DSS News 
                  D. J. Power, Editor 
           December 7, 2003 -- Vol. 4, No. 25
       A Bi-Weekly Publication of DSSResources.COM 


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* DSS Wisdom from "2001: A Space Odyssey"
* Ask Dan! - What are major research needs and questions related to 
building and using model-driven DSS?
* What's New at DSSResources.COM?
* DSS News Releases 


This year, 2003, is the 35th anniversary of the film "2001: A Space 
Odyssey" by Stanley Kubrick and Arthur C. Clarke. The paranoid, 
artificially intelligent HAL reminds us of one reason why the Decision 
Support Systems (DSS) vision keeps a person as the "dominant actor" in 
an important decision-making process. To quote HAL, "Look, Dave, I can 
see you're really upset about this. I honestly think you ought to sit 
down calmly, take a stress pill, and think things over. I know I've made 
some very poor decisions recently, but I can give you my complete 
assurance that my work will be back to normal. I've still got the 
greatest enthusiasm and confidence in the mission and I want to help 

-- HAL (Heuristic and Algorithmic Computer) 9000
For more information check

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Ask Dan!

What are major research needs and questions related to building and 
using model-driven DSS?

by Dan Power and Ramesh Sharda

This Ask Dan! column is a step along a path toward a more rigorous 
academic journal article tentatively titled "Studying Model-Driven DSS: 
Research Needs and Directions".  Dan Power (Univ. of Northern Iowa and 
DSSResources.COM) and Ramesh Sharda (Oklahoma State Univ.) are 
collaborating on this ambitious review and analysis.

On February 3, 2002, Dan Power's Ask Dan! column broadly explored "What 
are the 'hot' DSS research topics?" This column is an expanded 
discussion focused on model-driven DSS. Even after fifty years of 
research by economists, psychologists, operations researchers and 
management scientists we have only just begun to understand the 
behavioral and technical challenges of designing, developing and 
implementing model-driven DSS. Given the growing complexity and 
uncertainty in many decision situations, helping managers use 
quantitative models to support their decision-making and planning should 
be a "hot" topic.

By definition one or more quantitative models are the dominant component 
that provides the primary functionality of a model-driven decision 
support system. Also, by definition a model-driven DSS is designed so a 
user can manipulate model parameters to examine the sensitivity of 
outputs or to conduct a more ad hoc "what if?" analysis. 

Two characteristics differentiate a model-driven DSS from a decision 
analytic or operations research special decision study: 1) the model(s) 
in a model-driven DSS are made accessible to a non-technical specialist 
like a manager through an easy to use interface and 2) the DSS is 
intended for some repeated use in the same or a similar decision 
situation.  The general types of models used in model-driven DSS include 
algebraic and differential equation models, analytical hierarchy, 
decision matrix and tree, multi-attribute and multi-criteria models, 
forecasting models, network and optimization models, Monte Carlo and 
discrete event simulation models, and behavioral models for multi-agent 
simulations (cf., Power, 2002). A number of previous Ask Dan! columns 
have discussed one or more of these types of models, including AHP, 
behavioral models, and simulation models. Models in a model-driven DSS 
should provide a simplified representation of a situation that is 
understandable to a decision maker.

Early versions of model-driven DSS were called model-oriented systems by 
Alter (1980, p. 92) and category E DSS by Bonczek, Holsapple and 
Whinston (1981, p. 66).  Some authors have called them computationally 
oriented DSS.

Behavioral and technical research on model-driven DSS needs to address 
many unresolved issues associated with construction of the actual 
quantitative models, storage and retrieval of data needed by different 
types of models, communication of parameters among models and among DSS 
components, multi-participant interaction in model use and value 
elicitation, and the impact of user interface design alternatives on 
model-driven DSS effectiveness and ease of use. Also, researchers need 
to investigate issues associated with building, deploying and using 
Model-driven DSS. This broad listing of needs seems daunting, but it 
suggests more specific research issues and questions for further 
research. The spectrum of possible model-driven DSS is broad and as we 
learn more the implementation of model-driven DSS expands to incorporate 
new decision situations and/or new modeling approaches.

The remainder of this discussion of model-driven DSS research needs is 
divided into behavioral and technical questions that seem to warrant 
further study.

Behavioral Questions:

The following list of research questions focuses on topics related to 
understanding the behavioral impact of model-driven DSS.  The questions 
seem largely unresolved in the current literature, but we are 
identifying relevant prior research.

B1. Are users of a model-driven DSS who understand the model more likely 
to appropriately use the results?  Are they more frequent users? Do they 
have more confidence in the results?

The presumption has been that managers need to understand the 
quantitative model to benefit from using a model-driven DSS.  Inadequate 
research has investigated this topic.

B2. Do some users of a model-driven DSS attempt to bias or manipulate 
the parameters they can change to yield specific results?  If so, what 
types of users and when?  What conditions impact biased use of a 
model-driven DSS?  

The general perception is that some decision makers will bias an 
analysis.  This is supported by empirical research linked to behavioral 
decision theory.  The phenomenon has not been adequately explored in the 
context of model-driven DSS.

B3. Can some design alternatives and value elicitation methods reduce 
the occurrence of biased decision behavior?

Research associated with manual elicitation of subjective probabilities 
and values suggests that de-biasing can occur and that some techniques 
produce normatively better results than other.

B4. Does a "customized" user interface impact the subsequent use of a 
model-driven DSS? How much "customization" is needed and possible? What 
should the user interface software store from its interaction with a 
regular user?

Personalization of web portals and other Web interfaces is generally 
considered as desirable and some authors have speculated that because of 
individual differences among DSS users that a customized interface would 
improve usability, frequency of use and effectiveness of the 
model-driven DSS. 

B5. What is the impact of making decisions with a model-driven DSS in a 
"highly realistic", simulated decision environment on a person's actual 
decision making in the "real" decision environment? 

The increased capability of developing graphical, immersive, "highly 
realistic" decision situations creates new challenges and opportunities. 
Some research has begun to examine the impact of realistic decision 
training using decision support on actual decision making, but much more 
needs to be done.

B6. How can Visual Interactive Simulation (VIS) be used more effectively 
to examine the consequences of alternative decision strategies and 
policies.  Can a "realistic simulation of a specific firm" enhance 
firm-specific decision-making?

VIS has used Monte Carlo simulation and some technical issues remain, 
but the impact of this type of simulation deserves more investigation.

B7. What behaviors can and should be predicted using economic decision 

A number of innovative model-driven decision support tools have been 
developed and investigated, including decision markets and scenario 
databases (cf., Lang 2003).  The limits and possibilities of these 
approaches need more investigation.  Recent controversy over using a 
decision market to forecast events in the Middle East by the U.S. 
Defense Department indicates this issue needs investigation.

Technical Questions:

Technical issues related to building and using model-driven DSS have not 
been resolved.  Rapid technology innovation creates new challenges for 
researchers interested in more technical research questions.  The 
following questions are not adequately addressed in the current 

T1. What technology advances are needed to develop the next generation 
of model-driven DSS generators, especially for creating real time, 
model-driven decision support systems?

In this context, "real time" refers to a contemporaneous analysis using 
a model-driven DSS while data about events is being received and 
displayed.  The technologies need to provide speed in model execution 
and updating of data used by the model-driven DSS.

T2. How can "real-time" model-driven DSS be interfaced with "real-time" 
data-driven DSS to improve decision making in situations like routing of 
an emergency vehicle or selecting passengers to "bump" when a plane is 

Real-time data collection and storage issues need more investigation, as 
do technical issues associated with providing model-driven DSS for use 
in such an environment.

T3. Is a specific extensible mark-up language (XML) needed for 
communicating data about model parameters? If so, what mark-up tags can 
create a core for communicating data to various models?

Some exploratory research has been conducted on creating a decision 
support mark-up language (DSML).  The varying terminology in use and the 
variety of categories of DSS suggest that it may be advantageous to have 
more narrowly defined XML for specific types of models like optimization 
or discrete event simulation.

T4. Can the Uniform Modeling Language (UML) help developers and users of 
model-driven DSS better understand general categories of model-driven 
DSS like resource allocation, sourcing and estimating?

Identifying and modeling processes like resource allocation or 
scheduling using UML have been explored, but the possibilities seem 
promising and more should be done to specify decision processes where 
quantitative models might be useful.  Some processes that should be 
better defined include 1) assignment (of tasks, of staff, of resources), 
2) capacity planning (also queuing and congestion), 3) estimation of 
costs, quantities, revenues, 4) evaluation and selection (includes using 
cost-benefit analysis, financial analysis, multicriteria analysis), 5) 
location analysis (site selection), 6) routing (vehicles, network 
packets, people), 7) resource allocation, and 8) sequencing and 
scheduling (cf., Power, 2002).

T5. What is an appropriate analytical framework for aggregating results 
from multiple models?  Can web services provide a technical platform for 
implementing model aggregation?

Aggregating results depends upon why aggregation is desired and upon 
what model results seem to warrant aggregation. Web services are 
reusable application components that dynamically interact with each 
other using standard protocols over the Internet. It has been suggested 
that web services provide a means of dynamically aggregating model 
results when that is appropriate.

T6. What technical capabilities are most appropriate for developing a 
collaborative, model-driven DSS?

Collaborative building of model-driven DSS and collaborative use of 
model-driven DSS are both interesting areas for further research. The 
"how" of supporting collaboration may be the same or it may differ in 
these two situations. 

T7. What are the tradeoffs among various model-driven DSS delivery 
mechanisms such as a web browser, spreadsheet, immersive graphics, or 
peer-to-peer deployment? What innovative user interfaces should be 
incorporated in next generation model-driven DSS? When is a 3-dimension 
mouse or electronic ink most useful? 

The user interface has always been of enormous importance in building 
any type of DSS and new technologies may be especially useful in 
enhancing model-driven DSS interaction with a user.

T8. How can developers structure "communities of software agents" that 
imitate social structures like markets, organizations, or nations to 
assist decision makers for forecasting and planning? Can multi-agent 
simulations assist managers in understanding emergent behavior in a 
particular domain and help predict social trends?

Realistic simulations using multiple agents for decision support are an 
exciting frontier that provides many issues for technical research.

T9. What reusable model objects should be developed for use in an 
object-oriented, model-driven DSS development environment?

The range of potential quantitative model components is very large and 
diverse.  We need to investigate what objects will be useful and how 
they can best be implemented.


The behavioral issues associated with building and using model-driven 
DSS have often been avoided by relying on specialists and intermediaries 
to use complex models for analyses, that approach is limiting.  
Model-driven DSS developers have much more to learn about the management 
of models and there is a need for new development environments to 
advance the state of the art. Model management in distributed computing 
environments is now a requirement and not just a possibility. 
Model-driven DSS still need to be distributed more widely in 
organizations and they need to be used by managers and staff for 
planning and analysis.  The representations used in model-driven DSS for 
planning also need to be used in real-time data-driven decision support. 
This Ask Dan! presents an ambitious set of research opportunities for 
the decision support research community and especially those academic 
researchers and practitioners interested in using models, optimization 
and simulation to build model-driven DSS.


Alter, S.L. Decision Support Systems: Current Practice and Continuing 
Challenge. Reading, MA: Addison-Wesley, 1980.

Bonczek, R. H., C.W. Holsapple, and A.B. Whinston. Foundations of 
Decision Support Systems. New York: Academic Press, 1981.

Lang, C., "Professional and Collaborative Decision-Support: Many Ways to 
Improve Decision-Making", DSSResources.COM, 07/11/2003.

Power, D., Decision Support Systems: Concepts and Resources for 
Managers, Westport, CT: Greenwood/Quorum Books, 2002.

Please note: The ideas in this Ask Dan! will be presented and discussed 
at the pre-ICIS SIG DSS Workshop "Research Directions for Decision 
Support" Dec. 14, 2003 in Seattle, WA USA. Check


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What's New at DSSResources.COM?

12/05/2003 Posted Jessani, R., "Creating an Effective Data-Driven 
Decision Support System". Check the articles page.

11/28/2003 Posted case by Myron Messak, "Decision Support for Mayfield, 
NY Fire and Emergency Medical Services", 2003. Check the case studies 


DSS News Releases - November 24 to December 4, 2003
Read them at DSSResources.COM and search the DSS News Archive

12/04/2003 Competitive intelligence: trends and activities; SCIP annual 
conference March 22 - 24, 2004 in Boston, MA.

12/03/2003 Sun wins worldwide customers and partners for Java(TM) 
Enterprise System, announces new pricing for independent software 
vendors, OEMs, and very small companies.

12/03/2003 Zaplet announces partnership with Northrop Grumman's TASC 
unit to deliver collaborative BPM solutions to Intelligence and Military 

12/02/2003 BPM Partners to offer a preview of new industry-specific 
dashboards at DCI Performance Management Conference.

12/02/2003 Groove Networks webcast addresses distributed project 
management challenges.

12/01/2003 MicroStrategy provides free BI software to the Teradata 
University Network.

12/01/2003 Corporate Risk managers have a new tool to assess and manage 
catastrophe risk with the release of AIR's CATStation.

12/01/2003 BizTools exhibits business intelligence application at 
Intuit's 2003 IDN conference; Intuit's 2003 IDN conference attracts 
developers from around the world.

12/01/2003 selects MicroStrategy Report Services for 
enterprise reporting and analysis.

12/01/2003 Countrywide Home Loans looks to MindBox for loan decision 
support; expands MindBox's ARTEnterprise license to enable automated 
mortgage underwriting system across entire enterprise.

12/01/2003 Pentagon selects Sterling Management Solutions Corp.'s 
Performance Measurement/BI and Business Continuity Virtual Command 
Center (VCC) Dashboard solutions.

11/29/2003 Century 21 Co-founder launches expert systems; new IdeaFisher 
Expert Systems™ products ask thousands of probing questions, provides 
insightful answers.

11/28/2003 ABN AMRO insurance arm selects Fair Isaac decision tools for 
more efficient auto insurance underwriting.

11/26/2003 U.S. Department of Homeland Security looks to academia for 
help fighting terrorism.

11/26/2003 Tokyo University sets bandwidth record at SC2003 with Juniper 
Networks; Japanese research team exceeds 7.5 Gbps between Japan and the 
USA with T320 platform.

11/25/2003 Lombardi Software's TeamWorks 4 selected as finalist for 
Transform Magazine's Product of the Year 2003 awards.

11/25/2003 SPSS enables full customer view with new predictive text 
analytics solution.

11/24/2003 Infommersion wins best of COMDEX Las Vegas 2003 award for 
software; Xcelsius allows users to create real-time, interactive reports 
based on Excel spreadsheets.


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