- These notes are a rough transcription,
prepared as each individual presenter and/or commentator spoke at the ISSS
1998 conference. Gaps and errors have likely occurred. For more accurate
citations, please consult the original presenters. These notes have been
contributed to the ISSS by David Ing, of the IBM Advanced Business Institute
(sabi@systemicbusiness.org).

Reid Bailey, Georgia Tech

Topics:

- Motivation
- Story
- Types of models
- Selecting the right model type.

- Want to replace a real system with a representation of that system (so that you can learn about it)
- Why not interact with a real system? Cost, difficult, destructive, or too many players.
- Types of representations (tree from Gordon, 1978):
- Not interested in physical models.
- Math models are either static or dynamic.
- Analytical models have closed-form solutions.
- Thus, math and dynamic.

- A company trying to work within an industrial ecosystem: how do they work as a larger system?
- Would like to do what-if, before committing for real.
- Need to fit a tool to the problem.

- Discrete continuous models
- Instantaneous completion of event, e.g. hot dog vendor sells hot dog, or manufacturing line.
- Time skips over events.
- Deterministic stochastic (uncertainty) models
- Based on differential equations, which approximation through independent variables.
- e.g. draining of a lake.
- Hybrid / Mixture: both elements in the same model.

- Incoming parts, wait, paint, and then into another bin.
- Modeled as discrete shows oscillation around a trend, whereas a continuous model is a straight line, hybrid is joined between levels.

- For giving inputs, outputs always the same.

- For given inputs, behaviour is not fully determined.
- Stochastic isn't quite the same.

represent numbers with | ||

Fuzzy set | more qualitative than quant | membership function |

Bayesian statistics | only an estimated distribution can be determined | estimated distribution |

Stochastic | exact distribution known | exact distribution |

Interval arithmetic | upper and lower bound | |

- Weigh off ...
- purpose of the model (much more important, and unfortunately subjective) vs.
- characteristics of the system being modeled.

Discrete | Mixed | Continuous | |

Deterministic | microscopic, discrete, low accuracy | micro, mixed low | micro, continuous, low
OR macro, all, low |

Uncertainty | micro, discrete, high | micro, mixed, high | micro, continuous, high
OR |

- Fill the toolbox first, then select tools
- Purpose is paramount
- Difference between discrete and continuous is small, with end behaviours the same.
- More to do in the future ...
- Can also model with other methods.

- Looking at wholistic approaches to enterprises, where not connecting outflow from one industry into another.

- Have been looking at continuous, deterministic working on case studies of a power plant, heating system for a town.

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... to the ISSS home page at www.isss.org.