DSS News 
                  D. J. Power, Editor 
             July 6, 2003 -- Vol. 4, No. 14 
       A Bi-Weekly Publication of DSSResources.COM 


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* Ask Dan! - How can simulation be used for decision support?
* DSS News Releases 


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Ask Dan!
by Dan Power

How can simulation be used for decision support?

Questions about using simulation for building a DSS are reasonably 
frequent in my Ask Dan! email.  So this column has been in the works for 
some time, but my summer research project on advanced decision and 
planning support motivated me to move this column to the "front burner". 
Coincidentally, I received an email on Friday, July 4, 2003 from John 
Walker ( ). John wrote "I appreciate your 
newsletter. Keep 'em coming!" Thanks for the positive feedback.  Also, 
John thought I might be interested in a June 16, 2003 interview with 
Eric Bonabeau at Eric is the founder of Icosystem Corp., 
Cambridge, MA ( ).  Icosystem develops agent-based 
models and simulations. Agent-based or multi-agent simulations are the 
"latest and greatest" technology or approach in the simulation toolkit. 
Before I discuss agent-based simulations, let's review the basics of 
simulation. According to a number of sources, simulation is the most 
frequently used quantitative approach for solving business problems and 
supporting business decision making.  That generalization may be true, 
but simulation is still the province of management science 
"specialists". Simulation has not been made "manager friendly".

Simulation is a broad term that refers to an approach for imitating the 
behavior of an actual or anticipated human or physical system. The terms 
simulation and model, especially quantitative and behavioral models, are 
closely linked. From my perspective, a model shows the relationships and 
attributes of interest in the system under study.  A quantitative or 
behavioral model is by design a simplified view of some of the objects 
in a system. A model used in a simulation can capture much detail about 
a specific system, but how complex the model is or should be depends 
upon the purpose of the simulation that will be "run" using the model. 
With a simulation study and when simulation provides the functionality 
for a DSS, multiple tests, experiments or "runs" of the simulation are 
conducted, the results of each test are recorded and then the aggregate 
results of the tests are analyzed to try to answer specific questions. 
In a simulation, the decision variables in the model are the inputs that 
are manipulated in the tests.

In my DSS book (Power, 2002), Chapter 10 on Building Model-Driven 
Decision Support Systems notes "In a DSS context, simulation generally 
refers to a technique for conducting experiments with a computer-based 
model. One method of simulating a system involves identifying the 
various states of a system and then modifying those states by executing 
specific events. A wide variety of problems can be evaluated using 
simulation including inventory control and stock-out, manpower planning 
and assignment, queuing and congestion, reliability and replacement 
policy, and sequencing and scheduling (p. 172)."

There are several types of simulation and a variety of terms are used to 
identify them. When you read about simulation you will find references 
to Monte Carlo simulation, traditional mathematical simulation, 
activity-scanning simulation, event-driven simulation, process-based 
model simulation, real-time simulation, data-driven simulation, 
agent-based and multi-agent simulation, time dependent simulation, 
andvisual simulation.

In a Monte Carlo or probabilistic simulation one or more of the 
independent variables is specified as a probability distribution of 
values. A probabilistic simulation helps take risk and uncertainty in a 
system into account in the results. Time dependent or discrete 
simulation refers to a situation where it is important to know exactly 
when an event occurs. For example, in waiting line or queuing problems, 
it is important to know the precise time of arrival to determine if a 
customer will have to wait or not. According to Evan and Olson (2002) 
and others, activity-scanning simulation models involve describing 
activities that occur during a fixed interval of time and then 
simulating for multiple future periods the consequences of the 
activities while process-driven simulation focuses on modeling a logical 
sequence of events rather than activities. An event-driven simulation 
also identifies "events" that occur in a system, but the focus is on a 
time ordering of the events rather than a causal or logical ordering.

Simulation can assist in either a static or a dynamic analysis of a 
system.  A dynamic analysis is enhanced with software that shows the 
time sequenced operation of the system that is being predicted or 
analyzed. Simulation is a descriptive tool that can be used for both 
prediction and exploration of the behavior of a specific system. A 
complex simulation can help a decision maker plan activities, anticipate 
the effects of specific resource allocations and assess the consequences 
of actions and events. In a business simulation course, text materials 
usually focus on static, Monte-Carlo simulations and dynamic, system 
simulations (cf., Evan and Olson, 2002).

In many situations simulation specialists build a simulation and then 
conduct the special study and report their results to management. Evans 
and Olson (2002) discuss examples of how simulation has been used to 
support business and engineering decision making. They report a number 
of special decision support studies including one that evaluated the 
number of Hotel reservations to accept to effectively utilize capacity 
to create an overbooking policy (p. 161-163), a Call Center staffing 
capacity analysis (p. 163-165), a study comparing new incinerating 
system options for a municipal garbage recycling center (p. 176-179), a 
study evaluating government policy options, and various studies for 
designing facilities.Examples of model-driven DSS built with a 
simulation as the dominant component include: a Monte Carlo simulation 
to manage foreign-exchange risks; a spreadsheet-based DSS for assessing 
the risk of commercial loans (cf., Decisioneering Staff, 2001), a DSS 
for developing a weekly production schedule for hundreds of products at 
multiple plants; a program for estimating returns for fixed-income 
securities; and a simulation program for setting bids for competitive 
lease sales (cf., Evan and Olson, p. 190).

Sometimes in an effort to provide decision support an actual small-scale 
model or ecosystem is built and then it is "used in a simulated 
environment". For example, a physical model of an airplane may be built 
so that it can be tested in a wind tunnel to examine its design 
properties. Today a computer simulation might be used in place of a 
"physical model" for much of the design testing.  The case "Product 
development decision support at Lockheed Martin" by Silicon Graphics 
Staff posted at DSSResources.COM October 16, 2002 is an example of this 
use of simulation.

Agent-based or multi-agent simulation does not replace any of the 
traditional simulation techniques. But in the last 5 years, agent-based 
visual simulations have become an alternative approach for analyzing 
some business systems. According to Bonabeau, "People have been thinking 
in terms of agent-based modeling for many years but just didn't have the 
computing power to actually make it useful until recently. With 
agent-based modeling, you describe a system from the bottom up, from the 
point of view of its constituent units, as opposed to a top-down 
description, where you look at properties at the aggregate level without 
worrying about the system's constituent elements."

Multi-agent simulations can be used to simulate some natural and 
man-created systems that traditional simulation techniques can not. 
Bonabeau asserts agent-based modeling works best in situations where a 
system is "comprised of many constituent units that interact and where 
the behavior of the units can be described in simple terms. So it's a 
situation where the complexity of the whole system emerges out of 
relatively simple behavior at the lowest level."  Examples of such 
systems include shoppers in a grocery store, passengers, visitors and 
employees at an airport or production workers and supervisors at a 
factory. What is the objective of an agent-based simulation?  According 
to Bonabeau, "the objective is to find a robust solution" -- one that 
will work fine no matter what happens in the "real world".

A simulation study can answer questions like how many teller stations 
will provide 90% confidence that no one will need to wait in line for 
more than 5 minutes or how likely is it that a specific project will be 
completed on time and under budget? With a visual simulation decision 
makers or analyst can observe an airplane in a wind tunnel, a proposed 
factory in operation or customers entering a new bank or a construction 
project as "it will occur".

Based on my observations over the past 25 years, simulation has been 
used much more for one-time, special decision support studies than it 
has been used as the model-component in building a model-driven DSS. 
This is and can change with increased ease in creating visual 
simulations. Visuals imulation means managers can see a graphic display 
of simulation activities, events and results. Will Wright's games "The 
Sims", "SimCoaster" and "SimCity" (cf., ) are the 
precursors for advanced, agent-based, model-driven DSS. I am continuing 
my research on boids, sims, swarms, ants and other such agent 
technologies. So perhaps in another Ask Dan! I can discuss in more 
detail complex, realistic visual simulations based upon behavioral 
models. My sense is that current technologies can support development of 
complex, "faster than real-time", dynamic, agent-based, model-driven DSS 
for a wide variety of specific decision situations.


Decisioneering Staff, "SunTrust 'Banks' on Crystal Ball for assessing 
the risk of commercial loans", Decisioneering, Inc., November 1998, 
posted at DSSResources.COM March 16, 2001.

Eppen, G.D., F.J. Gould, and C.P. Schmidt. Introductory Management 
Science (Fourth Edition), Englewood Cliffs, NJ: Prentice Hall, 1993.

Evan, J. R. and D. L. Olson, Introduction to Simulation and Risk 
Analysis (2nd Edition), Upper Saddle River, NJ: Prentice Hall, 2002.

Rothfeder, J. "Expert Voices: Icosystem's Eric Bonabeau,", June 16, 2003,,3959,1124316,00.asp.

Silicon Graphics Staff, "Product development decision support at 
Lockheed Martin", sgi, Inc., 2002, posted at DSSResources.COM October 
16, 2002.


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