Journal of Behavioral Decision Making

J. Behav. Dec. Making, 19: 321–332 (2006)

Published online in Wiley InterScience

(www.interscience.wiley.com). DOI: 10.1002/bdm.530

Who will Win Wimbledon? The Recognition
Heuristic in Predicting Sports Events

SASCHA SERWE

1

*

and CHRISTIAN FRINGS

2

1

Giessen University, Germany

2

Saarland University, Germany

ABSTRACT

Goldstein and Gigerenzer (2002) described the recognition heuristic as a fast, frugal,
and effective decision strategy. However, most studies concerning the recognition
heuristic have been conducted in static domains, that is, in domains where it could
plausibly be argued that relevant variables stay relatively constant. Yet the question
is whether the heuristic would also work in dynamic environments where the quality
of the actors rises and falls, such as in sports. We tested performance of the recognition
heuristic in a dynamic environment and used it to predict the outcomes of tennis
matches in Wimbledon 2003. Recognition data of amateur tennis players and
laypeople was used to build recognition rankings. These rankings correlated with
official rankings and led to at least as good predictions. Simulations of individual
choices showed high recognition validities of both amateurs (0.73) and laypeople
(0.67). In a second study the recognition heuristic correctly predicted 90% of actual
individual choices. Overall, the recognition heuristic may be effectively generalized
to dynamic environments. Copyright # 2006 John Wiley & Sons, Ltd.

key words

decision making; recognition heuristic

INTRODUCTION

In research on judgment and decision making, the term ‘‘heuristics’’ is frequently used to describe simple
and naive decision strategies. However, two different views on the effectiveness of heuristics are discussed.
On the one hand, heuristics can be seen as suboptimal replicas of statistical computations that lead to
systematic biases and decision failures (e.g., Kahneman, Slovic, & Tversky, 1982; Kahneman & Tversky,
1996; Pohl, 2004). On the other hand, Goldstein and Gigerenzer (2002) emphasize that heuristics can be
‘‘ecologically rational’’, that is, they achieve accurate inferences by exploiting patterns of information in
the environment with the help of evolved psychological capacities. Moreover, due to their simple application

Copyright # 2006 John Wiley & Sons, Ltd.

* Correspondence to: Sascha Serwe, Giessen University, Department of Psychology, Otto-Behaghel-Str. 10F, 35394 Giessen, Germany.
E-mail: [e-mail]

they lead to fast, frugal, and therefore efficient results, when time, knowledge, and computational power
are limited. Gigerenzer and colleagues present several fast and frugal heuristics, for example, Take The Last,
QuickEst, etc. (Gigerenzer & Goldstein, 1996; Gigerenzer & Selten, 2001; Gigerenzer, Todd, & the ABC
Research Group, 1999; Todd & Gigerenzer, 2000). The simplest of all heuristics in their research program
is the recognition heuristic (Goldstein & Gigerenzer, 1999, 2002). The aim of this article is to test the effec-
tiveness and descriptive value of the recognition heuristic in a dynamic real world environment. In particular,
we predicted the outcomes of tennis matches in Wimbledon 2003 using the recognition heuristic and com-
pared its performance with predictions of two official rankings and the betting market.

RECOGNITION HEURISTIC REVISITED

Using the concept of ‘‘recognition’’ the world can be divided into two categories: the novel, unrecognized
objects (never heard or seen before) versus the previously experienced and recognized objects. If one has to
decide which of two objects has a higher criterion value (say for example, which city has a larger popula-
tion), this binary recognition information can result in predictions better than chance. Suppose there is an
environment where the probability of object recognition is positively correlated with the criterion (e.g., the
larger a city the higher is the probability that you know it). Then the recognition heuristic will perform well:
If one object is recognized and the other is not, infer that the recognized object has the higher value. Thus,
one crucial precondition that determines performance of the recognition heuristic is the correlation between
recognition and criterion. The strength of this relationship is measured by the recognition validity , which is
the number of correct predictions divided by the number of all discriminating pairs (i.e., excluding cases
where the recognition heuristic does not make a prediction).

Another precondition for the recognition heuristic is that there are at least some unrecognized objects.

More precisely, the recognition heuristic can be applied most often in a reference class of objects if half
of the objects are recognized. If you know more (or less) than half of the objects, the recognition heuristic
is applicable less often. Thus, if the recognition heuristic is the only decision rule and if > 0.5, the counter-
intuitive less-is-more effect (Goldstein and Gigerenzer, 1999, 2002) can be predicted. Suppose there are 100
cities and you have to decide, for all possible pairs, which city is bigger. Furthermore, there is a strong cor-
relation between recognition and population of a city. If you do not recognize any city, your only possibility
is to guess. If you recognize all cities, you cannot use the recognition heuristic either. However, if you recog-
nize half of the cities, the theoretical maximum of all pairs —for infinite samples it converges to 50% —
consist of one recognized and one unrecognized city. Due to the strong correlation many decisions in these
situations will be correct. If the recognition heuristic is not the only decision rule, further rules might
improve decisions in situations where both objects are recognized. The proportion of correct decisions in
these situations is defined as the knowledge validity . Yet as long as > , the less-is-more effect persists.

Empirical evidence
In the city population task described above usually about 90% of participants decide in accordance with the
recognition heuristic and achieve good results by using it (Goldstein & Gigerenzer, 1999, 2002). Moreover,
in group decisions with respect to city population, members who can use the recognition heuristic are more
influential than members who recognize both alternatives (Reimer & Katsikopoulos, 2004). However, the
recognition heuristic is domain specific and works only in environments in which the recognition validity
is high. Respectively the theory of the adaptive toolbox (Gigerenzer & Selten, 2001) predicts people to do
not use it mindlessly but only in situation where the subjective validity of the recognition cue is high.
For instance in the city population task people decide against recognized cities if they are tiny neighboring
towns or if they are (such as Chernobyl) recognized for reasons clearly unconnected to population size

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Journal of Behavioral Decision Making, 19, 321–332 (2006)

(Oppenheimer, 2003). Besides the city population task, Goldstein and Gigerenzer (1999, 2002) present
objective recognition validities above 0.6 for highest mountains, largest Italian provinces, largest deserts,
tallest buildings, largest islands, longest rivers, largest U.S. banks and largest seas.

All these domains have in common that the criterion stays relatively constant. Thus, the correct answer to

questions such as ‘‘Which island is larger—Borneo or Ellesmere Island?’’ is the same today as it was a few
years back and will be in the future. This might be essential for the recognition heuristic to work because the
correlation between recognition and the unknown criterion usually arises by mediators. This process, how-
ever, takes time. This is well documented for the city population task. Goldstein and Gigerenzer (2002)
counted in how many newspaper articles between 1985 and 1997 the name of the 83 largest German cities
appeared. There is a high correlation between the number of times a German city is mentioned during this
period and its population (ecological correlation) on the one hand. On the other hand, there is a strong cor-
relation between the number of times a German city is mentioned in American newspaper articles and its
probability of recognition by Americans (surrogate correlation). As a result, for Americans, recognizing a
German city allows inferring that in most cases this city has a larger population than an unrecognized
German city (recognition validity). Since the criterion (relative city population) stays constant, this inference
is facilitated. However, if the criterion itself varies, continuous and rapid adjustment of recognition memory
is needed. This might impair performance of the recognition heuristic.

Dynamic environments
Many everyday decisions have to deal with swiftly changing criterion values. How does the recognition heur-
istic perform in such dynamic real world environments? Besides the described static domains, so far only one
dynamic domain has been analyzed: There have been several attempts to use the recognition heuristic on the
stock market. On the one hand depots of recognized stocks seem to outperform depots of unrecognized
stocks, the market, chance depots and even mutual fonds (Borges, Goldstein, Ortmann, & Gigerenzer,
1999). On the other hand this does not hold in a bear market (Boyd, 2001) and the performance varies a
lot over time (Frings, Holling, & Serwe, 2003). However, the strategy used in these studies differed from
the city population task. In fact, aggregated recognition data has been used to build rankings, and several
portfolios have been derived from these rankings (e.g., one portfolio consisted of stocks from all companies
that have been recognized by more than 90% of the participants). This procedure is neither fast nor frugal and
has little in common with the original application of the recognition heuristic to two-alternative forced-
choice tasks. If you want to use solely your own recognition information for application of the recognition
heuristic to the stock market, you have to invest in stocks of all recognized companies and ignore stocks of all
unrecognized companies. This is again different from the standard application of the recognition heuristic.
Besides these methodical differences, it seems questionable whether performance of a strategy on the stock
market could be a valid test at all. Some researchers argue that no strategy can perform above-average on the
stock market —at least in the long run (see e.g., Cootner, 1967; Lucas, 1980). Even in the short run perfor-
mance of any strategy fluctuates to a large extent due to high volatility of the stock market and success in one
time period can hardly be generalized to other periods. Therefore, the inconsistent results of the recognition
heuristic on the stock market can not be interpreted safely. A further test in another dynamic environment,
which is perhaps better understood and which allows the application of the recognition heuristic in its ori-
ginal form, might be useful.

The prediction of tennis matches is such a test-situation: If you have to predict the winner of a tennis

match, you have to decide between two options and there are at least some pairs where you know one player
but do not know the other. In contrast to the stock market examples, it is a ‘‘natural two-alternative-forced-
choice task’’ and recognition heuristic can be applied fast and frugally in its original form. Additionally,
predictions for the same match can be derived from other strategies like official rankings or the betting mar-
ket which are known for their accurate predictions of sports events (Boulier & Stekler, 1999, 2003).

S. Serwe and C. Frings

The Recognition Heuristic

323

Copyright # 2006 John Wiley & Sons, Ltd.

Journal of Behavioral Decision Making, 19, 321–332 (2006)

Will the recognition heuristic successfully predict tennis matches in Wimbledon? On the one hand, the

chances of success seem to be good: It seems plausible, that there is an ecological correlation between the
success of a tennis player and the number of newspaper articles about him, analog to the city population
example. The more the media report on a tennis player, the higher is the chance to be recognized. Thus,
at least moderate recognition validities should be found, and people should infer that recognized tennis
players win against unrecognized players. Moreover, predictions of English soccer matches by foreign
(Turkish) students were nearly as accurate as the predictions by domestic (English) students (Ayton & O

¨ nkal,

1997). This is often interpreted in favor of the recognition heuristic. However, the students in this study seem
to use a familiarity cue rather than a recognition cue. Familiarity (Griggs & Cox, 1982) as well as availability
(Tversky & Kahneman, 1973), however, are graded distinctions among recognized items, and are therefore
more elaborated than the binary recognition heuristic (for a more detailed comparison between predictions
based on binary recognition information and predictions based on graded fluency of retrieval see Schooler &
Hertwig, 2005).

On the other hand, it seems questionable whether the recognition heuristic will work in a dynamic envir-

onment like tennis, because recognition may hardly be flexible enough: It takes time to become a reliably
successful tennis player who appears regularly in the media. Even then it might take time until the chance of
being recognized by laypeople becomes high. Thus, currently successful players might stay unrecognized
until they are established. Moreover, a successful tennis player who is recognized once might stay recog-
nized over a long period of time even if his success declines. To cope with declining strength systematic
forgetting is needed. Schooler and Hertwig (2005) demonstrate the benefits of forgetting for the recognition
heuristic. However, systematic rather than unsystematic forgetting is needed. These factors might impair
performance of the recognition heuristic.

OVERVIEW

To test the performance of the recognition heuristic in a domain with a dynamic criterion we predicted the
results of the most prominent tennis tournament of the world, the Lawn Tennis Championships at Wimble-
don. The aim of the study was twofold. Study 1 analyzes the performance of the recognition heuristic and
compared it to predictions of two official rankings and the betting market. We collected recognition data of
two groups with different experience in the domain of tennis (amateurs and laypeople). To measure the per-
formance of the recognition heuristic, an aggregated recognition ranking and the individual recognition data
was used to simulate predictions that would have been made if the recognition heuristic had been used con-
sistently. In Study 2 the descriptive value of the recognition heuristic was analyzed. We again collected
recognition data of amateurs and laypeople and assessed whether they actually used the recognition heuristic
for predicting tennis matches of the round of sixteen and quarter-final matches in Wimbledon 2003.

STUDY 1: SHOULD PEOPLE USE THE RECOGNITION HEURISTIC?

Method
Participants
Twenty-nine tennis players from Neukirchen-Vluyn (amateurs, AM) and 96 students from Jena who did not
play tennis (laypeople, LAY) were surveyed. The amateurs (20 men, 9 women) had an average age of
M

¼ 46.5 years (range from 14 to 72 years), and had been playing tennis for M ¼ 17.2 years (range from

1 to 50 years). The laypeople (12 men, 84 women) had an average age of M

¼ 21.8 years (range from 19

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Journal of Behavioral Decision Making

Copyright # 2006 John Wiley & Sons, Ltd.

Journal of Behavioral Decision Making, 19, 321–332 (2006)

to 30). The amateurs watched more tennis than the laypeople (M

AM

¼ 3.69, M

LAY

¼ 1.80, rated on a scale

from 1 to 5, where ‘‘1’’ indicates ‘‘never’’ and ‘‘5’’ means ‘‘very often’’). Data of 3 laypeople were excluded
because they did not recognize a single player.

Procedure
Participants were surveyed just a few days before the first match of Wimbledon 2003. They had to decide
whether they recognized the names (‘‘heard of before’’) of professional male tennis players or not. The list
comprised the 112 players who started in the Wimbledon Men’s single tournament 2003 without qualifica-
tion (another 16 starters had to qualify first). First, two recognition rankings were calculated by counting the
number of amateurs (REC-AM) and laypeople (REC-LAY) who recognized each player. These recognition
rankings were compared with each other and with two official rankings: the ATP Champions Race (ATP-CR)
and the ATP Entry Ranking (ATP-ER). The ATP-CR is the official worldwide ranking of male tennis players
for one calendar year. The ATP-ER is another official worldwide ranking procedure and results from success
in the last 52 weeks (with some minor exceptions, for more details on both rankings see Association of
Tennis Professionals (ATP), 2003). All rankings were used to predict the outcomes of each match played
in Wimbledon Men’s single tournament 2003 with an easy decision rule: ‘‘The player with the better rank
will win’’.

It is quite unusual to use recognition rankings to evaluate the performance of the recognition heuristic since

the recognition heuristic originally deals with individual decision behavior and its outcomes. Thus, for each
tennis match

1

played in Wimbledon 2003 a prediction by each participant was simulated using the individual

recognition data. Consistent with the rules of the recognition heuristic, if one out of two players was recog-
nized, this player was predicted to win. Each match with either both players recognized or both players not
recognized was ignored since no prediction can be derived from the recognition heuristic. If the recognition
heuristic was applied, two further predictions were simulated for the same match, namely predictions relying
on the actual rank of both players in the two official ranking procedures ATP-CR and ATP-ER. Again ‘‘The
player with the better rank will win’’ was used as simple decision rule for ATP-CR and ATP-ER. Moreover, we
also included another prediction relying on the betting market (BET) and we used as a decision rule for each
single match ‘‘The player with the lower odds will win’’. However, note that BET in contrast to the other pre-
dictors does not evaluate the absolute strength of a player but his chance to win against a given opponent in a
specific match. Additionally, BET has another advantage: It can change its evaluation of a player in the course
of the tournament. It is exactly for these reasons that it could be questioned whether BET is a comparable (and
fair) competitor.

2

Evaluation about the performance of the recognition heuristic is therefore focused on the

comparison between the recognition heuristic and the ATP rankings.

Results
Unless otherwise noted, all effects referred to as statistically significant throughout the text are associated
with p values less than 0.05, two-tailed.

1

Since for practical reasons no data was collected for qualificants, here and in the following text each match does mean ‘‘each match for

which data was collected’’ (96 out of 127 matches played at all).

2

We thank an anonymous reviewer for drawing our attention to this point. Please note that it could principally be argued, that adding

BET to the analysis destroys comparability and therefore BET should be excluded from analysis. However, we think that the comparison
to the betting market predictions is despite this problem a useful information: Even if it is unfair to compare the recognition heuristic or
ATP-rankings with BET (from a theoretical point of view), BET is practically one possible cue in a real world scenario. This cue can be
discovered as easy as the ATP rankings and analog to our simulation, the betting odds for a player change during the Wimbledon
tournament whereas the ATP rankings are updated afterwards and thus stay constant. Thus, in the real world, using betting odds can
prove to be a fast and frugal strategy.

S. Serwe and C. Frings

The Recognition Heuristic

325

Copyright # 2006 John Wiley & Sons, Ltd.

Journal of Behavioral Decision Making, 19, 321–332 (2006)

Recognition data
Amateurs recognized more tennis players than laypeople, M

AM

¼ 54.2 versus M

LAY

¼ 14.7, t(120) ¼ 10.95,

p

< 0.001. A closer look at the overall recognition data suggested that German participants recognized

German players more often than comparably ranked international players. That is in itself entirely compa-
tible with regard to the theory of the recognition heuristic—German press obviously focuses primarily on
German sports. However, the application of the recognition heuristic would disregard this potential bias.

Recognition rankings
The Wimbledon 2003 players were recognized on average by 9% (ranging from 0 to 70%) of laypeople,
respectively by 48% of amateurs (ranging from 3 to 100%). ATP-CR rankings varied between 1 and 168
including some ties (an additional fictitious rank of 169 was included since 6 players had not won any points
so far). With regard to ATP-ER every player in our sample was ranked explicitly, though again there were
some ties. As shown in the correlation matrix (cf. Table 1), all rankings correlated significantly and strongly
with each other. The 52-week ranking of ATP-ER and the ATP-CR of 2003 are very similar since the
Wimbledon tournament starts in June and almost half of a calendar year is over. The recognition rankings
correlate strongly with each other and significantly with both official rankings.

All rankings were used separately to predict the outcomes of each of 96 Wimbledon Men’s single matches.

Every prediction strategy was allowed to selectively sample the cases which they can reliably discriminate.
Figure 1 shows the percentage of correct predictions by each ranking. In an oneway-ANOVA with prediction
success as dependent variable and the four different strategies as independent variable no significant main
effect for strategies emerged, F(3)

¼ 0.38, p ¼ 0.77, ns. Yet, predictions by both recognition rankings

differed significantly from chance; that is, aggregated recognition data of both laypeople and amateurs
led to better than chance predictions with t(89)

¼ 3.01, p ¼ 0.003 for laypeople and t(95) ¼ 4.74,

p

< 0.001 for amateurs, respectively. The official rankings differ from chance, too, with t(95)

¼ 3.69,

p

< 0.001 for ATP-CR and t(95)

¼ 3.2l, p ¼ 0.002 for ATP-ER.

Individual recognition validities
For each of the 96 tennis matches individual predictions by each participant were simulated using his/her own
recognition data. However, application of the recognition heuristic was rarely possible. On average, for laypeo-
ple the recognition heuristic was applicable in 15% of all matches. Since amateurs knew more tennis players
(see above) application of the recognition heuristic was possible more often, namely in 37% of all matches.

The accuracy of the recognition heuristic is measured by recognition validity , that is, the number of

correct predictions by the recognition heuristic divided by the sum of correct and wrong predictions of

Table 1. Correlations between official rankings and recognition rankings

Variable

1

2

3

4

1. ATP-CR

—

0.738*

0.217*

0.333*

2. ATP-ER

—

0.275*

0.377*

3. REC-LAY

—

0.673*

4. REC-AM

—

*p < 0.05.
Note: ATP-CR

¼ ATP Champions Race; ATP-ER ¼ ATP Entry Ranking; REC-LAY ¼ recognition ranking for laypeople; REC-

AM

¼ recognition ranking for amateurs.

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Journal of Behavioral Decision Making, 19, 321–332 (2006)

the recognition heuristic (excluding those matches where the recognition heuristic was not applicable). The
mean individual recognition validity was

¼ 0.73 (SD ¼ 0.09) for amateurs and ¼ 0.67 (SD ¼ 0.11) for

laypeople, respectively. Individual for both amateurs and laypeople differs significantly from chance level,
t(28)

¼ 14.26, p < 0.001 for amateurs, and t(92) ¼ 15.76, p < 0.001 for laypeople, respectively.

Performance of the recognition heuristic compared to other strategies
As mentioned above, for each participant three further predictions were simulated for each match in which
the recognition heuristic could be used.

3

Analog to the individual recognition validity , individual values

based on ATP-CR, ATP-ER and BET were computed. The

ATP-CR

(

ATP-ER

,

BET

) was computed by

dividing the amount of correct predictions by ATP-CR (ATP-ER, BET) by the sum of correct and wrong
predictions by ATP-CR (ATP-ER, BET) for those matches where the recognition heuristic was used.

In a 4 (prediction: REC vs. ATP-CR vs. ATP-ER vs. BET)

2 (domain experience: laypeople vs.

amateurs) ANOVA with -values as the dependent variable, the main effect for prediction was significant,
F(3, 360)

¼ 42.59, p < 0.001 (see Table 2). No main effect for domain experience emerged, F(l, 120) ¼ 0.02,

p

¼ 0.90, ns. Yet, there was a significant interaction effect of domain experience prediction,

F(3, 360)

¼ 8.57, p < 0.001, indicating different -values depending on domain experience. For amateurs

68%

66%

72%

66%

50%

55%

60%

65%

70%

75%

80%

ATP-CR

ATP-ER

REC-AM

REC-LAY

Ranking

Percentage of correct predictions

Figure 1. Correct predictions of the official ranking procedures ATP Champions Race (ATP-CR) and ATP Entry Ranking

(ATP-ER) compared to performance of recognition rankings for amateurs (REC-AM) and laypeople (REC-LAY).

3

Note that selecting only matches were the recognition heuristic discriminates is in favor of the recognition heuristic; yet there are two

possibilities to avoid this problem. Firstly, one could simulate for every strategy those matches where the strategy itself discriminates.
However, this would not result in individual differences and this analysis is already reported in Part 1 where the rankings were compared
to each other. Secondly, one could simulate all matches for all strategies for each participant. However, each participant could use the
other strategies for predicting every match whereas his individual recognition validity will on average only predict 37% (amateurs) or
15% (laypeople) of matches; for all other matches, the participant will have to guess. Thus, the performance of the recognition heuristic
would diminish to the near of 50% and hence this analysis would bias the comparison of strategies, too. Since the issue of interest is here
whether the recognition heuristic can principally be used in a dynamic environment we consider only matches where this strategy could
be used.

S. Serwe and C. Frings

The Recognition Heuristic

327

Copyright # 2006 John Wiley & Sons, Ltd.

Journal of Behavioral Decision Making, 19, 321–332 (2006)

REC

(0.73) was significantly higher than

ATP-CR

(0.69), t(28)

¼ 2.76, p ¼ 0.01, and significantly higher

than

ATP-ER

(0.68), t(28)

¼ 3.22, p ¼ 0.003. On the other hand for laypeople

REC

(0.67) was significantly

lower than

ATP-CR

(0.72), t(92)

¼ 4.48, p < 0.001 and significantly lower than

ATP-ER

(0.72), t(92)

¼ 3.02,

p

¼ 0.003. As expected from the literature and consistent with the advantage to adjust predictions for each

match, regardless of domain experience,

BET

yielded better predictions than ATP-CR (t[121]

¼ 9.84,

p

< 0.001), ATP-ER (t[121]

¼ 11.73, p < 0.001), and REC (t[121] ¼ 9.31, p < 0.001).

Discussion
The aim of Study 1 was to test performance of the recognition heuristic in dynamic environments. In a first
step it was shown that recognition rankings correlate strongly with official ranking procedures and lead to at
least as good predictions. Furthermore, for both amateurs and laypeople the individual recognition validities
were significantly higher than chance level albeit ‘‘performance in tennis’’ is a criterion which is clearly not
time-invariant. Moreover, the recognition heuristic did surprisingly well if compared to predictions by offi-
cial rankings. The betting market was the only prediction that clearly outperformed the recognition heuristic.
However, the betting market information had several theoretical advantages (see above) and can be inter-
preted as a highly aggregated expert judgment leaving open how the experts arrived at their judgments in
the first place.

Overall, the recognition heuristic did surprisingly well. However, that does not necessarily mean that

people really use the recognition heuristic for their predictions. Thus, the second study analyzed whether
people actually decide in accordance with the recognition heuristic in this dynamic domain.

STUDY 2: DO PEOPLE USE THE RECOGNITION HEURISTIC?

Method
Participants
Twenty-one tennis players from Neukirchen-Vluyn and Mu¨nster (amateurs) with an average age of 44.4
(range from 12 to 63) and 39 students from Jena who did not play tennis (laypeople) with an average age
of 21.5 (range from 19 to 30) were surveyed. Both the tennis players and the laypeople did not participate in
Study I. Again the amateurs watched more tennis than the laypeople (M

AM

¼ 4.33, M

LAY

¼ 1.72).

Table 2. Prediction validities of the recognition heuristic, the official rankings and the betting market for amateurs and
laypeople

Prediction validity

Ranking

Amateurs

Laypeople

REC

0.73*

0.67*

ATP-CR

0.69*

0.72*

ATP-ER

0.68*

0.70*

BET

0.78*

0.79*

Note: REC

¼ recognition heuristic; ATP-CR ¼ ATP Champions Race; ATP-ER ¼ ATP Entry Ranking; BET ¼ betting market. For

significance of contrasts see text.

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Journal of Behavioral Decision Making, 19, 321–332 (2006)

Procedure
The participants were tested after the third round (before the round of sixteen) of Wimbledon 2003. The
questionnaire consisted of two parts. First the participants had to decide if they recognized the name (‘‘heard
of before’’) of the 16 remaining Wimbledon players or not. Second, for each match in the round of sixteen,
and all possible pairs in the quarter-finals, they had to predict the winner (later only those quarter-finals that
were played were included in the analysis).

Results
Again amateurs recognized more tennis players than laypeople (M

AM

¼ 12.8, M

LAY

¼ 3.6, t[58] ¼ 9.48,

p

< 0.001). Due to low recognition rate by laypeople, the recognition heuristic could be applied to only

24% of all matches. However, if the recognition heuristic discriminated between the two players (i.e., one
out of two players was recognized), the laypeople chose the recognized player in 88%. The concordance with
the recognition heuristic differs significantly from chance,

2

(1, N

¼ 110) ¼ 64.15, p < 0.001. Due to their

high recognition rate, amateurs could not use the recognition heuristic frequently either, that is, in only 23%
of all matches. However, if the recognition heuristic discriminated, 93% of predictions were in accordance
with it, which differs again significantly from chance,

2

(1, N

¼ 59) ¼ 44.09, p < 0.001.

Discussion
In the round of sixteen and quarter-final matches participants’ decisions were overall consistent with the
recognition heuristic. In nearly all situations in which the recognition heuristic could be used, people decided
in accordance with it. This result is the first proof for the descriptive value of the recognition heuristic in
dynamic environments, and confirms analog findings in the city population task (Goldstein & Gigerenzer,
2002) in which 90% of predictions could be described by the recognition heuristic, too. However, situations
in which the recognition heuristic could be applied were relatively rare in our data.

GENERAL DISCUSSION

Goldstein and Gigerenzer (1999, 2002) introduced the recognition heuristic as the simplest and fastest of all
heuristics. Nevertheless, they found surprisingly positive results concerning the performance of this heuris-
tic. A constraint of previous studies about performance of the recognition heuristic, however, was that the
positive assessment reported for the recognition heuristic stems from conditions characterized by relatively
static criterions. Yet, the question of whether or not the recognition heuristic works in dynamic environments
is not yet decided and important, especially when considering that many everyday decisions are made in
dynamic environments. So far, the recognition heuristic has been tested only once in a dynamic domain,
namely on the stock market, yielding inconsistent results. As argued above, it seems questionable whether
the stock market is an appropriate domain to test a heuristic at all.

In Study 1 the recognition heuristic was used to predict the winners of tennis matches in Wimbledon 2003.

On the one hand, it seems plausible that there is a correlation between the success of a tennis player and his
recognition rate. Thus, one crucial precondition of the recognition heuristic is fulfilled. On the other hand,
success of a specific tennis player swiftly changes and the recognition heuristic might face difficulties with
rapid adaptation and systematic forgetting. However, Study 1 showed that in Wimbledon 2003, the recogni-
tion heuristic would have led to above-chance predictions significantly for both amateurs and laypeople, and
for both recognition rankings and individual recognition validities.

There are two possible explanations for this surprising result. Firstly, there may be no—or not enough—

dynamic properties in the domain of tennis; for example, it might be that players with declining success tend to

S. Serwe and C. Frings

The Recognition Heuristic

329

Copyright # 2006 John Wiley & Sons, Ltd.

Journal of Behavioral Decision Making, 19, 321–332 (2006)

retire and therefore are not relevant for the success of the recognition heuristic. However, we found a significant
negative correlation between the time our participants actively played tennis and the recognition validity ,
r

¼ 0.54, p ¼ 0.004.

4

Thus, if you assume, that they were interested in tennis as long as they played tennis

themselves, a longer period of interest leads to worse predictions. This seems at first glance counterintuitive
but is entirely consistent with the assumption of dynamic processes: The longer someone is interested in
tennis, the more formerly (and perhaps no longer) successful players are recognized who negatively influence
success of the recognition heuristic. In contrast, a shorter period of interest in tennis leads to recognition
values that reflect a more actual state of affairs. Since this is only speculative evidence for the claim that
professional tennis is a dynamic environment, future research should prove the effects of dynamic processes
on the recognition heuristic in an experimental setting where dynamic aspects can be manipulated by the
experimenter.

A second explanation of the surprising result is that the recognition heuristic might be more adaptive than

previously assumed. We argued that a once recognized player stays recognized for a long period of time.
However, recently Schooler and Hertwig (2005) proposed a model for systematic forgetting that boosts
the accuracy of the recognition heuristic and makes it more compatible to dynamic environments: With
respect to the city population task, the frequency a city is mentioned in the newspaper does not only deter-
mine the chance to be recognized (the higher the frequency, the higher the chance to be recognized) but also
the chance to be forgotten (the lower the frequency, the higher the chance to be forgotten). This can be easily
transferred to the recognition rate of tennis players which we assume to be mediated as well by the number of
times a tennis player is mentioned in the media. For example, a formerly well-known tennis player who now
appears less frequently in the media could be forgotten. On the other hand if the media reports on a success-
ful newcomer in high frequencies he might become recognized faster than we assumed. Thus, with respect to
the model of Schooler and Hertwig (2005), the dynamic aspects of an environment can essentially be
described by the variation of frequencies. Future research on the dynamic properties of the recognition heur-
istic should focus on varying the frequency of appearance.

Study 2 provided support for the recognition heuristic from a descriptive point of view. Whenever the

recognition heuristic was applicable, nearly all decisions were consistent with it. Note however, that this
is a relatively weak test of the descriptive value of the recognition heuristic. Our results do not necessarily
mean that recognition alone (as sometimes stated by proponents of the recognition heuristic) influences the
decision process in a non-compensatory fashion. People might come to the same conclusions by using dif-
ferent facts and decision rules. For example, it seems plausible that someone who has been interested in
tennis for several years recognizes a top player with automatically activating his past success and other
details (if you are interested in tennis, you would probably recognize Pete Sampras with automatically
’knowing’ his legendary career). If so, then such a person would probably use all memorized information
for decision making and might not merely rely on the recognition heuristic. However, there is evidence that
in binary group decisions individuals who can use the recognition heuristic are even more influential than
individuals who know both alternatives and would decide differently (Reimer & Katsikopoulos, 2004).
Whether other information is ignored or integrated into the decision process, is an unanswered question open
for future research (see also, Newell & Shanks, 2004; Oppenheimer, 2003). Nevertheless, in our study the
recognition heuristic was able to describe the observed behavior.

The principle aim of this article was a ‘‘trial and error approach’’ to answer the question of whether the

recognition heuristic also functions within a dynamic real world environment. The results suggest that, at
least in the domain of tennis the performance of the recognition heuristic was better than expected and can
effectively describe decision behavior. Therefore, the recognition heuristic seems to be more adaptive than
previously assumed and its application can be ecologically rational even in an dynamic environment.

4

We are indebted to Ulrich Hoffrage for suggesting this analysis. Note, for this analysis the data of two participants who were extreme

outliers (Tukey, 1977) with respect to tennis experience were excluded.

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Journal of Behavioral Decision Making

Copyright # 2006 John Wiley & Sons, Ltd.

Journal of Behavioral Decision Making, 19, 321–332 (2006)

ACKNOWLEDGEMENT

We thank Sylvia Pro¨hl, Ronny Fu¨gert and Norbert Serwe for their help with the data collection and Ullrich
Ecker for improving the English of this article. We thank George Wright, Ulrich Hoffrage, and two anon-
ymous reviewers for their helpful comments on a previous version of this paper.

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S. Serwe and C. Frings

The Recognition Heuristic

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Journal of Behavioral Decision Making, 19, 321–332 (2006)

Authors’ biographies:

Sascha Serwe

is a graduate student at Giessen University, Germany. His research includes descriptive and prescriptive

aspects of human decision making. He is also interested in usability research, sensori-motor decision making and traffic
psychology.

Christian Frings

is an assistant professor for cognitive psychology at the cognitive psychology department of the Saar-

land University, Germany. His research includes the selective processing of attended and unattended information, inter-
individual differences in cognitive abilities, as well as human decision making.

Authors’ addresses:

Sascha Serwe

, Giessen University, Department of Psychology, Otto-Behaghel-Str. 10F, 35394 Giessen, Germany.

Christian Frings

, Institute of Psychology, Building 6, Saarland University, P.O. Box 151110, D-66041 Saarbru¨cken,

Germany.

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Copyright # 2006 John Wiley & Sons, Ltd.

Journal of Behavioral Decision Making, 19, 321–332 (2006)