DSS News 
                  D. J. Power, Editor 
           November 9, 2003 -- Vol. 4, No. 23
       A Bi-Weekly Publication of DSSResources.COM 


             Check interview with Ron Swift 
        "Comments on Decision Support and CRM".


* 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" ( 
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.


Bratman, M. E., D. J. Israel, and M. E. Pollack (1988). Plans and 
resource-bounded practical reasoning.  Computational Intelligence, 4: 

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,

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 

Swarm Development Group, Swarm is free software, 
released under the GNU General Public License (GPL).

The Sims, Electronic Arts and Maxis,

Vis-Sim, the Visual Simulation Resource, 
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.


DSS News Releases - October 26 to November 7, 2003
Read them at DSSResources.COM and search the DSS News Archive

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10/31/2003 INVITATION TO ATTEND: Pre-ICIS Workshop "Research Directions 
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10/27/2003 Kalido launches Data Warehouse Lifecycle Management Software 

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10/27/2003 Remarks by Bill Gates and Hillel Cooperman on the "Longhorn" 
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10/26/2003 Paradigm to display software products for improved workflow 
efficiencies for the oil and gas industry at SEG 2003.


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