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DSS News
D. J. Power, Editor
December 7, 2003 -- Vol. 4, No. 25
A Bi-Weekly Publication of DSSResources.COM
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Featured:
* 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
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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
you."
-- HAL (Heuristic and Algorithmic Computer) 9000
For more information check http://www.filmsite.org/
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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
markets?
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
literature.
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
canceled?
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.
Conclusions
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.
References
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
http://icis2003.cbe.wsu.edu/
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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
page.
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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
Agencies.
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 AutoTrader.com 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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DSS News is copyrighted (c) 2003 by D. J. Power. Please send your questions to
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