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DISTRIBUTED DECISION MAKING
7
B) offers similiar possibilities requiring detailed interdisciplinary research.
In these extensions it seems essential to stretch existing theories to cover
as much of the problem as possible, rather than leaving them with those
pieces of the puzzle with which they are most comfortable.
EMPIRICAL RESEARCH FOR DISTRIESI1ED DECISION MAKING
Applying existing theories to distributed dec~sion-making systems re-
quires some perception of which features of their design are most important
to their operation. ~ some extent, that perception may come from existing
research or direct experience with particular systems. However, to a large
extent, it represents disciplined speculation, extrapolating from existing
theory or data. Clearly, there are many questions bearing direct empirical
study In order tO guide theory development and provide direct input tO
designers. AS a first approximation, these topics can be divided into those
concerning the behavior of individuals acting alone, those conceding the
interaction of individuals with machines, those concerning the interactions
of multiple individuals, and those concerning organizational behavior-all
within distributed decision-making systems (although advances here would
often be of interest elsewhere). A sampling of such topics follows, drawn
from the discussions at the workshop. All have implications for human
factors specialists, helping them either to propose designs or to anticipate
the performance of designs proposed by others.
Research Topics in Individual Behavior
Mental Models
A distinctive feature of distn~uted decision-making systems is the
interdependence of many parts, each of whose status may be constantly
changing. Operators within the system must be mindful of the possibility of
changes as they decide what actions to take, what information to solicit, and
what information to share. Routine reporting requirements are designed to
help people keep up to date; however, they may not be appropriate for all
circumstances and may create a greater stream of information than can be
incorporated in operators' mental picture of the system's status. Standard
operating procedures are designed to direct actions in uncertain situations;
however, that very uncertainly often leaves some latitude for determining
exactly what situation exists at any moment.
lt woWd be very helpful to Mow how (and how weld people create,
maintain, and manipulate their mental pictures of such complex, dynamic
systems. A point of departure for such research might be studies of
people's mental models of formally defined physical systems (e.g., Chi,
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8
DISTRIBI=ED DECISION MATING
Glaser and Parr, 1988; Gentner and Stevens, 1983; Johnson-Laird, 1985~.
The uncertainty, dynamism, and lack of closure of distributed decision-
making systems would add some interesting wrinkles to that research.
Semi-analytic Cognition
The very complexity of such systems forces some serious thinking
regarding the nature of decision making within them. Normative theories
of decision making specify a laborious set of procedures, including listing
of all relevant alternative courses of action, identifying the consequences
potentially associated Path each action, assessing the likelihood of each
consequence, determining the relative importance (or attractiveness) of the
different consequences, and combining all these considerations according to
a defensible decision rule (von Winterfeldt and Edwards, 1986; Watson and
Buede, 1988~. Basic research into cognitive processes suggests that these are
difficult mental operations under any circumstances; specific research into
intuitive decision-making processes shows a variety of potential deficiencies;
the speeded conditions of decision making in distributed systems should
severely stress whatever capabilities people bring to it.
When questioned, the operators of complex systems often report a
process more like pattern matching than decision making (Klein, 1986;
Klein, CaldeIwood and Clinton-Cirocco, 1986~. That is, they figure out
what is happening in some holistic sense, then take the appropriate action.
Their reports include some elements of analytic decision making (e.g.,
they tank about alternatives and weigh consequences), however, it seems
to be much less than the full-blown treatment required by decision theory.
Additional descriptive studies are clearly needed, in order to elaborate on
these accounts and clarify how well actual behavior corresponds to this
recounting. Such studies could be accompanied by theoretical treatment of
the optimal (or suboptimality) of decision making by pattern matching.
If there is a case to be made for pattern matching, then one could examine
how it could be facilitated through the provision and display of information
(and perhaps what changes in analytic decision aids might make them seem
more useful). Conversely, system designers, in general, would want to know
when pattern matching leads one astray. In defense systems, designers
would want to know where it creates vulnerability to being led astray.
Relevant literatures include those regarding the validity of introspections
(e.g., Ericsson and Simon, 1980; Goldberg, 1968; Nisbett and Wilson, 1977)
and the diagnostic ability of experts (e.g., Chase and Simon, 1973; Chi et
al., 1988; Elstein, Shulman, and Sprafl
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DISTRIBUTED DECISION EKING
Decision Making in Real Life
9
As mentioned briefly in the preceding section, an extensive literature
has developed documenting deficiencies in intuitive judgment and dec~sion-
making processes (e.g., Dawes, 1979, 1988; Fischhoff, 1988; Hogarth,
1988; Kahneman, Slovic, and Iversky, 1982~. Although enough studies
have been done with experts performing tasks in their fields of expertise
to establish that these results are not just laboratory curiosities, the vast
majority of studies have still come from artificial tasks using lay subjects.
~ ~ r`~clllt th'~.re. i.~ the natural concern about their generalizability and
~ ~ ~ ~ _v ~ ~
even about whether apparently suboptimal behavior actually serves some
higher goal (e.g., reflecting the occasional errors produced by generally
valid judgmental strategies, reflecting strategies that pay on over a longer
run than that inferred by the investigator) (Berkeley and Humphreys, 1982;
Hogarth, 1988~.
When human performance is suboptimal, there is the need for training,
decision aids, or planning for problems. In addition to providing additional
impetus for addressing these topics of general interest, distributed decision-
making systems create a variety of new circumstances that may exacerbate
or ameliorate the problems. Appendix A to this report speculates on
some of these possibilities, which were discussed at some length during the
work Hop.
Interpreting Instructions
The pattern-matching process described above seems to involve inter-
preting concrete real-life situations in terms of some fundamental categories
that people (experts) have created through experience. A complementary
task which may have quite a different cognitive dynamic, is interpreting
real-life experiences in terms of general instructions provided by those
higher up in an organization. These might be contingency plans, of the
form "If X happens, do Y." or rules specifying goals at a fairly high level
of abstraction (e.g., "Act as though Quality is Job One"~. A cognitive
challenge ill the former case is achieving enough fluency with the abstract
categories to be able to identify them with actual contingencies in the way
intended by the contingency planners. If operators are unsuccessful, then
decision-making authority has not been distributed in the way intended.
A cognitive challenge in the latter case is to adapt hard abstract rules
to murky situations (e.g., "Should ~ really shut down the assembly line
because the paint looks a little spotty?"~. If operators are unsuccessful,
then system designers have failed to create the incentive schemes that they
thought they had. Points of departure for these topics include studies of
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D STRIBI=ED DECISION MATING
categorization processes for natural and artificial categories (e.g., Murphy
and Medin, 1985), interpretations of reward structures (e.g., Roth, 1987),
and lay theories of system behavior (e.g., Furnham, 1988).
Research Topics in Indi~dual-Machine Behavior
t
Distributed decision-maldug systems often execute their actions through
machines (e.g., missiles, reactor control rods, automatic pilots). They al-
ways coordinate those actions through machines (e.g., telecommunications
networks, automated monitoring systems, data exchanges). The human
operators of the system always must ask themselves whether the machines
can be trusted. Will they do what I tell them to? Are they telling me the
truth about how things are? Have they transmitted Me messages as they
were sent? Obvious (and different) kinds of errors can arise from trusting
too much and trusting too little. The designers of a system want it not
only to be reliable, but also to seem as reliable as it is. In some cases,
they might even want to sacrifice a little actual reliability for more realistic
operator expectations (Fischhoff and MacGregor, 1986~.
Expectations for the components of distributed decision-maldng sys-
tems presumably draw on cognitive processes akin to those used in predict-
ing the behavior of humans and machines in other situations (e.g., Fischhoff,
MacGregor, and Blackshaw, 1987; Furnham, 1988; Moray, 1987a, 1987b;
Moray and Rotenberg, 1989; Murphy and Rankler, 1984; Reason, in press).
An obvious research strategy is to examine the generalizabili~ of these
results. A second strategy is to study the impact of features unique to
distributed decision-making systems. One such feature shared by some
undistributed systems is what has been called the supervisory control prob-
lem (National Research Council, 1985), the need for operators to decide
when an automated system has gone sufficiently astray for them to override
it (Muir, 1988~. In doing so, they may be expressing mistrust, not only of
the system's choice of actions, but also of its reading of the real world (e.g.,
based on the reports of sensors and their interpretation) and its theory of
how to respond to that reality. A third strategy is to record the operation
of actual systems, eliciting operators' confidence in them in ways that can
be subsequently calibrated against actual perfo~ance. A fourth strategy is
to look at operators' interpretations of the clanns made for new equipment
before it is introduced and how those expectations change (for better or
worse) with experience.
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DISTRIBUTED DEC SION MAKING
Expert Systems
11
One variant on this general theme of trust and trustworthiness, dis-
cussed at some length at the workshop, concerned expert systems, that
is, computerized systems intended to incorporate the wisdom of the most
accomplished experts regarding a particular category of problem. These
systems could be allowed to operate on their own unless overriden by
operators (e.g., systems for deciding whether incoming mmsles are hostile)
or could be queried as to how the expert in the machine would respond to
the current situation, in terms of the way it has been deserted to it. There
is a strong technological imperative pushing the development of expert
systems for an ever-increasing range of situations. This imperative should
be particularly strong in distributed decision-making systems because the
promise of having a proxy expert online ~ the machines available at re-
mote sites seems like an obvious way of maintaining a consistent policy and
centralized control throughout.
Like any other decision aid, the contribution of expert systems to
system performance depends both on their capabilities and on the ap-
propriateness of the faith placed in those capabilities. In this light, any
improvements in expert systems should improve their usefulness for dis-
tnbuted decision-making systems, provided that their limitations are equally
well understood. Specifically, operators must understand what expert sys-
tems do and how well they do it. They must know, for example, what
values a system incorporates and how well those correspond to the values
appropriate to their situation (e.g., "Was the expert of the system more or
less cautious in reaching decisions than I want to bend. They must also
know how their world differs from that in which the expert operated (e.g.,
"Did the expert have more trustworthy reporting systems? Did the expert
have to consider deliberate deception when interpreting reports?"~. They
must know if they have advantages denied to the expert (e.g., the ability to
draw on additional kinds of expertise beyond that lodged in even the most
knowledgeable single individual in the world).
In addition to the cognitive challenge of unprov~ng the ~nterpretabilib
of expert systems for individual operators, there Is also the institutional
challenge of managing the allocation of responsibility for decisions made
by expert systems (or by the operators who override them). This need
aeates ~ special case of the general problem of understanding institutional
incentive structures.
System Stability and Operator Competence
No system in a complex, dynamic environment works exactly as
planned. That is why there are still human operators, even in cases in
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DISTRIBUTED DECISION AWING
which actions are executed by machine. The role of these operators must
therefore come from knowing things that are unknown to the machine,
perhaps because its formalized language cannot accommodate them, per-
haps because there is inadequate theoretical understanding of the domain
in which the system operates, perhaps because the theory has known flaws.
In any case, the operators must have some local knowledge or indigenous
technical knowledge or tacit knowledge allowing them to pick up where the
machine leaves off (Brokensha, Warren, and Werner, 1980; C:hi et al., 1988;
Foucault, 1980; Moray, 1987b; Polanyi, 1962; Rasmussen, 1983, 1986~.
Knowing more about the quality of this unique human knowledge
would obviously help in guiding the allocation of responsibility between
person and machine. Knowing more about the nature of this knowledge
would help to understand the impact of changes in a distributed decision-
maldng system on its operability and about procedures for maintaining
(or restoring) this kind of expertise. For example, is it better to examine
potentially significant changes constantly to determine their effect on one's
understanding? Or is it better to conduct periodic reviews, looking for
aggregate impacts that might be more readily discernible recognizing that
one may be functioning with an outdated model between reviews?
Finally, such knowledge should help manage those changes that are
controllable. There may be little that one can do to retard an opponent's
adoption of a new weapons system (with its somewhat unpredictable impact
on the operation of one's own systems) or on the spread of an iBiOt drug
or unfamiliar virus in the population (with their effect on the interpretation
of lab results). However, one may have some control over the introduction
of new technologies that can reduce operators' understanding of their own
system either by disrupting the operational patterns that they know well
or by reducing their direct contact with the system (a sort of intellectual
deskilling). Given the imperatives of innovation, it would take quite solid
documentation of operators' world views to resist changes in technology on
the grounds that it will reduce their understanding.
Displaying Uncertainty
If systems are known to be imperfect, it is incumbent on their designers
to convey that information. A fairly bounded design problem that came up
several times during the workshop was how to display information about
the uncertain in a system. This general category includes several different
kinds of uncertainly: that surrounding direct measurements (e.g., the
temperature in a reactor core, the altitude of an aircraft), that surrounding
interpreted data (e.g., the identity of an aircraft, its likely flight path),
and that surrounding its recommendations (e.g., whether to shoot). Such
displays would be attempts to create realistic expectations. Whether such
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DISTRIBUTED DECISION MAKING
13
good intentions achieve the intended effects is a potentially difficult design
question, especially when the uncertainty arises from imperfect theories
(even when those are applied to integrating perfect observations).
Research Topics in Multiple Individual Behavior
Shared Knowledge
The mdrnduals (or units) in a d~stnbuted decision-maldog system
are meant to have a shared concept of their mission (i.e., objectives and
situation) at a fairly high level of generality that nonetheless allows them to
function effectively in the restricted environment for which they have more
detailed knowledge. Achieving this goal is in part a matter of training,
so that distn~uted operators share certain common conceptions, and in
part a matter of distributing current information, so that they stay in
touch conceptually. Insofar as it is impossible to tell everybody everything,
the designers of a system need to know what is the minimal level of
explicit sharing needed to ensure adequate convergence of views. They
also need to know what kind of information is most effectively shared
(e.g., raw observations or interpretations). Conversely, they need to know
the drift in perceptions that arises from lack of sharing, whether due lo
individuals having too much to say, having too much to listen to, or being
denied the opportunity to communicate. Such knowledge would guide
them in determining the capacity needed for communication channels, the
fidelity needed for those channels, and the protocols for using them (e.g.,
when to speak how to interpret silence). Approaches to these questions
range from observational studies of the conversational norms of intact
communities to mathematical models of the impact of sharing on the
creation of communities (Carley, 1986a, 1986b, 1988; Grice, 1975; Hilton,
1988)
Bamers to Sharing
Communication involves more than just the transmission of proposi-
tional statements. People read between the lines of other people's state-
ments. People read the vocal intonations and facial expressions accompa-
nying statements for additional cues as to what is intended and what to
believe. These are well-worked topics in social psychology, whose implica-
tions for distributed decision-maldug systems need to be understood (e.g.,
Spencer and EkInan, 1986; Fiske and Taylor, 1984~.
In addition, there are special features of such systems that threaten
to disrupt normal patterns of communication, interpretation, and under-
standing. For example, modern telecommunications may deprive users of
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DISTRIBUTED DECISION MAKING
necessary interpretative cues, a fact that may or may not be apparent to
both transmitters and receivers of messages. They may disrupt the timing
(or sequencing) messages and responses, delaying feedback and reducing
coordination. Restricted communications can also prevent the unintended
communication of peripheral information Consider, for example, people
who come off poorly on television not because they are uncertain of their
messages, but because of discomfort with the medium. Or consider whether
there would be better communication between U.S. and Soviet leaders were
the current hot line replaced by a higher-fidelity channel, thereby letting
through more cultural cues that might be subject to misinterpretation.
These questions are beginning to receive systematic attention through both
controlled experunents and detailed observational studies (e.g., Hiltz, 1984;
Kiesler, Siegel, and McClure, 1984; Meshkati, in press; Sproull and Kiesler,
1986~. More research is needed, directed toward the particular conditions
created by distributed decision-making systems.
Distribution of Responsibility
Organizations of any sort must allocate responsibility for their various
functions. For distributed decision-making systems, this allocation must, by
definition, include the collection, sharing, and interpretation of information,
as well as the decision to undertake various classes of actions. These are
obvious parts of its design, which would exist even were there no technology
involved at ale Considering technology raises a few issues calling for
particular input from human factors specialists. One is how the distribution
of technical knowledge about the equipment affects control over the system.
Particularly under time pressure, technically qualified operators may have
to take actions without adequate opportunity to consult with their superiors
(e.g., the flight deck chiefs on carriers who are career noncommissioned
officers, yet subordinate to officers who are there because they have more
generalized expertise) ~Porte, 1984; Rochlin, in press). Even without
time pressure, differences in social status may restrict communication, so
that technically skilled operators are required to follow orders that do not
make sense to someone on the shop floor. In either circumstance, the
welfare of the system as a whole may require out-of-role behavior by its
operators. Designers should want a better understanding of when such
situations arise, how they can be minimized, and how to deal with their
aftermath without undermining an organiz~tion's authority structure.
A rather different impact of technology on the distribution of respon-
sibili~ is its effect on the opportunities for monitoring the performance of
operators. Successful organizations require an appropriate balance between
central contra! and local autonomy. Operators need some independence
both for motivational and functional reasons. Mo~civationally, they need to
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DISTRIBUTED DECISION MAKING
15
feel that someone is not on their case all the time. Functionally, they must
be able tO exploit whatever unique advantages their local knowledge gives
them, so that they can improvise solutions to problems that are not fully
understood by those at the top. Much organizational theory deals with
how to achieve this balance. In practice, though, these designs probably
specify no more than necessary conditions for balance, within which real
people might be able to negotiate workable arrangements. Any change in
technology (or in the external world) could destabilize this balance. The
increased capacity for surveillance may become a recurrent destabilizing
factor In distributed dec~sion-making systems. If those at the top of a
system must know everything they can know, they may then receive a good
of information that is inadequate to assert effective control, but enough
tO restrict the ability of local operators to innovate. Where this happens,
changes in the technology or its management are needed (Impair, Fischhoff,
and Johnson, 1988~.
Research Topics in Organizational Behavior
Most of the research topics described in the preceding sections concern
the reality facing individuals in distributed decision-making systems and
how their performance may be improved by better design of equipment
and procedures. A common assumption of these potential interventions is
that an organization will be better if the performance of its constituents
is improved. While this is doubtless true in general, certain phenomena
emerge most clearly at the organizational level. Although these topics
may seem somewhat distant from traditional human factors work, the
workshop participants felt that they were essential for deploying human
factors resources effectively and for understanding the impacts (intended
and unintended) of interventions.
Reliability,
Organizations can fail in many ways. Knowing the ways that are
most liked can focus efforts on improving design or help one to choose
among competing designs. Detailed quantitative modeling of organizational
reliability might highlight such vulnerabilities (Pate-Cornell, 1984, 1986) for
example, which methods of distributing information are most robust with
regard to noise and interruptions? Human factors specialists could not only
take direction from such analyses, but also give them shape by characterizing
the probability of failures arising from venous operator problems (Swain
and Gutman, 1983~. While the methods used for modeling mechanical
systems (e.g., McCormick, 1981; U.S. Nuclear Regulatory Commission,
1983) are an obvious place to start such analyses, there Is important
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DISTRIBUTED DECISION A~1~NG
theoretical ground to be broken by incorporating in them those forms
of vulnerabilities that are unique to single or interacting individuals (e.g.,
shared misconceptions, refusals to cooperate, the ability to deceive and be
deceived).
Conflicting Demands
Many organizations face conflicting demands. For example, they may
have to act in both crisis and routine situations; they may have to main-
tain a public face quite at odds with their internal reality (e.g., exuding
competence when all is chaos undemeath); or they may need to ad-
here to procedures (or doctrine) while experimenting in order to develop
better procedures. Each of these conflicting roles may call for different
equipment, different procedures, different personnel, different patterns of
authonty, and different incentive schemes. Mediating these conflicts is
essential to organizational survival. An understanding of these conflicts is
essential if human factors specialists are to see inhere they fit in and to
create designs that serve both purposes.
Learning
Like individuals, successful organizations are continually learning, both
about their environment and about themselves. Their design can facilitate
or hinder both the acquisition of such -understanding and its distribution
throughout the organization (both at any given tune and over time). Human
factors work may have leverage on learning processes through the methods
by which experience is accumulated and disseminated. Because such change
and maintenance are not always part of an organization's explicit mission,
they may need to be studied and identified lest they be ignored.
Research Methods for Distributed Decision Maldng
Each of the research topics cited in the previous section of the report
has particular methodological demands. Although those could be left to the
individuals undertaking each task, there are also some recurrent needs that
might be addressed profitably by research that is primarily methodological
in character. The workshop identified a number of such topics, which are
described below.
Analytical Measures
As mentioned, the label distributed decision making covers a very wide
variety of organizations. Although each deserves attention in its own right,
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DISTRIBUTED DECISION MAKING
17
respecting its peculiarities, the accumulation of knowledge requires the
ability to make more general statements about different kinds of distributed
decision-mal~g systems. That goal would be furthered by the development
of broadly applicable analytical measures. For example:
.
degree of d~stn~ution of decision-making authority
· degree of distribution of information
· heterogeneity of tasks across the system/degree of human and
physical asset specialization
· stability of external environment
· heterogeneity of external environments
· variation in organizational demands (e.g., across steady-state and
peak-load situations)
stability of internal environment (e.g., personnel turnover, techno-
logical change)
· irreversibility of actions
· time stress
Case Studies
Detailed case studies of actual distracted decision-maldug systems
are needed, both for individual disciplines to make contact between then
existing theories and this complex reality, and for them to make contact
with one another. Establishing a database of case studies created for
these purposes would help achieve these goals. Such studies would have
lo provide the information needed by the different relevant disciplines
and avoid preemptive interpretation of what has happened. Assembling
such a canonical set might begin with existing case studies, reviewing them
for the features that are missing and might be supplemented. Even if
individually adequate studies are currently available, the set of existing
studies would have to be reviewed for sampling biases. For example, it
might unduly emphasize crisis situations fin which organizations are tested)
and calamities (in which they fail the test).
Instrumentation
Case studies are usually fictions to some extent because they rely on
retrospective reports. Even central participants might not remember what
they themselves have done in a particular situation (e.g., because it no
longer makes sense, given what they now know about what was really
happening) (Dawes, 1988; Ericsson and Simon, 198~, Pew, Miller, and
Feeher, 1981~. Especially central participants may be reluctant to reveal
what they lmow, in order to present themselves in a more favorable light.
In addition, critical events may simply go unobserved. As a result, it would
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DISTRIBUTED DECISION MAKING
be helpful to automatically log or record ongoing systems operation for
the sake of later analysis. This might involve developing black boxes to
record events, online interrogation procedures to question operators about
what they think is happening, observational techniques cueing investigators
to potentially significant occurrences, or even the creation of experimental
systems, operating long enough for participants to achieve stable behavior
patterns under the watchful eyes of investigators (Moray, 1986~.
Capturing Mental Models
Inevitably, the study of distributed decision-making systems must rely
on operators' reports of what they think is or was happening. For the
foreseeable future, that is likely to be an irreplaceable source of insight re-
garding the subjective reality that it creates for them. Distributed decision-
making systems pose particularly difficult challenges for such elicitation.
The events are complex and dynamic; participants often have an incom-
plete understanding of what was happening; reports require inferences,
rather than mere observation; and critical events may have to be translated
from visual (or even visceral) experiences to verbal statements. Improved
methods are needed for eliciting what only participants can know (Gentner
and Stevens, 1983; Lehner and Adelman, 1987; Moray, 1987a).
Institutional Structure for Distributed Decision Making
Although there was considerable agreement among workshop partic-
ipants on the importance of studying the topics raised in this report, the
question of how distributed decision making should be studied was not fully
resolved. Participants agreed that significant research progress depends on
the creation of a research community that allows and reinforces sustained
interaction among leading scholars in the venous relevant disciplines, and
between these scholars and substantive experts familiar with the operation
of actual systems. Consideration of distributed decision-making systems
raises cutting-edge issues in many disciplines. These include both topics
that are unique to such systems and topics that occur elsewhere but have
yet to be addressed fully. Certainly the operators and designers of such
systems would benefit from universal access to existing scientific knowledge.
However, these systems really deserve the attention of creative investigators
breaking new ground.
There are several obstacles to creating such conditions for university-
based researchers. One is the centripetal force of academic disciplines
that typically reward scholars most for intradisciplina~y wore A second
obstacle is the need to master the nuances of concrete systems and additonal
methodological procedures, which simply demand more time than can be
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DISTIUBUTED DECISION MAKING
~9
allotted by many individuals facing the pressures of promotion and tenure.
A third is the frequent lack of respect within the academic community for
applied research and its often particularist conclusions.
One response to these obstacles is to look elsewhere for researchers
in settings less subject to the constraints of a university-based culture, say,
to a private research and consulting organization. This is an appropriate
solution when such an organization can provide the laud of enrichment
that comes from interdisciplinary exchanges comparable to Pose of an
academic environment and from the rigor that comes from peer review.
Some contract research organizations meet these standards; others do
not. These obstacles are not, of course, unique to research on distributed
decision making; that makes them no less real for sponsors of research and
the managers of related systems.
The recognition of workshop participants that obstacles do exist did
not dampen their enthusiasm for working on the problems that were raised
nor from considering ways in which they might work on them inside or
outside academia. Just how this might be done and which research topics
should be given the highest priorities were questions beyond the scope of
the workshop and constitute the basis for an agenda for some future effort.
a
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Representative terms from entire chapter:
factors specialists
