Skip to main content
UNPROGRAMMED DECISION MAKING
(T) Copyright 1967 by Peer Soelberg
he paper should not be reproduced in whole or in part,
by any process, without the author's permission.
I. T. LIBRARIES
UNPROGRAMMED DECISION MAKING ^
The research reported below has implications for management practice
if one accepts the following three propositions:
i. Information processing and decision making are central functions
in modern organizations,
ii. To improve management decision making it is useful to know how
organizations at present make decisions.
This parallels the now familiar argument why engineers ought to know
the science underlying their engineering rules of thumb: The less validated
the engineering principles are, the more an engineer needs to understand the
science on which his practice rests.
iii. So long as people remain the chief instrument of corporate
policy a key feature of management decisions will be the choice
processes of individual human beings.
This paper is a report of how individuals make important, difficult,
and highly judgemental decisions. It has become customary to contrast
so-called "non-programmed" with more highly programmed types of decisions.
The latter are choices, or actions, that follow routinely from the decision
maker's (Dm's) application of explicit decision rules to whatever stimulus
or input data face him in his task environment.
This paper summarizes findings and conclusions from the author's research
report A Study of Decision Making , Carnegie and Massaopusetts Institutes of
Technology, 1966, xix + 4*^3 pt>.
- 2 -
The management of most companies' daily operations abounds with highly
programmed decisions: Consider merely the highly routinized rules that norm-
ally guide the everyday management of inventories, production schedules,
machine and manpower allocations, cost estimation, mark-up pricing, etc. The
more famous scientific description of a case of highly programmed decision
making is perhaps G.P.E, Clarkson' s, in which it was demonstrated that the
portfolio selection decisions made by a bank trust investment officer were
so well programmed that his decisions could be predicted by a computer, six
months after his investment rules had been elicited by an interviewer.
This study, in contrast, focusses on highly unprogrammed decision making.
This is a subject that usually gets relegated to the mystical realm manager-
ial "judgement". Critical decisions are produced every day for which the
decision maker (Dm) has available no identifiable rules or pre-programmed
decision procedure. This is not to say that Dm may not be following a set
of generalized guidelines when rendering his so-called judgement. But if
you asked him directly , Dm would insist that the unprogrammed problem confront-
ing him had to be solved in its own unique context. Moreover, observing him
solve it, you would indeed find i_. that Dm applied few special-purpose
decision rules when arriving at his choice; ji- that a number of the deci-
sion criteria he applied were initially unoperational; iii . that many of the
choice alternatives he considered were unknown to start with; i_v. that inform-
ation about the alternatives' consequences and their relative worth was not
immediately available from the task environment; and iv. that Dm might not
even be able to specify the nature of an ideal solution to his problem a_
- 3 -
Yet it is precisely this type of non-programmed decision making that
forms the basis for allocating billions of dollars worth of resources in the
economy every year. And, ironically, until we better understand the nature
of such unprogrammed human decision processes, our sophisticated computer
technology will be of slight aid for making this type of decisions more
effectively. In other words, the potential pay-off to management of scien-r
tific understanding of the economic, psychological, sociological, and pol-
itical "laws" of non-programmed human judgement is truly enormous.
Available theories of unprogrammed decision making
Traditional economists have long tried to make do with the concepts
"utility" and "probability" for explaining unprogrammed choice among un-
certain alternatives. Utility functions, either cardinal or otdinal, are
linear preference otderings of all possible combinations of valued goods
and services, assumed to be adequate descriptions of Dm's value structure.
Likewise objective or personal ^probability measures are felt to cap-
ture the essence of how Dms think about whatever "factual" connections
they perceive between their available choice alternatives and the possible,
but uncertain, consequencs of their choosing ^ given alternative. Even
today mathematically oriented psychologists, management theorists, and
political scientists attempt to set along with little more than these
two deceptively elegant concepts, when trying to describe^ or prescribe,
nonprogrammed human choice behavior.
The best known exception to this generalization is the work of Herbert
A. Simon. The latter's notion of limited rationality, his "means-ends
satisficing" model of problem solving, and insistence on preserving
one-to-one process veridity in his theories of behavior have signif-
icantly reoriented and vitalized social science research on decision
, . (5)
Simon characterizes unprogrammed decision making in terms of the
following three-phase process model:
1. Intelligence — finding occasions for making a decisionj
2. Design — finding, inventing, developing, and analyzing
alternative courses of action;
3. Choice — selecting a particular course of action from those
In our research we used the following, slightly expanded phase
structure as our framework for analyzing unprogrammed decision
I. PARTICIPATION: the decision maker (Dm) is somehow induced to work
in a given task environment, in which he is then motivated to
attain one or more non-trivial objectives.
The Barnard-Simon inducements contribution theory is one
model that purports to explain under what conditions Dm
will decide to participate in a given task environment.
The research reported below may offer an alternate basis for
II. RECOGNITION AND DEFINITION: Dm surveys his task environment, i.e.
attends to it selectively, and then discovers, selects, or is
somehow provided with — and then defines operationally — the par-
ticular problem or part of a problem he intends to devote his
resources to resolving.
Problem recognition may occur in the form of:
i. Dm's discovery of a barrier to his progress toward a goal;
ii. someone else in his organization handing Dm a problem with a
request that he solve it;
iii. a serial performance indicator of some kind dropping
below Dm's target level ( a basis for simple, automated
problem recognition) ; or some
iv. j^eviously coded "problem" pattern appearing on Dm's per-
Problem definition may take the form of:
i. Dm's description of differences, along one or more attributes,
between his goal and his present state;
ii. the description of a previously encountered problem that
"fits" the stimulus configuration presently confronting
iii. Dm's description of an ideal solution to the encountered
III. UNDERSTANDING: Dm investigates his task environment, trying 1^ to
develop an appropriate set of event classifications, or concepts,
and 2_. to formulate and test hypotheses about the apparent cause-
effect relationships in the environment — which in turn might
suggest design operators for, or help generate, viable solution
Environmental understanding is in part made up of
i. Dm's deriving hypothesis about relationships among sets of
more primitive descritpors (i.e. concepts) in his image model
of the task environment;
ii. Dm's testing these hypotheses against his experiences, formally
recorded data, or experimental manipulation of the task environ-
iii. Dm's deriving normative operators, i.e. ways to achieve his
subgoals, from his subjectively validated knowledge of the
IV. DESIGN: Dm develops or searches for alternative courses of action
for solving his problem; he estimates or tries to ascertain ex-
pected consequences of choosing each perceived alternative.
Search for alternatives and estimation of consequences are the
two components of decision design that traditionally get
focussed upon by theorists of rational choice. As we demon-
strate below, however, Dm's search for, investigation
within, and estimation of alternatives include design components
that to date have been inadequately represented in available
V. EVALUATION: Dm assigns some sort of value measure to the estimated
consequences of his perceived decision alternatives.
Utility theory has (too) long been embraced as the sole
basis for formally representing Dm's value structure. This
study has a number of alternate suggestions to make in that
VI. REDUCTION: Dm reduces his set of viable decision alternatives
to a single one, i.e. he makes a choice.
According to traditional models this phase of decision making
is nearly trivial: Dm simply selects as his choice the best
of his previously ranked set of alternatives. This study
demonstrates that the choice reduction phase is not at all
this simple, that in fact it often constitutes a major
hurdle in decision making.
VII. IMPLEMENTATION: Dm introduces and manages his decision solution
in the task environment.
This phase of decision making, usually the more critical one
for practical purposes, is usually left out of formal models of
decision making ( a fact that operations research consultants,
if they have not formally recognized it in their decision
models, have long known in practice).
VIII. FEEDBACK AND CONTROL: Dm receives and evaluates information
from the task environment regarding the effects of his implemented
decision, and, if required, i^. changes his problem defin-
ition, or la. modifies his goals, or iii . takes appro-
priate follow-on action.
This is the feature of decision making that introduced
dynamics into the process. Most traditional models do
not adequately take into account the dynamic conse-
quences of decisions that are made at different points
in time, by Dms' related to each other by an organi-
zational network of constraints — even though their
temporal dynamics obviously constitute a major source
of the cyclical pathologies that are often observed in
rational decision making systems.
In order to explore empirically the detail structure of the above gen-
eralized decision process outline we would obviously have to investigate,
at great length, the information processes of a large number of Dms
solving many different types of decision problems, in different task environ-
ments. The criteria by which we chose a specific unprogrammed decision sit-
uation 'to study initially were the following, namely that the Dms we
were to focus on would be:
i. well trained for making decisions, as well as able and motivated
to talk at length about their information processing while actually
engaged in producing the decisions:
ii. highly involved with the problem confronting them, it being
personally very important for each Dm to reach the "right" decision;
iii. quite unfamiliar with the type of decision problem they were
faced with, having encountered few such problems before, and not
expecting to do so again in the near future;
iv. engaged in making the decision over a longer period of time,
like several weeks — in order to minimize possible observer
measurements effects, yet allow a number of observations to be
made at different phases of the decision process;
V. easily and inexpensively accessible to the investigator in reasonable
number, in order to minimize idiosyncratic interpretation of the data,
through cross-comparison of the thinking-aloud protocols of fairly
large samples of decision makers.
The above criteria for our choice of subjects were designed to help
us focus on as pure and "uncontaminated" a set of decision process obser-
vations as we thought could be found in industrial practice. However,
M.I.T. Sloan School of Management Master's and Doctor's candidates
making their post-graduate job decisions seemed to fit our bill reason-
ably well. And in addition to satisfying our selection criteria, these
subjects would allow us readily and validly to test whatever rejectable
hypotheses were generated by our initial investigation, on succeeding
years' samples of graduating management recruits.
Our research objective originallj^ was to design a longitudinal
questionnaire that could efficiently and adequately chart the course of
our Dm's job decision processes. For that purpose we constructed an
elaborate questionnaire instrument, which in its complete form it took
three or four hours for each Dm to complete, every week. This was
clearly too long, trying as it did to cover every possible theoretical
contingency. For example, one central part of the questionnaire derived
from classical probabilistic utility theory, according to which Dm was
asked to identify, weight, and then rate whatever goal dimensions he felt
entered into his decision. Perhaps not surprisingly, it turned out that
the goal weights which Dms provided during decision making could not
be trusted: The reported weights varied quite unreliably with i_. the
specific alternatives that Dm referred to when answering the goal
weight questions, and ii^. the temporal phasing of his decision process.
We therefore gave up the questionnaire as a poor job. It had
become increasingly obvious that unless our questionnaire was made up
largely of items that were closely compatible with the manner in which Dm
actually stored and manipulated his decision information "internally",
during his own thinking about the problem, the answers he provided to
our questions would, for explanatory as well as predictive purposes, be
spurious at best; entirely misleading at worst.
We therefore resolved to rely, almost exclusively at first, on periodic,
open-ended, and highly detailed interviews with the decision makers in
process. These interviews provided our first insight into some rather
surprising dspects of unprogrammed decision making. Preliminary analysis" of
nearly 100 open-ended interviews, each ranging from 1/2 hour to 2 1/2
hours in length, with 20 different decision makers over 3-5 month
choice periods, provided the basis for our first generalizable decision
processing model (GDP-1) . The latter was first presented at Carnegie
Institute of Technology in June, 1964.
Each interview protocol was thereafter reduced to comparable format
by the following 3-step method: First, each protocol was transcribed
verbatum and its decision phase structure, according to GDP-I, was annotated
in the margin. Thereafter the relevant protocol contents were summarized
in a synoptic coding language derived directly from the variables and
process hypotheses of GDP-I. Finally the current state of each Dm's de-
cision process and his active solution alternatives, at that point in time,
were entered on a multidimensional, Gantt type process chart. The stan-
dardized data produced by the last two steps of the analysis thereafter
served the basis for quantifying each protocol, to enable comparisions
of decision processes to be made across Dms . The latter curve-fitting
analysis provided ( post hoc ) support for a number of GDP-I hypotheses,
the more interesting of which are summarized below:
a^. Dm defines his career problem by deriving an ideal solution to
it, which in turn guides his planning of a set of operational
criteria for evaluating specific job alternatives.
b_. Dm believes a priori that he will make his decision by weighting
all the relevant factors with respect to each alternative, and
then "add up numbers" in order to identify the best one. In
fact, Dm does not generally do this; and if he does, it is done
after he has made a selection among alternatives.
c^. Dm will search in parallel for alternatives, by activating one
or more "alternatives generators" — procedures which, once
activated, allow Dm to search passivly, by deciding whether or
not to follow up investigating particular ones of a stream of
alternatives presented by his generators.
d^. Dm will usually be evaluating more than one alternative at a
time, each evaluation consisting of a series of investigation and
e_. Evaluation during the search phase takes the form of screening
each alternative along a number of noncompared goal dimensions;
no evidence of factor weighting is appearant at this stage.
f^. Search will not necessarily halt as soon as Dm has identified
an acceptable alternative, one that is not rejected by his
various screening criteris; conversely, when Dm ends his search
for new (initial processing of newly generated) alternatives he
will usually have more than a single acceptable alternative in
his "active roster".
g. When Dm terminates his search for new alternatives before his
search resources run out, he will already have identified a favorite
alternative in his roster of acceptable alternatives; this alter-
native (his choice candidate) can be identified by considering
Dm's primary goal attributes (usually one or two) alone.
h. At the point of search termination Dm will not generally have
compared his alternatives with one another, will not possess a
transitive rank ordering of alternatives, and will refuse to admit
that his implicit choice has been made.
i. Before Dm will recognize his choice explicitly he will engage in,
at times quite lengthy (two, three months) confirmation processing
of his roster of acceptable alternatives, during which alternatives
will get compared to each other, factor by factor.
j. During confirmation processing the roster of acceptable alterna-
tives, if greater than two alternatives, will quickly be reduced
to two alternatives — the choice candidate and a confirmation
candidate. If only one alternative, the choice candidate, is viable
at the time. Dm will try to obtain another acceptable alternative
(confirmation candidate) as soon as possible "in order to have
something to compare it with".
k. The goal of confirmation processing is a_. to resolve the residual
uncertainties and problems connected with the choice candidate,
and b^. to arrive at a decision rule which shows unequivocally
that the choice candidate dominates the confirmation candidate —
Pareto dominance being the ideal goal strived for.
1. During confirmation processing a great deal of perceptual and
interpretational distortion takes place, in favor of the choice
candidate, to the detriment of the confirmation candidate; goal
attribute "weights" are arrived at, or changed, to fit the per-
ceived data and the desired decision outcome.
m. The decision is made when a satisfactorily Pareto dominant decision
rule has been constructed, or when Dm runs up against a time
deadline during confirmation processing.
A follow-up longitudinal questionaire study
Having thus gained a fair degree of insight into the information
processes of unprogrammed human decision making we were nowi obviously' in a
much better position, than we were previously, to design a predictively
valid questionaire instrument for testing, on new and large samples of
Dms , some of the key hypotheses suggested by the GDP-I model. In contrast
to the protocol curve fitting exercise reported above, bur follow-up
investigation was a bona fide prediction study: All hypotheses,
with process-valid measures of the variables, were specified a priori .
Moreover, apart from our own personal belief in the GDP-I model, one
would have derived small prior likelihood estimates from any other, available
decision model that the hypotheses which we were about to test were in
fact true. To most orthodox theorists our predictions should indeed appear
to be "long shots in the dark".
Among the hypotheses that we tested by process questionnaires were
I. Search for new alternatives ends a significant period of time
before Dm is willing to admit having made his decision.
II. In observation periods prior to ending his search for new alterna-
tives Dm will, more often than not, already have available one or
more acceptable choice alternatives.
III. '^^len Dm ends his search for new alternatives he will report
significant uncertainty regarding which alternative he is
finally to select as his choice.
IV. Should Dm not have obtained a firm job offer from more than one
acceptable alternative at time of search termination, by the
time he is ready to announce his decision he will have tried
hard, and will usually have obtained, at least one other accept-
able offer, (according to GDP-I, in order to have something with
which to compare his choice candidate) .
V. When Dm ends his search for new alternatives his favorite
alternative can be identified by asking him a set of quite
simple questions. When Dm's subsequent confirmation processing
of alternatives ends, i.e. at time of choice announcement, his
decision will be to select that alternative.
VI. Affective dissonance reduction, in the form of a "spreading
apart" of Dm's liking for his accepted versus refected alterna-
tives, will not be generally observed after choice has been
Those familiar with sequential search, aspiration level, choice
models may recall that according to the latter theory the first five
hypotheses would not appear reasonable. Similarly, according to the
cognitive dissonance theory, the sixth would be a disturbing proposition.
Results of the longitudinal questionaire study
Below we can no more than summarize the findings pertaining to the
above six hypotheses — based on data from 256 questionaire response sets
provided by 32 members of the 1965 graduating class of M.I.T. Sloan School
of Management Masters and Doctoral candidates. Each Dm in the sample
provided answere to eight bi- weekly questionaires over the period when he
made his job decision. (For small numbers of Dms , a different subset with
respect to each hypothesis, the path of their decision processes, as
recorded by the questionaires, provided inadequate data with which to test
a given hypothesis. Thus the totals reported below may add t6
somewhat less than 32.)
Hypothesis I: 27 of 31 Dms (87%) terminated search for new alternatives
10 days or more before the date on which they reported
having made their decision. 15 of 31 Dms terminated
search 3 weeks or more before choice was made.
Hypothesis II: Using a highly conservative measure of an alternative's
acceptability, 17 or 24 Dms (74%) reported having avail-
able one or more acceptable alternative two weeks or
more before they terminated search for new alternatives.
Hypothesis III: The average personal probability distribution of 28 Dm's
reporting, at time of search termination, regarding the
likelihood that they would choose either of the alterna-
tives which we independently identified as being their
"choice candidate", their second most preferred alterna-
alternative, and "all other alternatives", was respect-
ively: [.29 .24 .47]. In other words, great uncertainity
was expressed by Dm at the time of search termination
regarding which alternative he was to choose.
Hypothesis IV: 13 of 16 Dms (81%) who did not have, or had not been
promised, an offer from more than one alternative at time
of search termination, did report having at least one such
other offer in hand before they made their decision.
Hypothesis V: 25 1/2 of 29 Dms (87%) [1/2 since one Um could reasonably
be classified either way] eventually selected as their
final decision that alternative which we, one to twelve
weeks, a median of three weeks, earlier — at time of
Dm's search termination — had independently identified
as being his favorite alternative, i.e. his choice
Hypothesis VI: No Dm reported a consistent dissonance reduction
"spreading apart" of his liking for accepted versus
rejected alternatives over the periods of observation
immediately following decision commitment. However, 2 Dms
exhibited what we might call latent dissonance reduction
i.e. one which took effect two or more weeks after Dm had
committed himself to the decision. 9 of 26 Dms (35%)
showed an initial "spreading apart" effect of their
relative liking for alternatives, a gap which, however,
was reduced again insubsequent periods of observation.
10 of 26 Dms (38%) exhibited no changes whatever in their
reported liking differentials, in the observation periods
following choice. The remaining 5 Dms exhibited
post choice dissonance expansion , i.e. a narrowing down
of their liking differential between alternatives after
they had made their decision.
In summary, the six decision process hypotheses described above were
thus supported rather convincingly by the data in our longitudinal predic-
Chief implications for a behavioral theory of decision making
Below are some of the study's more central implications for decision
theory, which may not yet be entirely obvious from the above, severely
summarized report of our findings:
A. Scalar utility theory is a poor way of representing the structure of
human values. Decision value attributes are usually multi-dimensional, i.e.
are not compared or substituted for each other during choice. No stable
utility weighting function can be elicited from Dm prior to his selection of
a preferred alternative; nor do such weights appear to enter into Dm's decision pro-
cessing. His non-comparison of goal attributes during the alternatives screen-
ing and selection phase also obviates Dm's need for, and the reasonableness
of our postulating the existence of, a multi-dimensional utility indiffenence map.
B^. Probability theory, either in objective frequency or personal-
Bayesion form, does not provide an adequate representation of Dm's perceived
uncertainty during unprogrammed decision making. The "probability" indices '
with which our highly trained Dms provided us, on direct questions about them,
were neither additive nor cardinally scaled. It seems that Dm does not nor-
mally think of his choice alternatives in terms of multiple consequences,
each of which is then seen to depend conditionally on a different reaction to
his decision by the task environment. Dm instead thinks of each choice alter-
native in terms of a set of non-comparable goal attributes. Uncertainty
in this context is more appropriately represented in terms of equally likely
ranges of a specific alternative's rating along its uncertain goal attributes.
In other words, decision uncertainty rarely takes the form of a "pure" or
probability-risk consequence uncertainty; more commonly it constitutes non-distri-
butive uncertainty with respect to Dm's goal attribute evaluation of an alternative.
The mathematics of how Dm compares such multiple-attribute uncertainty-
ranged choice alternatives is quite simple, but, unfortunately, would still take
too much space to illustrate here . By the same token of limited rationality,
one might argue that it is the simplicity of Dm's information processing comput-
tations that effectively prevents him from operating with the m conditional probability
distributions for each alternative, that according to distributive probability theory,
Dm should be associating with each multi-consequence, multi-vailueid alternative.
£. Search for alternatives is a paralleled process, i.e., several potent-
ially acceptable alternatives are considered by Dm at a time. This proposition
contrasts with sequential one-at-a-time search models. In addition, Dm's evalua-
tion of an alternative is a multi-stage affair, at each step of which new inform-
ation is collected and evaluated about some of the attributes of the given altern-
ative. In other words, search within alternatives is an important a process for
us to understand formally as the traditionally described search across alternatives.
During the search puase Lmi aoea not vxew axs evaluation of alternatives as final
alternatives that fall short on important goal attributes are rejected immediately,
but acceptable alternatives are merely put into Dm's "active roster," with little
or no systematic comparison .performed across the different acceptable alternatives,
until Dm is ready to make his final decision. In other words. Dm may well continue
to search for new alternatives, even though he has already discovered a perfectly
satisfactory one, (i.e. one that was not rejected by any of his important goal
D. Making the final decision, what we have called decision confirmation,
takes place after Dm has terminated search for new alternatives, and is cognitively
a highly Involved, affectively a most painful, process for Dm to engage in. This
is the period during which Dm has to reject alternatives that seem perfectly
satisfactory to him, in some ways perhaps better than the one he finally ends up
choosing. It is at this point that Dm is forced systematically to compare patently
It is a major thesis of this study that Dms generally solve this problem
in the simplest manner conceivable, by not entering into this difficult period
of decision making until one of the alternatives can be identified as an im-
plicit "favorite". In other words, decision making during its confirmation
phase is an exercise in prejudice, of making sure that one's implicit favorite
will indeed be the "right" choice. This proposition gives the key to a surprising
degree of predictability in decision making, demonstrated with the data of
Hypothesis V above, in which we predict 87% of the career jobs taken. two to
eight weeks before Dms would admit they had reached a decision.
It would be too lengthy here to go into details on the nature of the
confirmation process (see A study of decision making) . The following are two of its
more outstanding characteristics: a^. The criteria that Dm uses for identifying
his favorite alternative are very few, not more than one or two what we have
called primary goal attributes account for most of the observed varience.
b_. Dm's comparison among alternatives quickly reduces to a pro-con argument
between two, and only two, alternatives (see Hypothesis IV), the objective
being for Dm to bring i_. his perception of the facts, ii. and his evaluation
of goal attributes, into line with his predisposition that the preferred
choice candidate dominates his second-best alternative (which we call the con-
firmation candidate) on all of Dm's important goal attributes, secondary and
primary. Dm finally makes his decision when he has constructed himself
a satisfactory decision rule — a goal weighting function, if you please —
which enables him to explain the Pareto dominance of his choice candidate. (Un-
less, of coursej Dm is forced by some deadline to make his decision before that
time. If so, however, Dm will still choose his choice candidate, but with much
more felt uncertainty about the "rightness" of his decision.)
E. Dissonance reduction, in the sense that such has been described
by Leon Festinger, must be viewed as a conditional phenomenea: In a
loose sense, confirmation processing might be viewed as being part of Dm's
"dissonance reduction" process. But according to Festinger, the onset of
dissonance reduction awaits Dm's commitment to his choice, which in in our
data is synonomous with the point of Dm's choice announcement. And in our
study dissonance reduction after that point in time was observed in only 35%
of the cases, in all of which the effect dissipated during subsequent periods
We propose as a testable explanation of our data: Post-choice dissonance
reduction will be observed only when Dm, at the time of choice commitment, is
not satisfied with his confirmation decision rule — i.e. with the intellectual
rationale for why he chose the way he did. Thus dissonance reduction constitutes
an affective compensation, on part of Dm, for his lack of a socially acceptable
intellectual justification for his behavior.
This hypothesis also explains the observed second order dissonance reduction
effect: With time we expect all men to be able to invent better and better
rationales for why they behaved as they did. Correspondingly, we should observe
that any initial af f ective fdissonance reduction) compensation, with which Dm at
first may be protecting his decision, will be dissolved over time as his intell-
ectual argument gets better.
Some obvious consequences of the findings for management practice
Let us conclude by briefly considering some lessons of these findings
for management. The listener can surely think of other implications, which
I hope will be brought forward during our discussion^ Ifevertheless ,
here are a few obvious (Observations:
i^. Our generalizable decision process model (GDP-I) allows the
manager (Mgr) to recognize when others have reached an implicit decision,
i.e. when they are merely confirming their favorite alternative. Such
knowledge should enable Mgr not to waste time, resources, or face by re-
maining party to a choice process that for most purposes has already been
closed. This lesson should be particularly useful in situations where
Mgr, or his company, has been cast in the role of "confirmation candidate" by the
ii . The existence of a confirmation process that goes into effect
prior to public choice commitment emphasizes the desirability of getting
one's alternatives into the decision process early. On bids for government
research and development contracts, for example, Edward Roberts has uncovered
evidence that Mgr needs to get in there well before the official invitation
to bid on a contract has left the government agency — that at this time
one can predict with disturbing success which firm will get the contract, simply
by looking at the order of names on the list of those invited to bid.
iil . The confirmation process also suggests a way of manipulating
decision deadlines in Mgr's favor: If he has evidence that his alternative
is the favorite, Mgr can safely clinch the deal by imposing a stringent
deadline, trust to dissonance reduction to carry the day, and save himself
time, needless anxiety, and the risk of that rare alternative arriving on
Dm's horizon in time to upset the apple cart.
iv . The existence of the confirmation process also explains the
often observed asymmetry of administrative decision making: Once made, de-
cisions are usually very hard to unmake, or to get remade. A most obvious
explanation is that Mgr balks at having to go through all the pain of
changing his tailor-made decision rule to fit a new pair of alternatives.
(That might smell too much of rationalizing, and go against the
grain of men who like to think of themselves as principled, orderly de-
cision makers.) Besides, the decision rule offers ready arguments, in m
dimensions, why few alternatives can be expected to be as good as the
chosen one. And these arguments get themselves strengthened and elabor-
ated as time passes — partly through the self-fulfilling prophecy which will
bias all future interactions between Mgr and his rejected versus
V. Our description of the nature of the confirmation process also
offers a complementary explanation of the observed difficulty of changing people's
cognitive attitudes: Mgr can argue till he is blue in face with Dm's
decision rule; as soon as Mgr is successful in winning a battle on one
secondary point in Dm's rule, the latter will simply mend his
breach, either by pooh-poohing that particular goal attribute, or by
countering with a compensating argument along some other goal dimension.
Only if Mgr manages to zero in on Dm's truly primary goal attributes
(often carried around hy Dm quite inaccessibly^, as an uncommunicated ex-
istenialist "feel" for the problem situation) , can Mgr hope to change
Dm's decision behavior. But note, in the latter case Mgr will face the
difficult task of demonstrating convincingly to Dm that his old favorite
is clearly dominated by Mgr's own favorite alternative. (This proposition might
help explain why public debates should be so ineffective as a means of
changing anyone's voting behavior or political allegiance.)
Improvement of management decision
Below are three lessons that appear to follow from the study, re-
garding how to improve management decision making:
vi. Mgr should work on integrating his formal models of rational decision
making with his intuitive, judgemental, common sense manner of solving choice
problems — and seek to adapt the former to fit the latter, rather than submit
to a bastardization of his intuition in the name of some modern mathematical
technique. Mechanical aids to management decision, like computer based manage-
ment information systems, will be (and should be) resisted, i-^- not used, to the
extent that its structure is incompatible with a_. the manner Mgr codes rele-
vant decision information for his own use, or 'b. the manner in which Mgr
intuitively feels that information should, be reduced for arriving at a decision.
To be somewhat more specific, a formal goal attribute weighting scheme,
an imposed set of operating decision rules, or an explicit framework for
estimating and operating with personal probabilities will be circumvented
by reasonable managers, we hypothesize, to the extent that the area in which
the technique is to be applied has not been carefully chosen to match the
structure or Dm's intuitive (culturally learned) process of working through
multi-valued and uncertain decision alternatives. This is not to say, however,
that Mgr should not attempt to educate his own decision making judgement, though
the more effective way to accomplish this is still hotly debated in
schools of business. We would recommend that a major part of Mgr's effort
in that regard be directed toward making the processes of his managerial
judgement more explicit to himself, in a variety of different job situations.
vii. One way Mgr might start training himself to make better decisions
would thus be to become more aware of his personal predispositions and pre-
judices (i.e. of his primary goals) when operating in different task con-"
texts. Rapid feedback regarding his apparant decision behavior, from people
whom Mgr already trusts and respects, should be of much help in that regard.
Trying to avoid forming opinions early about complex sets of alternatives seems
(from anectdotal evidence) to create an uncomfortable state of tension in moSt
people. Perhaps Mgr might try devising private "holding" heuristics to allow
"sufficient" unbiased information about his available alternatives to be collected,
and to prevent him. from modifying his decision criteria until he has reached
an explicit decision to start doing so. A. counterargument, however, is tnat
decision making and action taking under time pressure is so difficult to get
accomplished under any circumstances, that Mgr neeciF to use ail t'.ie short -cu.:.3
ana tension-reducing rules of thuwb ht. can devise, even if in fo-ie cases tbene
lead to biased results.
viii. Our theory Would lead us to expect that different Mgrs will exhibit
different degrees of the tendency to commit themselves to alternatives early
in the decision process. Perhaps such a predisposition could be effectively
counteracted by pointing out to Mgr that this is the way he tends to operate;
but perhaps the characteristic is sufficiently difficult or expensive to
change that we should consider developing a standardized test situation for
helping screen out of critical managerial positions those persons who too
early, on too meager evidence, tend to jump to conclusions about the solution
of complex problems.
Notes and References
1. J.G. March and H.A. Simon, Organizations , New York: Wiley, 1958,
2. G.P.E. Clarkson, Portfolio selection: a simulation of trust invest-
ment , Englewood Cliffs, N.J.: Prentice-Hall, 1962.
3. K.J. Arrow, "Utility, attitudes, choices: a review note," Econometrica ,
1958, pp. 1-23.
4. See J.F. Rothenberg, The measurement of social welfare , Englewood Cliffs,
N.J.: Prentice-Hall, 1961, pp. 200-278.
5. H.A. Simon, Administrative Behavior , New York: MacMillan, 1947;
Models of Man , New York: Wiley, 1957; "Theories of decision making in
economics and behavioral science," Am. Econ. Review , 1959, 49_, pp. 255-
283; with A. Newell and J.C. Shaw, "Elements of a theory of human problem
solving," Psychol. Review , 1958, 65_, pp. 151-166; The shape of automation .
New York: Harper, 1966.
6. H.A. Simon, New science of management decision . New York: Haper, 1960.
7. J. A. Forrester, Industrial Dynamics , New York: Wiley, 1961.
8. L. Festinger, Conflict, choice, and dissonance reduction , Stanford
'University Press, 1964.
9. E.B. Roberts, "Questioning the cost/effectiveness of the R&D procure-
ment process," in M. Yovits, et al, (eds.). Research program effective-
ness , New York: Gordon and Breach, 1966.
OEC 5 '6%,
[^ W !''?&-
3 T06D 003 '^U2 b70
3 TOfiO 003 TOE bbE
3 TOflO 003 TD2 bEl
3 T060 003 "102 t.05
3 TOfio 003 avi blO
3 TOflO 003 fl71 bEfi
will LlDHMniLj ,,.,11111
i'^OfiO 003 fi71 b4M
3 TOfiO 003 fl71