DSS News 
                    D. J. Power, Editor 
              May 25, 2003 -- Vol. 4, No. 11 
       A Bi-Weekly Publication of DSSResources.COM 


   Check the case study "Optimizing Aircraft Maintenance
         Operations using a Document-driven DSS"



* 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 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, 


 Call for Papers: Special issue of DSS Journal on Web-based
 Decision Support. Contact,


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 

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.  


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.

Check the article by David Marco at DSSResources.COM

DSS News Releases - May 12 to May 22, 2003

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05/20/2003 Black & Decker drives superior customer service with 
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05/20/2003 Intergraph advances geospatial education with new online GIS 
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05/20/2003 Speedware introduces media symposium analytics.

05/20/2003 University of Toronto improves planning and budgeting with 

05/19/2003 Intergraph's IntelliWhere Division releases IntelliWhere 
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05/19/2003 WesCorp improves detailed business modeling with Hyperion 

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 
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05/19/2003 Black Pearl extends its Enterprise Decision Management 
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05/19/2003 SAP users select Cognos as top reporting solution.

05/16/2003 WaterOne selects SymPro treasury management software to help 
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05/15/2003 Harvard Pilgrim Health Care selects Fair Isaac's payment 
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05/15/2003 Transit agency uses GPS and wireless communications system 
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05/15/2003 CA's CleverPath positioned in leader quadrant in the Business 
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05/14/2003 Teradata extends industry lead in Data Warehouse availability 
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05/14/2003 CrownPeak Technology enables site creation via Content 
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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 
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05/13/2003 Databeacon makes web reporting and data analysis a team 

05/13/2003 ChoicePoint(R) helps financial institutions comply with 
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05/13/2003 Northern Colorado water agency lets the information flow with 
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05/13/2003 Veterans of Business Performance Management help companies 
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05/13/2003 ePocrates launches an Internet based desktop product to 
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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 
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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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