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OCR for page 275
SYNOPSIS OF GENERAL AUDIENCE DISCUSSION
-
bst of the points raised during Session IV and the general discussion
centered arc und two somewhat related issues:
1. the gap between behavioral (heuristic) and traditional (rule based)
approaches to decision making, and
2. how to HE with shortcomings in one or the other that detract f ~ u
system performance.
m e Gap Issue
m e observation was made that there seem to be two ways of thinking
about decision problems, each with its own philosophy and research
agenda, that are proceeding more or less independently. To some
extent, it was pointed cut, the two papers in the session highlight the
differences between the two approaches. me question was whether, and
if so how, they should be integrated or links morn cicsely.
Two conflicting views were offered. One was that since the
differences are deeply rooted in their respective traditions and
Captures, the barriers will not be broken down easily, and the
anticipated payoff for NINA would probably not justify the time and
cost necessary to bring about an integration. A number of other issues
should take precedence over this one. - ~ ~
. ~ ~ ~ . ~ ~ ~ . ~ .
The chairs visor was that the
two approaches snatch ne nether Denigrated, peaceably can be if NOVA
puts the issue on its r~r~ agenda, and in fact is being attempted
in a small way trough r~r~ currently in progress in Fischhoff's
Hi.
Among the suggestions for an Integrative approach were the whole
domain of fuzzy logic and the founded rationality concept (e.g.
defining general goals and then "fiddling with the model at the margin
as in 'satisficing"'). It was pointed out, however' that In the
context of expert systems such approaches reduce to writing a lot of
conditional rules over a large number of state variables. This-= one
cannot summarize -~.=ily what the system will do over the full range of
decision problems.
275
OCR for page 276
276
Applications, Or Dealing With Shortcomings
Several options ware suggested for minimizing the effect of
suboptimalities in human judgment. Training, while not universally
effective in overcoming biases, has produced some not~hie suc-~-=s~s
(e.g. weather forecasters). The key may well lie in the proper design
of training programs (something that merits a continuing research
effort). Increasing the trainee's sophistication in statistical
concepts, however, is clearly of little help.
Aiding in its various forms and with its inventory of existing
models has its place but also has limitations. MUltiattribute utility
theory, decision analysis, etc. are useful for solving well defined
problems, but "bring no knowledge to the party." Often their logic is
not trar~rent to the user and critical factors may be emitted. Thus
Fife output may not be satisfactory in either an absolute sense or as
E ~ hived by the user. When it conflicts with he m n intuition there is
a problem nartim,Qarlv if the human doesn't understand the logic.
~ , ~ ~
User acceptance of even improved decisions becomes crcbiematic.
. .
One approach to dealing with these deficiencies in the aiding models
was advocated by Davis: find cut what is missing an] build it An.
Intuition and creative thinking are not magic' but rancher,
"~iscaveredl rationality." R - earth Should try to expose that
rationality (or reasoning) and apply it in creating more rcit~ust models,
as well as mom transient ones. To Me extent that the research
subs, it should be incorporated into training as well as aiding
applications, and be result cock be better decisions and greater
acceptance of those decisions by users (who wed now be more likely to
appreciate the logic).
Representative terms from entire chapter:
transient ones
