Satisficing when buying information

ORGANIZATIONAL BEHAVIOR AND HUMAN DEEISION PROCESSES 51, 471-481 (1992)

Satisficing When Buying Information

JAMES BOWEN AND ZI-LEI QIU

The University of Texas at Arlington

In two studies expected value, expected utility, and satislicing approaches to one’s decision to buy information were compared. The task for the first study was to judge the maximum worth of information by bidding to reduce an ambiguous prospect to a risky one when given an option between a risky prospect and an ambiguous one. A within-subjects design was used in which, for the risky prospect, the probability of gain and expected value were varied. The results were inconsistent with both expected value and expected utility models, but were consistent with a satisficing strategy. The second study was similar to the first but with context changes in the stimuli and a slightly dif- ferent decision task. The results showed that the effects are robust across task and context. The main behavioral effect is that people appear more willing to buy information that may decrease an expected loss than information that may increase an expected gain. A decision justification principle is consistent with that effect. 6 1992 Academic Press, Inc.

Connolly and Set-t-e (1984) showed that the information purchasing strategies people use often lead to suboptimal purchasing behavior. Sub- optimality appears to result because most people buy a moderate amount of information, relative to what is available, regardless of cue validity. Thus too little information is purchased when cue validity is low and too much when cue validity is high. They claim (correctly) that their results are pertinent to a variety of decision tasks, such as consumer search for product information before purchase, physicians recommending diagnos- tic tests before diagnosis, oil companies collecting geological data before selecting drilling sites, potential employers administering aptitude and interest tests before hiring, and marketeers conducting market surveys before deciding whether to introduce a product to market.

We investigated a closely related feature of those decision tasks, i.e., the effect of the attractiveness of alternate courses of action on willingness to buy information. An oil company may have geological data about a particular drilling site and is considering whether to collect data about another. Whether the extant data are favorable may determine whether data about another site are collected. Employers may or may not test prospective employees depending on already obtained test results.

Address correspondence and reprint requests to Dr. James Bowen, Department of Psy- chology, Box 19528, University of Texas, Arlington, TX 76019.

471 0749-5978192 $3 .OO Copyright 0 1992 by Academic Press, Inc. All rights of reproduction in any form reserved.

472 BOWEN AND QIU

For each example, the decision maker(s) may choose a prospect about which they are well informed or about which they are ill-informed, or they may choose to acquire more information about the option for which they are ill-informed. In essence, then, the decision task is whether to choose between risky and ambiguous prospects or to pay to reduce ambiguity and then choose between risky prospects.

Simon (1955, 1956) argued that people ought to replace the goal of maximizing utility with the goal of satisficing, i.e., of choosing an action that is “good enough,” so rational decisions will be within the bounds of a human’s computational capacity. Since Simon proposed satisficing as a normative goal, behavioral research has shown that people have a satisticing attitude in the sense that they select simplifying strategies or use judgmental heuristics that give good results most of the time but occa- sionally yield undesirable outcomes (Kahneman, Slavic, & Tversky, 1982).

It is well established that the satisticing strategy one selects is context and task dependent (see Einhorn & Hogarth, 1981, and Payne, 1982, for literature reviews). Beach and Mitchell (1978) offer a cost/benefit framework for interpreting such contingent effects. The idea is that the decision maker goes through a two-stage procedure in making a decision. They first choose a decision strategy based on a cost/benefit analysis and then apply the selected strategy in choosing a course of action. Decision strategies are evaluated by balancing one’s desires to be correct and to min- imize decision effort. The benefit of the framework is flexibility for think- ing about or interpreting decision behavior. The cost is a lack of predictive power so that it is unclear what to predict for the task at hand.

Slavic, Fischhoff, and Lichtenstein (1982) and Curley, Yates, and Abrams (1986) provide more predictive power from subsets of the cost/ benefit framework with their ideas about the importance to people of being able to justify the decisions they make in order to assuage the potential effects of regret. According to them highly ambiguous decisions are more difficult to justify than risky ones. It follows directly that the more attractive the alternative to an ambiguous prospect the easier it is to justify choosing the alternative, so the value of information to people about ambiguous prospects should decrease monotonically as the attractiveness of alternatives increases. For if choosing an ambiguous prospect is difficult to justify and one is considering an ambiguous prospect and an unattractive risky prospect, then both are difficult to justify and ambiguity reducing information should be valuable. If one is considering an ambiguous prospect and an attractive risky prospect, choosing the risky prospect is justifiable and ambiguity reducing information should have little value. The questions, then, are whether people value information in the

BUYING INFORMATION 473

pattern specified and, if so, whether that pattern corresponds to normative patterns; i.e., does one get good results by using that strategy?

The monetary value and the utility of uncertainty reduction can be computed (e.g., Lindley, 1985, for a lengthy and lucid development of the computational procedures) and it will be useful to compare human judgment with expected value (EV) and expected utility (EU) as models for evaluation purposes. A fundamental principle is that information that is independent of the decision task has zero value; otherwise its value is positive. There is also a symmetry property such that information that reduces an expected loss by amount c has the same value as information that increases expected gain by amount c.

The classical literature (Mosteller & Nogee, 1951; Davidson, Suppes, & Siegel, 1965) shows that attractiveness increases with expected value for risky prospects. Thus the expected value of an alternate risky prospect may affect willingness to buy information about an ambiguous prospect. Such an effect by itself, however, does not distinguish between a non- normative cognitive strategy and EV or EU theory. To cope with differences among these approaches, we varied both expected value and probability of gain for the risky prospects.

For the present study we gave people choices between risky and ambiguous prospects with the option of bidding for information that would reduce the ambiguity to risk before making a choice. Because the value of information, V(I), is always nonnegative and usually positive (Lindley, 1985) people should generally be willing to pay for it. We varied V(1) and independently varied the probability of gain and the expected value of prospects. As V(1) does not reflect one’s attitude toward risk unless one is risk neutral, we simulated risk averse and risk seeking utility functions with power functions of the payoffs and calculated corresponding values for the utility of information, U(1). See Fig. 1 for the patterns of information buying behavior predicted from the value and utility functions.

For the risky prospects the probabilities of gain were .25 and .75 and did not affect V(1). The EV’s of the prospects were - 1, 0, or 1 dollar. The V(1) were 50 cents for EV = - I or 1, and 90 cents for EV = 0, thus showing the pattern of bidding behavior for people who are willing to pay the objective worth of information. There should be no effect for probability of gain and a quadratic effect but no linear effect for expected value. For the range of outcomes we used, the value and utility functions should be similar. We selected power functions so that the utility functions were in the neighborhood of the values. For risk seekers, there was a concave trend over expected value at p = .75 and a decreasing trend over expected value at p = .25. For risk avoiders, there was a concave trend over expected value at p = .25 and a decreasing trend over expected value at

474 BOWEN AND QIU

Expected Value

* pm.25 -a- pm.75

U(l) for Risk Averse 1

/

--.-.--..__ -____.__.__

0.75 -..__...___ ._. _._. __ __

o,5 - . .,-m _ _ _

0.25

t

-.--_.-..__ .._._.._.._

01 1 -1.5 -1 -0.5 0 0.5 1 1.5

Expected Value

* pm.25 - pm.75

U(l) for Risk Seekers

0.25 _ _ _ _ _ _ _ _ _ _ . . _ . _ _ _ . _ _ _ -

0 I -1.5 -1 -0.5 0 0.5 1 1.5

Expected Value

* pm.25 - pm.75 FIG. 1. Value and utilities of information.


p = .75. Boh tlt f t u i i y unctions generate interactions for values of U(1). Values of U(1) converge to V(1) as the gain-loss exponents converge to one. The interaction effect increases as U(1) differs from V(1). For a computational example, one may represent a risky prospect for a risk seeker as (x1.* , p, - y.‘) and for a risk avoider as (CC*, p, -yl.*). Then, for risk seekers, the U(1) at p = .25 are .62, .51, and .45 for expected values - 1 , 0, and 1 dollar, respectively. At p = .75 the U(1) are .48, .65, and .43. For risk avoiders, at p = .25 the U(1) are .43, .65, and .48; at p = .75 the U(I) are .45, .51, and .62. See Fig. 1 for a graph of the patterns.

The patterns generated from EV and EU theory differ from that predicted by a satisficing strategy based on justification principles. Thus the simplifying strategies people use may not in general give good results for the complicated task of judging the worth of information.

EXPERIMENT 1

Method

Subjects. The subjects were 36 students satisfying a course requirement at The University of Texas at Arlington.

Stimuli. Expressions of the form “(x, p, - y)” denote risky prospects and signify “win x dollars with probability p, otherwise lose y dollars.”

The risky prospects were (1, .75, -7), (3, .75, -9), (3, .75, -5), (5, .25, - 3), (9, .25, - 3), and (7, .25, - 1). The ambiguous prospect was (3, p, - 3) with p unknown but with possible values of 0, .25, .50, .75, or 1.0. All values of p were equally likely.

Procedure. The subjects were to report in groups of four. Because of a few failures to report, some groups had only three subjects. Each group listened to instructions together and observed demonstrations of the type of choice they were to make. They were to be presented with a risky prospect and a standard ambiguous prospect (3, p, -3). They could choose between them as presented if they wished or they could make a bid for knowledge of the value of p. To encourage maximized bids, they were allowed, for each pair of prospects, one bid only for information and told that bids below a critical but unrevealed value would be rejected. They were told that after completing their tasks one among them would be randomly chosen and they would be committed to one of their chosen prospects. At that time they would learn whether their bid, if they made one, was high enough to warrant ambiguity reduction, and, if so, make their final choice between risky prospects. They were warned that a loss was possible and were allowed the option of leaving at this point if they so desired. No one did. The people then were isolated in separate experimental chambers and run independently with independent random se-

476 BOWEN AND QIU

quences of choices. The stimuli were presented on computer monitors and responses were computer recorded from keyboards. The monitors and keyboards were serviced by a PDP-11 computer.

After all had finished, one was selected by chance and one of their prospects realized by a random draw from a can with appropriate proportions of red or blue chips. If an ambiguous prospect was chosen, the person selected a can without knowing its contents and then drew a chip. They were paid if they won, but no money was collected from losers. The total cost was 26 dollars.

Design. Each subject served in all conditions, thus yielding a repeated- measures design with one dependent variable and two crossed independent variables. The dependent variable was the amount of money a person was willing to pay to learn the probabilities of either winning 3 dollars or losing 3 dollars. The probability of winning 3 dollars had possible values of 0, .25, SO, .75, and 1 .O. All were equally likely to occur. The independent variables were the expected value of the alternate prospect with possible expected values of - 1, 0, and 1 dollar and the probability of winning with the alternate prospect with possible values of .25 and .75. The payoffs for the risky prospects ranged from a gain of 9 dollars to a loss of 9 dollars. Each person thus made six choices in two stages. A person first compared a risky prospect with a standard ambiguous prospect. The person could choose at that juncture or make a monetary bid for information that would reduce the ambiguous prospect to risk with a chance of later choosing between two risky prospects.

Results

For expected values - 1, 0, and 1, respectively, the mean bids in dollars for information were, for p = .25, 1.08, 1.04, and .77. For p = .75 they were 89, .74, and .59. An analysis of variance showed there was no interaction (F(2,70) = .46) and that both main effects were significant (for probability of gain, F(1,35) = 5.22, p < .03; for expected value, F(2,70) = 4.95, p < .Ol). Because the interaction did not approach significance, only the main effects are of interest. The mean bid for p = .75 was .74 dollars and for p = .25 it was .% dollars. For expected values - 1, 0, and 1, respectively, the mean bids were 99, 89, and .68 dollars. The linear trend for expected value was significant (F(1,70) = 6.38, p < .Ol) but the quadratic trend was not (F(1,70) = .27). For expected values - 1, 0, and 1, respectively, the numbers of people who chose without bidding for information, i.e., made bids of zero dollars, were, for p = .25, 6, 7, and 16. Forp = .75 there were 9, 14, and 19. For the cases of nonbidding, the person chose the risky prospect. Given the results of the statistical analysis, the pattern of nonbidders is not surprising but nonetheless supports that analysis.


The above ANOVA shows population trends that are consistent with a satisficing attitude toward information worth. Because the data were pooled over people, they may not represent any individual’s judgments. A strict criterion for counting the number of people whose data are consistent with a justification mechanism is to count those who show strictly decreasing bids over increasing expected value at both probabilities of gain. Nine people met the criterion. Four others met a strict criterion for V(I), i.e, a clear quadratic trend for both probabilities of gain. Two and three others met strict criteria for risk averse and risk seeking behavior, respectively, i.e., one strictly monotonic trend and one clear quadratic trend, each for the appropriate probability of gain. No more than three people displayed any other unique pattern.

A less strict criterion is to count the number of people who show decreasing marginal mean bids as expected value increases. Computa- tions show a quadratic marginal trend for all three normative patterns. Seventeen people showed decreasing marginal trends and 10 showed clear quadratic marginal trends.

Discussion

Because there was no interaction and no concave trends over either probability value, the pooled data tit none of the computed patterns. However, the results are consistent with a satisficing heuristic. That is, when one has an option between unpleasant alternatives, one of which is unpleasant because it is ambiguous, one is willing to continue the search for an attractive prospect by paying to sharpen one’s knowledge about the ambiguous prospect. The observed pattern is consistent with an under- lying justification mechanism and that may be the most common cognitive process operating in decision tasks of the type modeled here.

EXPERIMENT 2

As Payne (1982) observed, seemingly minor changes in task or context can have profound judgmental effects. One difference that affects final preference order is whether one makes holistic judgments about options instead of direct choices among options. Another is the kind of knowledge one has about the outcome generating mechanism. Tversky and Heath (1989) showed that whether one’s knowledge about uncertainty is alea- tory or epistemic may affect judgment. Experiment 2 is similar to Exper- iment 1 with the main differences being that uncertainty is more epistemic; yes-no rather than holistic judgments were made; larger amounts of money were used; no real gambling occurred; and, for the risky prospects, there were three levels each for probability of gain and expected value. Results similar to those of Experiment 1 would show that those

478 BOWEN AND QIU

effects are insensitive to changes in task and context. Each person was asked whether, in choosing between a risky and an ambiguous investment and with 1000 dollars to invest in a 90-day venture, they would pay a stockbroker a fee to reduce the ambiguous investment to a risky one. The risky investments had probabilities of gain of .25, SO, and .75. The expected values were - 25, 0, and 25 dollars.

For all probabilities of gain the values of information were, for expected values of -25 and 25 dollars, 5 dollars and, for 0 dollars, 15 dollars. Simulations of utility functions again showed an expected value by probability of gain interaction. Risk avoiders should show a marginal increasing tendency to buy information as expected value increases while risk seekers should show a marginal decreasing tendency to buy as expected value increases.

Method

Subjects. The subjects were 26 students satisfying a course requirement at The University of Texas at Arlington.

Stimuli. The standard ambiguous investment return prospect was (50 dollars, p, -50). The alternate risky investment return prospects were, for expected values -25, 0, and 25 dollars, respectively, at p = .25, (50, .25, -5O), (150, .25, -5O), (250, .25, -50); atp = .5, (50, .5, - IOO), (100, .5, -lOO), (150, .5, - 100); at p = .75, (50, .75, -250), (50, .75, -150), (50, .75, -50).

Procedure. The subjects were run in one group and each responded in questionnaire style to one of three random sequences of stimulus presen- tation. In deciding how to invest 1000 dollars for 90 days, each subject indicated whether they would choose the risky return prospect or the ambiguous return prospect or pay a knowledgeable and reliable stockbroker 25 dollars to reduce the ambiguous return prospect to risk. The stockbroker would advise that the probabilities of gain were 0, .25, .50, .75, or 1.0.

Results

The main concern was willingness to buy information, so, for each risky prospect, each subject was coded 1 for deciding to buy information and 0 otherwise. The coded scores were dependent variables in a 3 x 3 within-subjects design. The total scores were, for expected values -25, 0, and 25 dollars, respectively, at p = .25, 8, 4, and 4; at p = .50, 13,9, and 7; at p = .75, 6, 7, and 2. To discriminate most directly among EV, EU, and judgmental heuristic approaches, an ANOVA was run on the coded scores. The analysis showed a significant effect for expected value (F(2,50) = 4.82, p < .02) but not for probability of gain (F(2,50) = 2.89,


p < .07) or the interaction (F(4,lOO) = .98, p < .43). Trend analyses were done on both marginal distributions to aid in interpreting the data. For expected value there was a significant linear trend (F(1,50) = 9.61, p < .004) but no quadratic trend (F(1,50) = 0, p = 1). For probability of gain there was no linear trend (F(1,50) = .024, p < .88) but a significant quadratic trend (F(1,50) = 5.75, p < .02).

Those who decided against buying information chose between the risky and the ambiguous prospects. For expected values - 25,0, and 25 dollars, respectively, the conditional probabilities of choosing the risky prospect were .39, .69, and .78. For probabilities of gain .25, .50, and .75, respectively, the conditional probabilities of choosing the risky prospect were .65, .69, and .59.

Discussion

The proportions of people willing to pay a fee tit a satisficing approach but do not fit either EV theory or EU theory. The data do not fit EV theory because there was no quadratic trend for expected value and there was an effect for probability of gain. The data do not fit EU theory because there was no expected value by probability of gain interaction.

GENERAL DISCUSSION

The two experiments are consistent in showing a decreasing willingness to buy information about a prospect as alternatives become more attractive (Fig. 2). Both show that EV theory is a poor description because there is no quadratic trend over expected value and there are differences due to probability of gain. Without an interaction between expected value and probability of gain, EU theory is not a good description of people’s information buying behavior. The best fit from among the normative functions is that for risk seekers, as, for both Experiments 1 and 2, risk seekers should show a decreasing linear tendency to buy information as expected value increases. The implication is that people appear to buy information that may reduce an expected loss but not information that may increase an expected gain. Unlike the outcomes of other satisficing strategies, such an appearance may rarely correspond to a normative pattern.

Though Experiment 2 confirmed the theoretically interesting parts of Experiment 1, there were differences in results. The most notable difference was for probability of gain. For Experiment 1 there was a marginal difference in mean bids between probabilities of gain .25 and .75; for Experiment 2 there was no linear trend and thus no difference in the proportion of people willing to pay the fee between probabilities of gain .25 and .75. The lack of consistency may be because, in Experiment 2, the

480 BOWEN AND QIU

Experiment 1

Observed Bids

1.25

0.25

-1.5

I -1

I I I -0.5 0 0.5

Expected Value

4 p=.25 4 pm.75

I I 1 1.5

Experiment 2

Proportion Buying Information

100% ._.----.-.._-._._____._____.____-

F

75% __.------___---__________________

50% ---- __ _ ___._______________ _ __ __

F=+b

25% __ ______ _________ ___________

O%l I

I I I I I I -30 -20 -10 0 10 20 30

Expected Value

* pm.25 4 p=.so * pm.75 FIG. 2. Observed worth of and proportions of people willing to pay a fee for information.

subjects were less cautious because the risk was imaginary. A high probability of gain may be seen as safer when the risk is perceived as real. The quadratic trend observed in Experiment 2 may be because it is easier to compare an ambiguous prospect with a risky one if the probability of gain is .50 and so the tendency to avoid ambiguity may be strongest at that value.


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Curley, S. P., Yates, J. F. & Abrams, R. A. (1986). Psychological sources of ambiguity avoidance. Organizational Behavior and Human Decision Processes, 38, 230-256.

Davidson, D., Suppes, P., & Siegel, S. (1957). Decision making:An experimental approach. Stanford, CA: Stanford Univ. Press.

Einhom, H. J., & Hogarth, R. (1981). Behavioral decision theory. Annual Review of Psy- chology, 32, 53-88.

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Kahneman, D., & Tversky, A. (1979). Prospect theory. Econometrica, 47, 263-292. Lindley, D. V. (1985). Making decisions (2nd ed). London: Wiley. Mosteller, F., & Nogee, P. (1951). An experimental measurement of utility. Journal of

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Tversky, A., & Heath, C. (1989). Ambiguity and confidence in choice under uncertainty. Twelfth Research Conference on Subjective Probability, Utility, and Decision Making, Moscow.

RECEIVED: January 24, 1990

Satisficing when buying information

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