Geography Reference
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Decisions: problems, frames and heuristics
Because one of the desired outcomes of an effort to make uncertainty more usable through
visualization is the support of a more informed decision-making process, it would be benefi-
cial to incorporate theories and research from decision science and information science into
uncertainty visualization methods. Research in the psychology of decision making focuses
on the means by which people merge beliefs (knowledge, expectations) and desires (personal
values, goals) to make a decision (choose between alternatives or courses of action; Hastie,
2001). Decision making is often seen as the sequence of steps that people pass through in
order to make a decision, including several components: a course of action, expectations
about outcomes and processes, and beliefs (a decision maker's expectation) or utilities (what
the decision maker wants) that define the consequences associated with the outcomes of
each event or action (Hastie, 2001). Throughout this process, decision makers often attempt
to identify or seek out information to support or identify a potential course of action. Infor-
mation seeking refers to the variety of methods and actions that people employ to discover
and gain access to information resources (Wilson, 1999). Not surprisingly, uncertainty is
an essential and ubiquitous component of information seeking (Kuhlthau, 1991; Wilson,
1999; Wilson
et al
., 2002).
Uncertainty is often considered to be negative and detrimental by information seekers,
leading to a reduction in confidence (and an increase in anxiety). However, information
science research has shown that the presentation of uncertainty can have a positive influence
on problem-solving, encouraging individuals to take action and work through uncertainty to
eventually generate knowledge (Anderson 2006). Information, in this case, has the potential
to both reduce and produce uncertainty, depending on the context of the problem and the
individual. The ultimate goal in problem solving is the reduction of uncertainty to acceptable
levels, not necessarily the elimination of uncertainty. In the case of uncertainty visualization,
consideration of the connotation of uncertainty (positive or negative) may play a role in
the design of uncertainty representations. A simple semantic rewording of a legend, for
example, from '50% uncertain' to '50% certain' may be an avenue by which some users
would become more comfortable with uncertainty, allowing them to use it in productive
and not detrimental ways.
The degree to which an information seeker can decide on what information to access
(view, read or listen to), how long to access it, and in what order, is called
information
control
(Ariely 2000). In website design, for example, information can be 'pushed' onto a
user without the seeker's request, or it can be 'pulled' onto the display through a particular
request for information. There are legitimate and productive reasons for both approaches,
and it has been suggested that the degree to which an information seeker should have
control of information is dependent on the experience, ability and/or knowledge of the
user (Shapiro, Schwartz and Astin 1996; Wu and Lin 2006). This so-called
match theory
of
information has provided a theoretical foundation to evaluate the influence of information
control on the decision making of consumers.
For example, Wu and Lin (2006) found that experts should be given a high level of freedom
to search for relevant information, while novices, who are less able to differentiate between
relevant and irrelevant information, should have less information control. This suggests
that there may be an optimal amount of information to present to users depending on
their individual characteristics. Match theory should be adapted to visualization research,
answering questions about the relationships between expertise, interactive affordances and
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