Rational Behaviour in Everyday Situations

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European Sociological Review, Vol. 18 No. 4, 401^415

Rational Behaviour in Everyday Situations Ju« rgen Friedrichs and Karl-Dieter Opp

Introduction Recall what you have done for the last two or three weeks: you went to work where you engaged in routine behaviour most of the time, you visited friends or relatives, you watched television, you went to a restaurant or theatre, you bought some food or other things, or you talked to your wife or husband. The vast majority of these actions are lowcost and low-bene¢t repeated behaviours. Although most everyday behaviours individuals engage in are of this sort, rational choice theory (RCT) has largely neglected their explanation. To be sure, many applications of RCT refer to everyday behaviour such as studies on voting in national elections or consumer behaviour. It is also well known that there are ‘anomalies’ in everyday life that violate some postulates of ‘rationality’ ^ whatever these postulates may be (see e.g. Dawes, 1988, 1998; Tversky and Kahnemann, 1990). But there are no attempts to & Oxford University Press 2002

treat everyday behaviour in general as an explanatory problem of RCT and to test the respective hypotheses in natural situations. It seems that standard RCT has problems in explaining everyday behaviour. This is illustrated by the explanation of voting behaviour (see e.g. Riker and Ordeshook, 1973; for recent summaries and discussions see Aldrich, 1997; Fiorina, 1997; Kanazawa, 1998). The bene¢ts of voting are negligible because in most elections a single voter has only a very small in£uence on the outcome; because voting incurs a cost there is no incentive to participate. Therefore, people will in general abstain from voting. Nevertheless, we observe that large numbers of people vote ^ up to 80 per cent in Germany ^ and that participation rates vary in di¡erent elections. The prediction of standard RCT is thus wrong. In general, it is argued that

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Rational choice theorists are divided on what the most adequate version of rational choice theory is and on the kinds of behaviour it can explain. Furthermore, scholarly evidence di¡ers with respect to which decision rules are predominantly used by individuals. Present versions of rational choice theory can be placed on a continuum ranging from a neoclassical economic model of man to a ‘soft’ bounded rationality model. Depending on the model the answers to the following questions di¡er: Do people calculate or is most behaviour habitual? To what extent are existing behavioural alternatives, behavioural consequences, and probabilities considered? Do individuals use decision heuristics and, if so, which ones? In order to answer these questions we have conducted an empirical study on everyday behaviour such as buying a car or a computer, travelling to a certain destination, choosing a place for a vacation, going to a restaurant, and choosing one’s occupation. The major results are that decision situations exhibit a low complexity, and decision processes can be described by three major decision heuristics. The dominant type is binary-sequential. Furthermore, in everyday behaviour individuals perceive fewer behavioural alternatives, but unexpectedly they consider more behavioural consequences and probabilities than in other behaviours. The paper suggests an explanation for this ¢nding. Another ¢nding is that the decision process for everyday behaviour extends mostly over one phase only. In the ¢nal section, consequences for rational choice theory are discussed.

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Defining ‘Everyday Behaviour’ ‘Everyday behaviour’ is here de¢ned as behaviour that individuals engage in regularly. This means that the time that elapses between behaviours is similar or the behaviour occurs once or several times within consecutive time periods. For example, voting occurs every four years. Thus, the time that elapses between di¡erent acts of voting is similar. A vacation is taken once or several times within

consecutive time periods ^ once or twice a year, but within these time periods the behaviour may occur at di¡erent times ^ in one year in spring, in another year in winter. Our de¢nition implies that a regular behaviour may vary in regard to frequency. We do not characterize everyday behaviour as habitual behaviour or as behaviour where people do not calculate. We prefer to use a wide de¢nition (including habitual everyday behaviour) that allows us to explore empirically whether characteristics such as calculating actually exist. For example, if we de¢ne everyday behaviour as behaviour where people do not calculate it is no longer possible to investigate empirically whether people really calculate. We neither de¢ne everyday behaviour in terms of low-cost and high-bene¢t behaviour for two reasons. First, the distinction is not clear in many cases. For example, the price of a computer ranges from, say, $200 to $2000. Which price is to be denoted as low cost and which one as high cost is largely arbitrary. Secondly, what is a low- or a highcost decision (and, likewise, a low- or high-bene¢t decision) depends on the actor’s evaluation and opportunity cost and not upon the attribution by the researcher. The same amount of money for buying a good and the same e¡ort such as the required time of an action may involve quite di¡erent costs for a person. A price of $300 for a computer is a high cost for a student with a low income, but a low cost for a manager of a large enterprise. De¢nitions cannot be right or wrong but only more or less useful. We chose the preceding de¢nition because we want to deal with behaviour that seems to be di⁄cult to explain by standard RCT, and because we hypothesize that everyday behaviour and other types of behaviour di¡er in several respects that will be discussed below.

The ‘Rationality’ of Everyday Behaviour The most general version of the rational actor model posits that the choice of individuals among the behavioural alternatives open to them is determined by their preferences and constraints. Actors choose the alternative that maximizes their utility.2 In applying this proposition to everyday behaviour, several questions arise.


such low-cost behaviour which typically occurs in everyday life is di⁄cult or impossible to explain by RCT (see e.g. Green and Shapiro, 1994: 27^28, Mensch, 2000;Tsebelis, 1990: 32^33). This raises the question of whether RCT in general is suitable to explain voting ^ and other similar everyday or regularly performed behaviour ^ or whether some assumptions of the standard version of RCT have to be modi¢ed in order to explain everyday behaviour. Another reason for making everyday behaviour a special subject of study in the tradition of RCT is that decisions in everyday life are of interest in their own right. Qualitative sociology has focused on this type of behaviour for a long time, as the work of Gar¢nkel or Go¡man testi¢es. In this paper we will suggest some hypotheses about decisions in everyday situations and provide empirical data collected in real life situations. These data have been collected by means of interviews with largely open-ended questions. Although there is a voluminous literature on decision-making (e.g. Arkes and Hammond, 1986; Dawes, 1998; Kahneman, Slovic, and Tversky 1982; Gigerenzer et al., 1999) we have not found any work that systematically investigates the kind of behaviour and the questions we are focusing on in this paper in natural settings. There are also no empirically tested general propositions based on a rational choice framework focusing on everyday decisionmaking behaviour in natural settings.1 In contrast to that literature, we do not focus on biases in perception and judgement; nor are we interested in describing the decision processes of certain groups (such as policy-makers) or of political events (such as the Camp David negotiations ^ see Rai¡a, 1982, reprinted in Arkes and Hammond, 1986). Our research is intended to supplement this literature.

RATIONAL BEHAVIOUR IN EVERYDAY SITUATIONS

not worth the e¡ort of considering all the behavioural consequences of performing one or other action. Opportunity costs are low (Mensch, 2000). Therefore, individuals may pick only one or two ‘salient’ aspects of a situation when they perform a behaviour. Another hypothesis is also plausible. Individuals know everyday situations very well: they have performed various behaviours a large number of times and have information on the behavioural consequences that occurred and may occur. Therefore, individuals are able to scan very quickly a relatively large number of characteristics of a situation that matter to them. For example, in selecting a restaurant individuals may have a clear conception of what is important to them: the interior of the restaurant, the kind of people who attend the restaurant, the composition of the menu, the prices, etc. Thus, individuals may have acquired a competence in everyday life to consider a relatively large number of behavioural consequences before they make their choice. Here the question is: how many behavioural consequences do individuals take into account when they perform everyday behaviours? (3) Utility theory and value-expectancy theory make the assumption that utilities of behavioural consequences are weighted by subjective probabilities. In situations of uncertainty and risk these are di⁄cult to determine (Little, 1991: 47). In contrast, it may be argued that in everyday situations it is too complicated or time-consuming to take probabilities into account. People know everyday situations very well and, thus, there are only probabilities of zero and one. On the other hand, everyday situations are subject to change. Therefore, it seems plausible that probabilities are taken into account, although in many situations simply by using the extremes, i.e. very high and very low probabilities. The question is thus: to what extent and how do individuals consider subjective probabilities in everyday situations? (4) A further question that rational choice theorists have addressed is the existence ofdecision heuristics. Heuristics of choices under uncertainty can be de¢ned as‘rules which systematically simplify search through the problem space by disregarding some elements of the problem space. Alternative simpli¢cations represent di¡erent heuristics’ (Johnson and Payne, 1985: 396). Thus, the heuristic is a decision


(1) It is often claimed that everyday behaviour is mostly habitual in the sense that no behavioural alternatives are considered. Individuals have learned to perform certain types of action in certain situations and they perform this behaviour when the situations occur. People simply perform the behaviour they performed in the past. Again, an alternative hypothesis seems plausible too: although there are certainly habitual behaviours the bulk of everyday behaviour may involve clearly structured behavioural alternatives that require decisions from the point of view of the actor. For example, people are faced with buying several goods of the same kind in a supermarket or with several possible ways of spending their vacation. These examples suggest that even in everyday situations people take into account several behavioural alternatives and choose among them. Hence the question is: to what extent do people consider behavioural alternatives in everyday situations? (2) If everyday behaviour is habitual it is plausible to assume that individuals do not consider any behavioural consequences of their action. If this claim holds true RCTwould indeed be incapable of explaining everyday behaviour ^ unless it is held that all action has an intrinsic reward value. However, an alternative proposition seems plausible too. Even when performing routine behaviour individuals often explore what consequences their behaviour might have. For example, if one moves to a new neighbourhood one will ¢rst explore the possible ways of reaching the workplace, e.g. by foot, by car, by bus, or by bike. Then a decision is made. Thereafter, the behaviour chosen is repeated time and again and the actor may forget the reasons for having chosen this and no other action ^ the behaviour has become a habit. The decision underlying a habit is not revised until new behavioural consequences emerge. It may therefore only seem to an outside observer that behavioural consequences are not considered. But even if behaviours become a habit individuals may continually ‘recalculate’ to make sure that the behavioural consequences taken into account have not changed and, thus, that their habit is the best behavioural choice for them. It could further be argued that behavioural consequences are not taken into account because at least a large part of everyday behaviour is low-cost and lowbene¢t behaviour. When there is little at stake it is

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(because they have relevant information) than in other behaviour. Due to the assumptions made above we would also suggest that the number of decision phases is lower in everyday behaviour than in other types of behaviour.Thus: Proposition 2. The number of decision phases in everyday behaviour is lower than in other types of behaviour. However, it is also plausible that for everyday behaviour people consider more behavioural consequences and probabilities because they dispose of a relatively large stock of knowledge and can thus reason in a more complex way. In contrast, for other behaviour people may partition a complex decision into several phases and consider relatively few behavioural alternatives, consequences, and probabilities in each phase. Existing theory does not allow us to choose between these predictions. The result of our research will show which hypothesis is correct.

Research Design The data were collected in Cologne by the ¢rst author.3 Several types of situations were selected. Questionnaires were developed referring to the questions mentioned above.The data were collected by students participating in a research seminar. In order to answer the questions outlined in the previous section we selected the following decision situations involving regular (i.e. everyday behaviour) as well as non-regular behaviour: (1) People in a travel agency deciding about their holiday trip (‘travel agency’): N¼13; (2) People in the charter lobby of the Cologne/Bonn airport waiting for their £ight to a holiday resort (‘charter lobby’): N¼20; (3) Students of a technical college who had decided upon their vocational training (‘job career’): N¼31; (4) University freshmen who were asked about their decision to choose a particular subject (‘university career’): N¼27. Only the ¢rst two behaviours are ^ according to our de¢nition ^ everyday behaviours; the other behaviours were chosen to ascertain whether our hypotheses hold across various types of behaviours.


rule. Several rules have been suggested in the literature, the most common being: Maximin (or the Conjunctive Rule), Elimination by Aspects, Equiprobable, Expected Value, and Most Likely (Bettman, 1979; Svenson, 1979; Thorngate, 1980; Tversky and Kahneman, 1974; van Raij, 1988). Which decision heuristic do individuals apply to arrive at a decision in a very short period of time? We will examine the extent to which our subjects used such heuristics and the types of heuristics. A decision heuristic may consist of phases of a decision process. Findings from experimental studies suggest that individuals choose among a given set of alternatives by sequentially eliminating alternatives, as ¢rst described by Tversky (1972). This raises two questions we want to address in our empirical study: ¢rst, are there typical decision processes in everyday behaviour; and secondly, do decision rules di¡er among phases of the decision process? Is it possible to suggest some preliminary general propositions that answer the previous questions? The general assumption of RCT that people try to reach their goals given the constraints they face can be applied to everyday behaviour as well. What would follow for the number of behavioural alternatives, consequences, and probabilities that are taken into account? The following assumptions seem realistic: 1. People want to make decisions quickly, i.e. the time required for a decision is a cost. 2. There are cognitive constraints in regard to the processing of information. 3. People want to avoid negative consequences of ‘wrong’ decisions, i.e. of options they do not choose. Applied to everyday behaviour we would expect that people have already stored information about most of the decisions they make in their everyday a¡airs; therefore, they will probably regard the costs of a wrong decision as low.This would suggest the prediction: Proposition 1. (a) For everyday behaviour, the number of behavioural alternatives and behavioural consequences that people consider in their decisions is lower than for other types of behaviour. (b) In regard to probabilities, people more often consider not intermediate but only extreme probabilities of 0 or 1 in everyday behaviour


cassette recorder. The questionnaire thus consisted of some pre-formulated questions and, in addition, hints for the interviewer about what she or he had to ascertain in a free conversation.Thus, our studies were ‘process tracing’ insofar as they ‘intended to reveal the cognitive process that led to the ¢nal decision’ (Svenson, 1979: 98). The coding of the data proceeded in three steps. Step 1: two interviewers listened to the taped interview. They reduced the information into a transcribed version including the major elements of the respondent’s report. Step 2: the resulting paper version of the interview was transformed into a code-sheet in which the information was systematized by listing the alternatives, behavioural consequences, and probabilities for each phase of the decision process. Coding categories were derived from the hypotheses and established before coding. It was further coded whether or not additional information was collected. Step 3: the information from this code-sheet was systematically coded to a ¢nal code-scheme. Then these data were turned into an SPSS system ¢le. In contrast to analyses reported in the literature, we used neither an experimental method nor did we present a standardized questionnaire. However, this method of semi-structured interviewing was adequate for the purposes of our study.The reliability ^ and hence the validity ^ rests heavily upon two conditions: the qualities of the interviewers and the completeness of the respondents’ reports. We tried to realize the ¢rst condition by having two interviewers for each respondent and by taperecording the interview. Our data are based on post-decision reports. Initiated by Newell and Simon (1972), the analysis of decision processes via verbal protocols has since been questioned for its validity (see Carrol and Johnson, 1990: 32^38), as indicated by the controversial views of Nisbett and Wilson (1977) and Ericsson and Simon (1984). Several biases may occur. First, certain answers to the interviewers may be regarded as socially desirable. For example, respondents may be reluctant to admit that they expected certain behavioural consequences that did not occur. However, in the situations we inquired about nothing was at stake for the psychic and social well-being of a respondent. It is therefore implausible that social desirability distorted the results of


In a welfare state like Germany where more than 70 per cent of the employees have six or more weeks of vacation per year those behaviours are regular. The 91 interviews were conducted in November 1993. We chose travelling (situations 1 and 2) as an example of everyday behaviour, because travelling does not happen very often and is usually a pleasant experience.Therefore, we assumed that respondents will give reliable information about their decision and would not refuse to be interviewed. We chose occupational choice (situations 3 and 4) because each respondent has already made such a decision and was thus expected to give reliable information. The pre-test of the study was run with a standardized questionnaire, which decomposed the decision process into phases; the perceived alternatives, behavioural consequences, and probabilities for the situations selected were ascertained. We further asked whether additional information, and if so, from which source, was collected. The consistent result of the pre-tests in all four situations was that the respondents were puzzled and gave only short and inconclusive answers. Evidently, they were unable to decompose the decision process into the analytical elements imposed by us. Instead, it seems that the decision process was stored by its chronology, and only if we adjusted the questionnaire to accommodate this method of storage were we able to retrieve the information. This result led to a drastic change in the method used.We chose an unstandardized type of interview. The students now had only a one-page questionnaire, starting with an introductory question and listing some additional questions the interviewers suggested using to ascertain the behavioural alternatives.4 The interviewers were instructed to start with the initial decision problem and then trace the decision process as exactly as possible. We followed a suggestion by Ericsson and Simon (1984: 27) that ‘the accuracy of verbal reports depends on . . . the relations between the requested information and the actual sequence of heeded information.’ To ensure that the reports were as complete as possible, interviews were now conducted by two students: one asked the questions and the other coded the answers into the analytical frame and could ask for missing information, e.g. concerning additional consequences of the behavioural alternatives. Interviews were recorded with a small

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Results How ‘Rational’ is Everyday Behaviour? Table 1 shows the extent to which propositions 1 and 2 are con¢rmed. There are striking di¡erences

between ‘travel’as an everyday behaviour and ‘occupation’ as a lifetime decision situation. Travel decisions exhibit lower numbers of behavioural alternatives than occupational choices.This concurs with proposition 1a. For example, 10.3 per cent of the respondents looking for an occupation consider three alternatives, whereas only 3 per cent of those who wish to travel take into account three alternatives. Furthermore, 30.3 per cent of those travelling see one alternative while only 20.7 per cent have one alternative for choosing an occupation.5 According to proposition 1a, this result seems plausible: for most individuals occupational choice is a more important decision regarding the costs of a ‘wrong’ decision and, therefore, it seems worthwhile to expend e¡ort to explore alternatives. This con¢rms an assumption already made by Stigler (1961), that individuals’ search for information is positively related to the costs of the decision. It is also consistent with proposition 1a that about 91 per cent considered at most two behavioural alternatives. However, the following ¢nding is not in line with proposition 1a: regarding behavioural consequences and probabilities, travel decisions involve more behavioural consequences and more (and not, as proposition 1a suggests, fewer) behavioural consequences and probabilities per alternative than decisions about occupational choice. This is also apparent from Tables 2 and 3: The average number of behavioural consequences is, in total, larger for travel than for occupational decisions. The same holds for the average number of probabilities (i.e. how often respondents take into account probabilities greater than 0 and smaller than 1), as Table 3 indicates.

Table 1. Number of alternatives, behavioural consequences and probabilities for everyday behaviour (travel) and other behaviour (choice of occupation) in the ¢rst phase ofthe decision process (%) Travel (N¼33)

Occupation (N¼58)

Total (N¼91)

........................................

........................................

........................................

Alternatives

Behav. Cons.

Probabil- Alternaities tives

Behav. Cons.

Probabil- Alternaities tives

Behav. Cons.

Probabilities

.............................................................................................................................................

0 1 2 3 4 and more

0 30.3 60.6 3.0 6.1

6.1 9.1 18.2 12.1 54.5

42.4 6.1 6.1 9.1 36.3

0 20.7 62.1 10.3 6.9

1.7 8.6 22.4 22.4 44.9

24.1 31.0 19.0 6.9 19.0

0 24.2 61.5 7.7 6.6

3.2 8.8 20.9 18.7 48.4

30.8 22.0 14.2 18.7 25.3


our research. Secondly, if a behaviour is performed people may retain in their memory not the expected but only the real consequences of their behaviour ^ if the two di¡ered. Such a distortion would be expected when making a mistake would cause high cognitive dissonance. However, making mistakes in everyday life is so common that admitting mistakes does not represent a cost to an actor and thus giving wrong information to the interviewer is unlikely. Thirdly, respondents may forget what happened in a decision situation due to the passage of time and invent reasons, behavioural consequences, etc. during the interview. However, because the decisions were made only recently (decisions 1 and 2 of the study) and/or were important for the subjects (decisions 3 and 4 of the study) it is implausible that our respondents had forgotten the events we were trying to measure. Fourthly, ‘rationalization’ may have occurred: respondents may have invented expected behavioural consequences, decision heuristics, etc. that ¢tted into their cognitive structure or ‘identity.’ We do not see any reason why behaviours such as travelling or occupational choice could hurt a respondent’s self image to the extent that she or he would deceive the interviewers. In general, from a rational choice and a cognitive dissonance perspective, lying and cognitive restructuring are quite costly.

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Table 2. Arithmetic means of number of behavioural consequences for di¡erent numbers of behavioural alternatives, by decision situation in the ¢rst phase ofthe decision process Average number of utilities .....................................

Travel

Occupation

Total

.....................................................................

1 2 3 Total

2.6 5.2 5.7 4.4

2.2 3.7 6.1 3.9

2.4 4.2 6.3 4.1

Note: Eta¼0.52; p¼0.000.

Table 3. Arithmetic means of number of probabilities for di¡erent numbers of alternatives, by decision situation in the ¢rst phase of the decision process

Decision Phases In this section we will address two questions: ¢rst, to what extent are decisions made in di¡erent phases, i.e. does the decision process take place in one or several clearly identi¢able time periods; and secondly, can we identify typical decision heuristics? The vast majority of decisions, i.e. 92.1 per cent, were made in one or two phases (Table 4). The simplest one-phase decision situation one can imagine is the one-option^one-choice situation: an individual takes into account only one behaviour and performs this behaviour. To illustrate this, for one of our respondents the only perceived option for an occupation was to become an electrical engineer. Table 4. Number of phases, by decision situation (%)

Average number of probabilities ...........................................

Travel

Occupation

Decision situation Total

...................................

.....................................................................

1 2 3 Total Note: Eta¼0.22; n.s.

1.7 2.7 4.3 2.6

1.3 1.8 2.8 1.8

1.5 2.2 3.2 2.1

Travel

Occupation

Total

.....................................................................

1 2 3 and more Note: Eta¼0.28; p¼0.01.

63.6 36.4 0

39.2 48.2 12.6

48.3 43.8 7.9


How can this be explained? It is important to note that these ¢gures refer to the ¢rst phase of the decision process. As Table 4 indicates, there are more decision phases for occupations than for travel decisions. To be sure, it happens in later phases; as we shall see later, new behavioural alternatives do occur, but this happens very rarely. The di¡erence between the two types of decision processes may thus be that everyday behaviour is a short decision process with few behavioural alternatives, but a relatively large number of behavioural consequences; in contrast, behaviour such as occupational choice may be relatively simple with regard to the number of choices but each individual passes through several decision phases. In short, travel decisions are typically complex in the ¢rst ^ and in most cases only in the ¢rst ^ phase, whereas occupational decisions run over several, less complex decision phases. We will take up this explanation in the Summary and Discussion section (seeTradeo¡s in Complexity).

It is interesting to see that the number of behavioural alternatives reported in Table 1 correlates positively with the average number of behavioural consequences per alternative (Table 2, ¢nal column) as well as with the average number of probabilities per alternative (Table 3, ¢nal column). Accordingly, the average number of behavioural consequences should correlate with the average number of probabilities. This is indeed the case: the Pearson correlation coe⁄cient is r¼0.50 ( p¼0.001). These results suggest that as the number of behavioural alternatives increases, individuals take more behavioural consequences into account. The reason may be that as decision situations become more complex in the sense of a relatively large number of perceived behavioural alternatives, search processes are set in motion that lead to the consideration of a relatively large number of behavioural consequences. Moreover, the situation may be regarded as riskier in the sense that behavioural consequences are no longer categorized by reducing probabilities to either extremely low or extremely high.

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Figure 1. Threephases decision heuristic

therefore, he decided to choose the Fachabitur, which has lower entry requirements. Two aspects of this decision process are of importance and we will take them up later. In the third phase, one alternative is neglected because it turned out that the costs of choosing it were extremely high, i.e. there is a strong restriction on one alternative. Another feature of this decision situation is that in each phase several options are considered at the same time, i.e. simultaneously. Our data suggest that decisions are made in sequences for di¡erent reasons. One is that some options at a later time are only open when an individual has chosen particular behavioural alternatives at an earlier time and if certain outcomes of the behaviour chosen were obtained. Furthermore, there is uncertainty in regard to these outcomes. This situation typically occurs in educational careers, as our previous example suggests. Individual goals can only be reached stepwise. Reaching the next step in such a decision process is dependent on outcomes of the former step. This process is imposed on the individuals by institutional constraints: goal attainment (such as becoming a medical doctor) is channelled in di¡erent periods where the outcomes of each period are screened, and where the individual has only limited choices in each period. A further reason for stepwise decisions seems to be the cognitive problem of complexity. Only after having decided among two alternatives, further di¡erentiations are perceived. It is the stepwise approach to the complexity of reality by gradually perceiving potential options, which were not perceived initially. It is the exclusion of alternatives that gives the individual the cognitive capacity to perceive or search for new alternatives. A ¢nal reason for a stepwise decision process is the cost of collecting all relevant information for the ¢nal decision in the ¢rst phase, as the following example illustrates (see Figure 2). The respondent wanted to decide where to spend his holidays. At ¢rst he applied the criterion ‘nice climate’ to reduce the number of potential holiday resorts. He found ¢ve resorts ful¢lling this condition.Then he applied another decision criterion: ‘¢ve-star hotel at a reasonable price’ allowing him to cancel four of the ¢ve alternatives: the resort with the ¢ve-star hotel o¡ering the most reasonable price was chosen. It


All his friends as well as his brother worked in this occupation. A low number of phases may also be due to‘framing’. This somewhat ambiguous concept denotes, among other things, a selection criterion (Enste, 1998, Esser, 1990, 1993, 1996, 1999; Lindenberg, 1993). In our context, a frame may be de¢ned as a criterion the actor applies at a very early stage of the decision process. It is a way of structuring situations. The frame will allow him or her to eliminate a large number of behavioural alternatives before proceeding with the decision process itself. This can be exempli¢ed by the ‘travel’decision: An actor may decide to have a sports holiday ^ as opposed to a recreational holiday ^ and thus consider only holiday resorts with sports facilities. The following example illustrates a decision situation that extended over three phases (see Figure 1). In phase 1, the respondent had to decide whether to repeat the last year of school (9th year) or to start a practical training course. He decided to accept the o¡er of training in a metal construction company. After having completed this training, he had to decide whether to work as a skilled worker or to continue his education (phase 2). He decided to do the latter and now, in phase 3, he perceives three alternatives: to take up retraining as a technician; to go back to school and receive the Fachabitur quali¢cation (limited access to university); or to take the Abitur (unlimited access to university). The retraining is not subsidized by the Labour Agency, and


Decision Heuristics Heuristics of choices under uncertainty can be de¢ned as‘rules which systematically simplify search through the problem space by disregarding some elements of the problem space. Alternative simpli¢cations represent di¡erent heuristics’ (Johnson and Payne, 1985: 396). Thus, the heuristic is a decision rule. In the decision processes we analysed there are three di¡erent heuristics, as Table 5 shows: we ¢nd simultaneous decisions in 46 per cent of the cases (see the last column of Table 5, lower panel). A simultaneous decision exists if at least two behavioural

Figure 2. KO-criterion decision heuristic

alternatives are evaluated simultaneously. In 9 per cent of the cases, decisions are made by applying a knock out criterion, i.e. a criterion or utility of high importance dominating the decision process, drastically reducing the number of alternatives. There are 18 per cent of decisions where at least one alternative is nearly impossible due to high costs, i.e. where a strong restriction obtains. There are 27 per cent one-choice ^one-alternative decisions. Furthermore, individuals try to avoid decisions among several alternatives simultaneously; instead they decompose the process into a binary sequential process. In our sample, decision heuristics are not significantly related to the type of decision ^ travel vs. occupation ^ although the simultaneous decision heuristic is more frequent for occupational choice (Table 5). However, the mean number of phases of occupation decisions was signi¢cantly higher than for travel decisions (p50.001), as an analysis of variance revealed. How are heuristics related to the perceived number of alternatives and behavioural consequences? We found that individuals applying a ‘knock out’ criterion perceive more behavioural alternatives and more behavioural consequences: 25 per cent report three and more alternatives, in contrast to 14.6 per cent applying the simultaneous and 12.5 per cent applying a restriction heuristic. Furthermore, individuals using a ‘knock out’ criterion report an average of 5.9 behavioural consequences, whereas the other two report 4.3 and 4.5 behavioural consequences (these results are not presented here). This result contradicts an assumption byJohnson and Payne (1985: 407) that simultaneous heuristics should exhibit more alternatives. These authors argue that the alternatives are ‘more open’ because other heuristics have reduced the number of consequences. Our ¢ndings re¢ne typologies of decision rules and heuristics suggested in the literature (e.g. Bettman, 1979; Svenson, 1979; Thorngate, 1980; van Raaij, 1988), since we systematically took the phases of the decision process into account. Some processes we found correspond to the Expected Value heuristic: individuals compare alternatives by their probabilities and behavioural consequences. In some processes, individuals apply mixed decision rules: in the ¢rst phase they used an Equiprobable


would have been too costly to scan all possible ways of spending a holiday. To reduce these costs, the individual made up his mind and found one feature or aspect that is most important to him: a nice climate. In other words, a holiday in a resort with a nice climate has the highest net utility for the respondent. Next he selects another alternative that further narrows the options: a certain type of hotel. Again, staying in a ‘¢ve-star hotel at a reasonable price’ has the highest net utility. In this process, which exempli¢es an ‘elimination by aspects’ decision heuristic, information costs are reduced: respondents use a ‘knock out’ criterion which saves time and e¡ort to compare holiday resorts according to a host of criteria. Further research is needed to specify other conditions for the number of phases of decision processes. In particular, it would be interesting to know to what extent institutional constraints inhibit the behavioural alternatives as well as the time sequences of decisions in everyday life situations.

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Table 5. Decision processes and decision heuristics

No. of phases

Travel Occupation Total (N¼33) (N¼58) (N¼91)

.....................................................................

1 2 3 or more

84.4 12.1 3.5

62.5 26.8 10.7

71.0 21.2 7.8

Decision heuristics .....................................................................

42.4 12.1 15.1 30.3

48.2 7.2 19.6 25.0

46.0 9.0 18.0 27.0

rule, choosing the alternative with the highest payo¡ by ignoring the probabilities; whereas in the second and third phase they apply the Expected Value rule. Some processes are stepwise decisions corresponding to the Most Likely rule: decisions are made by comparing all alternatives with respect to one consequence and selecting the alternative exhibiting the highest probability for that utility. Still other processes follow the Elimination by Aspects rule, where individuals exclude alternatives with a highly negatively evaluated consequence.We did not observe Maximin strategies.

Summary and Discussion The most important results of our study can be summarized in the following way. 1. Everyday decisions are made on the basis of low complexity: few alternatives, few behavioural consequences, and even fewer probabilities are taken into account. In contrast, other decisions, such as choosing one’s occupation, are more complex and often extend over more than one phase. 2. However, everyday decisions are more complex in the ¢rst phase than other decisions. 3. Probabilities are estimated in a very crude way by using extreme values. 4. There seem to be three decision heuristics that are most frequent in everyday situations: simultaneous decisions between several options;


Simultaneous decision KO-criterion applied Restriction exists One alternative

applying a‘knock out’criterion to choose a behavioural restriction; and strong restrictions that reduced the number of options. 5. In the great majority of cases individuals decide between only two alternatives. When perceiving more than two alternatives, they exclude alternatives by imposing restrictions or criteria on the consequences. In these cases, the alternative is excluded entirely on the basis of one consequence. 6. About half of the decision processes involves only one phase and in these cases again the majority consists of a decision between two alternatives. 7. The other half of the decision processes extend over two or more phases. A new phase often began when the selected alternative at one point in time is further di¡erentiated into two or more alternatives later on. It is basically a binary-sequential process. 8. Individuals often use di¡erent decision rules at di¡erent phases of the decision process, as already reported in the literature (e.g. Johnson and Payne, 1985: 408; Payne, 1976; Wright, 1975). Several authors hold that individuals do not perform ‘mental algebra’ (van Raaij, 1988: 93) or an ‘algebraic model’ (Svenson, 1979: 109) in complex decision situations. The ¢ndings of our study support this view, but modify it as well. Individuals use sequential decisions to reduce or even avoid such an algebraic e¡ort. Our ¢ndings suggest that everyday decision processes are cognitive e¡orts to reduce the multitude of possible options as quickly and e⁄ciently ^ from their point of view ^ as possible. We might therefore extend Simon’s (1957, 1983: 34) assumption of ‘bounded rationality’ not only to the limited perception of alternatives and consequences but also to the avoidance of information search costs. The most general cause for these limitations is the limited cognitive capacity of individuals. With respect to information sources, we ¢nd that (trustworthy) persons are more important information sources than institutions (or written material). This ¢nding is corroborated by evidence from more recent decision-making studies on buying a computer, ¢nancial investment, and choice of an elementary school (summarized in Friedrichs,


1999). Finally, a decision based on additional information from persons and institutions becomes more complicated, which is indicated by a higher number of behavioural consequences and probabilities reported.

The ‘Generalizability’ of the Findings

(i.e. number of phases, number of behavioural consequences, and of probabilities) of a decision.

Is Rational Choice Theory Falsified? The answer to this question depends on the assumptions that one considers to be part of RCT. The assumptions range from a ‘thin’ to a ‘thick’ version (Elster, 1983; Taylor, 1988) or from a ‘narrow’ to a ‘wide’ version (Opp, 1999).The ‘thin’or ‘narrow’ version consists of a number of relatively restrictive assumptions. For example, it is assumed that individual actors are egoistic, that ‘the agent’s actions are instrumental in achieving or advancing the given aims in the light of the given beliefs’ (Taylor, 1988: 66), and the kinds of incentives allowed in explanations are limited to material or economic incentives (Taylor, 1988: 66). A‘thick’ or ‘wide’ version of RCT admits, for example, altruism and ‘soft’ incentives such as social rewards. A version of RCT that is rather close to the ‘thick’ version is value-expectancy theory (VET).6 It is beyond the scope of this paper to discuss the arguments that are or may be advanced for or against those di¡erent versions.7 Instead, we will focus on the extent to which the version of RCT that we think is most fruitful for sociological applications and that underlay the questions our research sought to answer is con¢rmed. This is VET. It asserts, in brief, that the behaviour to which individuals attribute the highest net utility of an action is performed. The net utility is, by de¢nition, the sum of the products, each consisting of the utility and subjective probability of the behavioural consequences of a given action. We found, that for travelling as well as for occupational choice 24.2 per cent of our respondents reported that they only perceived one behavioural alternative, i.e. the one they performed (seeTable 1). In other words, even everyday behaviour does not often involve decisions in the sense that at least performing or not performing a behaviour is the option taken into account. This does not falsify VET because it asserts that the behaviour with the highest net utility is performed, and this may be just one behaviour. But it runs counter to versions of RCT that assume that each behaviour is a


The question arises to what extent our results hold for all kinds of everyday and other behaviours. Obviously, this question cannot be answered on the basis of our research alone because inductive ‘generalizations’ are logically £awed, as is well known. However, it is common in scienti¢c research to begin with some hypotheses (or alternative hypotheses) that are empirically tested. If the data con¢rm one of the hypotheses there is no reason to discard them. Further research on other behaviours is necessary to further test the propositions. Nonetheless, we will provide two hypotheses about conditions under which some of our results are supposed to hold particularly well. We de¢ned ‘everyday behaviour’as regular, i.e. as repeated behaviour. A person who has performed an action has already gone through a decision process and has learned about the factual behavioural consequences. This information is stored in the person’s memory and is used when the decision situation comes up again. This assumption yields a testable prediction: the results of our study will probably hold particularly well for those behaviours that occurred relatively often in the past. If this reasoning is correct the distinction between regular, i.e. everyday behaviour, and other behaviour is theoretically useful. It could be argued that regularity explains not the di¡erences between everyday and other behaviour but the ‘importance’ (i.e. costs) or the costs of a ‘wrong’ behaviour (i.e. the opportunity costs) for the respondent. Thus, the argument may be that everyday behaviour is low-cost and low-bene¢t behaviour, in contrast to other behaviour. However, a decision about a holiday destination need not be low-cost or low-bene¢t behaviour. This theoretical argument yields a testable prediction for further research: The regularity and not the costs or the opportunity costs are relevant for the complexity

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behaviour that is chosen. A falsi¢cation would also be obtained if known behavioural consequences of a behavioural alternative are disregarded that lead to the performance of an action that does not have the highest net utility.We did not ¢nd any evidence for such decision rules. It seems that in the decision processes of our respondents two decision rules are applied. One is to exclude behaviours from further consideration that have a relatively low net utility; but if the situation changes the respective behaviour is reconsidered. The second decision rule applied seems to be that respondents make clear to themselves what their major goal is and that they select behaviours according to these goals. As a previous example shows, a respondent ¢rst selected holiday destinations according to climate, then according to price. This procedure ^ ‘elimination by aspects’ ^ is certainly consistent with VET because alternatives that are relatively costly are eliminated. The results of our research are in line with the ‘thick’ or ‘wide’ version of RCT. They further indicate that a major argument against such a version is mistaken: the objection of tautology says that a ‘thick’ version is ad hoc because any incentive can be claimed to have been brought about the behaviour.8 We do not introduce incentives ad hoc but tried to ascertain them by the methods of empirical research. This is the usual procedure of research carried out to test VET.

Trade-offs in Complexity We found that decision processes regarding travelling have only one phase, but more behavioural alternatives, behavioural consequences, and probabilities are involved. In contrast, occupational choice is a stepwise decision process. In each phase few behavioural alternatives, behavioural consequences, and probabilities are considered. As was noted before ^ see the section on the ‘Generalizability’ of the Findings ^ it seems that this can be explained by the fact that travelling is a regular behaviour. In the case of occupational choice, however, a multi-phase decision ‘requires’ the individual to consider fewer behavioural alternatives, behavioural consequences, etc. If such a process is not due to institutional constraints there seems to be a


decision situation in the sense that people always consider behavioural alternatives. Regarding the number and kind of perceived behavioural consequences and probabilities taken into account,VETdoes not impose any restrictions. Our ¢ndings indicate that assumptions claiming that only material or economic incentives matter or that individuals’ behaviour is instrumental ^ from the point of view of an outside observer ^ are not con¢rmed. Do the decision processes and the heuristics we found falsify VET? The decisions of our respondents consist of the following behavioural steps: (1) Consideration of a series of behavioural alternatives A1, . . ., An; (2) consideration of a series of behavioural consequences C1, . . . , Cn; (3) performance of an action (not including search for information); (4) consideration of new behavioural alternatives (due to information search, other events, or to action); (5) consideration of new behavioural consequences (due to information search, performed action, or other events). The behavioural alternatives may also be a search for information before a decision is taken. Each of these ¢ve steps may occur in di¡erent orders, the simplest sequence being: 1, 2, 3. The existence of such steps or sequences is not a falsi¢cation of VET. If a sequence consists of behaviours VET can be applied. If a sequence is a cognition (such as step 4) VET cannot be applied because it can only explain behaviour and not the formation of cognitions. VET would only be falsi¢ed if individuals choose the behavioural alternative whose net utility is lower than that of another alternative, but there is no indication in our data that this holds true. However, allowing for di¡erent decision rules at single phases of the decision process is inconsistent with the conclusion ‘SEU mis¢ts decisions’, drawn by Slovic et al. (1977: 94) in their review of the earlier literature. Empirical research, including our own investigation, indicates that individuals use decision rules to simplify the decision process. This simpli¢cation would be a falsi¢cation of VET if a decision rule leads individuals to disregard intentionally behaviour that has a higher net utility than the


trade-o¡ in complexity: few phases of a process go with many behavioural alternatives, consequences, and probabilities; many phases go with few behavioural alternatives, consequences, and probabilities.

Future Research

quantitatively. The often claimed incompatibility of these two approaches does not exist in our research.

Notes 1. An exception is the article by Enste 1998; he addresses decision-making in everyday life, viz. investing money, but this is not everyday behaviour as it is de¢ned in this article. Nor did we include the work of the ABC Group of the Berlin Max-Planck Institute for Human Development, a major institution focusing on decision research (e.g. Gigerenzer et al., 1999, Gigerenzer, 2000). However, their work does not pertain to the exploration of everyday decisions in natural settings by interviewing subjects. 2. For details see Becker, 1976; Frey, 1999; Kirchgssner, 1991; Radnitzky and Bernholz, 1987. From a sociological perspective see particularly Coleman, 1990; Goldthorpe, 2000;Voss and Abraham, 2000. 3. The second author conducted a comparable study in Hamburg (before he moved to the University of Leipzig). Because of time limitations this study was more of an exploratory nature and less well coordinated than the Cologne studies. Nonetheless, as far as the results of both studies are comparable they are consistent. Because of limitations of space we will not refer to the Hamburg study in this article. 4. To illustrate this point, in the one-page questionnaire regarding the choice of studying at a university the interview began with a few sentences introducing the interviewers, their institutional a⁄liation, and the goal of the research. The latter was described as a study focusing on how individuals make decisions such as how one chooses a vacation destination or buys a car. Then the questionnaire continued: ‘I would like to know how you decided to study at a university.What do you study? Kind of study, combination of subjects.’ This was information for the interviewers indicating what they should ¢nd out in a free conversation. In order to make the task easier, the ¢rst question to be asked could be read from the questionnaire. Another question was: ‘Did you consider studying other subjects?’ The word ‘other’ was added. Again, this means that the interviewer had to elicit statements about which other subjects were taken into account and in what order. 5. These results are consistent with ¢ndings by Lu«demann (1997): citizens of Bremen were asked when they recently went downtown and which behavioural alternatives they considered for getting there. Only 27.6 per cent said they had thought about alternatives, and 58 per cent indicated that they had not


Our research is just the initial step in an exploration of the rationality of everyday behaviour in natural settings. Further research is needed in many respects. Speci¢cally, we need studies on decision processes by extending the range of situations and exploring possible other heuristics. We also recommend a more careful study of the situation preceding a given decision process. Such studies could refer to the problem of framing options or consequences. An analysis of decision processes in dyads or groups would be interesting. For example, how does a family decide about moving? How do three persons, coming from a movie theatre, decide about jointly going to a restaurant and choose which one? Furthermore, we may analyse ongoing decision processes. For example, persons leaving a travel agency undecided could be re-interviewed by telephone in order to ascertain their decision process. The results of our study allow us to draw several important conclusions for future research referring to methodological problems in decision research in real life situations. With respect to the method used, it seems very di⁄cult to ascertain decision processes by standardized questionnaires. Unstandardized questionnaires, with a list of questions as guidelines, and the tape-recording of interviews seems to be the adequate method. However, the more we know about real life decision processes the more unstandardized questions may be replaced by standardized ones. We further suggest the use of two interviewers posing the questions. This allows for a constant change in the two tasks of formulating questions and coding the reported material. Changing between these two tasks requires intensive training of the interviewers. This type of study is an excellent example of combining qualitative and quantitative analyses. The data were collected qualitatively, but analysed

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Author’s Address Ju«rgen Friedrichs, Forschungsinstitut fu«r Soziologie, Universitt zu K˛ln, Institut fu«r Soziologie, Greinstr. 2, D-50939 K˛ln, Germany. Fax. +49 221^470 5180. E-mail: [email protected] Manuscript received: September 2000.


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