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about computerized Decision Support Systems.


                        DSS News
                  D. J. Power, Editor
            September 11, 2005 -- Vol. 6, No. 20

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          "Aligning BI with Business Strategy:
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* Ask Dan! - Can computerized decision support systems impact,
eliminate, exploit, or reduce cognitive biases in decision making?
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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

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.


Cognitive Technologies, "Biases in Decision Making",

Computer Reservations System (CRS) Regulations,,
Office of the Secretary, Department of Transportation, January 31,

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,

Tversky, A. and Kahneman, D. "Judgment under uncertainty:
Heuristics and biases".  Science, 185, 1974, 1124-1131.

Wikipedia, Cognitive bias, .

Wikipedia, Decision making, .


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DSS Conferences 

Upcoming Conferences 

1. Teradata PARTNERS User Group conference, September 18-22, 
2005, Orlando, Florida. Check .

2. 2005 NPRA Plant Automation and Decision Support 
Conference, October 18-21, 2005, Gaylord Texan Hotel, 
Grapevine, Texas. Check .

3. ACM 8th International Workshop on Data Warehousing and 
OLAP (DOLAP 2005), November 4-5, 2005, Bremen, Germany. 
Check .

4. Water Management Decision-Support Software Workshop 
November 16 - 17, 2005 - Niagara Falls, New York, USA,
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:

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:

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 .


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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.

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