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DSS News
D. J. Power, Editor
May 25, 2003 -- Vol. 4, No. 11
A Bi-Weekly Publication of DSSResources.COM
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Check the case study "Optimizing Aircraft Maintenance
Operations using a Document-driven DSS"
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Featured:
* DSS Wisdom
* Ask Dan! - What DSS interface design is "best" for eliciting values?
* What's New at DSSResources.COM
* DSS News Releases
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DSS News has more than 875 subscribers from 50 countries.
Please forward this newsletter to people interested in DSS.
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DSS Wisdom
Thomas Crowley (1967) of Bell Labs concluded in his introductory book on
computers "... if computers are used properly, they will be a tremendous
boon to mankind; if they are used improperly, they will do serious harm.
... If we assume that this feeling is correct and that the effects of
computers are neither inevitably good nor bad, but depend on their use,
an important conclusion can be drawn. It is important that a reasonably
good basic understanding of the functioning and use of computers should
be a part of everybody's general body of knowledge in order to maximize
the possible benefits and minimize the possible harm. Debates such as
those attending the widely publicized congressional hearings on a
proposed national data center will almost inevitably arise over whether
a certain use is good or bad. It is important that decisions be made on
the basis of some understanding rather than by some completely
irrational process. Such understanding hasn't prevented our making
mistakes in the past, and undoubtedly it is no guarantee for the future,
but at least it leaves room for hope." (pps. 124-125)
from Crowley, Thomas H. Understanding Computers. McGraw-Hill, New York,
1967.
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Call for Papers: Special issue of DSS Journal on Web-based
Decision Support. Contact bhargava@computer.org, power@uni.edu
or daewons@psu.edu
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Ask Dan!
What DSS interface design is "best" for eliciting values?
by D. J. Power
When I teach a course about DSS, I require students to work on a team to
develop a model-driven DSS in Microsoft Excel. Students are able to use
and improve their skills with Excel and they get to experience the
process of decision support design and development. Despite my
exhortations, many teams do not seem to devote sufficient attention to
the design of the user interface. Some student teams have difficulty
creating a user interface that supports "what if?" analysis for the
decision task. Another problem that is especially notable is associated
with eliciting the values for the parameters of the model(s) used in the
DSS. Students seem to think that "asking for the value" is all that is
required. In general, inadequate attention is given to how values should
be entered by the anticipated users of the DSS. In some cases, the order
of input fields seems random or disorganized, vague stimuli are often
given to the user about what values are sought, an anchor may be used
that biases the user, or an inappropriate elicitation approach may be
used.
This Ask Dan! discusses what it means to elicit values in the context of
building a model-driven DSS, discusses three approaches for eliciting
values, and then proposes some hypotheses and design guidelines for a
DSS interface design that is "best" for eliciting values.
What is a value? Decision analysts sometimes use the term narrowly to
mean a measure of worth or utility, but a broader definition seems
warranted. In building a model-driven DSS, a designer needs to be
concerned about eliciting certain and uncertain quantities and
qualities, objective and subjective probabilities, utilities and
weights. It may be necessary to elicit probability point estimates,
probability distributions, utility fuctions, monetary values and
monetary estimates, preferences, integer quantities, distances, scale
values, and priorities. Values can describe objective and subjective
measures of concrete objects and appraisals of feelings, beliefs and
attitudes. Values may be estimated or based on actual measurement using
a scale. The scales may involve physical or perceptual units.
Values are elicited from a decision maker, assessor, estimator or
appraiser -- the user of a model-driven DSS. In general, a question or
another type of stimulus indicates what value is being elicited. Values
are elicited as part of a valuation or elicitation process. The
elicitation approach in a specific DSS may reduce or increase errors in
the values that are obtained.
A number of years ago, I made a presentation on computerized elicitation
of values at a Subjective Probability, Utility and Decision Making
(SPUDM) conference (Power, 1987). My presentation focused on three
primary approaches for eliciting values: 1) numerical, 2) graphical, and
3) verbal elicitation. In a model-driven DSS, a question or stimulus can
directly request a numerical parameter. A graphical object can be
manipulated to enter or change a value. A text response from a pull down
menu or a free form response can also be used to collect a "value"
response. Let's briefly examine the different approaches.
Numerical Elicitation. Directly eliciting numerical values is the most
traditional mode of gathering user information about values. Changes in
the computing environment have expanded the capabilities for this type
of user interface. Colors can highlight input fields and data validation
rules can check the reasonableness of values that are entered in fields.
Graphical Elicitation. A slider or spinner type of graphical display
is probably the most common elicitation in this category. Also, DSS
users may move arrow keys or a mouse to control the height or position
of vertical "value-bars" or of a "probability wheel". Also, a simple
value line with a scale can be used effectively.
Verbal Elicitation. In one type of verbal elicitation, a DSS user
chooses a verbal description from a menu to which the DSS designer has
assigned a value. For example, a person may be asked to enter a
description from the following choices: certain, very likely, likely,
unlikely, very unlikely. A second type of verbal elicitation requests
the user to enter a natural language phrase and then the program parses
the response and assigns a value based on a rule.
Some people can process certain types of value information much more
effectively using visual displays and graphical input modes than they
can process them numerically or verbally. Each approach for eliciting
values has strengths and weaknesses that a software designer must
recognize. Current evidence suggests that unbiased values can be
elicited using any of the approaches. Also, none of the approaches is
inherently better than the other two for accurately assessing all types
of values. The appropriate approach seems to depend on the task and the
skill and training of the user. So what advice, hypotheses and design
guidelines would I offer to create a DSS interface design that is "best"
for eliciting values? Let me suggest 8 hypotheses:
Hypothesis 1: Graphic elicitation is best for providing "what if?"
analysis for a decision task. Values used as parameters for "what if?"
analysis should be changed using a manipulation object like a spinner or
a scroll bar.
Hypothesis 2: Graphic elicitation is most useful when only a few values
are elicited and the values are uncertain or may vary over a wide range.
Hypothesis 3: Static objective values are more accurately entered using
a numeric input field. When it is possible input validation should be
used to ensure that the requested value is in the anticipated range.
Hypothesis 4: Use numeric elicitation if and only if a value can be
clearly defined and the elicitation stimulus is unambiguous.
Hypothesis 5: If a large number of values will be elicited, provide for
multiple inputs on a single screen and insure that the elicitation is
not excessively time consuming and tedious.
Hypothesis 6: Subjective values will be more reflective of a person's
opinion, belief or preference if a graphical elicitation method is used
rather than numerical or verbal elicitation.
Hypothesis 7: It is most appropriate to use abstract word inputs or
anchors for eliciting feelings and sentiments.
Hypothesis 8: If a DSS is eliciting feelings or preferences, a color
scale may help a decision maker/assessor input a value. For example, a
color scale from dark green to light green, or from light red to dark
red with a slider may help capture a person's feelings.
These hypotheses are a starting point, I would appreciate your comments
on them and any suggestions for improving the elicitation of values in
model-driven DSS. One hopes that over the next 10 years empirical
studies will provide additional guidance.
References
Power, D.J., "Computer-aided Elicitation of Values," paper presented at
Subjective Probability, Utility and Decision Making conference
(SPUDM-11), Cambridge, UK, August 1987.
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What's New at DSSResources.COM
05/17/2003 Documentum Staff, "Optimizing Aircraft Maintenance Operations
using a Document-driven DSS", Documentum, Inc., 2001. Check the case
studies page.
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Check the article by David Marco at DSSResources.COM
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DSS News Releases - May 12 to May 22, 2003
05/22/2003 Titan awarded $69 million multiyear GSA task order to provide
geospatial information and services support to pacific USAF bases.
05/21/2003 eBay selects MicroStrategy platform for enterprise business
intelligence.
05/21/2003 Appian launches industry's first procurement solution with
integrated e-commerce, collaborative workflow, and document management
functionality.
05/20/2003 Black & Decker drives superior customer service with
Manugistics global logistics sourcing solutions.
05/20/2003 Intergraph advances geospatial education with new online GIS
training program.
05/20/2003 Speedware introduces media symposium analytics.
05/20/2003 University of Toronto improves planning and budgeting with
Cognos.
05/19/2003 Intergraph's IntelliWhere Division releases IntelliWhere
TrackForce for mobile resource management.
05/19/2003 WesCorp improves detailed business modeling with Hyperion
Software.
05/19/2003 Firstdoor teams with Oracle to deliver human resource content
support for compliance and policy issues.
05/19/2003 Albertsons standardizes its product information with Trigo
Technologies software to drive strategic business initiatives, increase
collaboration with suppliers.
05/19/2003 Black Pearl extends its Enterprise Decision Management
platform with OneChannel merger.
05/19/2003 SAP users select Cognos as top reporting solution.
05/16/2003 WaterOne selects SymPro treasury management software to help
attain efficient investment operations.
05/15/2003 Harvard Pilgrim Health Care selects Fair Isaac's payment
optimizer to detect fraud and abuse.
05/15/2003 Transit agency uses GPS and wireless communications system
from Radio Satellite Integrators to streamline fleet operations.
05/15/2003 CA's CleverPath positioned in leader quadrant in the Business
Rules Engine Sector -- Knowledge-driven DSS development software.
05/14/2003 Teradata extends industry lead in Data Warehouse availability
for business users.
05/14/2003 CrownPeak Technology enables site creation via Content
Management System for the first time.
05/14/2003 Southern Company selects Intergraph's Solution for Mobile
Workforce Management.
05/14/2003 Manugistics' latest release delivers powerful scalability and
performance, lower total cost of ownership.
05/14/2003 INSIGHT enhances best optimization software for supply chain
planning, releases SAILS V3.
05/13/2003 Databeacon makes web reporting and data analysis a team
sport.
05/13/2003 ChoicePoint(R) helps financial institutions comply with
identity verification, USA PATRIOT Act compliance.
05/13/2003 Northern Colorado water agency lets the information flow with
Hummingbird Enterprise(TM) for ESRI.
05/13/2003 Veterans of Business Performance Management help companies
reap the benefits of BPM.
05/13/2003 ePocrates launches an Internet based desktop product to
complement its PDA software applications.
05/12/2003 Bell(R) Sports selects Logility for global sourcing
management; solution to optimize sourcing decisions and extend supply
chain visibility.
05/12/2003 U.S. Army to use Super Computer, Inc.'s gaming middleware
platform: Andromeda, including Master Browser Services (MBS) to power
'America's Army: Special Forces' game.
05/12/2003 Airlines expand use of Market Intelligence Data (MIDT) and
Shepherd Systems' MarketMaster(SM) technology to optimize revenues,
enhance customer service.
05/12/2003 Cognos cited as a leader in business intelligence by
independent research firm.
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DSS News is copyrighted (c) 2003 by D. J. Power. Please send your
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