The Role of the Goal for Generating Actions*

THE ROLE OF THE GOAL FOR GENERATING ACTIONS* Helmut JUNGERMdNN, Ingrid von ULARDT, and Lutz HAUSMANN Technical University, Berlin

Abstract In thls paper the initial phase of decision processes is conceptualized as the develop ment of a structural representation of relevant knowledge. Goals are viewed as playing an important role in representing decision problems when they have some specific content and are not purely formal (e. g,maximize SEU). A network model is proposed f a the representation of goals and actions, and several assumptions are made regarding the spread of activation through the network. In an experiment, hypotheses about the effects of two factors were investigated: Goal explidtness (E) was varied by presenting to Ss goal hierarchies of different speciflcity (one to three levels), and goal importance (R)was varied by letting Ss either rank-order goals with respect to their personal priorities, or not. The results show that the number of actions generated increases with the degree of goal explicitness, thus supporting the Ss creative search process, whereas the number of actions is lower for Ss who focus on their own values compared to Ss who do not, thus pointing to ego involvement as a factor restricting creativity. On the other hand, the actions generated by the personally involved group were rated higher on goal achievement scales than the actions generated by the other group. The results axe in accordance with the model which, however, needs elaboration

The Structural Representation of Decision Problems Any decision problem can be assumed to be structurally represented somehow. This representation may be more or less explicit, more or less precise, more or less aware to the subject, of course, but in any case it reflects the subject's perspective of the situation from which the decision

* We would like to thank Susanne Dlbbelt for her cooperation in the preparation of the experiment, and we are indebted to Robin Hogarth and Lennart Sjoberg for comments on a previous version of this paper. Requests for reprints should be sent to Helmut Jungermann, Institut f i i Psychologie, Technische Universitat Berlin, Doverstr. 1-5, D-1000 Berlin 10, West Germany.

224

H . Jungermann, I. von Ulardt,and L. Hausmann

process starts. A structure is a set of components of a complex whole and their interrelations; developing a structure, then, implies the generating of the components of the problem, and relating these components to each other. Both processes are closely intertwined and can be distinguished only analytically: The generating process is mostly guided by some implicit assumptions about the relations among the elements (e.g., their similarity or their mutual influences), and the structuring process often leads to a redefinition of the element set (e.g., adding or eliminating elements). In decision problems, the components may be possible actions, relevant events or states, potential outcomes, or goals and objectives; relations may be of a categorical or means-end sort (e.g., in goal hierarchies) or of a causal sort (e.g, in decision trees). A person’s representation of a problem, may it be ’real’ or ’experimental’, draws on two sources: (a) Knowledge already stored in memory, that is activated and retrieved in a particular situation; (b) Information that is searched for in the environment, and subsequently stored and integrated in permanent memory, Knowledge may be stored as relatively unconnected pieces of information that are structured only in a specific situation, or it might be stored in some already existing structure, e.g., as a schema or script. The mechanisms and strategies people use to generate and structure the components of a decision problem are not yet well understood. This is important, though, since it is this first initial phase of a decision process in which an ill-defined problem becomes well-defined, and the definition of a problem, of course, predetermines strongly the subsequent process. For instance, the selection of actions taken into consideration, or not, is certainly an important decision made before the usual decision process starts (i.e., the selection of one of the actions). This is particularly important when actions are not somehow given (e.g., sites for energy facilities), but must be designed or created (e.g., in urban planning), i.e., when imagination, phantasy, creativity are required. Whereas many authors have pointed out the importance of studying the process of representation and the factors that affect it (e.g., Vlek and Wagenaar, 1979; Jungermann, 1980; von Winterfeldt, 1980; Einhorn and Hogarth, 1981; Pitz, t h s volume), little empirical research exists on the issue. Exceptions are Gettys and Fisher (1979), who studied how people generate hypotheses about possible states of the world, or Pitz, Sachs, and Heerboth (1980), who investigated the generation of options and goals.

ROLE OF THE GOAL FOR GENERATING ACTIONS

225

The Goal Concept in Decision Theory Of particular importance for the problem formulation is the goal concept, but its function for representing decision problems has received little theoretical attention in decision theory. 'Goals' have been used in different meanings and functions: - Originally, the 'goal' of a decision maker was understood as nothing else than to 'maximize' sometiung. A goal in this sense does not have any particular content, it is a purely 'formal' goal, it is conrext-free. Its function is to serve as a criterion for selecting a course of action. - With the extension of the classic approach towards a 'multiple-objectives' approach, however, the goal concept got another meaning: goals stand for specific outcomes the decision maker tries to achieve (Keeney and Raiffa, 1976). A goal has a particular 'content'; it is contextbound- which has consequences for rhe understanding of 'rationality' and 'optimality' (cf. Einhorn and Hogarth, 1981; Pitz, this volume). Mostly so far, the function of this goal concept has been to generate and structure outcomes, i.e., to define attributes or dimensions on which potential outcomes may be evaluated. T h s is one of the goal(s) in representing decision problems. Another role might be considered, however, namely, to generate and structure actions, i.e., prior to all further steps (evaluation and selection) to design possible alternative courses of action. It is surprising that tlus interpretation has not been explored more systematically, since we can think of no other way of defining the set of actions than to consider the goal or goals to be achieved. The reason for this neglect might be that mostly decision-theoretic approaches, particularly the prescriptive ones, assume a well-defined problem in the sense that the options are given; the focus of these 'option-driven' approaches is on evaluation and selection. Only recently, in what may be called 'goal-driven' approaches, ill-defined problems have come under closer study, in the sense that the options are not given but must be created (e.g., Toda, 1978; Vlek and Wagenaar, 1979; Hogarth, 1980; Pearl, Leal,andSaleh, 1980; Pitz, Sachs, and Heerboth, 1980). Pearl, Leal and Saleh (1980) were apparently the first who, within a decision-theoretic framework, used the subject's goal(s) for generating actions, GODDESS is a computerized goal-directed decision structuring system for representing decision problems. The system allows the user to state relations among aspects, effects, conditions, and goals, in addition to actions and states which are the basic components of the traditional decision-theoretic approach. The system begins with assessing a structure of goals and subgoals and then elicits possible actions that would help produce improvements in each of the subgoals. 1s

226

H. Jungermann,l. von Ulardt,and L. Hausrnann

In an experimental study, Pitz, Sachs, and Heerboth (1980) investigated the effect of techniques on the generation of options and objectives, based on means-end analysis as used in the GODDESS system, and on script theory. Groups were given a decision problem and a list of objectives related to this problem, and were then asked to generate as many reasonable options as possible. Three groups, for example, were given the list of objectives and asked to generate options that satisfied the objectives one at a time, two at a time, or all simultaneously. Although the differences were small, there was a tendency for the goup focusing on one objective at a time only to produce more options than the group who tried to meet all objectives simultaneously. Our present interest is to study this role of goals for creating actions, i.e., in situations in which these are not given a priori. We want to know whether and how the explicit consideration of the goal, and various ways of thmking about the goal, affect the representation of the problem in terms of alternative actions. For instance, is the representation of a problem dependent on the framing of the goal (e.g, in terms of seeking positive or of avoiding negative consequences, of Tversky and Kahneman, 1981)? Under which conditions does it help and under which does it hinder people to create actions when they think about their goals(s) beforehand (Pitz, this volume)? What effect has the degree of detail with whch people think about their goals for the representation of a problem? Are there situations in which thinking about the goal results in the creation of actions which do not need to be evaluated any more, because a solution has ’emerged’ meanwhile (Beach and Wise 1980)? A Cognitive Approach to Study the Role of the Goal

Our approach to study the role of the goal is based on conceptions of the representation of knowledge in human memory. Specifically, we made use of the idea that human memory can be modeled in terms of an associative network of concepts and schemata. The basic unit of thought is a proposition. The basic process is activation of the network’s nodes, i.e., concepts; activation presumably spreads from one to another by associative linkages (Anderson and Bower, 1973; Collins and Loftus, 1975). We assume generally that goals, actions, events, and outcomes are components of the memory structure; in the present study, however, we focus on goals and actions only. We do not distinguish here between goal-directed and causally-directed knowledge (Wilks, 1977), but rather treat goals and actions as elements of the same knowledge structure.

ROLE OF THE GOAL FOR GENERATING ACTIONS

221

A god is assumed to be represented as a node that is connected to many subordinate and superordinate goals by associative pointers. The relation between a subordinate and a superordinate goal can be interpreted as 'is meant by' or 'is implied by'. This assumption entails the idea of a cognitive goal hierarchy. Connected to goals are actions that, by reasoning or by experience, are related to that goal. The relation between an action and a goal can be read as 'helps achieve'. These actions may differ in their efficiency with respect to the goal. For simplicity we assume further that actions are directly connected to the more specific (lower level) goals and only indirectly connected to the more abstract (higher level) goals (see Figure 1). \ \

\

\

7

-\

0 , , ,

, ,

Figure 1. Schematic Representation of a Cognitive Network Structure with Hierarchically Ordered Coals G and Actions A as components. (The numbers attached to action components indicate hypothetical efficiencies, and shaded actions indicate their belonging to a personal set.)

15*

228

H. Junprmann, 1. von Ulardt, and L. Hausmann

Activation of a goal node spreads throughout the nodes in the network to which it is connected, creating excitation at those nodes. Activation of a node might be interpreted as the attention the element is getting from the subject. The degree of excitation of an action node is dependent on the distance, i.e., the number of elements between the action node and the activated goal node; the greater the distance, the weaker the excitation. We postulate that, whether and how goal nodes are activated, influences which actions or sets of actions will be excited, retrieved, and generated. Specifically, the following assumptions are made with respect to the generation of actions: (1) When the activation of a goal concept is increased (e.g., by activating more of the goal nodes constituting the concept), then the excitation of action nodes connected to the goal concept will also be increased. If the chance of an action to be generated is a function of the excitation level of the node, which appears reasonable, the number of actions that can be generated increases with the degree of the general goal activation. For each individual, a goal concept has an impersonal (seman(2) tic) but also a personal (episodic) meaning which forms a part of the semantic meaning. When a subject considers a goal from his or her own perspective, i.e., what it means to him or her personally, only the elements of the personal goal concept, including the associated action nodes, will be activated. Consequently, the number of actions that can be generated is lower when goals are personally interpreted than when they are not. A further assumption refers to the quality of actions generated for the achievement of personal goals: Relating goals to personal preferences might also induce an unequal distribution of activation among the goals, i.a, higher-valued goals might be stronger activated than lower-valued goals. This implies, due to the spread-of-activation effect, that the action nodes connected with the higher-valued goals are also more excited than the action nodes connected with lower-valued goals. Since discrimination and search among a set of more excited actions should be easier than among a set of less excited actions, the quality of actions generated with respect to some goal is assumed to be a function of the value that goal has for the subject.

ROLE OF THE GOAL FOR GENERATING ACTIONS

229

The Experiment Material and Variables We chose an issue that was presumably familiar to all subjects: The goal was "I want to have a nice vacation", and the actions to be generated and evaluated were the possible means to achieve this goal. Two independent variables were manipulated in a 3 x 2 design: The first factor (E) was the goal explicitness, i.e., the degree of elaboration of the goal; there were three levels of E: In E l , Ss were only given the general goal, i.e., "I want to have a nice vacation". In Ez,Ss were presented a two-level hlerarchy in which the elements on the second level represented aspects of the top-level goal. In &, Ss were presented a three-level hierarchy with elements on the third level representing aspects of the elemensts on the second level. The goal hierarchy had been developed beforehand with the help of 20 student Ss, using a sorting technique. A part of the three-level hierarchy is shown in Figure 2. The second factor (R) was the way in which Ss had to think about the goals; there were two levels: In condition R,,Ss had to rank-order the goals that were presented to them, according to their personal priorities. In Rz,Ss were requested t o think about the goals in a general way, i.e., as goals people commonly have for their vacation. Procedure In the first step, Ss were presented the goal, or goal hierarchy, and requested to think about it for a while. To ensure that they actually considered each element, they had to give a specific example for each. Depending on the experimental condition, they then had to rank-order the goals, or not. In the second step, Ss were asked to generate actions that would help to achieve the goal. They had to put together packages consisting of 8 elementary actions, one from each of 8 categories (location, transport, accommodation, company activity, transport at destination, food, all other). Ss were given a booklet, and used for each package a new sheet. The first 'vacation package' was to represent their personal, ideal combination; they had then to form as many other packages as possible that might be attrao tive to other people. The number of different elementary actions generated was the first dependent variable. In the third step, all Ss were shown the complete hierarchy, had to rank-order the goals (if they had not done this before) and then evaluated their own ideal package on a 0 to 100 scale as to which degree it met each second-level goal. The rating on the respeo tive scales was the second dependent variable.

230

H . Jungermann, I. von Ulardt, and L. Hausmann

ROLE OF THE GOAL FOR GENERATING ACTIONS

231

Hypotheses The hypotheses can be stated as follows: H 1 (General Activation Hypothesis): With increasing goal explicitness, as operationalized through factor E, an increasing number of actions will be generated. H 2 (Set Activation Hypothesis): Ranking of goals with respect to personal importance, as operationalized through factor R, will reduce the number of actions generated. H 3 (Evaluation Hypothesis): The personal package of actions will have higher values on the goal achievement scales for the group who rank-ordered the goals with respect to personal importance before generating the actions than for the group who did not.

Subjects Ss were 130 students, nurses, secretaries, and post office workers. The number of Ss was not the same in all groups: In group El /Rl , N=21; in group E2/RI, N=19; in group E3/R1, N=17; in group EI/Rz, N=34; in group Ez/R2,N= 19; in group E3/Rz, N=20.

Results For each S, three scores were determined: The P-Score represents the number of packages the S has generated; all packages that were either incomplete (i.e., one or more categories left out) or impossible under the given contingencies (e.g., three weeks vacation) were not counted. The A-Score represents the number of generated actions, the variable of main interest in our study; only semantically or functionally different elements were counted (e.g., a VW and a Ford were not counted as two different means of transportation). The E-scores represent the evaluations of the personal package with respect to the goal achievement scales. Our main two hypotheses concerned the number of actions generated, depending on goal explicitness (factor E) and goal importance (factor R). The A-scores, that are of primary interest here, are given in Table 1. Goal explicitness (factor E). The General Activation Hypothesis (H 1) said that Ss who think more explicitly about the goal will produce more possible actions. The data support this assumption: The number of elementary actions (A-score) was significantly different among the three

H. Jungermann, I. von Ulardt ,and L. Hausmann

232

Table 1. Mean Number of Generated Actions (A-score)

I Importance of Goal

EI

E2

I

E3

ii

Rl

29.90

2758

32.18

29.8 1

R2

28.21

38.1 1

42.55

34.71

28.85

32.84

37.78

~

x

groups El, E2, and E3 (Kruskal-Wallis test: H = 16.15; df = 2; p < 0.001). In particular, group E3 generated more actions (A = 37.78) than group Ez (A = 32.84), and group E2 more than group El (A = 28.85). The main effect of E was also found when P-scores were analysed: The mean number of packages ('P) produced by E l , E2, and E3 were 5.36, 7.42, and 8.08, respectively. These differences are highly significant (Krwkal-Wallis H = 21.43; df = 2; p < 0.0001). Goal importance (factor R). In our Set Activation Hypothesis (H 2) it was assumed that Ss who rank the goals with respect to personal importance will generate fewer actions than Ss who do not. The data provide evidence for this hypothesis also: Group R1 produced significantly less elementary actions (i = 29.81) than group R2 (i = 34.71) (KruskalWallis H = 6.16; df = 1; p < 0.02). The analysis of the P-sere shows the same effect. For group R I , 5 = 6.37, and for group Rz,P = 7.03 (Kruskal-Wallis H = 7.75; df = 2; p < 0.01). Some further evidence on E and R effects. Since an analysis of variance could not be applied to explore possible interactions, separate analyses with the Kruskal-Wallistest were performed of the effects of E on the two levels of R, and of the effect of R on the three levels of E. The analysis of E effects shows that only under condition R2 are the differences between E l , Ez, and E3 significant (p < O.OOOl), but not under condition R 1 in which Ss ranked the goals with respect to personal importance. The analysis of R effects shows that only the differences on levels Ez and E3 are significant (for both, p < 0.01), but not the difference on level El . Evalwtion. The Evaluation Hypothesis (H 3) postulated that Ss who relate the goals to themselves will produce better personal packages in t e r n of potential goal achievement than Ss who do not. Table 2 shows the mean evaluations (E-scores) of groups RI and R2 with respect to each of the 8 second-level goals. As predicted, the values for group R1 are

ROLE OF THE GOAL FOR GENERATING ACTIONS

233

1

2

3

4

5

6

7

8

R1

88.8

86.9

85.8

19.3

81.1

70.1

75.3

66.6 N = 5 1

RZ

89.6

84.1

18.8

18.0

73.6

76.1

66.0

58.6 N = 19

significantly higher than the values for group Rz (Kolmogorov-Smirnov test: Chi' = 11.91; p < O.Ol).However,ifonesplitsthe datain evaluations for the four most important goals and the four least important goals, the groups differ significantly only on the latter scales. Discussion The results support the hypotheses and are basically in accordance with our theoretical approach based on a network model of the representation of knowledge. The main effects of goal explicitness (factor E) and goal importance (factor R) were evident in our data. The finding that factor E is not effective when Ss rank the goals may be seen as an indicator for the strong effect of R irrespective of the other factor: Relating goals to personal values and thus interpreting them from a personal perspective seems to level the effect of goal explicitness. The finding that factor R is not effective on level El of the goal is relatively easy to understand: Under condition El, only one very general goal is presented to Ss, namely, the top element of the goal hierarchy. An instruction to relate to the self, as given in R1,might actually be expected not to have any particular effect, although, of course, some Ss might implicitly rank aspects of this goal with respect to their importance. However, the lack of any difference between groups El/Rl and El/Rz seems more plausible. Finally, we have a very tentative explanation for the finding that evaluations differ only on the less important goal achievement scales. Ss in group R2,when asked to generate their personal ideal package, had only a very brief time to reflect about their own goals, and they might have directed their attention therefore primarily t o the most important goals while neglecting the less important ones. Consequently, they could select relatively efficient actions with respect to the more important goals, thus

234

H . Jungerrnann, I . von Ulardt,and L. Hausrnann

not differing from Ss working under condition R1,but produce only less efficient actions with respect to the other goals. Our results demonstrate the importance of goals for the process of generating options. On the one hand, the consideration of goals can support the creative activity of subjects, namely, when goals are made explicit and specific. On the other hand, goals may limit subjects’ search for options when the perspective is strongly self-oriented. In both cases, however, goals direct the attention and effort subjects spend on generating actions for goal achievement; the effect differs only depending on the way goals are introduced and framed. Thus result may be linked to other psychological research. For instance, in problem-solving psychology it has always been said that one needs to define the goal status in order to be able to generate the operators that help transform the status quo into the desired status, and various theoretical and empirical approaches have studied problemsolving processes using this conception (e.g., Newel1 and Simon, 1972; Dorner, 1976). Effects of set and direction on creative problem-solving has clearly been demonstrated in many studies (e.g., Hyman, 1961), and characteristics of people’s handling of goals in complex decision situations (like running a town) have recently been investigated by Dorner (in press). Another area is the research on goal setting and task performance. A recent review (Locke, Shaw, Saari, and Latham, 1981) concluded that the “beneficial effect of goal setting on task performance is one of the most robust and replicable findings” (p. 145). One particular finding is that specific goals lead to higher output than vague goals such as ”do your best”. This, of course, sounds similar to the contrast between concrete, substantial goals and abstract, formal goals like ”maximize your SEU”, as discussed above in Section 11. Of course, the model proposed in this paper is very simple. However, the use of models of long-term memory for research on decision processes is only beginning. More specific models of the acquisition, storage and retrieval of decision-relevant knowledge must be developcd and tested. Questions worth to be studied would include, for instance: Is the knowledge that is retrieved in decision situations actually structured in terms of actions, events, and outcomes, as decision theory assumes? How are goals connected to these elements? Which factors influence the retrieval process in which ways? Can the effects of heuristics suchas availability or representativeness (Tversky and Kahneman, 1975) be explained with the help of models of the representation of knowledge? One problem for such research is that it seems difficult to design experiments and to develop measurement procedures for eliciting subjects’ r e p resentation of decision problems or for studying factors that affect this

ROLE OF THE GOAL FOR GENERATING ACTIONS

235

representation. New experimental paradigms are needed that parallel those in cognitive psychology, e.g., recognition and recall. Finally, what are the implications for the application of decision theory? In decision analyses, clients are required to retrieve, and then externalize knowledge in terms of the prescriptive conception (i.e., actions, events, and outcomes), and this seems usually to work, at least it does not seem to be counterintuitive. However, this approach might not be sufficient when the representation of the problem is itself an important step of the decision process, as many decision analysts claim it is most of the time. But it is exactly this step where attention and memory play an important role. Our conclusion is that, if we want to improve our understanding of people’s decision behavior, and also if we want to improve our ability to aid people making decisions, we will have to study how this process of representing problems is performed and what techniques we need to develop for adequately eliciting people’s knowledge about the problem. If the representation of a problem depends on which knowledge is activated and retrieved, the procedures used for eliciting this knowledge and their effects on cuing attention and activation are as important as the techniques for eliciting utilities and subjective probabilities.

References Anderson, J.R. and C.H. Bower, 1973. Human Associative Memory. Washington, D.C.: V.H. Winston & Sons. Beach, L.R and J.A. Wise, 1980. Decision emergence: A Lewinian perspective. Acta Psychologica, 45, 343-356. Colhs, A.M. and E.F. Loftus, 1975. A spreading-activation theory of semantic memory. Psychological Review, 82, 407-428. Dorner, D., 1976. Problemlosen als Informationsverarbeitung. Stuttgart: W. Kohlhammer. Dorner, D. Heuristics and cognition in complex systems. In: R. Groner, M. Groner, and W.F. Blschof (eds.), Methods of Heuristics Hillsdale, N.J.: Lawrence Erlbaum (in press). Einhorn. H.J. and R.M. Hogarth, 1981. Behavioral decfsion theory: Rocesses of judgement and choice. Annual Review of Psychology, 32, 53-88. Cettys, C.F. and S.D. Fisher, 1979. Hypothesis plausabillty and hypothesis generation. Organizational Behavior and Human Performance, 24, 9 3 - 100. Hogarth, R.M, 1980. Judgment and Choice: The Psychology of Dedsion. Chichester, England: Wiley & Sons. Hyman, R, 1969. On prior Information and creativity. Psychological Reports, 9, 151- 161.

Jungermann, H., 1980. Structural modeling of decision problems. Paper presented at American Psychological Association, Montreal.

236

H. Jungermann, I. von Uiardt, and L. Hausmann

Keeney, R.L and H. Raiffa, 1976. Decisions with Multiple Objectives: Preferences and Value lladeoffs. New York: Wlley. Lmke, E.A., K.N. Shaw, L.M. S d , and G.P. Latham, 1981: Goal setting and task performance: 1969- 1980. Psychological Bulletin, 90, 125- 152. Newell, A. and H.A. Simon, 1972. Human Problem Solving Englewood Cliffs, N.J.: Rentice-Hall. Pearl, J., A. Leal, and J. Saieh, 1980. GODDESS: Agoal-directed decision structuring system UCLA-ENG-CSL-8034. School of Engineering and Applied Sciences, University of California, Los Angeies. Pitz, G.F. Human englneering of decision alds. In this volume, 205 -221. Pit& G.F., N.J. Sachs, and J. Heerboth, 1980. Procedures for eliciting choices In the analysts of Individual decislons. Organizational Behavior and Human Performance, 26, 396-408. Toda, M., 1976. The dedslon process: A perspective. International Journal of General Systems, 3, 79-88. Tversky, A. and D. Kahneman, 1976. Judgment under uncertainty: Heuristics and blases. In: D. Wendt and C. Vlek (eds.), Utility Probability, and Human DecC sion Making DordrechtlBoston: D. Reidel. Tversky, A. and D. Kahneman, 1981. The framing of dedslons and the psychology of choice. Science, 2 1 I , 4 5 3- 458. Vlek, Ck and W.A. Wagenaar, 1979. Judgment and declsion under uncertalnty. In: J.A. Michon, E.G. Eijkman, and L.F.W. DeKlerk (eds.), Handbook ofPsychonomics, 11. Amsterdam: North-Holland, 253-345. von Winterfeidt, D., 1980. Structuring decision problems for dedsion analysis. Acta Psychologica, 45, 11-93. WiIks, Y., 1977. What sort of taxonomy of causation do we need for language understandlng? Cognitive Science, 1, 235 -264.