In the past two
    centuries scientific and engineering knowledge
    has produced technologies that affect the
    interaction between societal and natural systems.
    For example increased scientific knowledge of
    stratospheric ozone identified an increased
    cancer risk due to the depletion of stratospheric
    ozone caused by CFCs and other industrial
    emissions. Although scientists and engineers are
    in positions to advise on policies to address
    problems involving societal/natural system
    interactions, they are not always successful. The
    proposed research seeks to develop an improved
    understanding of the dynamic interactions between
    society, natural systems, scientific and
    engineering knowledge, and public policy. The
    proposed research will offer insight to
    scientists and engineers on effectively
    influencing public policy on these issues and
    will offer insight to policy makers on how to
    incorporate scientific and engineering knowledge
    into effective public policies.
  • 1 Portions of the text have been taken from
    Integrated Natural Hazard Risk Management
    Modeling the Interactions of Nature, Science,
    Society, and Public Policy by Vedlitz,
    Lindquist, and Ford (2007). However, the
    development and description of the research
    program is the work of the author. The author can
    provide specific information.

71
System Dynamics Modeling Interest Meeting The
System Dynamics Group Bergen, Norway
Figure 4 Interaction of natural and societal
systems, public policy, scientific and
engineering knowledge, and technology
72
System Dynamics Modeling Interest Meeting The
System Dynamics Group Bergen, Norway
Figure 6 Expanded nuclear industry example
described using the dynamic hypothesis
73
System Dynamics Modeling Interest Meeting The
System Dynamics Group Bergen, Norway
Figure 8 Expanded stratospheric ozone example
described using the dynamic hypothesis
74
System Dynamics Modeling Interest Meeting School
and Earth and Environmental Sciences
75
System Dynamics Modeling Interest
Meeting Rockefeller College of Public Affairs
Policy
Initiative for System Dynamics in the Public
Sector Rod MacDonald, Director The Initiative
for System Dynamics in the Public Sector develops
system dynamics computer simulation models of
specific problems identified by government
agencies and nonprofits. These models are used to
test scenarios and develop understanding about
the possible implications of different policies
and programs prior to implementation. Studies for
the Division of Disability Determinations, The
Governors Traffic Safety Committee and Home
Health Care Association of New York are among the
Initiatives recent projects as well as providing
training for Office of the State Comptroller and
Regional School Support Centers . Current
projects include an examination of scenarios
regarding the implementation of technology on the
criminal justice system for the National
Institute of Justice.
76
System Dynamics Modeling Interest Meeting Water
Resources Management Program - UNLV
Dr. Krystyna A. Stave, Associate Professor The
water resources management program in the College
of Sciences is a flexible, interdisciplinary
course of study leading to a master of science
degree. It is a technically and scientifically
based program that blends the physical aspects of
the hydrologic sciences with policy and
management issues. The program, recommended for
those with undergraduate degrees in physical,
biological, natural sciences and engineering,
social sciences, management, environmental
studies, or related disciplines, encourages
multidisciplinary study and research with
participating faculty at UNLV from the colleges
of Sciences, Business, Urban Affairs,
Engineering, and Liberal Arts, the Boyd School of
Law, as well as participating faculty from the
Desert Research Institute and federal, state, or
local agencies.
77
System Dynamics Modeling Interest Meeting Water
Resources Management Program - UNLV
Dr. Krystyna A. Stave, Associate Professor I
use a systems approach to study relationships
between people and the biophysical environment,
drawing on social ecology to examine the ways
that a person's biosocial context affects the way
she or he perceives and interprets environmental
conditions, defines environmental problems, and
develops ideas about how the environment should
be used and managed, and on hydrology and
ecosystem ecology to trace the effects of human
activity through the biophysical system. I teach
courses in system dynamics modeling, social
ecology, and environmental problem-solving. My
research focus is to improve stakeholder
participation in environmental decision-making.
This includes environmental education to increase
public awareness and understanding of
environmental issues and the development of
computer simulation models to support
decision-making.
78
Description Elements Benefits
( necessary to progress to a Virtual World )
  • Representative Elements
  • A report consisting of text, Vensim software
    conceptual diagrams, graphs, etc.
  • Model sectors for key stakeholders
  • Information and physical flows
  • Goals
  • Policies
  • Boundaries included and excluded
  • Causality between key variables
  • Interaction between key variables
  • Feedback loop
  • identification
  • operation
  • polarity
  • Variable definitions and units
  • Representative Benefits
  • A basis to reason about
  • range of potential performance
  • drivers of normal and abnormal performance
  • feedback loop strength and time-based dominance
  • monitoring and control points
  • data collection needs
  • tradeoffs
  • robust policies
  • appropriate project design
  • scope of potential virtual world
  • Improved focus, scientific reasoning,
    communication, and influence within and between
    teams, executives, partners, contractors,
    lenders, etc.
  • Improved knowledge retention and transfer

79
Virtual World Elements Benefits
( incremental to Description elements and
benefits )
  • Representative Elements
  • A transparent, internally consistent, integrated,
    and verifiable Vensim software computer model
    that
  • represents and simulates description
  • checks units and equations
  • includes a wide range of analysis tools, e.g.,
    causal tracing, feedback loop identification,
    deterministic and stochastic information, etc.
  • produces time-series data
  • allows comparison of historical to model behavior
  • allows controlled experiments
  • User interface that shields user from unnecessary
    complexity while allowing quick scenario
    configuration and analysis
  • Optimization and Monte-Carlo analysis
  • Stand-alone PC and BP Intranet Biz-Sims
  • Representative Benefits
  • A basis to validate
  • range of potential performance
  • drivers of normal and abnormal performance
  • feedback loop strength and time-based dominance
  • monitoring and control points
  • data collection needs
  • tradeoffs
  • robust policies
  • appropriate project design
  • Reduced risk of implementation failure through
    use of role reversal gaming
  • A means to satisfy assurance inquiries
  • Useful for helping new project managers quickly
    gain years of experience

80
Why is System Dynamics distinctive?
  • What features make business dynamics models
    distinctive?
  • how humans actually make decisions (versus how
    they say they make them)
  • delays
  • feedback
  • non linear relationships
  • validation process
  • tightly coupled and integrated
  • actions of others
  • historical data matching
  • process design and management policies

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