In this lecture, we briefly introduce System Dynamics Modeling (SDM) and Agent-Based/Individual-Based Modeling (ABM/IBM) as the two ends of the simulation modeling spectrum (from low resolution to high resolution). The introduction of ABM describes applications in life sciences, social sciences, and engineering (Multi-Agent Systems, MAS)/operations research. NetLogo is introduced, and it is used to present examples of running ABM's as well as the code behind them. At the end of the ABM/NetLogo introduction, comments about the previous lab on Monte Carlo simulation are given. These comments focus on interval estimation (which is right 95% of the time, as opposed to point estimation that is right 0% of the time) and the role of non-trivial distributions of random variables (as opposed to just their means).
Subscribe to:
Post Comments (Atom)
Popular Posts
-
In this lecture, we close out our review of DES fundamentals and hand simulation. After going through a hand-simulation example one last tim...
-
This lecture covers Variance Reduction Techniques (VRT) for stochastic simulation, covering: Common Random Numbers (CRNs), Control Variates ...
-
In this lecture, we review basic probability space concepts from the previous lecture. We then go on to discuss the common probabilistic mod...
-
In this lecture, we review topics from the first half of the semester that will be tested over in the upcoming midterm. Most of the class in...
-
In this lecture, we introduce the detailed process of input modeling. Input models are probabilistic models that introduce variation in simu...
-
In this lecture, we review pseudo-random number generation and then introduce random-variate generation by way of inverse-transform sampling...
-
In this lecture, we introduce the three different simulation methodologies (agent-based modeling, system dynamics modeling, and discrete eve...
-
In this lecture, we (nearly) finish our coverage of Input Modeling, where the focus of this lecture is on parameter estimation and assessing...
-
In this lecture, we continue to discuss hypothesis testing -- introducing parametric, non-parametric, exact, and non-exact tests and reviewi...
-
In this lecture, we wrap up the course content in IEE 475. We first do a quick overview of the four variance reduction techniques (VRT's...
No comments:
Post a Comment