************************************************************ 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" ************************************************************ Featured: * DSS Wisdom * Ask Dan! - What DSS interface design is "best" for eliciting values? * What's New at DSSResources.COM * DSS News Releases ************************************************************ DSS News has more than 875 subscribers from 50 countries. Please forward this newsletter to people interested in DSS. ************************************************************ 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. ************************************************************ Call for Papers: Special issue of DSS Journal on Web-based Decision Support. Contact bhargava@computer.org, power@uni.edu or daewons@psu.edu ************************************************************ 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. ************************************************************ Subscribe to DSSResources.COM. One month $10, six months $25. Visit http://dssresources.com/subscriber/subscriber.html ************************************************************ 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 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. ************************************************************ Tell your friends! Get DSS NEWS free -- send a blank email to dssresources-subscribe@topica.com. ************************************************************ DSS News is copyrighted (c) 2003 by D. J. Power. Please send your questions to daniel.power@dssresources.com. |