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DSS News
D. J. Power, Editor
November 9, 2003 -- Vol. 4, No. 23
A Bi-Weekly Publication of DSSResources.COM
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Check interview with Ron Swift
"Comments on Decision Support and CRM".
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Featured:
* Ask Dan! - How can behavioral models be used for decision support?
* What's New at DSSResources.COM?
* DSS News Releases
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Ask Dan!
How can behavioral models be used for decision support?
by Dan Power
Editor, DSSResources.COM
In the Ask Dan! of July 6, 2003 I discussed the question "How can
simulation be used for decision support?" That column provided an
overview, but I left open the possibility of discussing in more depth
complex, realistic visual simulations based upon behavioral models. This
Ask Dan! tries to clarify the "how" of agent-based modeling and
reinforce the advantage of using this approach for finding robust,
workable decision alternatives in complex, uncertain environments.
What is a behavioral model? Let's begin with defining the term. A
behavioral model is an explicit statement of variables that impact the
observed actions of a system of objects or of a specific object or
entity. A behavioral model is used to help understand, explain, and
predict behavior. Behavioral models are usually specified as
mathematical equations or as computer programs, rather than as verbal
descriptions. Models of behavior exist in almost every academic
discipline including Physics, Chemistry, Economics and Psychology. A
behavioral model is built by observing the previous behavior of an
entity or a system; the resulting model can then be used to predict
future behavior and performance.
In realistic, visual simulations many models are needed to "drive" the
simulation. Models of the physical environment ensure that natural laws
are not violated. For example, the simulation "logic" would specify
that 2 objects can not occupy the same space. From a decision support
perspective, the really interesting simulations are those that help a
decision maker anticipate human behavior, i.e. customers, voters, enemy
soldiers. These simulations need to imitate physical reality, but more
importantly multiple "human-like" agents need to be included in the
simulation. Currently this is happening in 2 ways. Some simulations
use "real actors" who make choices in a simulated on-line environment.
A multi-player simulation like "America's Army" (www.americasarmy.com)
is an example of this approach. Another approach is to use behavioral
models as the "actors/agents" that are making choices in the simulated
environment. We can refer to these approaches as multi-player and
multi-agent simulations. Today a multi-player simulation usually also
includes some computer-based agents.
So what is a behavioral model for a human-like "agent"? There is no
single model, rather various models from Psychology, Management and
Marketing can potentially be used. In some cases behavioral models are
heuristically composed by the developer and lack any theoretical or
empirical foundation. Conceivably simulations can be built using well
known models like Maslow's Need Hierarchy, Expectancy Theory, or
Herzberg's theory. In the current Artificial Intelligence literature the
belief-desire-intention (BDI) model of "practical reasoning" (cf.,
Bratman et al., 1988) seems to dominate the discussion and the research.
According to d'Inverno et al (1998) and others the BDI model is
currently implemented in a distributed Multi-agent Reasoning System
called dMARS. This system helps a developer program the beliefs, goals,
intentions and plans for artificial agents. The plan library is the
procedural knowledge of the agent. In building this type of simulation
we are trying to capture the global behavior of a large number of
interacting autonomous artificial agents.
In general an agent should choose its actions and make decisions for
reasons similar to those that a human decision maker would use, i.e.
hunger, seeking pleasure, or avoiding pain. Will Wright's game "The
Sims" is perhaps the most widely known example of a software program
involving behavioral modeling and visual simulation. In an informal
class poll recently, many of my 22 and 23 year old students had not
however played the game.
In an attempt to try out a behavioral simulation and see what it "felt
like" I used "The Sims" software. To remove the game aspect and create a
more meaningful simulation my sons helped me use what's called a cheat
code to get unlimited funds so I could build a representation of my home
here in Cedar Falls. I populated the simulation with Sims of myself and
my wife and of my 15 year old and 9 year old sons. The behavioral model
is simple. I could enter values for each of the following five
personality variables in the behavioral model: playful, neat, active,
nice, and outgoing. The Sim Creator let the user allocate a maximum of
10 of 25 total points to each variable. Once all 25 points are allocated
an increase in a value for one variable forces a decrease in some other
variable. The Sim Creator has an easy to use graphical interface.
Alternatively, I could have chosen the astrological sign of each of us
and the default values on the variables would have been displayed for me
to adjust. The simulation continually calculates the hunger, comfort,
hygiene, bladder, energy, fun, and social scores for each Sim based on
what is occurring. These scores "motivate" actions and initiate plans.
Using the Power Family simulation has been interesting and insightful.
You can let the simulation run with out intervention in normal speed,
high speed and ultra speed. Rather than intervening and directing the
Sims (which one does in the game) I have been observing the simulation
play out over simulated days without intervention. I can zoom in or out
on the simulation and change the speed. You can try this experiment
yourself at a modest cost to get a "feel" for multi-agent simulation.
What is a decision support example? I'm trying to find a good case
example with screen shots for DSSResources.COM, but that is a few months
away. Gimblett, Durnota and Itami (1996) reported a project to develop
an intelligent decision support and simulation system that used
autonomous agents to assist natural resource managers in assessing and
managing dynamic recreation behavior, social interactions and resulting
conflicts in wilderness settings. They linked dMARS (Distributed
Multi-agent Reasoning System), the Swarm Multi-agent Simulation System
and a GIS system to develop the model-driven DSS. They calibrated the
autonomous agents using survey data from people using a recreation
facility in Sedona, Arizona. The realistic simulation was intended to
support forest management activities and assist in evaluating proposed
practices for recreation use in the recreation facility.
One of the challenging tasks facing DSS researchers is to identify
decision situations that justify implementing realistic decision support
simulations for experimentation and repeated analysis. Another
challenge is to understand how this type of model-driven decision
support might impact decision makers. One claimed impact is that
counterintuitive phenomenon may be identified in an agent-based
simulation that alters a decision makers perception of a situation. My
limited experience suggests this is true. Although multi-agent visual
simulation will not completely replace traditional simulation, it can be
used to simulate some complex systems that traditional techniques can
not model or cannot help a user completely understand. Multi-agent
simulation provides another lense for anticipating the future of a
complex system.
Agent-based modeling can simulate complex systems and it can be a useful
management decision support tool. As always the key to simulation is
believability. Any model is a simplified description of the real world
and that is true for behavioral models. We have much to learn and we
need to develop reusable objects and components that academic DSS
researchers can use to build innovative, multi-agent simulation decision
support applications. I'm currently exploring the costs and difficulty
of creating a Java development environment using dMARS, Swarm and other
existing tools. Any suggestions or assistance in this quest would be
much appreciated.
References
Bratman, M. E., D. J. Israel, and M. E. Pollack (1988). Plans and
resource-bounded practical reasoning. Computational Intelligence, 4:
349-355.
d'Inverno, M., D. Kinny, M. Luck, and M. Wooldridge (1998). A formal
specification of dMARS. In Intelligent Agents IV: Proceedings of the
Fourth International Workshop on Agent Theories, Architectures and
Languages, Singh, Rao and Wooldridge (eds.) Lecture Notes in AI, 1365,
155-176, Springer-Verlag.
Gimblett, R., B. Durnota and R Itami (1996). Spatially-Explicit
Autonomous Agents for Modelling Recreation Use in Complex Wilderness
Landscapes. Complexity International, vol. 3,
http://journal-ci.csse.monash.edu.au/ci/vol03/.
Sims, Karl (1994). "Evolving 3D Morphology and Behavior by Competition,"
Artificial Life IV Proceedings, ed. by R. Brooks and P. Maes, pp. 28-39.
SimAnt: The Electronic Ant Colony, release date: 1991, published by
Maxis.
Swarm Development Group, http://swarm.org/. Swarm is free software,
released under the GNU General Public License (GPL).
The Sims, Electronic Arts and Maxis, http://thesims.ea.com/.
Vis-Sim, the Visual Simulation Resource, http://www.vis-sim.org.
Currently offline.
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What's New at DSSResources.COM?
11/07/2003 Posted an interview with Ron Swift "Comments on Decision
Support and CRM". This is a planned new series at DSSResources.COM.
11/05/2003 Posted Longman, C., "Data Warehousing at the Speed of
Business". Check the articles page.
11/02/2003 Updated Top 5% DSS Sites. Submit nominations. Check the page.
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