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Topic: System Dyanmics model for preserving privacy and security in smart cities.
Building a System Dynamics Model is a series of papers written to demystify the model building process. This paper is the first in the series and explains the first stage of the model building process called conceptualization. The paper examines in depth the following steps of conceptualization:
- Define the purpose of the model.
- Define the model boundary and identify key variables.
- Describe the behavior or draw the reference modes of the key variables.
- Diagram the basic mechanisms, the feedback loops, of the system.
The first step, deciding on the model purpose, means focusing on a problem and narrowing down the model’s audience before concretely stating the model purpose.
Defining the model boundary involves selecting components necessary to generate the behavior of interest as set by the model purpose. After defining the model boundary and identifying key variables, some of the most important variables are graphed over time as a reference mode.
Points to Ponder for Project
You could lose points because of the following reasons (not necessarily limited to):
- Inadequate definition of model purpose
- Implicit criteria for model evaluation
- No description of model context
- Casual choice of scale for the system-of-interest
- Inclusion of too many irrelevant components
- Careless categorization of system components
- Inclusion of circular logic
- Lack of precision in conceptual model diagram
- Reluctance to make initial hypotheses about system behavior
- Selection of inappropriate mathematics
- Choice of inappropriate time unit for simulations
- Construction of mathematical descriptions without verbal descriptions
- Underestimation of the importance of graphical representations
- Use of functional relationships that are not interpretable
- Careless definition of dimensional units of model components
- Use of coefficients without meaning to obtain dimensional consistency
- Reluctance to use qualitative information
- Decision to remove a functional relationship due to lack of data
- Reliance on automated model parameterization
- Underestimation of the importance of negative feedback and time lags
- Careless definition of baseline conditions
- Reliance on automated solutions to mathematical and programming problems
- Underestimation of the importance of qualitative aspects of evaluation
- Acceptance of conceptually flawed functional relationships
- Acceptance or rejection of surprising model behavior without explanation
- Inappropriate interpretation of the initial phase of model behavior
- Inappropriate interpretation of statistical tests used in model evaluation
- Careless design of sensitivity analysis
- Tendency to equate model sensitivity with model inadequacy
- Careless experimental design for model application
- Tendency to overestimate the range of model applicability
- Inappropriate interpretation of statistical tests used in model application
- Failure to communicate numerical results
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