Hoffrage - How causal knowledge simplifies decision-making.pdf - Psychologia - traditradi

Mind Mach (2006) 16:365–380

DOI 10.1007/s11023-006-9035-1

How causal knowledge simpliﬁes decision-making

Rocio Garcia-Retamero Æ Ulrich Hoffrage

Received: 23 February 2005 / Accepted: 14 November 2005 /

Published online: 11 August 2006

Springer Science+Business Media B.V. 2006

Abstract Making decisions can be hard, but it can also be facilitated. Simple

heuristics are fast and frugal but nevertheless fairly accurate decision rules that

people can use to compensate for their limitations in computational capacity, time,

and knowledge when they make decisions [Gigerenzer, G., Todd, P. M., & the ABC

Research Group (1999). Simple Heuristics That Make Us Smart . New York: Oxford

University Press.]. These heuristics are effective to the extent that they can exploit

the structure of information in the environment in which they operate. Speciﬁcally,

they require knowledge about the predictive value of probabilistic cues. However, it

is often difﬁcult to keep track of all the available cues in the environment and how

they relate to any relevant criterion. This problem becomes even more critical if

compound cues are considered. We submit that knowledge about the causal struc-

ture of the environment helps decision makers focus on a manageable subset of cues,

thus effectively reducing the potential computational complexity inherent in even

relatively simple decision-making tasks. We review experimental evidence that

tested this hypothesis and report the results of a simulation study. We conclude that

causal knowledge can act as a meta-cue for identifying highly valid cues, either

individual or compound, and helps in the estimation of their validities.

Keywords Causal knowledge Æ Compound cue Æ Cue selection Æ Fast and frugal

heuristics Æ Search processes Æ Take the Best Æ Take the Best Conﬁgural Æ

Validity estimation

)

Max Planck Institute for Human Development, Lentzeallee 94, D-14195 Berlin, Germany

e-mail: rretamer@mpib-berlin.mpg.de

U. Hoffrage

Ecole des Hautes Etudes Commerciales, Universit ´ de Lausanne, CH-1015 Lausanne-

Dorigny, Switzerland

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Introduction

When we are faced with a decision, for example, which restaurant to go to or which

meal to order, it is often impossible to consider all the available alternatives and to

gather and process all the information regarding these options. For instance, we

generally do not consider every restaurant in the city, and when we do select one, we

often do not have much detail about the entrees on the menu (e.g., the amount of

cholesterol, fat, or preservatives in the dishes, the cooking methods used, or how

they taste) to help us infer which one we would like most. In fact, in real-life

situations such as this, we often make fast decisions based on little information.

Recently, Gigerenzer, Todd, and the ABC Research Group ( 1999 ) have suggested

that we use simple heuristics in these situations, that is, fast and frugal but never-

theless fairly accurate strategies for making decisions with a minimum of informa-

tion (see also Todd & Gigerenzer, 2000 ). These rules are fast because they do not

involve much computation, and they are frugal because they search for only some of

the available information in the environment.

One of the fast and frugal heuristics proposed by the ABC research group is

Take The Best (TTB; Gigerenzer & Goldstein, 1996 , 1999 ). This heuristic is de-

signed for so-called two-alternative forced-choice tasks and can be used to infer

which of two alternatives has a higher value on a quantitative criterion, such as

which of two university professors earns more money. The alternatives are de-

scribed on several dichotomous cues such as gender or whether the professor is on

the faculty of a state or a private university. These cues allow to make probabilistic

inferences about the criterion. Like each of the fast and frugal heuristics that has

been proposed in the context of this research program, TTB is constructed from

building blocks, which are the precise steps of information gathering and pro-

cessing involved in making a decision. Speciﬁcally, this heuristic has a search rule ,

which deﬁnes the order in which to search for information (TTB looks up cues in

the order of their validity , i.e., the probability that a cue will point to the correct

decision given that it discriminates between the alternatives); a stopping rule ,

which speciﬁes when the search is to be stopped (TTB stops after the ﬁrst

discriminating cue); and a decision rule , which speciﬁes how to use the information

that has been looked up when it comes to making a decision (TTB chooses the

alternative favored by the ﬁrst discriminating cue).

The TTB heuristic and an extension of TTB for comparisons among more than

two alternatives have been subjected to empirical tests in a number of studies (e.g.,

Br¨ der, 2000 , 2003 ; Br¨ der & Schiffer, 2003a ; Newell, Rakow, Weston, & Shanks,

2004; Newell & Shanks, 2003 ; Rieskamp & Hoffrage, 1999 ). There is accumulating

experimental evidence for the use of this heuristic, particularly when there are

search costs for accessing cues (see Br¨ der, 2000 , 2003 ; Br¨ der & Schiffer, 2003b ), or

when decisions have to be made under time pressure (e.g., Rieskamp & Hoffrage,

1999). In addition, Newell, Weston, and Shanks ( 2003 ) tested the building blocks of

TTB separately and reported that 75% of participants followed TTB’s search rule by

validity. Furthermore its stopping and decision rules were obeyed in 80% and 89%

of the trials, respectively (see also Newell & Shanks, 2003 ).

However, these experimental results on the use of TTB need to be qualiﬁed. In

most of these studies, participants were encouraged to use cues in the order of their

validity by being informed about cue validities or the validity order (see Br¨ der,

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2000, 2003 ; Br ¨ der & Schiffer, 2003b ; Newell et al., 2003 ). In two studies that tested

search by validity against alternative search orders, validity was not the search cri-

terion that predicted participants’ searches best (L¨ ge, Hausmann, Christen, &

Daub, 2005 ; Newell et al., 2004 ) because participants were instead making use of

simple rules for ordering cues based on trial-by-trial learning (Dieckmann & Todd,

2004; see also Garcia-Retamero, Takezawa, & Gigerenzer, 2006). The cue orderings

established through such rules do not necessarily converge toward the ordering

established by validity. Therefore, participants might have had difﬁculties computing

cue validities and then ordering cues accordingly even though they were dealing in

those experiments with relatively few cues (i.e., four to six).

The problem of ﬁnding a good cue ordering appears even more severe if one

considers that in most situations, there are myriad potential cues that could be used

to make a decision, and it is practically impossible to keep track of them all and to

compute their validities for any potentially relevant criterion (Juslin & Persson,

2002). Cue selection is further complicated if potential combinations of cues (i.e.,

compound cues) are taken into account. Yet sometimes an accurate decision re-

quires us to do so. For example, some medications might have side effects, such as

nausea, if ingested together with alcohol, whereas neither the drug nor the alcohol

would cause any problems if ingested alone (of course, this would also depend on the

amount of alcohol that is consumed). Thus, the relationship between one cue (the

ingestion of a medication) and the criterion (nausea) depends on the presence of

another cue (the ingestion of alcohol). The problem is that in real-world environ-

ments, there exist a multitude of potential combinations of cues to form compounds,

rendering it nearly impossible to keep track of them all. As a consequence, a strategy

that processes all possible compound cues as conﬁgurations would be too compu-

tationally demanding. Nor is it plausible to assume that the brain comes ‘‘prewired’’

with a representation for each of the combinations of elementary stimulus inputs

(Kehoe & Graham, 1988 ).

Bearing these comments in mind, the interesting question is whether there is a way

in which the relevant cues, individual or compounded, can be selected from the

abundance of possibilities in the environment. We hypothesize that people do not

process all possible cues in their natural environments but rather use their causal

knowledge, that is, their knowledge about causal relationships between events in the

environment, to focus on a small and manageable subset of relevant cues. We further

assume that causal knowledge might also aid learning of cue validities. In sum, causal

knowledge might allow decision makers to deal adaptively with the huge number of

individual and compound cues that appear in the environment by directing them to

those that are potentially relevant. In the remainder of this paper, we offer more

precise predictions about how causal knowledge can inﬂuence decision-making

processes and review several experiments and a simulation study, all conducted within

the fast-and-frugal heuristic framework, in which these predictions were tested.

The adaptive value of knowledge about the causal texture of the environment

The adaptive importance of causal processing has been stressed by many authors in a

wide range of disciplines (see Gopnik & Schulz, in press, for a review), including

computer science (e.g., Pearl, 2000 ), philosophy (Glymour, 1998 ; Harre & Madden,

1975; Hume, 1987 ; Kant, 1965 ; Mackie, 1974 ; White, 1995 ), developmental

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R. Garcia-Retamero, U. Hoffrage

psychology (Gopnik et al., 2004 ; Koslowski & Masnick, 2002 ; Schlottmann, 1999 ),

and cognitive psychology (Ahn & Kalish, 2000 ; Cheng, 1997 ; Garcia-Retamero, in

press; Waldmann, Holyoak, & Fratianne, 1995 ). We have a general tendency to

consider connections between events in terms of causal relationships, and we

understand, predict, and control our environment by positing underlying causal

mechanisms that generate our sensory experience (Lagnado & Sloman, 2004 ).

When it is said that a cause brings about an effect, the implication is that there is a

stable causal link between the cause and the effect and an underlying causal

mechanism that is an essential property of this link (Ahn & Kalish, 2000 ; Glymour &

Cheng, 1999 ). Such a link between a cause and an effect goes beyond the mere

covariation between them as the cause produces the effect (Cheng, 1997 ; Novick &

Cheng, 2004 ). Having causal knowledge about the cues in the environment is

adaptive for individuals because it allows them to make predictions about future

events and to intervene in the ecology to bring about new events (Gopnik et al.,

2004; Lagnado & Sloman, 2004 ; Steyvers, Tenenbaum, Wagenmaker, & Blum, 2003 ;

Waldmann & Hagmayer, 2005 ). Therefore, it is quite conceivable that natural

selective pressures have, over the course of evolution, established some genetic basis

for causal thinking (Shultz, 1982 ). Causal beliefs are not isolated but are tightly

connected with other causal beliefs in a broad base of knowledge that represents the

causal structure of the environment, henceforth referred to as a causal mental model .

There are two main approaches in the psychological literature to explain how

causal links between events can be inferred. The bottom-up approach assumes that

observing or experiencing correlations among events could help in the generation of

these causal links or in the adjustment of existing ones (Cheng, 1997 ; Glymour &

Cheng, 1999 ; Gopnik et al., 2004 ; Koslowski & Masnick, 2002 ; Novick & Cheng,

2004; Shanks & Dickinson, 1987 ; Spellman, 1996 ). The top–down approach was

advanced by Waldmann (Waldmann & Holyoak, 1992 ; Waldmann et al., 1995 ; see

also Ahn & Kalish, 2000 ; Harre & Madden, 1975 ; White, 1995 ), who argued that

people’s abstract knowledge about causality (such as causal directionality) shapes

how data are interpreted.

When it comes to decision-making, we posit that causal knowledge is advanta-

geous for three reasons. First, causal knowledge might act as a meta-cue for iden-

tifying valid cues in the environment. Second, causal knowledge might help us focus

on certain cues, which, in turn, facilitates learning of cue validities. Third, causal

knowledge might guide people in the selection of the relevant compound cues in the

environment. We now elaborate on each of these three advantages in more detail.

Considering the ﬁrst advantage, we hypothesize that cues that are causally linked

to a criterion tend to be more valid than other cues lacking such a connection to the

criterion (Garcia-Retamero, Wallin, & Dieckmann, 2006 ; see also Ahn & Kalish,

2000; Wallin & G¨ rdenfors, 2000 ). For instance, lung cancer (here, an effect) is more

likely to be predicted from a well-established smoking habit (i.e., a cause) than from

yellowed ﬁngers (i.e., a second effect of the common cause; see Boyle, 1997 ). Fur-

thermore, correlations between events that are causally linked are likely to be more

robust across environments, that is, less sensitive to contextual changes, than those

without such a connection (Pearl, 2000 ; Reichenbach, 1956 ). Following our example,

the correlation between smoking and lung cancer would be more robust across

different series of patients than the correlation between lung cancer and yellowed

ﬁngers would be. We could expect this to be the case even if we control for other

alternative causes that could bring about yellowed ﬁngers (e.g., being a painter) that

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Causal knowledge and simple heuristics

369

might reduce their predictability for lung cancer. We hypothesize that this asym-

metry between causal and non-causal cues that holds in the physical world would be

reﬂected in human cognitive processes. We therefore expect decision makers to use

their causal knowledge as a meta-cue for selecting highly valid and robust cues in the

environment.

Besides facilitating the selection of valid cues in the environment, causal

knowledge might reduce the number of cue–criterion correlations to keep track of

when computing cue validities (Garcia-Retamero et al., 2006 ). This hypothesis is

supported by research using multiple cue probability learning (MCPL) tasks. In this

paradigm, participants have to predict the criterion of a given object from multiple

cues that are probabilistically related to this criterion. Previous empirical studies

using this paradigm (see Kruschke & Johansen, 1999 , for a review) suggest that there

exists interference effects when multiple cues are available and, consequently, cue

validities have to be learned concurrently. For instance, if irrelevant cues are present

in such a task, the utilization of valid cues is reduced and, consequently, the accuracy

of people’s judgments is lower as compared to a condition in which these irrelevant

cues are not included (Castellan, 1973 ; Edgell & Hennessey, 1980 ). One can explain

this ﬁnding, which can be observed even after a large number of learning trials,

by assuming that in the condition with the irrelevant cues it is harder for participants

to identify and focus on the valid cues. In contrast, when participants can learn

cue–criterion relationships sequentially, that is, for one cue after another, their

judgments more closely correspond to the ecological correlations (Brehmer, 1973 ).

Based on this ﬁnding we suggest that in multiple-cue settings people equipped with

causal knowledge might be able to use this knowledge to focus on certain cues,

which, in turn, might facilitate learning of cue validities.

Note, however, that causal knowledge about the cues in the environment also has

to be learned. Therefore, our argument holds only if the acquisition of causal

knowledge is simpler than the learning of cue validities. We think that this is, in fact,

the case. Consider, for instance, learning of causal Bayes nets. Such learning is

certainly not necessarily simple, but it could be simpliﬁed if prior speciﬁc or abstract

domain knowledge about the structure of the environment (e.g., causal direction-

ality) constrains the number of potential causal relations that need to be considered

(see Tenenbaum, Grifﬁths, & Niyogi, in press; Waldmann, 1996 ; Waldmann &

Martignon, 1998 ). 1

The third advantage of causal knowledge is that it can guide people in the

selection of the relevant compound cues in the environment that should be repre-

sented as conﬁgurations (see Garcia-Retamero, Hoffrage, Dieckmann, & Ramos, in

press b). Speciﬁcally, we hypothesize that when people perceive several cues to act

through a common causal mechanism in bringing about an effect, they will consider

the possibility that these cues might also interact with each other in bringing about

that effect. This possibility may lead them to check whether the accuracy of their

predictions would be increased through representing these cues as a conﬁguration.

1 Along these lines, research in the ﬁeld of artiﬁcial intelligence has recently proposed a number of

algorithms capable of easily inferring causal relations from covariation patterns (e.g., the TETRAD

II program; Spirtes, Glymour, & Scheines, 1993 , 2000 ). These algorithms use causal models to

generate a certain pattern of statistical dependencies and then search for certain clues that reveal

fragments of the underlying structure. These fragments are pieced together to form a coherent causal

model. Obviously, these systems do not provide information about how humans learn causal links,

but they do tell us how such a task might be solved.

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