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LIBRARY 

OF THE 

MASSACHUSETTS INSTITUTE 
OF TECHNOLOGY 



UNPROGRAMMED DECISION MAKING 
Peer Soelberg 



239-67 



(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. 






PFCEIVED 
EB21 1957 
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>. 



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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 

(2) 
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_ 
priori . 



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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- 

(3) 
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, 

(4) 
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 



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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) 
making. 

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 
available. 

In our research we used the following, slightly expanded phase 

structure as our framework for analyzing unprogrammed decision 

processes : 

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 
explanation. 

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. 



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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- 
ceptual horizon. 

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 
Dm; or 
iii. Dm's description of an ideal solution to the encountered 
problem. 

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 
alternatives . 

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- 
ment ; 
iii. Dm's deriving normative operators, i.e. ways to achieve his 



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subgoals, from his subjectively validated knowledge of the 
task environment. 

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 
decision models. 

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 
regard. 

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. 



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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. 



-8- 



Research strategy 

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. 



-9- 



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. 



-10- 

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 



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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 
evaluation cycles. 

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". 



-12- 



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. 



-13- 

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 
the following: 

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. 



-14- 

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 
announced. 

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. 



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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 



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"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 
candidate. 

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 



-17- 



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- 
tion study. 

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. 



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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. 



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£. 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 
attributes) . 

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 
non-comparable alternatives. 



-20- 

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.) 



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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 
of observations. 

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. 



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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 
decision maker. 

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 

(8) 
by looking at the order of names on the list of those invited to bid. 



-23- 

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 
accepted alternatives. 

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 



-24- 

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. 



-25- 
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. 



-26- 

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, 
pp. 169-182. 

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. 



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