on decision rules and information processing strategies for choices among multiattribute...

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On decision rules and information processing strategies for choices among multiattribute alternatives

HENRY MONTGOMERY OLASVENSON

Abstract.-It is suggested that a decision in a complex situation can be described as a sequential process in which different decision rules and information processing strategies can be used at different points in time. Ex- amples of possible decision rules are presented in an approximate order of complexity. Two ways for processing the information in a decision situation, viz., breadth-first or depth-first strategies, are discussed and suggestions are made about their relationship to particular decision rules. Finally, it is proposed that the order of application of particular rules in a decision process is guided by a tendency to minimize cognitive effort.

Models of human decision making are often parti- tioned into static and dynamic models (cf. Rapoport & Wallsten, 1972). Whereas dynamic models typically deal with a number of interrelated decisions which are made sequentially in time (Rapoport, 1975) recent studies have demonstrated that a single decision also can be described as a dynamic process in which the decision maker seeks ar?d evaluates information sequentially. For example, Payne (19746) and Svenson (1974) each reported process tracing studies of choices among homes, and Payne (1974a) demonstrated that it is possible to simulate the idormation processing behavior involved in choices between simple gambles.

A dynamic description of a single decision might be particularly relevant when the decision maker is faced with complex multidimensional or multiattribute alternatives as in the choice among job offers or the purchase of a new home. For example, consider a person who is in the market to buy a house and is attempting to choose between two of them. First, he investigates whether each of the two houses in turn is acceptable on all relevant attributes such as price, number of rooms, and distance to town. In doing so, it might be possible for him to eliminate one of the alternatives as being un- acceptable. However, realizing that both houses

University of Goteborg, Sweden University of Stockholm, Sweden

are acceptable on all relevant attributes, he at- tempts to reach a decision by investigating whether only one of the houses is clearly more attractive than the other house on any of the relevant attributes. Unfortunately, he discovers that this choice procedure is not applicable, since each of the houses is better than the other one on a particular attribute. He then rank orders the differences between the two houses with respect to attractiveness on the attributes. Finally, he chooses the house which is more attractive on the attribute with the greatest attractiveness difference.

The above example illustrates some of the cognitive components of the information processing which precedes a single decision. First, the decision maker may use some system for representing the alternatives such as a multiattribute representation as in the above example. Secondly, various procedures for processing the information may be applied. The example illustrates the use of two such procedures. Initially, the decision maker evaluates one alternative on all relevant attributes before the other alternative is judged. Later in the decision process, the cpnyerse procedure is used. That is, both alternatives are evaluated on the same attribute before another attribute is considered. Thirdly, the decision maker may use various decision rules in order to eliminate alternatives or to find out whether one alternative is better than the others. Fourthly, as the example reveals, more than one decision rule may be used before the final decision is made, and these decision rules might be applied sequentially in time.

By way of summary, it is concluded that at least four components should be cansidered in studies of cognitive processes leading to single decisions. To facilitate the subsequent discussion, these four components will be considered in the following order: ( a ) subjective representation of the decision

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alternatives, (b) decision rules for finding the best alternative, (c) procedures for processing the information about the alternatives, and (d) principles for the order of application of particular decision rules. The aim of the present paper is to discuss each of these aspects of the decision making process and to comment on possible relationships among them.

H . Montgomery and 0. Svenson

SUBJECTIVE REPRESENTATION OF CHOICE ALTERNATIVES

Most studies of human decision making assume that the decision maker’s representation of the choice alternatives can be described on a number of dimen- sions or attributes. This assumption is followed also in the present paper. That is, it is proposed that each possible outcome of a decision may be characterized by a set of aspects which correspond to values on a set of attributes, such as price or size of an apartment. It is furthermore assumed that each aspect may be mapped on a scale of attractiveness specific for each attribute. In the subsequent discussion, a measure of an aspect always refers to the subjective scale of attractiveness re- lated to an attribute. For example, an apartment of the size of 100 m2 will be represented not by its objective size, nor by its subjectively experienced size, but by a symbol denoting its degree of attractiveness on the attribute of size. The remainder of this section is devoted to a discussion of this type of representation of a set of choice alternatives.

The first point to be discussed is the metric level of the attractiveness of aspects in a decision situation. In the simplest case, it may be sufficient to consider only ordinal relationships between the degrees of attractiveness of different aspects. For example, when a decision maker confronts a situation where the choice alternatives differ on only one critical attribute, he will be able to make his decision by choosing the alternative which is most attractive on the critical attribute. In more complex cases, however, it might be necessary to use information on a higher metric level, such as, a rank order of intervals between attractiveness values as was the case for the potential home purchaser in the previous example of a dynamic decision process.

In some situations the decision maker may use an ordinal representation of attractiveness as a first approximation, but over time change his conception of attractiveness toward a higher metric level in

order to obtain a more optimal decision. As an example of such a successive change of the metric level, consider a housewife who wants to buy one of two brands of marmelade, A and A 2 . Initially, she thinks that the price information, on an ordinal metric level, is sufficient for a decision to buy the cheapest brand, A,. But realizing that brand A 2 is better in quality, she also finds it useful to rank order &he differences in attractiveness of price and quality between the two products. In that case, the choice might have been the marmelade associated with the greater attractiveness on the attribute where the alternatives differed most. Unfortuna- tely, at this point the poor housewife realizes that the jars contain unequal weights of marmelade. This entices her to use interval scales of attractiveness of price, quality, and weight in order to reach a final decision.

Another component of the decision maker’s conception of attractiveness is the comparability, or commensurability, of the attractiveness of aspects across different attributes. In some situations, it may be difficult to compare the attractiveness on different attributes. For example, it may be hard to compare the attractiveness of a healthy life with the attractiveness of a given income. In other cases it may be impossible to compare the attractiveness across attributes because information may be lack- ing (cf. Slovic & MacPhillamy, 1974). However, sometimes it may not be necessary to make interattribute comparisons of the attractiveness of aspects in order to make a decision. This is the case, for example, when one of two alternatives is not acceptable on some attribute, whereas the other alternative is acceptable on all attributes. In that situation, it seems perfectly rational to choose the acceptable alternative regardless of the differences in attractiveness across different attributes between the two alternatives.

DECISION RULES

As noted previously, the decision maker may use various decision rules to find the best choice alternative. In the following, some representative examples of decision rules are presented. The rules differ as to their requirements on the metric level of attractiveness and on the commensurability of attractiveness across attributes. The requirements which different decision rules may impose on the subjective representation of the choice alternatives

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seem to be an important factor in determining the type of information processing preceding a single decision. That is, the decision maker may tend to apply rules requiring a relatively low complexity of the subjective representation of the choice alternatives before he applies a rule that requires a more complex subjective representation. In the account of decision rules to follow, an attempt has been made to present the rules in an approximate order of complexity.

The rules presented below usually concern choices between two alternatives, A , and A,, but it is easy to generalize the rules to more alternatives. The attractiveness of the aspects character- izing the two alternatives on a set of n attributes will be denoted ( a , , ..., a,,,) and ( a z , ..., am) , where the first subscript denotes the alternative and the second subscript the attribute. As discussed previously, the attractiveness of an aspect is con- ceived of as being on different metric levels and the attractiveness of aspects on different attributes are not necessarily commensurable. If the attractiveness of the aspects is commensurable over all attributes, then the attractiveness of an aspect will be called utility, denoted u .

Ordinal attractiveness, no commensurability In this group three examples of decision rules are presented, viz., the rule of dominance, the conjunctive decision rule and the disjunctive rule.

(1) The dominance rule is one of the comer stones in theories of rational decision making (cf. Lee, 1971). In the present context this rule prescribes that if alternative A , is better than alternative A, on at least one attribute, and if A , is not worse than A , on any other attribute, A , will be chosen. Stated more formally, the dominance rule implies that if, and only if, for at least one attribute k, a&w*, k = l , ..., n and allPal for i = l , ..., n , i+k . alternative A will be chosen.

(2) The conjunctive decision rule implies that the attractiveness of every aspect for the chosen alternative must exceed a criterion specific for each attribute and that at least one of the aspects of the other alternative falls below one of the critical values of the attractiveness. Thus no compensation for a value smaller than the cntenon is allowed by this rule in its pure form. Let the cntenon values be represented by cl,. . . , c.. Then the conjunctive rule implies that ifa,,Pc, for all i = l , ..., n and if, and only if, for at least one k, aak(ck then A , will be chosen. This rule is a multidimensional generalization of the satisficing principle presented by Simon (1955).

(3) The disjunctive decision rule states that the attractiveness of at least one aspect for the chosen alternative must exceed a criterion specific for that attribute and that all aspects of the other alternative fall below a cri-

terion specific for each attribute. In its pure form this decision rule allows for total compensation for any low value on the other attributes. Let the criterion values be represented by d ,, . . . , d,. Then the disjunctive rule implies that if n,>dk for at least one k and if a l i s d i for all i = l , ... ,n, i+k , then A , will be chosen if, and only if, aasdi for i = l , ..., n.

The conjunctive and disjunctive rules have been proposed and axiomatized by Coombs & Kao (1955), Coombs (1964), and Dawes (1964a, 19646) and have received some empirical support in studies by Einhorn (1970, 1971).

Because of the low level of metric information used, the above decision rules do not always lead to a choice. Consider, for example, A , ( 2 , 2 , 3 ) and A , ( 2 , 3 , 2 ) , where the values in parentheses repre- sent attractiveness values on three attributes. In this case the dominance rule does not lead to a choice since each of the two alternatives is more attractive than the other one on at least one attribute. Moreover, the conjunctive and disjunctive rules would not lead to a choice if, for example, the conjunctive criteria (cl, c,, and c,) all are equal to 2 and the disjunctive criteria (dl, d,, and d3) all are equal to 3 .

Ordinal attractiveness, lexicographic order, no commensurability A lexicographic ordering of attributes implies that the attributes are rank ordered in terms of importance without compensation for aspects in attributes lower in rank. This rank order is used in the next decision rules.

(4) The lexicographic decision rule implies that the choice will be the alternative which is more attractive on the most important attribute. If the aspects of this attribute are equally attractive, the decision will be based on the attribute next in importance. For instance, if the order of the attributes in the previously given examples A l (2,2,3) and A, (2,3,2) correspond to their lexicographic order, then alternative A , will be chosen because A, is more attractive than A , on the second attribute. The lexicographic rule was axiomatized by Fishburn (1970).

( 5 ) The elimination by aspect rule was proposed by Tversky (1972). The rule may be interpreted as a combi- nation of the lexicographic rule and the conjunctive rule. Like the lexicographic rule, this rule assumes that the attributes are ordered according to their importance. A decision is assumed to be based on the following procedure: First the most important attribute is selected and all the alternatives that do not exceed the conjunctive cri- tenon on this attribute are eliminated. This procedure is then repeated with new attributes, selected according to their order of importance, until all the alternatives are eliminated but one.

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Ordinal attractiveness differences, lexicographic order, no commensurability The following two rules may be seen as psychologi- cally more reasonable versions of the lexicographic rule. Both rules have in common that they require a rank order of the intervals, that is, a hyperordinal scale (Suppes & Zinnes, 1%3) of the attractiveness values on each attribute.

(6) The lexicographic semiorder rule works like the lexicographic rule with the additional assumption that for the most important attribute there is a minimum difference A. That is, if the difference between two alternatives on this attribute is greater than A, then the alternative with the more attractive aspect on the attribute is chosen. However, if the difference between the attractiveness values on the most important attribute is less than or equal to A, then the decision maker considers the attribute that is next in importance and chooses the alternative with the higher degree of attractiveness on that attribute as in the pure lexicographic rule.

The lexicographic semiorder rule was proposed by Tversky (1%9) who suggested that this rule might be used when the most important attribute is noisy as a consequence of imperfect discrimination or unreliability of available information. Empirical evidence that subjects use the lexicographic semiorder rule was presented by Tversky (1969) and also by Montgomery (1975).

(7) The minimum difference lexicographic rule is suggested as a generalization of the lexicographic semiorder rule inasmuch as for each attribute there is a minimum difference A,. Thus, only differences greater than A, between the attractiveness values of two alternatives on the same attribute i may determine a decision. As an example, let A,=1.5 for all i, and let the choice alternatives be At (2,2,3) and A, (2,3,1). The successive comparisons will then be the following. There is no difference on the first attribute (2-2) there is a small but insufficient difference on the second attribute (2-3) and finally, the difference on the last dimension is great enough (3-1) for a choice of A


Ordinal attractiveness, commensurability The following rules also require only ordinal relationships between the attractiveness values on each attribute. However, in contrast to the previously mentioned rules, the attractiveness values should be comparable across different attributes.

(8) Maximizing number of attributes with greater attractiveness implies that the alternative with the greater number of favorable attributes is chosen. That is, if one alternative is more attractive than another alternative on a greater number of attributes, then the former alternative should be chosen. Thus, the rule does not lead to a choice when the numbers of positive and negative attractiveness differences between two alternatives are identi- cal. This is the case, for example, in the above examples A , (2,2,3) and A 2 (2,3,2) where there is one positive and

one negative attractiveness difference between the two alternatives. However, if the choice is made between A l and A, (3,3,2), the rule may be used, since there are two positive differences in favor of A, against one in favor of Al. May (1954) reported an experiment where this rule may have been used in choices of hypothetical marriage partners.

The following two wles are proposed as ana- logues to the maximin and maximax principles in game theory, respectively (Luce & Raiffa, 1957).

(9) Elimination by least attractive aspect implies that the decision maker chooses that alternative which is not associated with the least attractive aspect.

(10) Choice by most attractive aspect means that the decision maker should choose that alternative which is associated with the most attractive aspect.

Ordinal attractiveness differences, commensurability The following decision rule exemplifies a structure where a rank order of intewals between attractiveness values is defined over attributes.

(1 1) Choice by greatest attractiveness dgference implies that the decision maker first determines the attribute that is associated with the greatest attractiveness difference. Then the most attractive alternative on this attribute is chosen irrespectively of the other attributes. When there are only two alternatives, this rule is equivalent with a choice of that alternative which is not associated with the less attractive aspect on the attribute with the greatest difference in attractiveness. Stated in this way, the rule becomes analoguous with the minimax regret principle in game theory (Luce & Raiffa, 1957).

Interval attractiveness (utility), commensurability In this group of decision rules the concept of attractiveness is exchanged for the term utility. Two examples of this type of rules are given below.

(12) The addition of utilities rule prescribes that a decision should be based on a summation of all utilities corre- sponding to the aspects for each alternative. The alternq: tive associated with the greater sum of utilities will then be chosen, that is, this rule implies that alternative A , should be chosen overA2 ifF'u,>Pu,.

Numerous studies have shown that linear m&- els, of which the addition of utilities rule is a special case, are extremely powerful for predicting decisions from component cues (cf. Slovic & Lichten- stein, 1972). This is the case particularly when data are averaged over several subjects (Shepard, 1964). For individual subjects, other rules such as the conjunctive or disjunctive rules may provide a better fit (Einhorn, 1970, 1971).

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attractiveness difference rule, and the addition of utility differences rule are examples of rules requiring intra-attribute comparisons. On the other hand, the depth-first strategy is typically associated with rules requiring interattribute comparisons such as the addition of utilities rule.

Tversky (1%9) noted that there are several general considerations which favor intra-attribute comparisons before interattribute ones. For example, in the case where one alternative is slightly better than another one on each of a set of attributes, a sequence of intra-attribute comparisons will make this apparent and the choice will be easy. Furthermore, intra-attribute comparisons might facilitate the use of approximation methods such as “cancelling out” differences that are equal. or nearly equal, which imp1ie.j that the decision malter can reduce the number of attributes to be considered. Finally, Tversky argued that intra-attribute comparisons are easier to carry out than interattribute ones insofar as the compared aspects may be expressed in the same units.

It can be concluded that a breadth-first procedure involving a sequence of intra-attribute comparisons offers some general advantages relative to a depth- first procedure involving a sequence of interattribute comparisons. However, the decision maker can also make absolute evaluations on a single attribute within an alternative which refer to a subjective criterion value. Three of the decision rules listed above require such absolute evaluations, viz., the conjunctive rule, the disjunctive rule. and the elimination by aspects rule. These evaluations cannot be associated uniquely with a breadth-first or a depth-first procedure. (Note, however, that the elimination by aspects rule prescribes that the aspects should be evaluated according to a breadth- first strategy.) Consider, for example, the judg- ments that are made when the conjunctive rule is applied. In that case, the decision maker could either select a single attribute and check whether the choice alternatives are acceptable or not on this attribute before he considers another attribute; or he could select a single alternative and check whether this alternative is acceptable or not on a number of attributes before he judges another alternative.

In some decision situations, however, the depth- f i s t procedure may be more appropriate than the breadth-first one. For instance, if information about different choice alternatives is not available simul-

(13) The addition of utility differences rule states that a decision is based on the differences between utilities on the same attribute, i.e., quantities of the form (uII- ua). “TO each such quantity, one applies a difference function n, which determines the contribution of the particular subjective difference to the overall evaluation of the alternatives” (Tversky, 1%9, p. 41). More formally this rule states that alternative A , should be chosen over A 2 if there exist increasing continuous functions c p , , ...,rpn defined on some real intervals such that Z”cpi(u,i-u21)>0, where v* (u - u d = -vi (uI -u d.

The addition of utility differences rule was suggested by Tversky (1%9). The rule is algebraically equivalent with the addition of utilities rule if, and only if, the difference functions are linear. It should be noted, however, that algebraic equivalence between two decision rules does not necessarily mean that the rules suggest the same information processing procedures (Hoffman, 1%0, 1%8; Tversky, 1%9; Payne, 1974b).

The rules presented above do not necessarily prescribe the same choice. That is, two different rules may lead to conflicting decisions. As an example, consider a choice between two alternatives A5 (3 , l ) and As (2,3) with the disjunctive criteria (2.5, 3.5) which leads to a choice of A5. If an interval scale of attractiveness is used, however, so that the addition of utilities rule applies, then As should be chosen since the sum of attractiveness values for A, (2+3) is greater than the correspond- ing sum forA, (3+1).

INFORMATION PROCESSING PROCEDURES

A familiar distinction in information processing studies of problem solving behavior is the one between breadth-first and depth-first search (Newel1 & Simon, 1972). This distinction can also be made in studies of decision processes (Payne, 19746). Breadth-first search would then imply that a number of alternatives are evaluated on the same attribute before another attribute is considered. By contrast, depth-first search would imply that one alternative is evaluated on a number of attributes before another alternative is judged.

The decision rules listed above often suggest a particular information processing strategy. The breadth-frst strategy seems to be appropriate when decisions are made according to rules requiring intra-attribute comparisons. The dominance rule, the lexicographic rules, the choice by greatest

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taneously, then a depth-first procedure might make fewer demands on the decision maker’s memorizing capacity than a breadth-first procedure. Moreover, in contrast to breadth-first search, depth-first search does not require that the choice alternatives can be characterized on a common set of attributes. This implies that a depth-first procedure may be advantageous when it is difficult to conceive the choice alternatives in terms of the same set of attributes as might be the case, for example, when a person chooses between spending money on a trip abroad or on a new sofa for his living room.

Payne (19746) and Svenson (1974) showed in their process tracing studies of choices between homes that the relative proportion of depth-fist vs. breadth-first strategies varied strongly over different individuals. On the other hand, Montgomery (in press) found in a protocol analysis of choices between simple gambles, that all his subjects almost exclusively seemed to base their decisions on intra- attribute comparisons, that is on a breadth-fiist strategy. This result could partly be due to the fact that the subjects had been selected as being poten- tially intransitive in their choice behavior. Howev- er, the strong predominance of intra-attribute comparisons also might be due to the narrow range of stimulus values that were used in Montgomery’s study. That is, the small differences between the alternatives might have pushed the subjects to- wards considering whether they could ignore or not a difference between the gambles on a particular attribute.

It can be concluded that the information processing preceding a single decision may vary as a function of both the individual and the situation. However, further research is needed in order to identify the factors which affect the decision maker’s information processing strategies. It was demonstrated above that several decision rules require the use of a particular information processing procedure. Thus, knowledge of the factors which affect the decision maker’s information processing strategy may also shed light on the decision rules which might be used by a particular person in a particular situation.

ORDER BETWEEN DECISION RULES

As noted previously, different decision rules may be applied in succession in the same choice situa-

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tion. It is also possible that the decision maker uses the same rule more than once during a decision process but with different degrees of differentiation of aspects in terms of their attractiveness. He may also change the criterion levels ( c , d, A) or, put in other terminology, the aspiration level (Simon, 1955) used in some of the rules.

Applications of new rules, changes in criterion levels or in differentiation between aspects may occur for various reasons. One obvious reason is that a given decision rule may turn out to be impossible to apply because its requirements on the representation of aspects are not fulfilled. It might also occur that the decision maker wants to con- f i i the results of the application of a particular decision rule by also applying some other rule and/ or by using the same rule again but with a new representation of aspects before he reaches the final decision. Furthermore, when there are more than two choice alternatives, the final decision might be based on successive elimination of alternatives, which implies that different decision rules might be used at different stages in this elimination process.

An empirical illustration of two decision rules applied in succession was given by Slovic (1975). He reported a series of experiments where the decision alternatives were first pairwise equally valued by subjects. The decision that the alternatives were equal in value could be achieved by application of a decision rule requiring, at least, a rank order representation of attractiveness differences and commensurability among attributes. When the subjects were asked to make a choice within each pair of alternatives it was found that the majority of subjects consistently selected the alternative that was superior on the most important attribute. In the present notation this means that the subjects first made a choice according to one of the rules 1 1 , 12, or 13 and that subseqoently they applied rule 10 in order to reach a final decision.

It was mentioned that different decision rules require a more or less complex representation of the choice alternatives. It seems reasonable that the use of a more complex representation is cognitively more demanding. Thus, different decision rulesmay require different amount of cognitive eflort so that the rules may, in theory, be ordered on an effort continuum of at least an ordinal type. The concept of cognitive effort may be compared with Bruner, Goodman & Austin’s (1956) term “conceptual

Decision rules and information processing 289

Codins of informa-

Has dominance been tested before?

Can any aspects be iknored? befinition of decision situation.

Decision

0 Conjunctive rclr: Are minimum requirements met by aspects of one alternative onl. 9

I I ’ I

Disjunctive rule: I s one.alternative superior than the criterio on one aspect?

inirnum r e -

i c combina-

strain”, and with Newel1 and Simon’s (1972) use of the terms “effort” and “effort limit”.

It seems plausible that a decision maker tries to minimize the amount of cognitive effort spent in a given situation. This implies that during the decision process he generally will tend to apply rules requiring less cognitive effort before he applies rules that require more effort. Payne (1974b) presented verbal protocol data which corroborate this conclusion. He found that some subjects ini-

Fig. 1. A tentative structure for the description of a decision process.

tially seemed to use the conjunctive rule in order to quickly eliminate alternatives until a few alternatives remained as possible choices. After this the subjects appeared to employ a more cognitively demanding decision rule, e.g., addition of utility differences, in order to make the final decision.

Another consequence of this minimization of cognitive effort principle may be that the decision maker will not use more cognitive effort than is needed for reaching the level of confidence that he

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wishes to obtain from a decision in a particular situation. This level may be determined by the decision maker’s conception of the importance of the situation, implying that he will not spend too much


tion processing and decision making within a sub- ject and across subjects.

CONCLUSIONS effort on unimportant decisions. If the level of confidence required by the decision maker cannot be achieved by considering all present information, he may search for new information instead of trying to apply another decision rule. The search for more information may also lead to more or less explicit inferences of missing aspects. Another strategy in such a situation is to postpone the decision in the hope of getting new relevant information in the future.

An example of a possible latent structure for a decision making process A decision process may be described as following different paths in a flow diagram as the one in Fig. 1. The diagram is not complete but incorporates a few of the decision rules in a temporal pattern. In the diagram it is assumed that the dominance rule requires least cognitive effort and that it is the rule first applied in the process after the coding of information. Therefore this rule is shown at the top in Figure 1. In general, rules higher in the flow chart are assumed to require less cognitive effort in this hypothetical illustration. If the decision is judged unimportant it may be hypothesized that only rules higher in the diagram will be used.

The choice function in the lower part of the figure indicates the possibility of alternative ways de- pending on, e.g., earlier parts of the process, the cognitive effort the decision maker is willing to spend, and the information about the alternatives. If it was found empirically, as illustrated in Fig. 1, that a decision maker always used the dominance rule before the conjunctive rule, in a particular situation, this would constitute an example of what may be called a functional chain of decision mech- anisms. This functional chain would then be a unit which may be used in the analysis of the dynamic process leading to a decision.

The flow chart in Figure 1 can only serve an il- lustrative purpose as a possible example. When it comes to empirical descriptions of single decision makers it is necessary to map unique processes and to try to find functional chains working in succession as parts in the cognitive processes. After the identification of such functional chains it may then be easier to describe important patterns of informa-

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The present approach treats the act of making a decision as a deliberate and sequential process in which different search procedures and decision rules may be used at different points in time. We have already noted that this approach is most appropriate in complex muitiattribute choice situations. We have also noted that the expenditure of information processing effort may be positively re- lated to the decision maker’s conception of the importance of the situation. Thus, the present approach may be of little relevance in simple or unimportant choice situations. Moreover, the present approach also requires that the choice situation is not too similar to previous choice situations in which the decision maker already has made a decision. That is, if the decision maker experiences a choice situation as similar to a previous one, he may reach his decision by simply importing the previous decision to the present situation. This implies, that studies of decision processes in relatively new choice situations may be of particular relevance since the outcome of such processes may determine the decisions to be made in future similar situations.

Finally, it has been tacitly assumed in the present paper that all choice alternatives are available and well-defined at the beginning of the decision process. This is clearly not the case in many choice situations in everyday life. However, the information processing approach that has been advocated herein may be a more suitable point of a departure than a more static approach for the development of a general psychological decision theory which also may cover ill-defined choice situations.

This investigation was supported by a grant from &he Swedish Council for Social Science Research. The authors wish to thank David Magnusson, Paul Slovic, Amos Tversky and Charles Vlek for their valuable com- ments on earlier versions of the paper.

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