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Abstract In a recent Mind & Society article, Evans (2005) argues for the
social and communicative function of conditional statements. In a related
article, we argue for satisficing algorithms for mapping conditional statements
onto social domains (Eur J Cogn Psychol 16:807–823,2004). The purpose of
the present commentary is to integrate these two arguments by proposing a
revised pragmatic cues algorithm for pragmatic conditionals.
Keywords Conditionals Æ Pragmatics Æ Satisficing algorithms
1 Introduction
According to Evans (2005), speakers use conditional statements of the form
‘‘if P, then Q’’ to influence the actions and beliefs of listeners. And they do so
in a context, by having them imagine the actual possibility of P and the
practical consequence of Q, before making a decision on how to act or what to
believe. Take his example of an editor telling an author: ‘‘if you submit your
paper to our journal, we will publish it’’. For Evans, this is an instance of a
Juliet: If they do see thee, they will murder thee—Shakespeare’s Romeo and Juliet II ii 76
(Luhrmann 2002)
A. Lo
´
pez-Rousseau (&)
Amor de Dios 4, 28014 Madrid, Spain
e-mail: lopezrousseau@yahoo.com
T. Ketelaar
Department of Psychology, New Mexico State University,
Las Cruces, NM 88003-8001, USA
e-mail: ketelaar@nmsu.edu
123
Mind & Society
DOI 10.1007/s11299-006-0009-z
ORIGINAL ARTICLE
Juliet: If they do see thee, they will murder thee.
A satisficing algorithm for pragmatic conditionals
Alejandro Lo´ pez-Rousseau Æ Timothy Ketelaar
Received: 11 October 2005 / Accepted: 29 November 2005
Ó Fondazione Rosselli 2006
promise, because it strongly encourages the act of submission by the listener,
as the reward of publication is controlled by the speaker. More broadly, it is a
statement of the speaker meant to induce an action of the listener.
Figure 1 shows our taxonomic representation of conditional statements
analysed by Evans (2005), including their defining terms and features
according to him. It is not an exhaustive taxonomy, as it does not include
conditionals not discussed by Evans, such as permissions and obligations,
requests and orders, and so on.
We basically agree with Evans’ (2005) conditionals, except for his distinc-
tion of inducement and advice in terms of influence strength. According to
him, an inducement is stronger than an advice, because in an inducement the
speaker controls the consequent event, whereas in an advice the speaker does
not. Take his above example of a promise and compare it to his other example
of a colleague telling an author: ‘‘If you submit your paper to their journal,
they will publish it’’. For him, this is an instance of a tip, because it weakly
encourages the act of submission by the listener, as the reward of publication
is not controlled by the speaker, but by others.
We do believe that the speaker’s control of the consequences is the dis-
criminating feature between an inducement and an advice, but we do not be-
lieve that this feature makes an inducement necessarily stronger than an advice.
Take the example of a modern Juliet telling her Romeo: ‘‘if my brothers see
you, they will kill you’’. According to Evans (2005) and ourselves, this is a
warning, because it seeks to deter the act by Romeo, and the punishment is not
controlled by Juliet, but by her brothers. However, this warning is stronger, not
weaker than the threat of Juliet telling Romeo: ‘‘if my brothers see you, I will
kill you’’. Although now the punishment is controlled by Juliet, not her
brothers. Or take a pharmacist’s advice: ‘‘if you take this pill, it will calm you’’.
Fig. 1 The taxonomic representation of social and communicative conditionals
Mind & Society
123
And compare it to a pharmacist’s promise: ‘‘if you take this pill, I will calm
you’’. Evidently, an inducement is not necessarily stronger than an advice. In
fact, it is context that determines the strength of a conditional. For example, a
medical warning is stronger when made by an expert than a novice doctor.
2 A satisficing algorithm for pragmatic conditionals
However, the point of this commentary is another, namely, to integrate Evans’
(2005) detailed analysis of pragmatic conditionals in his recent article with a
satisficing algorithm for pragmatic conditionals we advanced in a related
article (Lo
´
pez-Rousseau and Ketelaar 2004). Particularly, because in his
suppositional approach, Evans does not address the possibility of conditional
reasoning being driven by satisficing processes.
Figure 2 shows the pragmatic cues algorithm for classifying conditional
promises, threats, advices, warnings, permissions and obligations (Lo
´
pez-
Rousseau and Ketelaar 2004). This algorithm was introduced as a partial
answer to the question: When confronted with a conditional, given that all
conditionals are formally equivalent, how do people know whether they are
facing a promise, a threat or something else? Certainly, because of the content
and context of the conditional as conveyed by linguistic and non-linguistic
cues. But it is not always clear. Take the following example (Newell 2004)
Kirsten: If you fail me, there will be consequences.
Julia: Are you threatening me?
Fig. 2 The pragmatic cues algorithm. Source: Lo
´
pez-Rousseau and Ketelaar (2004) ‘‘If ...’’:
satisficing algorithms for mapping conditional statements onto social domains (Eur J Cogn
Psychol 16:812)
Mind & Society
123
It is not clear whether Kirsten’s conditional is a threat to Julia, and this is why
Julia asks Kirsten whether she is threatening her. Apparently, people’s cog-
nitive algorithm for classifying conditionals is not optimal but satisficing,
namely, a simple serial procedure sufficing for satisfactory classifications in
most cases, but not in all cases. So, exactly how is this cognitive algorithm?
The pragmatic cues algorithm is meant to simulate people’s cognitive
algorithm for classifying conditionals. The algorithm is restricted to six
pragmatic conditionals and three linguistic cues. In fact, the algorithm is
meant to be the most simple by including the minimum possible of three cues
to classify those six conditionals. Also, the algorithm is meant to be serial by
adopting the sequential form of a decision tree, which simplifies the classifi-
cation by discarding three conditionals after the first cue, and two more
conditionals after the second cue. And the algorithm is meant to be satisficing
by producing correct classifications in most but not all cases. In this regard, the
pragmatic cues algorithm would misclassify any excluded conditional (e.g.,
requests) or any included conditional based on excluded cues (e.g., gestures).
Evidently, people’s cognitive algorithm would include all conditionals and all
cues (for details, see Lo
´
pez-Rousseau and Ketelaar 2004).
Given that a number of complex, parallel or optimizing algorithms can be
used for classifying conditionals, an empirical test was run on how well the
pragmatic cues algorithm approximates the performance of people’s cognitive
algorithm. Briefly, conditional promises, threats, advices, warnings, permis-
sions and obligations were collected from people, and given to other people
and the algorithm for classification. Their corresponding performances were
then compared. Results show that people classified most conditionals cor-
rectly, and that the pragmatic cues algorithm did almost as well as people.
Both the algorithm’s and people’s classifications were far better than chance,
and their misclassifications were randomly distributed. These findings indicate
that the pragmatic cues algorithm approximates well the performance of
people’s cognitive algorithm for classifying conditionals, and suggest that this
satisficing algorithm might be an integral part of that cognitive algorithm (see
Lo
´
pez-Rousseau and Ketelaar 2004).
Now back to Evans (2005). Figure 3 shows that by simply discarding per-
missions and obligations from the pragmatic cues algorithm, its first two cues
readily account for all four of Evans’ pragmatic conditionals, namely, advices
(tips), promises, threats and warnings. Take again the example of Juliet telling
Romeo: ‘‘if my brothers see you, they will kill you’’. The pragmatic cues
algorithm would process this conditional by applying its first cue, and asking
whether the conditional’s consequent Q is meant as a benefit for the listener
(Romeo). Because being killed is not a benefit for the listener (Romeo), the
algorithm would follow its ‘no’ branch to the second cue, and ask whether the
conditional’s consequent Q involves an act of the speaker (Juliet). Because
the killing is not done by the speaker (Juliet), the algorithm would then follow
its ‘no’ branch to the warning domain, and stop there. Thus, according to the
algorithm, Juliet’s conditional is a warning to Romeo.
Mind & Society
123
Now take again the example of Kirsten telling Julia: ‘if you fail me, there
will be consequences’. According to the pragmatic cues algorithm, it is not
clear whether Kirsten’s conditional is a threat to Julia. Actually, it is unclear to
herself as well, and this is why Julia asks Kirsten whether she is threatening
her. To the algorithm, it is not clear firstly whether the stated consequences
are meant as a benefit for the listener (Julia) or not, and secondly whether
these consequences involve an act of the speaker (Kirsten) or not. The con-
ditional’s context suggests that the consequences would not mean a benefit for
the listener (Julia) and could involve an act of the speaker (Kirsten). Thus,
Kirsten’s conditional is probably a threat to Julia.
In fact, a new analysis using the same empirical data obtained by us
before (Lo
´
pez-Rousseau and Ketelaar 2004) reveals that this reduced
pragmatic cues algorithm does almost as well as people in classifying con-
ditional advices (tips), promises, threats and warnings. Figure 4 shows the
percentage of correctly classified pragmatic conditionals by the algorithm
and people (people’s average 95%, range 90–100%; algorithm’s average
92%, range 86–96%). Both people’s and the algorithm’s classifications are
far better than chance (25%), and both misclassifications are randomly
distributed. These findings indicate that the pragmatic cues algorithm for
pragmatic conditionals approximates well the performance of people’s cog-
nitive algorithm. The small difference is probably due to additional cues the
cognitive algorithm depends on. These findings thus suggest that the parsi-
moniously simple, serial and satisficing pragmatic cues algorithm might be an
integral part of people’s cognitive algorithm for classifying pragmatic con-
ditionals.
Fig. 3 The pragmatic cues algorithm for pragmatic conditionals
Mind & Society
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3 Conclusion
In sum, this commentary discusses how the pragmatic cues algorithm ad-
vanced by us (Lo
´
pez-Rousseau and Ketelaar 2004) readily accounts for the
pragmatic conditionals analyzed by Evans (2005). Reordering and rewording
our cues to follow his analysis, Fig. 5 shows the revised pragmatic cues algo-
rithm for pragmatic conditionals. Actually, this satisficing algorithm discrim-
inates over 90% of the conditional promises, threats, tips and warnings people
make. Certainly, the integration of both approaches sheds more light onto the
social function and satisficing functioning of pragmatic conditionals than each
approach alone.
Fig. 4 The percentage of correctly classified pragmatic conditionals by the algorithm and people
Fig. 5 The revised pragmatic cues algorithm for pragmatic conditionals
Mind & Society
123
References
Evans JStBT (2005) The social and communicative function of conditional statements. Mind Soc
4(1):97-113
Lo
´
pez-Rousseau A, Ketelaar T (2004) ‘‘If ...’’: satisficing algorithms for mapping conditional
statements onto social domains. Eur J Cogn Psychol 16:807-823
Luhrmann B (2002) William Shakespeare’s Romeo and Juliet [DVD] 20th Century Fox home
entertainment
Newell M (2004) Mona Lisa Smile [DVD]. Columbia Tri-Star home video
Mind & Society
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