DSS News is a free biweekly newsletter from DSSResources.COM about computerized Decision Support Systems. *********************************************************** DSS News D. J. Power, Editor September 11, 2005 -- Vol. 6, No. 20 A Free Bi-Weekly Publication of DSSResources.COM 1,190 Subscribers ************************************************************ Check the article by Bair, Fox, Hunt, and Meers "Aligning BI with Business Strategy: How a Mission Mapped Architecture can help" ************************************************************ Featured: * Ask Dan! - Can computerized decision support systems impact, eliminate, exploit, or reduce cognitive biases in decision making? * DSS Conferences * What's New at DSSResources.COM * DSS News Releases ************************************************************ Visit DSSResources.com; Support our advertisers Advertise here! ************************************************************ Ask Dan! by Dan Power Can computerized decision support systems impact, eliminate, exploit, or reduce cognitive biases in decision making? YES. In the early days of computerized decision support the American Airlines Sabre Reservation system favorably exploited human information processing limitations to increase sales of tickets on American Airlines flights. Since then the US Department of Transportation and the US Courts have restricted and prohibited such practices. The Computer Reservations System (CRS) Regulations originally adopted in 1984 prohibited display bias. The current regulation notes "Display bias has been a concern since the systems were first developed. Experience has demonstrated that travel agents are likely to book one of the first services displayed by a system in response to a travel agent's request for information, even if services shown later in the display would better satisfy the customer's needs. If systems give preferential display positions to one airline's services, that display bias will harm airline competition and cause consumers to be misled." Cognitive biases exist. People are predisposed to make choices by the way information is presented and the way analyses are conducted. But debiasing or unbiased presentation has often been a secondary motivation for building DSS. It is often easy for managers to accept that some people are biased decision makers, but that doesn't mean they think their decision making is biased or at least not in the situation where a proposed DSS will be used. Also, DSS builders assume their targeted users are rational thinkers (cf. Power, 2004). In general, cognitive bias has been an issue raised more by academic researchers than one raised by industry consultants and practitioners. If DSS builders are consciously attempting to expand the boundary of rational managerial decision making behavior, then they must be familiar with the cognitive biases that can impact human information processing. We MUST ask and explore how DSS can reduce or even eliminate significant cognitive biases. Also, DSS can encourage and even promote biased decision making, building such systems may not however be ethical or legal. As DSS builders we must ask if it is ever desirable and ethical to reinforce or exploit known cognitive biases when building a DSS. And if it is, when and in what circumstances? This Ask Dan! won't resolve or even offer guidance on these questions. Below is a list of common cognitive biases with comments related to building decision support systems. The list is based upon various sources (cf., Tversky and Kahneman, 1974; Kahneman, Slovic, and Tversky, 1982) including Wikipedia. Anchoring and adjustment - Decision-makers "anchor" on the initial information they receive and that influences how subsequent information is interpreted. So for example, in a data-driven DSS for business performance management the dashboard screen metrics will significantly impact how subsequent data and analyses are interpreted. Attribution - Decision-makers tend to attribute successes to their own actions and abilities, but attribute failures to bad luck and external factors. Also there is a tendency to attribute a competitor's success to good luck, and a competitor's failure to mistakes. In a data-driven DSS, managers should be encouraged to ask why questions about summary data values. Why did profit increase 25% in the most recent quarter? Why did the in-process inventory increase 20%? Availability - Decision-makers estimate the probability of an outcome based upon how easy that outcome is to imagine. In a model-driven DSS, when probabilities are elicited a DSS should encourage information gathering prior to the input of any probability estimates. Competing scenarios can potentially reduce this bias. Causal attribution - Decision-makers tend to ascribe causal explanations even when the evidence only suggests correlation. In data-driven DSS, cross-tabulation displays should be "interpreted" when possible or a disclaimer should be provided about this problem. Confirmation - Decision-makers tend to explain away inconsistent and negative evidence. Negative evidence is sometimes used to confirm a pre-existing hypothesis. A data-driven DSS should be used early in a decision making process and multiple decision-makers should have access to and use a specific DSS. Conservatism, tradition and inertia - Decision-makers repeat previously successful thought patterns and analyses when confronted with new circumstances. In a knowledge-driven DSS, it is important to periodically check that circumstances have not changed. Model-driven DSS also need to be periodically reviewed and updated. Decision makers need to monitor changes in situations and circumstances. Escalating commitment - Decision-makers often look at a decision as a small step in a sequential decision process and this encourages commitment to a course of action. DSS that are tightly linked to a particular strategy reinforce this tendency. Also, the selection of critical success factors in data-driven DSS can reinforce commitment to a course of action. Managers needed to periodically revisit the metrics used to monitor organization performance. Experience limitations - Decision-makers are often constrained by past experiences. A planning-oriented DSS should include a wide-range of scenarios from multiple stakeholders to expand the experience horizon of decision-makers. Faulty generalizations - Decision-makers simplify complex interactions and group ideas, things and people. These generalizations influence decisions. A DSS builder should explicitly state assumptions and generalizations about the models in a DSS. Inconsistency - Decision-makers do not consistently apply the same decision criteria in similar decision situations. Screening and evaluation models in model-driven DSS can help insure consistency. Consistency is only desirable however when the criteria are appropriately identified and appropriately weighted. Premature closure - Decision-makers tend to terminate the search for evidence quickly and accept the first alternative that is feasible. Data and document-driven DSS can make search easier and a user friendly interface can encourage further search. Recency - Decision-makers tend to place the greatest attention on more recent information and either ignore or forget historical information. When possible, data-driven DSS should put recent information in a context of historical information. For example, the current month, prior month and the year ago month's sales data should be presented. Repetition - Decision-makers often believe what they have been told repeatedly and by the greatest number of different sources. Data and document-driven DSS need to help identify the source of data and a single source should not be presented many times to bolster the same conclusion. In web-based search, the same source can often appear in many results. Representativeness -- Decision-makers often judge events, people and things based upon how similar they are to a prior case example. This approach can work effectively in some situations and it is used in case-based reasoning in some knowledge-driven DSS. DSS builders need to monitor systems that rely on a representativeness heuristic. Role fulfillment - Decision-makers often conform to the expectations that others have of them. If the expectation is that a manager will use a specific DSS, then it is more likely s/he will use the DSS. The reverse of this also holds. DSS builders should examine the role of a decision maker/user as part of DSS analysis and design. Selective perception - Decision-makers actively screen-out information that is considered as irrelevant and unimportant. This perceptual bias helps reduce information load, but if the decision-maker is prejudiced about the decision outcome then important information will be ignored. A data-driven DSS can present predefined information and the rationale for what information is presented can be examined and disclosed. Selective search for evidence - Decision-makers tend to gather facts that support certain conclusions, but ignore other facts that might support different conclusions. This tendency encourages some managers to use DSS to bolster previously made decisions and to rationalize their conclusions. When possible, DSS should attempt to encourage unbiased search. Often a review of search efforts can identify additional search topics. Source credibility - Decision-makers sometimes reject information because of the source. A healthy skepticism about source credibility should be encouraged in data and document-driven DSS. Information about a source's race, nationality, religion or other potentially prejudicial source information should not however be readily available to DSS users. Source information should focus on relevant qualifications. Underestimating uncertainty and having an illusion of control - Decision-makers tend to underestimate uncertainty about future events and outcomes. This occurs because people believe they have more control over outcomes than they often do. The tendency is to believe one has adequate control to minimize potential problems from decisions. If decision-makers will use DSS for contingency planning, such systems can potentially help reduce this bias. Wishful thinking and unwarranted optimism - Decision-makers tend to assume the "best" outcome will occur. It is a natural tendency to view events in a positive frame of reference and this bias can distort perception and thinking. DSS should present multiple scenarios when possible including "worst case" scenarios. According to Wikipedia on Decision Making, "Due to the large number of considerations involved in many decisions, decision support systems have been developed to assist decision makers in considering the implications of various courses of action. They can help reduce the risk of errors." Do you agree? As always your comments and suggestions are appreciated. References Cognitive Technologies, "Biases in Decision Making", http://www.cog-tech.com/projects/Biases.htm Computer Reservations System (CRS) Regulations, http://www.dot.gov/affairs/Computer%20Reservations%20System.htm, Office of the Secretary, Department of Transportation, January 31, 2004. Kahneman, D., P. Slovic, and A. Tversky (Eds.). Judgment under Uncertainty: Heuristics and Biases. Cambridge, UK: Cambridge University Press, 1982. Power, D., Do DSS builders assume their targeted users are rational thinkers? DSS News, Vol. 5, No. 21, October 10, 2004. Psych Central, http://psychcentral.com/psypsych/List_of_cognitive_biases Tversky, A. and Kahneman, D. "Judgment under uncertainty: Heuristics and biases". Science, 185, 1974, 1124-1131. Wikipedia, Cognitive bias, http://en.wikipedia.org/wiki/Cognitive_bias . Wikipedia, Decision making, http://en.wikipedia.org/wiki/Decision_making . ************************************************************ Purchase Dan Power's DSS FAQ book 83 frequently asked questions about computerized DSS http://dssresources.com/dssbookstore/power2005.html ************************************************************ DSS Conferences Upcoming Conferences 1. Teradata PARTNERS User Group conference, September 18-22, 2005, Orlando, Florida. Check http://www.teradata.com . 2. 2005 NPRA Plant Automation and Decision Support Conference, October 18-21, 2005, Gaylord Texan Hotel, Grapevine, Texas. Check npra.org . 3. ACM 8th International Workshop on Data Warehousing and OLAP (DOLAP 2005), November 4-5, 2005, Bremen, Germany. Check http://gplsi.dlsi.ua.es/congresos/dolap05/ . 4. Water Management Decision-Support Software Workshop November 16 - 17, 2005 - Niagara Falls, New York, USA, Check http://www.ceatech.ca/eventsd.php?eid=1027. Abstracts due August 26, 2005. 5. Call for Papers: Fourth workshop on e-Business (WEB 2005), a pre-ICIS workshop sponsored by AIS SIGeBIZ. Workshop URL: www.web-workshop.org 6. Call for Papers: Third Annual Pre-ICIS Workshop on Decision Support Systems sponsored by AIS SIG DSS, December 11, 2005, Las Vegas, Nevada. Workshop URL: mis.temple.edu/sigdss/icis05 7. Call for Papers: International Conference on Creativity and Innovation in Decision Making and Decision Support (CIDMDS 2006) sponsored by IFIP WG 8.3, June 28th - July 1st 2006, London, UK. Check http://www.ifip-dss.org/ . ************************************************************ Please tell your DSS friends about DSSResources.COM ************************************************************ What's New at DSSResources.COM 09/10/2005 Posted an article by John Bair, Stephen Fox, Morgan Hunt, and Dan Meers "Aligning BI with Business Strategy: How a Mission Mapped Architecture can help". Check the articles page. ************************************************************ DSS News Releases - August 29 to September 9, 2005 Read them at DSSResources.COM and search the DSS News Archive 09/09/2005 Geac launches MPC 7 performance management software. 09/08/2005 Lake Hospital System chooses Landacorp's Maxsys II Medical Management software. 09/08/2005 Powerful line-up of industry experts, practitioners set to present at Siebel Business Intelligence Summit. 09/08/2005 MicroStrategy selected by NWEA for comprehensive student assessment program. 09/07/2005 Planalytics says Katrina's effect on consumers likely to linger. 09/07/2005 Plansmith Corporation announces Open Solutions' adoption of new modeling system. 09/07/2005 'State of Workforce Mobility' study sheds light on use and understanding of mobile technology. 09/02/2005 More California hospital quality information now available to consumers. 09/02/2005 Source for information on community disaster planning, preparation. 09/02/2005 Risk Management Solutions expects economic loss to exceed $100 Billion from Hurricane Katrina and the Great New Orleans Flood. 09/01/2005 Independent cost estimating leader helps government agencies comply with OMB's mandate. 09/01/2005 Industry analyst report cites Fair Isaac as revenue leader in analytic applications market. 08/31/2005 Supply chain executives familiar with RFID overwhelmingly recognize critical nature of the technology. 08/31/2005 Silverton Casino deploys Teradata(R) and Compudigm Solutions to enable growth in hypercompetitive gaming market. 08/31/2005 ILOG optimizes Ameriprise Financial's key financial planning tool. 08/30/2005 Entrepreneur Mouli Cohen talks about the art of making tough business decisions. 08/30/2005 Ronin Capital selects Insightful's S-PLUS(R) 7 to deliver predictive information to investment decision-makers. 08/30/2005 Wells Fargo announces added improvements to nonprime mortgage lending practices. 08/30/2005 Experian-Scorex enables enterprise-wide decisioning for Standard Bank of South Africa. 08/30/2005 Carphone Warehouse calls on Hyperion for business performance management. 08/30/2005 Carlson Wagonlit Travel of Brazil cruises to excellence with Business Objects. 08/29/2005 Oracle announces general availability of Oracle(R) Collaboration Suite 10g. ************************************************************ DSS News is copyrighted (c) 2005 by D. J. Power. Please send your questions to daniel.power@dssresources.com. |