Dynamic Modeling I
Live Webinar Series: Beginning Wednesday, October 9
12:00 Noon - 1:00 PM EDT (New York Time)
Dynamic Modeling I bolsters your model building confidence by focusing on building models in every single class.
Join Dr. Karim Chichakly as he guides you, step by step, through some of the key components in the process of effective model creation. During each 55-minute class, you'll learn the
ins and outs of model creation as he shares his personal workflow and additional tips and tricks that he’s learned in more than 20 years of experience in the field.
Each class is followed with a question and answer session with Dr. Chichakly. Online access to these class recordings, sample models, handouts, and homework assignments are
included to cement your learning.
Class 1: Model Formulations - October 9, 2019
The most difficult part of building a model is deciding how to formulate the relationships and the underlying equations. In this class, we will first review the
model formulations studied in Introduction to Dynamic Modeling I
and Introduction to Dynamic Modeling II and
then add new formulations to your toolset.
Class 2: Main Chains - October 16, 2019
Multiple stocks connected together by flows, called main chains, form the backbone of many of our systems. Several main chains, including the ubiquitous
aging chain, will be presented. Some time will also be devoted to steady-state initialization of your models.
Class 3: Intangibles - October 23, 2019
Not everything we wish to model is tangible. We often need to include soft variables, such as Morale, Reputation, and Loyalty, in our models. This class explains
how to include soft variables in your model and covers some deeper details about graphical functions, which are necessarily part of modeling intangibles.
Class 4: Model Parameterization - October 30, 2019
As you develop your model, how do you pick your parameters? Once your model is complete, how do you confirm you’ve made the proper choices for your
parameters and your graphical functions? This class explores the different ways to choose your parameters, to modify them as you develop your model, to
perform sensitivity analysis on them, and to reduce the sensitivity of your parameters.