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Commitment and Evolution
Connecting Emotion and Reason
in Long-term Relationships
István Back

ii
The research described in this thesis was carried out under the auspices of
the Interuniversity Center for Social Science Theory and Methodology (ICS)
and the Faculty of Social and Behavioral Sciences (GMW) at the University
of Groningen (RuG). Funding was generously provided by the Ubbo Emmius
Bursary (2003) and by the Netherlands Organization for Scientific Research
(NWO).
© 2007 by István Back
All rights reserved. No part of this publication may be reproduced, stored in a
retrieval system, or transmitted in any form or by any means without written
permission of the author.
The document was typeset using L
A
TEX2ε
and Stasinos Konstantopoulos’s RuGthesis.cls.
Printed by Mesterprint Kft., Budapest, Hungary.
ICS Dissertation series (nr. 133)
ISBN 978-90-367-3113-3

RIJKSUNIVERSITEIT GRONINGEN
Commitment and Evolution
Connecting Emotion and Reason
in Long-term Relationships
Proefschrift
ter verkrijging van het doctoraat in de
Gedrags- en Maatschappijwetenschappen
aan de Rijksuniversiteit Groningen
op gezag van de
Rector Magnificus, dr. F. Zwarts,
in het openbaar te verdedigen op
maandag 17 september 2007
om 14.45 uur
door
István Henrik Back
geboren op 19 november 1979
te Boedapest, Hongarije

iv
Promotor: Prof. dr. T.A.B. Snijders
Copromotores: Dr. H. de Vos
Dr. A. Flache
Beoordelingscommissie: Prof. dr. M.W. Macy
Prof. dr. S.M. Lindenberg
Prof. dr. A. Riedl

Acknowledgements
I would like to thank my supervisors, Henk de Vos, Tom Snijders, and espe-
cially Andreas Flache for their invaluable input throughout the last four years.
Their encouragement, inspiring ideas and complementary expertise was in-
strumental to carrying out this interdisciplinary piece of research. I received
further assistance from many others working in the graduate school ICS in
Groningen, Utrecht and Nijmegen, most notably Vincent Buskens who always
kept a watchful eye on my research, provided me with ideas and helped to
carry out a large proportion of my experiments; Károly Takács and Michael
Mäs, who were always ready to read and discuss my drafts; Rita Smaniotto
who helped me keep my enthusiasm for evolutionary theory; Sigi Lindenberg;
Jeroen Weesie; Inneke Maas; Frans Stokman; Richard Zijdeman, Eva Jaspers,
Ellen Verbakel, Nienke Moor, Janneke Joly and Stefan Thau; Jessica Pass, Ja-
cob Dijkstra, Christian Steglich, Lea Ellwardt and Jurre van den Berg. I thank
Michael Macy and graduate students at the Sociology Department of Cornell
University, especially Arnout van de Rijt and Ma Li for making my visit there
so rewarding not only professionally but also personally; and David Sloan
Wilson at Binghamton University for thought-provoking conversations about
human evolution.
I owe big thanks to Ji Wenxi (Wendy) who not only provided me with in-
valuable support during my experimental work in China but continues to be a
window into the oriental mind and thinking; Fan Xuejuan at East China Nor-
mal University, Xu Bo and Xu Longshun at Fudan University who generously
provided the means to carry out my experiments in Shanghai; Zhao Kanglian
at Nanjing University. Xu Yu, Gerbren Kuiper and Huixin, Yorgos Vleioras,
Justin Park, Simon Dalley, Bori Takács, Tamás Bíró, Ela Polek, Andrea Szent-
györgyi, Gábor Imre, Li Kun and Wang Zhuo for keeping me company in
Groningen; and Evelien de Roos, the best land-lady in the Netherlands.
Finally, I would like to thank my friends in Hungary, Miki Rosta, Levente
Skultéti, Attila Máté, György Hermann, Zoli Gedei, Kristóf Bajnok and Laura
Radics, for their friendship which has been one of the key sources of motiva-
tion behind this piece of work.
v

Contents
1 Introduction 1
1.0.1 A brief word on “commitment” . . . . . . . . . . . . . . . 4
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.1.1 Commitment to a course of action . . . . . . . . . . . . . 6
1.1.2 Interpersonal commitment . . . . . . . . . . . . . . . . . 7
1.2 Toward an evolutionary explanation . . . . . . . . . . . . . . . . 12
1.2.1 Separating ultimate and proximate explanations . . . . . 15
1.2.2 How evolutionary theory helps to explain seemingly ir-
rational behavior . . . . . . . . . . . . . . . . . . . . . . . 15
1.2.3 Constructing an ultimate explanation for interpersonal
commitment . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.2.4 Proximate mechanisms for interpersonal commitment . 20
1.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.4 Outline of chapters . . . . . . . . . . . . . . . . . . . . . . . . . . 22
I An ultimate explanation 25
2 The Competitive Advantage of Commitment 27
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.2 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.2.1 Modeling strategies . . . . . . . . . . . . . . . . . . . . . 33
2.2.2 Evolutionary dynamic . . . . . . . . . . . . . . . . . . . . 37
2.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.3.1 Simulation setup . . . . . . . . . . . . . . . . . . . . . . . 39
2.3.2 The unconditionality of Commitment . . . . . . . . . . . 41
2.3.3 Explanation: the importance of strong ties . . . . . . . . 46
2.3.4 Sensitivity to initial parameters . . . . . . . . . . . . . . . 48
2.4 Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . . 50
vii

viii CONTENTS
3 The Evolutionary Advantage of Commitment 53
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.2 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
3.3 Conjectures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.4.1 Initial parameters . . . . . . . . . . . . . . . . . . . . . . . 61
3.4.2 Stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
3.4.3 The importance of interpersonal commitment . . . . . . 62
3.5 Discussion and conclusions . . . . . . . . . . . . . . . . . . . . . 67
4 Fairness and Commitment under Inequality 71
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4.2 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.3 Conjectures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.4.1 Initial parameters . . . . . . . . . . . . . . . . . . . . . . . 80
4.4.2 Stability . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
4.4.3 The importance of interpersonal commitment . . . . . . 81
4.4.4 The relative importance of fairness, commitment and ca-
pability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
4.4.5 Sensitivity to initial parameters . . . . . . . . . . . . . . . 84
4.5 Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . . 85
II Proximate explanations 89
5 Commitment Bias 91
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
5.2 Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
5.3 Experimental design . . . . . . . . . . . . . . . . . . . . . . . . . 98
5.3.1 Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
5.3.2 Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
5.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
5.4.1 Hypothesis 5.1 and 5.2 – The commitment bias . . . . . . 101
5.4.2 Hypothesis 5.3 – Effect of affect . . . . . . . . . . . . . . . 103
5.4.3 Hypothesis 5.4 and 5.5 – Cross-cultural similarities and
differences . . . . . . . . . . . . . . . . . . . . . . . . . . 103
5.4.4 Self-reported reasons for exit . . . . . . . . . . . . . . . . 107
5.5 Discussion and conclusions . . . . . . . . . . . . . . . . . . . . . 107

CONTENTS ix
5.5.1 The evolutionary roots of commitment . . . . . . . . . . 109
5.5.2 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . 111
6 Commitment and Networking under Uncertainty 113
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
6.2 Theory and Hypotheses . . . . . . . . . . . . . . . . . . . . . . . 115
6.3 Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
6.3.1 Sample 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
6.3.2 Sample 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
6.3.3 Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
6.3.4 Manipulation of uncertainties . . . . . . . . . . . . . . . . 124
6.3.5 Manipulation of dilemma . . . . . . . . . . . . . . . . . . 125
6.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
6.4.1 Final sample . . . . . . . . . . . . . . . . . . . . . . . . . . 126
6.4.2 Hypothesis 6.1 – Social uncertainty and commitment . . 127
6.4.3 Hypothesis 6.2 – Resource uncertainty and commitment 127
6.4.4 Hypothesis 6.3/6.4 – Interaction between uncertainties . 128
6.4.5 Hypothesis 6.5/6.6 – Trust and Optimism . . . . . . . . . 129
6.5 Robustness of results across different dilemmas . . . . . . . . . 130
6.6 Conclusion and Discussion . . . . . . . . . . . . . . . . . . . . . 131
6.6.1 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . 132
7 Conclusions 133
7.1 Summary of results . . . . . . . . . . . . . . . . . . . . . . . . . . 134
7.2 General discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 136
7.2.1 In defense of evolutionary theory in the social sciences . 137
7.2.2 Placing our work . . . . . . . . . . . . . . . . . . . . . . . 138
7.2.3 Innovations of the present work . . . . . . . . . . . . . . 141
7.2.4 Possible criticism . . . . . . . . . . . . . . . . . . . . . . . 142
7.2.5 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . 143
7.2.6 Avenues for future research . . . . . . . . . . . . . . . . . 144
Bibliography 147
Summary 159
Samenvatting - Dutch summary 163
Összefoglalás - Hungarian summary 167
Zhai Yao - Chinese summary 171
A Analytical solution of the simplified dilemma 175

x CONTENTS
B Parameter values used in ecological simulations 177
C Pseudocode of simulation core 179
D Parameter values used in evolutionary simulations 181
E Pseudocode of evolutionary dynamic 183
F Experiment instructions 185
F.1 Initial instructions . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
F.2 Instruction text from the experiment . . . . . . . . . . . . . . . . 185
F.3 Screenshot from the experiment game . . . . . . . . . . . . . . . 187
G Experiment instructions 189
G.1 Instructions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189
G.2 Screen shots from the experiment . . . . . . . . . . . . . . . . . . 190

Chapter 1
Introduction
“We may call the part of the soul whereby it reflects, rational; and the other
with which it feels hunger and thirst and is distracted by sexual passion and
all the other desires, we will call irrational appetite, associated with pleasure in
the replenishment of certain wants...
What of that passionate element which makes us feel angry and indignant? Is
that a third, or identical in nature with one of those two?”
—Plato, The Republic
The tendency to establish lasting personal relationships is a fundamental
aspect of human sociality. Throughout life we build friendships, collect ac-
quaintances, forge business alliances, become attached to intimate partners.
Many of these relationships follow us through our lives and integrate us into
a complex social fabric of interpersonal connections. At the same time, estab-
lishing and maintaining long-term relationships involves substantial invest-
ment of one’s time, effort and other resources. Moreover, many relationships
by definition require exclusivity. For example, we can only have one best
friend at a time, in many cultures only one spouse, and in many business set-
tings only one supplier of some product. To a certain extent all relationships,
i.e. non-exclusive ones as well, are competitive with each other, given that we
have finite attention and resources. This means that we occasionally have to
forgo relationships with potentially better alternative partners. And to com-
plicate matters, even when we do our best to invest in a relationship, we have
to live with the risk of being dumped for someone else or unknowingly being
taken advantage of by our partner.
Why do people establish and maintain long-term relationships when these
are costly, risky and exclusive? A simple but powerful answer from rational
1

2 Chapter 1. Introduction
choice theory is that it is in their best interest to do so. More precisely, people
become committed to each other if and only if the benefits of having a rela-
tionship outweigh its maintenance costs and its alternative costs. In particular,
having a long-term relationship with a partner provides valuable information
about the trustworthiness of the partner compared to other partners (trust ex-
planation, see Kollock, 1994; Yamagishi and Yamagishi, 1994; Yamagishi et al.,
1994) and at the same time creates a strategic incentive to cooperate in order to
avoid retaliation and stabilize long-term mutual collaboration (reciprocity ex-
planation, see Trivers, 1971; Friedman, 1971; Axelrod, 1984; Fehr and Schmidt,
1999; Fehr and Gächter, 2002; Falk et al., 2001).
But at the same time, there seems to be much more to long-term inter-
personal relationships than just trust and reciprocity. There are numerous
cases, for example, when people keep relationships even after their partner
has proved to be untrustworthy (e.g. Roy, 1977; Strube, 1988; Rusbult and
Martz, 1995). There are also examples of relationships where a partner has no
means of reciprocating in the future (e.g. Monahan and Hooker, 1997). What
is it that makes battered wives return to their abusive husband when there are
hardly any prospects for change? And why does someone take care of a life-
long partner with Alzheimer’s disease who will never be able to recognize the
caretaker? Why do subjects in controlled laboratory experiments give costly
gifts to their long-term exchange partners when their identity will never be
revealed to each other?
A great wealth of empirical evidence suggests that people are engaged
in long-term relationships with their full emotional repertoire (cf. Baumeis-
ter and Leary, 1995). People create social relationships with great ease even in
the absence of materialistic benefits or other ulterior motives, and strongly re-
sist the dissolution of these relationships, well beyond rational considerations
of practical advantages. Many of the strongest emotions people experience
in their life, both positive and negative, are linked to long-term relationships.
The evidence suggests that being accepted, included, or welcomed leads to
positive emotions such as happiness, elation, contentment, and calm, whereas
being rejected, excluded, or ignored leads to anxiety, depression, grief, jeal-
ousy, and loneliness, etc. Indeed, the evidence is sufficiently broad and con-
sistent to suggest that one of the basic functions of emotion is to regulate be-
havior so as to form and maintain social bonds (Baumeister and Leary, 1995).
There is further evidence that people observe and evaluate alternative
partners with a biased vision, systematically dependent on how committed
their current relationship is (Johnson and Rusbult, 1989). Moreover, we
know that even in anonymous exchange settings, positive emotions develop
toward frequent exchange partners, and toward the relationship itself, being
perceived as an object of value (Lawler and Yoon, 1996). These emotions
provide a positive feedback for commitment behavior and lead to a systematic
divergence from instrumental rationality.

3
But why is it that our relationship-related emotions are so often out of tune
with what is usually regarded as rational? What is the source of emotions that
make us consistently more committed than our best interest seems to dictate?
Is there, in fact, something fundamentally rational behind seemingly irrational
commitments?
In order to resolve the paradox between rational and emotional expla-
nations of interpersonal commitment, we put forward an evolutionary ex-
planation. During countless years of prehistoric evolutionary adaptation in
the human ancestral environment, people lived together in small groups and
fought for daily survival in a world more hostile than today’s (Sterelny, 2003).
With many of the formal and informal helping institutions of modern society
missing, people had to rely on interpersonal relationships to a much larger
extent than today. Sometime during the Pleistocene epoch (roughly 1.8 mil-
lion years to 12 thousand years before the present) humans moved from rain
forests to the savannah, which increased the need for collective hunting and
mutual protection from large predators. This in turn created a selection pres-
sure for increased social complexity (c.f. Smaniotto, 2004). At the same time,
life-threatening situations produced more opportunities for bonding and deep
friendships. Being capable and willing to establish and maintain long-term
stable relationships substantially increased one’s survival and reproductive
chances. As a consequence, those whose cognitive arsenal was equipped with
better tools and stronger preferences for making interpersonal commitments
gradually increased their presence in the population over many generations
(cf. Nesse, 2001a).
In lack of a direct test, evolutionary theories are difficult to empirically fal-
sify and therefore problematic to find convincing support for. Therefore, our
strategy in this dissertation is twofold. We first examine a theory of natural
selection acting on commitment in closer detail in Part I (An ultimate expla-
nation)1
. The main motivating question for this effort is: Could a trait of inter-
personal commitment have been selected for in human evolutionary history,
especially in the face of other, more or less cooperative, traits? Building on
previous work (especially de Vos et al., 2001) that relies on anthropological
knowledge about conditions of the human ancestral environment, we create
formal computational models of the ancestral environment. The purpose of
these models is to test the internal consistency of an evolutionary theory about
deeply rooted (or “hardwired”) emotions that facilitate interpersonal commit-
ments.
Then, in Part II (Proximate explanations) we move on to empirically test
the existence of an evolved commitment trait. The main question this part
addresses is: Are there features of contemporary social behavior that are in
line with an ancestral trait for commitment but cannot readily be explained by
1See more about the important distinction between ultimate and proximate explanations in evo-
lutionary theory under Section 1.2.1 of this chapter.

simpler, existing theories? In order to test the existence of such a proximate
mechanism (which we term the commitment bias), we conducted laboratory
experiments at six locations in three different countries (the Netherlands, USA
and China). In particular, we aimed to find support for mechanisms that are
difficult to reconcile with current exchange theoretical and (social) psycholog-
ical theories but become intelligible in light of the evolutionary explanation.
In the remainder of this introductory chapter we are first going to clarify
an important issue about the use of the word commitment. We then address
the vast literature of commitment from philosophy, economics, game theory,
exchange theory, psychology, sociology, and evolutionary psychology, point-
ing to how the dichotomy of emotional and rational explanations permeates
the subject throughout these disciplines. Building on this broad background,
we set out to construct an evolutionary theory that aims to bring the two sides
of the dichotomy closer to each other and thus present the diverse literature
of commitment in a new light.
1.0.1 A brief word on “commitment”
Before we turn to the substantial discussion of commitments, a brief clarifi-
cation of the term itself is inevitable. The word “commitment” is used ex-
cessively in different meanings, within different contexts, which may lead to
misunderstandings. It also creates seemingly unrelated research lines in vari-
ous disciplines across the humanities and behavioral sciences. The first known
record of the word entering the English language is from 1386, when Geoffrey
Chaucer advised “commit the keeping of your person to your true friends...,
who are the best «physicians» and most reliable help and healing” (Wyatt,
1999).
Thus, in its original sense, commitment is a promise or threat, pledge,
agreement, contract or dedication, made to oneself or to others, to do some-
thing or to act in a certain way in the future. “Being committed to protect
one’s country from enemies” or “committing oneself to not getting married”
are examples. Commitment in these cases is similar in meaning to persistence
or consistence (see Section 1.1.1 “Commitment to a course of action” below).
By extension of meaning, commitment came to refer to a bond, or loyalty
toward a social entity, such as an organization, a group of people, or another
person. The basis for this extension is that in such cases one acts in accordance
with one’s expressed or understood promise to the entity, and membership
therein. A friendship, a marriage vow, an employment contract, or simply re-
fraining from extra-couple romance are examples. Commitment in this sense
is related to meanings of belonging, stay behavior, loyalty or faithfulness (see
Section 1.1.2 “Interpersonal Commitment” below).
Arguably, these different meanings are not independent, and a closer look
reveals a number of common characteristics. Firstly, commitment always re-

1.1. Background 5
quires behavioral consistency, in other words acting repeatedly in the same
way with regard to the target of commitment. Secondly, commitment entails
opportunity costs for the individual due to sacrificing potential rewards from
alternative courses of action, that are not explored due to behavioral consis-
tency. Finally, commitment is always temporally embedded – it has a duration
in time, or at least it is in some sense about the future. It is by definition con-
tinuous in time because one cannot uphold the same commitment in disjoint
fractions of time.
Given these conceptual similarities behind different forms of commitment,
it is surprising that hardly any interdisciplinary research has systematically
explored links between commitment in the action and in the interpersonal
sense. This is not our major undertaking either but as we will demonstrate
through a brief literature review below, there is at least one crucial point on
which most theories of commitment converge. This common point is the
duopoly of two competitive explanations: one that advocates rational reasons
and another that points to deeply rooted emotions. Ignoring either type of
explanation leaves a theory potentially vulnerable to criticism by the other
side. Our goal is therefore to derive and test hypotheses within an evolution-
ary framework that is able to accommodate both types of explanations and
resolve some of the contradictions arising between them within the context of
interpersonal commitment.
1.1 Background
The idea of interpersonal commitment is conceptually embedded into the
more general notion of commitment to a course of action. To identify the
implications of the more general concept for the more specific, we start our
theoretical discussion with this broader idea of commitment. From here
we proceed to our core topic of interpersonal commitment, the tendency to
maintain long-term relationships. The existing literature of interpersonal
commitment can be separated into two, largely disconnected fields. The
first field, researched mostly by economists, focuses on exchange and social
networks. The second, researched mostly by psychologists, is more directed
at close relationships, such as married and romantic couples. We review
some of the most important contributions within each field, pointing to the
presence of the emotion-rationality dichotomy throughout. Finally, we briefly
touch upon a hybrid area, organizational commitment, which is closest to
business and management research, although it originally grew out of the
psychological field of interpersonal commitment.

1.1.1 Commitment to a course of action
Committing ourselves to a course of action means that we voluntarily give up
some of our freedom of choice, by agreeing to do (or not to do) something
at some point in the future. The willingness to make such commitments has
long intrigued scientists and philosophers alike, going back as far as ancient
times. The paradoxical benefits of this seemingly self-defeating behavior was
already recognized by ancient Greeks. Xenophon, a talented general, when
facing a superior enemy, ordered his troops to take up a position with their
backs to an impassable ravine (cf. Schelling, 2006) in order to eliminate all
their routes of escape. By doing so he signaled both to the enemy and to his
own men that there was no alternative for survival, except victory.
A major theoretical advance came in 1785 with Immanuel Kant’s “Grundle-
gung zur Metaphysik der Sitten”, where he proposed to distinguish between two
sources of commitment (cf. Levinger, 1999). He argued that commitment, on
the one hand, can grow out of desire or affection. In the case of commitment of
desire, people act out of inclination, for example, because they like to or enjoy
it. Kant considered this type of commitment transient and therefore weak and
untrustworthy. The other form of commitment stems from duty or moral obli-
gation, in which case people act in accordance with principles. Kant argued
that this type of commitment is more enduring and far better morally.
With this theoretical distinction between “having to” and “wanting to”,
Kant essentially created the fundamental dichotomy between rational and
emotional explanations that still dominates the discourse over commitment.
The next major contributor to the theory of commitment was Thomas
Schelling with his seminal book “The Strategy of Conflict” (1963). For Schelling,
commitment is a strategic tool, deliberate action, the purpose of which is to
influence someone else’s choices. Schelling recognized the importance of
being able to make commitments in situations where each actor’s outcome
mutually depends on other actors’ actions. In such situations each actor
needs to take into account what others are likely to do next. The fact that one
makes a commitment to act in a certain way radically alters the expectations
and decision processes of others (Schelling, 2006). The very possibility to
make commitments is a key mechanism for achieving collectively desirable
outcomes that are otherwise difficult to agree on (see e.g. Raub, 2004).
According to Becker’s side-bet theory (1960), making a commitment links
investment in an extraneous interest (a side-bet) with a consistent line of ac-
tion. In Becker’s example, a man wants to buy a house. The man makes an
initial offer of sixteen thousand dollars to the owner. The owner insists on
having twenty thousand. Our well-prepared buyer, however, reaches into his
pocket to produce certified proof that he had made a bet of five thousand dol-
lars with a third-party that he will not pay more than sixteen thousand for the
house. The seller has no choice but to accept the buyer’s standpoint.

1.1. Background 7
In this example, the buyer uses a “credible threat” (a commitment) to mod-
ify his own payoff structure, thus also modifying the strategic interdepen-
dence between the two. Becker’s theory has received extensive attention,
and was widely tested empirically, albeit with mixed success (cf. Cohen and
Lowenberg, 1990; Wallace, 1997). Another conceptualization of side-bets is
voluntary hostage posting (e.g. Raub, 2004). Hostage posting means surren-
dering an object of value to a trustor in order to increase trust in the trustee’s
willingness to uphold the promise (commitment) made to the trustor. The
hostage promotes trust (at least in the economic sense) by binding the trustee
through reducing his incentives for abusing trust, by providing compensa-
tion for the trustor in case trust is abused, and by serving as a signal for the
trustor about unobservable characteristics of the trustee that are related to the
trustee’s opportunities and incentives for abusing trust (Raub, 2004; Snijders
and Buskens, 2001).
An interesting case of commitment is the tendency to escalate investment
in a failing course of action, in other words, “throwing good money after bad”
(cf. Karlsson et al., 2005; Brockner, 1992; Staw, 1976, 1997). Also known as
the sunk cost effect, this motivates people to continue investment in a project
despite unsuccessful prior investments of money, effort, or time (Arkes and
Blumer, 1985). It is important to recognize that thinking in terms of sunk costs
is a departure from rational calculation, in the sense that it distorts actual costs
and benefits associated with possible outcomes.
1.1.2 Interpersonal commitment
Interpersonal commitment, or becoming committed to long-term partners, is
regarded as a special case of commitment to a course of action by some game
theorists, economists and also others (cf. Frank, 1988; Nesse, 2001a). The core
idea behind this association is that commitment in long-term relationships is
based on an implicit or explicit promise to stay with the partner, and to uphold
a general conduct that is aligned with the interests and expectations of the
partner.
This dissertation focuses on long-term interpersonal relationships, such as
marriage, friendship and acquaintanceship, asking the question: why do peo-
ple become committed to each other when it is seemingly not in their best in-
terest? Just as commitment to uphold a certain course of action entails a seem-
ingly irrational decision to reduce one’s set of available choices in the future,
interpersonal commitment involves sacrificing interaction with potentially su-
perior, alternative partners. Yet, as the former type of commitment proves to
be not only rational but, in fact, essential for success in society, could the same
be said about interpersonal commitment? The tentative answer is yes.

Commitment, exchange and uncertainty
In social exchange, two or more actors exchange some form of material or so-
cial benefit among each other in order to arrive at an advantageous outcome.
Exchange theory presumes that people exchange repeatedly with the same
actors when success occurs but move to others when failure occurs. The un-
derlying mechanism may be simple reinforcement learning (Homans, 1961;
Emerson, 1972; Macy and Flache, 2002) or rational choice (Kollock, 1994; Cook
and Whitmeyer, 1992).
Exchange often motivates actors to unilaterally modify the balance of ex-
change to their own advantage without prior knowledge of the partner, in
other words to cheat them in some way. This inevitably leads to uncertainty
about the outcome of the exchange for both partners. When actors repeat-
edly exchange resources, they learn more about one another, find each other
more predictable, develop mutual trust, and infer that they have similar ori-
entations to the exchange task (Lawler, 2001). Therefore, a standard insight
of exchange theory is that frequent exchange with the same partner reduces
uncertainty about cheating, and thus decreases the likelihood of exchanging
with strangers.
More specifically, the uncertainty-reduction hypothesis was tested by Kol-
lock (1994) who showed that commitment is more likely to form in markets
where the quality of the products is unobservable at the time of the exchange.
Kollock (1994) also simulated different market environments under controlled
laboratory conditions. In one condition (high uncertainty), sellers could de-
ceive their potential buyers about the quality of the product they were selling.
In the other condition (low uncertainty), it was not possible to deceive buy-
ers. A key finding of Kollock’s experiment was that commitment formation
between a particular seller and a particular buyer occurs more frequently in
the high-uncertainty condition than in the low-uncertainty condition.
In the same vein, Yamagishi and Yamagishi (1994) argue that committed
relations give a solution to the problem of uncertainty, for multiple reasons.
First, committed partners accumulate information about each other over
time. Second, mutually committed people enact “hostage-taking” behaviors
(Raub, 2004) – ranging from the formation of mutual emotional attachments
to the establishment of relation-specific assets (Helper and Levine, 1992).
Hostage-taking behaviors provide deterrence against unilateral defection
(Shapiro et al., 1992). Finally, conditionally cooperative strategies such as
Tit-for-Tat can be used to control each other’s behavior (Axelrod, 1984).
The main underlying argument for the uncertainty hypothesis is that in-
dividuals tend to avoid unpredictable or uncertain decision contexts (Tver-
sky and Kahnemann, 1974; Kahneman and Tversky, 1979, 1996), which are
created by a lack of first-hand knowledge about a potential partner’s trust-
worthiness (“social uncertainty”). But is the trust problem the only source of

1.1. Background 9
uncertainty in social exchange? Different exchange partners have different re-
sources and may offer different benefits. The size and range of these potential
benefits leads to a conceptually new source of uncertainty. Does this kind of
“resource uncertainty” also increase commitment, independently from social
uncertainty? We will examine the question of resource-inequality between
exchange partners more closely in Chapter 4, and return to the concept of re-
source uncertainty in Chapter 6.
Yamagishi and Yamagishi (1994) list several reasons for the difficulty peo-
ple have in leaving a committed relationship even when it becomes a liability.
One is that the mutual attraction and loyalty that have developed through the
relationship keep partners together. Another is that a temporary better offer
from outsiders may not be sufficient for someone who has already invested
in relation-specific assets to leave the current relationship. Social and psy-
chological assets, such as the warm memory of a pleasant past and mutual
understanding, may be considered relation-specific assets that keep people in
these relationships. Finally, commitment to a particular partner often reduces
the level of trust in “outsiders” (see Kiyonari and Yamagishi, 1996, for exper-
imental support), creating a vicious cycle of distrust of outsiders: those who
do not trust “outsiders” tend to stay in committed relationships, and because
they avoid “outsiders” they become even less trusting of “outsiders.”
Yamagishi et al. (1998) further connect the tendency to form a committed
relationship with the individual’s low level of general trust in others. They
show in a cross-cultural setting (comparing the USA and Japan) that those
who have high trust in others in general are less likely to form committed
relationships. In Chapter 5, we follow up with a cross-cultural study (com-
paring the USA, China and the Netherlands), which shows that simple mere
exposure is sufficient to increase commitment, even without an actual solution
to the trust problem.
Yamagishi et al. (1998) argue that general trust (or trust in people in gen-
eral) provides a psychological springboard for people who have been “con-
fined” to committed relationships to move out into the larger world of op-
portunities. However, as we argue in Chapter 6, general trust addresses only
one of the concerns about switching to new partners. It mitigates concerns
about social uncertainty, but not about resource uncertainty. On the psycho-
logical level, a different antidote is required for resource uncertainty, such as
general optimism. We argue that general trust and optimism together serve as
two mechanisms that help people to explore new relationships with strangers,
thus decreasing commitment.
Next to the uncertainty reduction mechanism, exchange theorists have re-
cently started to recognize the importance of emotions in exchange commit-
ments. Ed Lawler, the main proponent of the emotion argument postulates
that in repetitive exchange, groups and relations become salient social objects
that have a cognitive or subjective reality to actors (Lawler et al., 2000; Lawler,

2001). As such, these relations or groups may take on objective value and
become ends in themselves (cf. Lawler and Yoon, 1996).
Lawler and Yoon (1996) contend that success at exchange makes people
feel good, while failure makes them feel bad. Their theory of relational cohe-
sion states that individually felt emotions unleash a cognitive process through
which the emotion is attributed in part to the relation or group that constitutes
the context of the exchange. In this way, groups can become objects of intrinsic
value to actors due to the positive emotions generated from exchange.
Commitment in close relationships
A special case of interpersonal commitment is close relationships, such as
marriage and intimate partnership. Close relationships research has been the
realm of psychology and social psychology, and so it is little wonder that it
has identified the duality of emotional and rational explanations much earlier.
Many studies in close relationships psychology refer to this duality as at-
traction and constraints (Adams and Jones, 1999). According to Goode (1960),
the attraction (or “positive pull”) aspect is strong for example in romantic cou-
ples having a mutually satisfying and harmonious relationship. Both partners
actively work together to ensure the future of the relationship. On the other
hand, a constraining mechanism could similarly produce stay behavior. Even
a marriage that exhibits no attraction anymore for either partner could never-
theless continue to exist due to external reasons, such as the sake of children’s
well-being or to uphold appearances in a society where divorce is unaccept-
able (an “empty shell” marriage in Goode’s terms).
Hinde (1979) creates a similar dichotomy when he distinguishes endoge-
nous from exogenous commitment. Endogenously committed people strive
to maximize the outcomes of their relational partner, even at the cost of their
own interest. In contrast, exogenous commitment is based on the legal and
social environment in which the relationship is embedded.
In marriage commitment, Johnson (1973; 1991) introduces a third aspect by
distinguishing between personal, structural (constraint) and moral-normative
commitments. Personal commitment is the individual preference for staying
in the marriage (because one wants to); structural commitment comes from
avoiding negative consequences of the dissolution of the relationship (because
one has to). Finally, moral-normative commitment arises from a sense of obli-
gation, to do the right thing, to uphold personal behavioral consistency (be-
cause one ought to). A key psychological source of moral-normative commit-
ment is the avoidance of cognitive dissonance – divorce may be in conflict
with one’s view about marriage, or having made a public declaration through
marriage vows. Another source is a sense of obligation to one another, regard-
less of what others think: one may want to remain true to the promise made
in the wedding vow.

1.1. Background 11
From our perspective of an emotional-rational dichotomy in commitment,
moral-normative commitment occupies a special position. It could be classi-
fied under rational explanations, simply as a factor that modifies instrumental
properties of outcomes within a deliberative thought process. On the other
hand, it could be part of an emotional explanation, inasmuch as norms are
internalized and modify the emotional preferences of the individual.
Within interpersonal relationship research, it is perhaps Rusbult who
comes closest to establishing a rational choice framework for commitment.
Building on Becker’s side-bet theory (1960) and Blau’s work on commitment
(1967), Rusbult created an investment theory for interpersonal commitment
(1980; 1983). According to the investment theory, the level of commitment to
a relational partner is determined by multiple interconnected factors, such
as relational satisfaction (the ratio of rewards and costs in the relationship),
the quality and availability of alternatives or alternative states (e.g. single-
ness), and prior investment in the relationship. Having a highly rewarding
relationship increases commitment, but so does not having satisfactory
alternatives.
Yet, in other works, Rusbult gives implicit indication that a rationality
framework is insufficient to explain many aspects of interpersonal commit-
ment. Johnson and Rusbult (1989) show, for example, that people uncon-
sciously devalue potential alternatives the more committed they are to their
current partner. Doing so, people distort key variables of a rational choice
equation.
Organizational commitment
A large body of research studies commitment to organizations. In the dis-
course of organizational commitment, commitment refers to the attachment of
a member or employee to an organization. It is sometimes used interchange-
ably with other concepts, such as cooperativeness and stay behavior, or even
more broadly, organizational citizenship behavior (see Moorman and Blakely,
1995; Organ, 1988).
Organizational commitment research is largely motivated by the insight
that members who are more committed, will perform better and regard the
interest of the organization as common with their own, are less stressed, and
less likely to leave the organization.
Meyer and Allen (1991) integrated many of the divergent conceptualiza-
tions and measurements of commitment into a coherent theoretical frame-
work. Their model is based on the recognition that there are three main as-
pects (or “mindsets”) of organizational commitment:
1. Affective Commitment is the employee’s emotional attachment to the or-
ganization. It refers to identification with the goals of the organization
and a desire to remain a part of the organization. The employees commit

to the organization because they “want to”. In developing this concept,
Meyer and Allen drew largely on Mowday et al.’s (1982) concept of com-
mitment.
2. Continuance Commitment lies behind the commitment of an individual
who perceives high costs of losing organizational membership (cf.
the side bet theory, Becker, 1960), including economic losses (such as
pension accruals) and social costs (friendship ties with co-workers) that
would have to be given up. The employees commit to the organization
because they “have to”.
3. Normative Commitment is created by feelings of obligation to the organi-
zation. For instance, the organization may have invested resources in
training an employee who then feels an obligation to put forth an effort
on the job and stay with the organization to repay the debt. It may also
reflect an internalized norm, developed before the person joins the orga-
nization through family or other socialization processes, that one should
be loyal to one’s organization. The employees stay with the organization
because they “ought to”.
According to Meyer and Herscovitch (2001), an employee has a “commitment
profile” at any point in time that reflects high or low levels of all three of these
factors, and different profiles have different effects on workplace behavior
such as job performance, absenteeism, and the chance to quit.
These three factors are thought to jointly determine the overall level of an
employee’s commitment to the organization Meyer and Allen (1991). Com-
pare how similar this trichotomy is to Johnson’s model above (1973; 1991) un-
der “Commitment in close relationships”.
1.2 Toward an evolutionary explanation
With the advent of sociobiology, and later the rapid growth of evolutionary
psychology, many aspects of human behavior have been convincingly ex-
plained from an evolutionary perspective, relying on dynamics of genetic and
cultural evolution.
The major argument of evolutionary psychology (see Cosmides, 1989; Cos-
mides and Tooby, 1993) is that human ancestors spent a vast amount of time
in a relatively stable environment of the Pleistocene, starting 1.8 million years
ago and spanning until about 12,000 years ago. During the time spent in this
ancestral environment, human brains and some of the most fundamental so-
ciocultural institutions respectively, underwent a long adaptation process.
During evolutionary adaptation (Darwin, 1859), the characteristics of an
individual (trait) undergo random changes (mutation) that are inherited by

1.2. Toward an evolutionary explanation 13
their offspring. Through mutation new traits may appear, increase in strength
or disappear2
. When the combination of traits (phenotype) of an individual in-
creases reproductive success relative to other individuals, i.e. by increasing
the chances of the individual surviving until a reproductive age, the traits of
this individual become more prevalent in the population, through the rela-
tive increase in the number of offspring possessing the trait (natural selection).
Traits that specifically increase mating opportunities, usually through some
highly observable physical trait (e.g. the peacock’s colorful tail) may spread
even faster (sexual selection).
This process led to the stabilization of those cognitive abilities and social
preferences which solved problems frequently encountered in our prehistoric
ancestral environment. Due to rapid changes in our civilization in the last few
millennia, many of these stable adaptations are no longer beneficial but never-
theless continue to influence the behavior of contemporary humans. One ex-
ample is that, although an estimated 132,687 people sustain gunshot wounds
that result in death or emergency treatment in the USA annually (Beaman
et al., 2000), and only a handful of people are killed or injured by snakes and
spiders, people learn to fear snakes and spiders roughly as easily as a pointed
gun, and much more easily than an unpointed gun, rabbit or flowers (Öh-
man and Mineka, 2001). The explanation from evolutionary psychology is
that snakes and spiders were a large threat in the ancestral environment but
guns, rabbits and flowers were not.
Several attempts have been made to construct a similar evolutionary ex-
planation for commitment (in the general sense of promises and threats) that
brings together the emotional and rational sides. As one of the main propo-
nents of this line, Nesse (2001a), puts it:
[There are] abundant examples of the importance of commit-
ment in human social life. The evidence is so compelling that one
cannot help but wonder why explanations for cooperation have
been so narrowly dependent on methodological rationalism and
individualism. I suspect the reason is the absence of a framework
that can account for actions that seem irrational. In the framework
of commitment, such behaviors are not only explicable, they are
expected. Certain emotions seem opposed to reason because they
are opposed to reason. In the short run they seem mysterious, but
in the long run on average they give advantages that shape psy-
chological traits that change the structure of human society. These
psychological traits must be incorporated into our model, however
difficult that may be (p. 161).
2Note that according to the theory of cultural evolution (Boyd and Richerson, 1985) such an
evolutionary process need not take place on a genetic level. They showed that culture can evolve
by a very similar dynamic as genetically based traits evolve by natural selection. Culture also
undergoes mutation, individuals have cultural offspring, etc.

The answer he proposes is to regard deep rooted emotions related to com-
mitment (to an action) as evolutionary adaptations that serve a good purpose
in general and in the long run but due to their hardwiring easily come in con-
flict with rational deliberation.
He argues that the parts of the human brain that evolved latest in our his-
tory, the frontal lobes, closely match the abilities needed to use commitment
strategies (p. 34). It appears that the frontal lobes are especially well-suited
to calculating trade-offs between short-term costs of giving up options and
long-term benefits that may or may not be obtained. Such calculations are
inherently complex, because they involve considerations about social capital,
and would be impossible without specialized mental hardware. According to
Nesse, the frontal lobes are also involved in the ability to empathically iden-
tify with another person, which is essential to predicting whether the other
will fulfill a commitment.
The weakness of Nesse’s argument is that it attempts to cram too much
under the explanatory umbrella of natural selection. In his book, he integrates
works from psychology, game theory, ethology, law, medicine, religion and
mythology. Doing so, his argument gets fragmented and lost in the myriad
aspects of general commitment. In the end, some of the phenomena and mech-
anisms considered can only be linked to natural selection through smaller or
larger jumps in the argument. In fact, Nesse tries to explain human coop-
eration and non-kin altruism arguing for a capacity for making threats and
promises (commitment in the broad sense) in general, but his argument relies
heavily on long-term relationships (commitment in the interpersonal sense). It
could possibly strengthen his theory if the evolutionary argumentation were
restricted only to the simpler and more specific idea of interpersonal commit-
ment.
Another proponent for the crucial role of emotions in commitment is
Robert Frank. In his seminal book “Passions within Reason” (1988) he argues
that social environments naturally produce situations where commitment
could potentially play a pivotal role, yet there is little room for formal commit-
ment devices, such as contracts or other tangible hostages. In these cases, the
best solutions are emotional commitments. One of the social emotions Frank
argues for, as a relatively hard-to-fake signal of commitment, is sympathy
(Frank, 2001). Sympathy enables people to detect other’s emotional state and
experience it to some extent. Detecting sympathy in others helps to make
promises about future cooperation more credible.
Another social emotion that makes commitments credible without tangible
assurances is anger. In a world of purely rational self-interested people who
have perfect self-control, all acts of defection where the costs of retaliation
outweigh benefits would go unpunished. An angry person, however, seldom
gets recognized as a rational one, leading to an increase in the credibility of his
threat of punishment, and thus decreasing the expected benefits of defection

in the first place (Frank, 1988).
1.2.1 Separating ultimate and proximate explanations
When attempting to construct an evolutionary explanation for any kind of
behavior, it is important to separate parallel explanations on at least two dif-
ferent levels of causality, the proximate and the ultimate level (Mayr, 1961).
Proximate explanations identify environmental stimuli that trigger mechanisms
within the individual as the causes of physical expression of the behavior. For
example, in answer to the question, “why do songbirds sing?”, one might ar-
gue that increased daylight in the spring leads to increased testosterone pro-
duction which activates parts of the brain in male songbirds. This explanation
identifies a proximate mechanism (a neurobiological one in this case) in re-
sponse to a direct stimulus (increased sunshine) to explain behavior (singing).
Such a proximate explanation, however, might leave one with a sense of un-
satisfied curiosity.
In order to answer why such a proximate mechanism came to exist in the
first place, one needs to look for an ultimate explanation, on a more general level
of evolutionary causation. The reason why male songbirds sing is that singing
attracts females and defends territory from other males. Consequently, those
males who sing have better chances of reproducing and spreading their habit
of singing into the next generation of songbirds, than those males who do not
sing. Such an ultimate theory has the advantage of explaining behavior, while
at the same time encompassing and justifying the proximate explanation.3
1.2.2 How evolutionary theory helps to explain seemingly ir-
rational behavior
There are at least three systematic4
ways in which evolved behavior may de-
part from the seemingly rational. The first two result from the fact that ulti-
mate functions are implemented through proximate means, and the third is
based on fundamental constraints on information processing.
Proximate mechanisms are always imperfect in the sense that they were
the first solution, discovered randomly by natural selection, which addressed
a specific problem of survival and reproduction in the simplest and most cost-
efficient way in a certain environment. As soon as there is a change in the
environment, a proximate mechanism can easily lose its efficiency or even turn
3Ultimate (also called holistic) explanations are subject to criticism by reductionists who claim
that because ultimate explanations are functional, they lack a sufficient causal argument. We
provide a counterargument to this criticism in Chapter 7, page 137.
4By “systematic” we mean that behavior fails to be rational in the same way within the same
context for a large number of individuals, i.e. not as isolated occurrences of some random or
transient mistake in individual reasoning.

against the individual. Consider in the previous example the appearance of a
human hunter who learns to imitate the calling of the male bird and thus easily
captures female birds. In this case, females who have evolved a preference for
males’ songs experience a serious decrease in their survival and reproductive
chances. By definition, proximate mechanisms lead to stable behavior across
different contexts. But while they create a clear adaptive advantage in one
context, they could lead to maladaptive behavior in another.
The first possibility for such errors is that a stimulus from the environment
is falsely interpreted by the individual as a trigger for a proximate mechanism
(a “false positive”, or “type I error” in statistics). The reason why the proxi-
mate mechanism could still be left in place by evolution is that the relative cost
of the false alarm is smaller than the cost of not recognizing the real stimulus (a
“type II error”). According to Error Management Theory (Haselton and Buss,
2000; Haselton and Nettle, 2006), humans acquired a large number of biases
that increase the amount of false positives, when false negatives are extremely
costly. An interesting example is that people develop a strong aversion to a
certain kind of food, if its consumption was closely followed by sickness in the
past (Garcia et al., 1966). This mechanism protected ancestral humans against
consuming poisonous food sources (cf. Sripada and Stich, 2004). Such behav-
ior could also be regarded as rational if information collection and processing
are assumed to be costly. Therefore, the departure from standard rationality
in this case is not so much the crude causal approximation between poisonous
food and sickness but the fact that the aversion is manifest as a discomfort-
ing sensation in the gut, and not as the end-product of a deliberative thought
process.
An example with regard to interpersonal commitment is the laboratory
studies carried out by Lawler and collaborators (Lawler and Yoon, 1993, 1996;
Lawler et al., 2000). In these experiments, people became committed to their
partners and reaffirmed their commitment with costly gifts when in fact they
had never met these partners face to face and were ensured by the experimen-
tal setting that they never would. In this case, the bias for interpersonal com-
mitment, a proximate mechanism, misfired in an inappropriate context (termi-
nology from Sripada and Stich, 2004).
The second way in which rationality could fail is when a signal is correctly
recognized but the response given to it is no longer adaptive due to changes in
the environment itself. A core assumption of evolutionary psychology (Cos-
mides, 1989; Barkow et al., 1992) is that the environment we live in today is
radically different from the environment of evolutionary adaptation (ancestral
environment). Therefore, some of the evolved stimulus-response mechanisms
have become maladaptive.
An example from the domain of interpersonal relationships is the very re-
cent phenomenon of Internet addiction taking place among a worryingly large
portion of ordinary people. The majority of these people turn to on-line chat

rooms and role-playing games in search of social support, sexual fulfillment,
and an opportunity to safely express forbidden aspects of their personalities.
Adverse results include social withdrawal in the real world and loss of con-
trol, which are typical of other forms of addiction (Henry et al., 1997). In other
words, people follow their evolved need for socialization but given the trans-
formation of our social environment due to rapid technological development,
the individual’s fitness is negatively affected.
The third way in which human decision-making may depart from rational-
ity is linked to information. In order to make rational decisions by choosing
between different actions that lead to different outcomes, one needs informa-
tion about these outcomes. If evolution ultimately favored rationality, it would
also have favored mechanisms that help to obtain and process information ac-
curately. There is mounting evidence that evolution sometimes works in the
exact opposite direction. This is most notable in the case of evolved cognitive
biases and optical illusions (see e.g. Haselton and Buss, 2000; Haselton and
Nettle, 2006; Gigerenzer and Todd, 1999). Among the numerous examples,
consider Evolved Navigation Theory. According to this theory, humans were
selected to perceive physical characteristics of the environment (e.g. height
and altitude) not as precisely as possible, but rather with a factoring in of
the dangers they represent for individual fitness. Researchers in an experi-
ment (Jackson and Cormack, 2006) asked one group of people to estimate the
height of a very tall lookout point by looking at it from its bottom and another
group to do the same from the top. It was found that people on the top con-
sistently overestimated altitude, in proportion to the increased risk of falling. This
shows how evolution can build safeguards into our cognitive apparatus that
act against standard rational calculation.
Consider now the finding of Johnson and Rusbult (1989) from earlier in
this chapter, which shows that people systematically underestimate alterna-
tive partners, the more committed they are. If evolution ultimately favored
the choice for a rational decision, it would have made sure that information
about alternative partners is as accurate as possible at the time of making a de-
cision. If, however, evolution aimed at stabilizing interpersonal commitments,
it would have biased decisions in exactly this direction.
1.2.3 Constructing an ultimate explanation for interpersonal
commitment
Although in his 1988 book Frank sets out to summarize empirical support for
an evolutionary explanation for commitment in the general sense, many of
his examples are more relevant for commitment in the interpersonal sense.
Frank refers to marriage as a key example for a commitment dilemma. People
search for the perfect mate, but settle for someone after a certain period of
exploration despite knowing that there is certainly someone else out there, not

yet encountered, who would make a better spouse. And although a marriage
contract may create a formal token of commitment, this is hardly the reason
why people stop exploring further mates. A far more secure commitment
is ensured by emotional bonds of affection (Frank, 2001). These emotional
bonds ensure that even if someone kinder, better looking, or richer, who would
originally have been preferred over the current partner, comes along now, the threat
to the current commitment is diminished.
But what does this have to do with evolution? There is growing acceptance
among biologists of the idea that marital commitment is a key factor in en-
hancing the reproductive success of humans (Hrdy, 1999; Martin, 2003; Foley,
1996; Geary, 2000; Pillsworth and Haselton, 2005), indeed more so than in the
case of any other primate species. In order to be able to pass through the birth
canal of their mother with their large brain unharmed, human infants need to
be born at an earlier developmental stage than other primate offspring (Hrdy,
1999). Consequently, they are more helpless and require substantially longer
parenting (Martin, 2003). Therefore, finding a committed father who is present
and cooperative during this extended period of parenting is instrumental for
the reproductive fitness of humans5
(Foley, 1996; Geary, 2000; Pillsworth and
Haselton, 2005).
Indeed, there is a wealth of empirical findings in psychology and social
psychology that gives further support for the existence of a consistently bi-
ased emotional-cognitive framework facilitating interpersonal relationships
and commitment (cf. Baumeister and Leary, 1995). People in every society
on earth belong to small primary groups that involve face-to-face, personal
interactions (Mann, 1980). Festinger et al. (1950) found that mere proxim-
ity is enough for people to develop social bonds, and is especially suitable
to compensate for differences in age or race (Nahemow and Lawton, 1975).
Ostrom et al. (1993) showed that people memorize things related to close ac-
quaintances on a person basis, whereas information related to looser contacts
is stored and organized based on attribute characteristics (e.g. traits, prefer-
ences and duties).
There is evidence that forgiving a misconduct of a committed partner di-
rectly enhances psychological well-being of the one who forgives (Karremans
et al., 2003). It has also been shown that when people evaluate potential alter-
native partners, they unconsciously devalue potential alternatives the more
committed they are to their current partner (Johnson and Rusbult, 1989). Kiy-
onari and Yamagishi (1996) give experimental support that those who stay
committed to steady partners not only increasingly trust their partner, but
5Indeed there is a possibility that next to natural selection, sexual selection also contributed
to the proliferation of a commitment trait. Since human women need to find potentially commit-
ted mates to ensure the survival of their offspring, showing interpersonal commitment in social
relationships in general could have served as a costly signal of males’ willingness and ability to
become committed fathers.

also increasingly distrust outsiders, leading to a “vicious cycle of distrust in
outsiders”.
On the one hand, a strategy of commitment appears to be efficient in forg-
ing beneficial relationships, yet it also loses out by letting potentially good
alternatives slip away, and moreover, it gives way to exploitation within the
relationship. To better understand these mechanisms and their interaction
under a complex, evolutionary dynamic, we create formal (computational)
models of the ancestral environment. Our models for the evolution of deeply
rooted emotions underlying interpersonal commitment rely on a series of pre-
vious works by Henk de Vos and his collaborators (de Vos and Zeggelink,
1997; de Vos et al., 2001; Zeggelink et al., 2000). These researchers designed
an agent-based computational model based on the following minimalistic as-
sumptions about conditions of the ancestral environment:
1. People lived together in relatively small groups.
2. The environment was harsher, its impact less buffered, and resources
more scarce than today.
3. In lack of many modern social institutions, help from fellow individuals
was more important for survival than today.
4. The environment and subsistence technologies were more stable over
an extended period of time than in modern civilizations, which made it
possible for evolutionary pressures to hardwire preferences.
De Vos and colleagues created a help exchange model, in which members of
a relatively small group are dependent on the help of others to survive an
event of distress from time to time. They compared two major contestants in
their simulations of the evolution of exchange strategies, a strategy based on
calculative reciprocal cooperation and a strategy based on commitment. De
Vos and collaborators found that when each of the strategies competes against
opportunistic players – i.e. actors who are unwilling to help but accept help
from others – commitment is more viable than calculative reciprocity.
De Vos et al. tentatively concluded from their computational experiments
that under conditions of the human ancestral environment, selection pressures
might have shaped a tendency towards commitment and largely uncondi-
tional cooperation. This tendency may still be present in contemporary hu-
mans, even though the pressures that formed it are weakened or no longer
in place. However, their studies were strictly limited by the small number of
strategy variations they examined. This presents a problem because overly
cooperative agents following a commitment strategy could easily fall prey to
smart cheaters, a possibility that their model could not account for. More-
over, as Binmore (1998) argued forcefully, the outcome of computer tourna-
ments and simulations of evolutionary dynamics strongly depends on the set
of strategies that are initially present in a population.

To address whether and to what extent these two potential problems re-
duce the viability of commitment, we propose in Part I (Chapter 2) a method to
considerably and systematically enlarge the set of behaviors examined in the
original analysis of the help exchange model. The core idea is to represent be-
haviors as determined by a set of individual preferences, or traits with respect
to possible exchange outcomes. Agents in our model are boundedly and sub-
jectively rational in the sense that they make decisions to cooperate, defect and
change partners with the goal of maximizing subjective utility (or satisfaction)
given their preferences. However, maximizing subjective utility based on in-
dividual preferences in our model does not necessarily lead agents to optimal
exchange outcomes. We assume that individual preferences or strategies are
subject to evolutionary pressure that selects for successful strategies based on
the objective fitness consequences of the behavior resulting from the strategy.
This approach is similar to the “indirect evolutionary approach” proposed by
Güth and Kliemt (1998).
1.2.4 Proximate mechanisms for interpersonal commitment
Is there support for an ultimate explanation for commitment through a cor-
responding proximate mechanism? More precisely, do contemporary humans
have a stable, hardwired tendency to become committed to their previous in-
teraction partners in an emotional way when it is not in their instrumental
self-interest? In Chapter 5 we empirically test the existence of such a poten-
tially hardwired tendency for commitment through a series of cross-cultural
laboratory experiments.
When arguing for the evolutionary origins of any aspect of sociality, it is
usually better to rely on cross-cultural data, in order to rule out cultural expla-
nations. Since the environment of evolutionary adaptation mostly predates
the break-up of modern cultures, adaptations associated with the ancestral en-
vironment should be present in all cultures. This is not to say, of course, that a
cross-culturally stable phenomenon necessitates an evolutionary explanation,
or that a lack of cross-cultural evidence rules one out. Culture intricately in-
terplays with how people decide and behave, which itself has implications for
biological evolution (Boyd and Richerson, 1985).
The idea behind a proximate explanation for commitment is that through
repeated positive interactions, people’s view of a committed relationship be-
comes systematically biased in comparison with a strictly instrumental per-
spective. When the relationship later takes a negative turn, this positively bi-
ased perspective for commitment makes stay behavior and cooperation more
likely than otherwise expected. According to the mere exposure effect (orig-
inally described by Zajonc, 1968), when being repeatedly subject to a non-
repulsive stimulus, one develops a positive affect toward the stimulus. For
example, the more we listen to the same piece of music, the more we appre-

1.3. Methodology 21
ciate it. We argue that such a mere exposure effect exists between long-term
interaction partners. There is evidence, for example, that the more we see the
same face, the more attractive we find it (Rhodes et al., 2001). What is even
more interesting, is the finding that people also trust others more if they have
been exposed to them more times, even in the absence of any interaction that
could support actual inferences about trustworthiness (Moreland and Beach,
1992).
1.3 Methodology
Studies reported in this volume rely largely on two key methodological ap-
proaches, agent-based computational modeling and laboratory experiments.
Below is a brief description of both, and an explanation of their usefulness for
answering our research questions.
Part I is aimed at testing an evolutionary theory that posits the stabilization
of a commitment trait under selection pressures of the human ancestral envi-
ronment. Its goal is to compare the strengths and weaknesses of commitment
to other social preferences, such as calculative reciprocity (fairness). Drawing
on earlier work in this domain (de Vos et al., 2001) and weighing the complex-
ity of the modeling task, we decided to apply a type of social simulation to
our problem, agent-based computational modeling (ABCM).
Whereas social scientists usually model social processes as interactions
among variables, ABCM studies interactions among adaptive agents who in-
fluence one another in response to the influence they receive (Macy and Willer,
2002). In ABCM, all modeling information about the properties of individ-
ual agents and their behavioral rules are transformed into a formal language
(e.g. a computer program). Subsequently, the dynamics of the model, as well
as conclusions on the macro-level can be deduced through step-by-step com-
putation from given starting conditions (Flache and Macy, 2005). The ad-
vantages of ABCM are especially apparent when modeling dynamic phenom-
ena in groups that are highly complex, non-linear, path-dependent, and self-
organizing. The obvious advantage is that the explanation draws on local
interactions among agents and not on predefined global characteristics of the
group (Macy and Willer, 2002).
A possible alternative methodology in Part I would be game theoretical
(e.g. equilibrium) analysis. The benefit of such analysis is that it is able to
provide more universal hypothesis tests that benefit from the strength of a
mathematical proof. Its drawback is that given the complexity of our model,
coupled with the evolutionary dynamic, this methodological approach seems
unfeasible. This is also illustrated by the relative complexity of a brief analysis
of a strongly simplified version of our model in Appendix A.
In Part II, we aim to empirically test the evolutionary argument through

identifying peculiarities in decision-making in exchange relationships among
contemporary humans. The majority of our hypotheses predict links between
specific, well-defined conditions and exact, quantifiable measures of commit-
ment. Therefore, strict control over conditions and measurement is indispens-
able. Accordingly, we decided to test our hypotheses in laboratory experi-
ments with human subjects, using anonymous, computer-based settings.
A possible alternative here would be to rely on representative cross-
national surveys, or data collected among contemporary small-scale (e.g.
hunter-gatherer) societies. Such secondary data analysis, however, would
seriously restrict the scope of hypotheses that we could test.
1.4 Outline of chapters
The rest of this dissertation is organized as follows. Part I lays the theoreti-
cal foundation for studying interpersonal commitment using formal compu-
tation models. These three chapters investigate whether a commitment trait
could have been adaptive under the conditions of the ancestral environment,
with each chapter gradually putting commitment under a stricter test. More
specifically, Chapter 2 examines whether improving on a known weakness of
fair reciprocity (the main contestant of commitment in earlier work of de Vos
et al., 2001) eliminates the competitive advantage of commitment. We create
an agent-based model that is capable of incorporating previous models, and
at the same time offers more flexibility and robustness to study the relative
viability of commitment strategies. This chapter also tries to answer whether
the exchange network structures, which are formed spontaneously in the sim-
ulated populations, help to explain the relative differences in viability.
Chapter 3 puts the viability of commitment strategies under a stricter test
by extending the previous ecological model through accounting for evolution-
ary dynamics of selection and mutation. This also means that the strategies
examined here are no longer a priori invented and specified by the modeler,
but emerge spontaneously through random walks in the strategy space. In
comparison with previous work, this can possibly lead to the emergence of
more sophisticated opponents that may take advantage of the weaknesses of
commitment.
A defining characteristic of interpersonal commitment is that one forgoes
interaction with potentially better alternatives in favor of a long-term part-
ner. Chapter 4 puts the emphasis on inequalities between potential interaction
partners, and asks whether a preference for high-resource (or highly capable)
others is more important than a preference for old partners (commitment). It
also examines whether becoming committed to average or low-value partners
undermines the efficiency of commitment. In order to do so we extend our
previous model by introducing an inheritable trait for high-resource others,

1.4. Outline of chapters 23
and non-inheritable inequality in individual capabilities/resources.
Part II turns to the experimental investigation of whether an ancestrally
evolved commitment trait influences behavior in contemporary humans.
Chapters 5 and 6 examine commitment in various exchange situations using
laboratory experiments with human subjects. These two chapters take up the
two major lines in explaining commitment in exchange: positive emotions
and uncertainty reduction. In Chapter 5, we study whether people have a
tendency to escalate commitment to previous interaction partners, when it is
not in their self-interest. The purpose of this study is to test whether people
have a cross-culturally stable emotional preference for previous partners
that acts as a decision-making bias even in anonymous, economic setting.
At the same time, in Chapter 5 we also find that uncertainty can decrease
commitment, which leaves us with a new puzzle.
To resolve the puzzle about the effect of uncertainty on commitment, in
Chapter 6 we propose to refine the explanatory framework that has been used
in the literature of commitment in exchange. More precisely, we identify an
important assumption that previous works have left implicit, about the coop-
erative intentions of people, that qualifies the effect of uncertainty on commit-
ment. In addition, Chapter 6 is also an empirical counterpart of the theoretical
Chapter 4, which argued that a preference for previous partners (commitment)
should be stronger among contemporary humans than a preference for high-
resource partners. Accordingly, we argue that partner selection situations can
create not only social uncertainty (about trustworthiness) but also resource
uncertainty, and that each type of uncertainty has its independent effect on
commitment. At the same time, we contend that individual characteristics,
such as general trust and optimism, may also influence the level of commit-
ment.
Finally, Chapter 7 contains a summary of results, with a general discussion
of the findings, and an evaluation of the strengths and weaknesses of the work
accomplished. We locate our work within past research and point to possible
avenues of future research.
Since Chapters 2, 3 and 4 have been accepted for publication in interna-
tional peer-reviewed journals and a book, and the material that makes up
Chapters 5 and 6 is currently under review, these chapters are kept in their
original article format. As a result, some overlap may be detected between
different chapters, especially in their empirical motivation and model descrip-
tions. On the positive side, this leaves each chapter stand-alone and self-
explanatory.

Part I
An ultimate explanation
25

Chapter 2
The Competitive Advantage
of Commitment1
Abstract
A prominent explanation of cooperation in repeated exchange is reci-
procity (e.g. Axelrod, 1984). However, empirical studies indicate that
exchange partners are often much less intent on keeping the books balanced
than Axelrod suggested. In particular, there is evidence for commitment
behavior, indicating that people tend to build long-term cooperative relation-
ships characterized by largely unconditional cooperation, and are inclined
to hold on to them even when this appears to contradict self-interest. Using
an agent-based computational model, we examine whether in a competitive
environment commitment can be a more successful strategy than reciprocity.
We move beyond previous computational models by proposing a method
that allows systematic exploration of an infinite space of possible exchange
strategies. We use this method to carry out two sets of simulation experiments
designed to assess the viability of commitment against a large set of potential
competitors. In the first experiment, we find that although unconditional
cooperation makes strategies vulnerable to exploitation, a strategy of commit-
ment benefits more from being more unconditionally cooperative. The second
experiment shows that tolerance improves the performance of reciprocity
1This chapter is based on Back, I. and Flache, A. (2006). The Viability of Cooperation
Based on Interpersonal Commitment. Journal of Artificial Societies and Social Simulation 9(1),
http://jasss.soc.surrey.ac.uk/9/1/12.html.
27

28 Chapter 2. The Competitive Advantage of Commitment
strategies but does not make them more successful than commitment. To ex-
plicate the underlying mechanism, we also study the spontaneous formation
of exchange network structures in the simulated populations. It turns out that
commitment strategies benefit from efficient networking: they spontaneously
create a structure of exchange relations that ensures efficient division of labor.
The problem with stricter reciprocity strategies is that they tend to spread
interaction requests randomly across the population, to keep relations in
balance. During times of great scarcity of exchange partners this structure is
inefficient because it generates overlapping personal networks so that often
too many people try to interact with the same partner at the same time.
2.1 Introduction
The most prominent explanation of endogenous cooperation in durable rela-
tionships is reciprocity under a sufficiently long “shadow of the future” (Ax-
elrod, 1984; Friedman, 1971). In this view, actors engage in costly cooperation
because they expect future reciprocation of their investment or because they
feel threatened by future sanctions for non-cooperation (Fehr and Schmidt,
1999; Fehr and Gächter, 2002; Falk et al., 2001). Roughly, these analyses show
that even in a competitive environment with changing exchange partners,
strategies that reciprocate cooperation with cooperation and defection with
defection, such as the celebrated “Tit-for-Tat”, are far more successful than
strategies that aim to exploit their opponents. Evolutionary game theory has
demonstrated that if exchange relations persist long enough, cheaters are out-
performed by reciprocators. This is because reciprocators benefit from ongo-
ing mutually cooperative exchanges, while cheaters gain at best a short-term
advantage at the outset of the exchange. This, however, cannot offset the long-
term losses caused by the early disruption of the exchange relationship.
It has been suggested that this reciprocity explanation of cooperation ap-
plies to a number of domains ranging from business ties between organiza-
tions to interpersonal relationships. However, recent empirical studies of co-
operative behavior, in particular in interpersonal relationships, indicate that
often reciprocity may be much less strict and actors much less intent on keep-
ing the books balanced than the original reciprocity argument suggests. A
short excerpt from Nesse (2001b) offers good examples:
Perhaps the strongest evidence that friendships are based on
commitment and not reciprocity is the revulsion people feel on dis-

2.1. Introduction 29
covering that an apparent friend is calculating the benefits of act-
ing in one way or another. People intuitively recognize that such
calculators are not friends at all, but exchangers of favors at best,
and devious exploiters at worst. Abundant evidence confirms this
observation. Mills has shown that when friends engage in prompt
reciprocation, this does not strengthen but rather weakens the rela-
tionship (Mills and Clark, 1982). Similarly, favors between friends
do not create obligations for reciprocation because friends are ex-
pected to help each other for emotional, not instrumental reasons
(Mills and Clark, 1994). Other researchers have found that people
comply more with a request from a friend than from a stranger, but
doing a favor prior to the request increases cooperation more in a
stranger than a friend (Boster et al., 1995).
Moreover, there is solid empirical evidence indicating that people have a
tendency to build long-term cooperative relationships based on largely uncon-
ditional cooperation, and are inclined to hold on to them even in situations
where this does not appear to be in line with their narrow self-interest (see
e.g. Wieselquist et al., 1999). Experiments with exchange situations (Kollock,
1994; Lawler and Yoon, 1993, 1996) point to ongoing exchanges with the same
partner even if more valuable (or less costly) alternatives are available. This
commitment also implies forgiveness and gift-giving without any explicit de-
mand for reciprocation (Lawler, 2001; Lawler and Yoon, 1993). One example
is that people help friends and acquaintances in trouble, apparently without
calculating present costs and future benefits. Another, extreme example is the
battered woman who stays with her husband (Rusbult and Martz, 1995; Rus-
bult et al., 1998).
Since the seminal work of Axelrod (1984), a range of studies has used evo-
lutionary game theory to refine the strategy of strict reciprocity and adapt it
to empirical criticism. One line of work focused on the advantages of “relaxed
accounting” in noisy environments (e.g. Kollock, 1993; Nowak and Sigmund,
1993; Wu and Axelrod, 1995). Broadly, these experiments confirmed the hy-
pothesis that uncertainty favors “tolerant” or “relaxed” conditionally coop-
erative strategies that do not always retaliate after defection of an opponent.
Kollock (1993), for example, found that in noisy environments (with mistakes
and miscues), strict reciprocity is prone to needless recrimination that can be
avoided by looser accounting systems. However, these studies cannot address
the empirical phenomenon of commitment to long-term exchange partners,
simply because they apply a repeated game framework in which there is no
possibility to exit from an ongoing exchange in order to seek a new partner.
A number of authors have explored variations of Tit-for-Tat that combine
looser accounting under uncertainty with selective partner choice. Computa-
tional analyses of exit effects (Schüssler, 1989; Vanberg and Congleton, 1992;
Schüssler and Sandten, 2000) put the role of the shadow of the future for

emergent cooperation into perspective. The route to emergent cooperation
that these studies uncover is commitment of cooperators to cooperators, with
the consequence of exclusion of defectors from relationships with cooperative
partners. This is based on the principle “be cooperative but abandon any-
one who defects.” When enough members of a population adopt this strat-
egy, cooperative players stay in stable relationships, leaving defectors with no
one but other defectors to interact with. As a consequence, defectors perform
poorly and conditional cooperation thrives even under anonymity conditions
where unfriendly players can hide in a “sea of anonymous others” (Axelrod,
1984, 100) after they “hit and run”. Considering more complex agent archi-
tectures, Schüssler and Sandten (2000) show that strategies that are to some
degree exploiters may survive under evolutionary pressure but even then the
most successful strategies will have the property of staying with a cooperative
partner who turns out to be difficult to exploit. Other computational studies
that include partner selection and arrive at similar conclusions are, for exam-
ple, Yamagishi et al. (1994), or Hegselmann (1996) (cf. Flache and Hegselmann,
1999b).
While previous work using evolutionary game theory could demonstrate
the viability of relaxed accounting and commitment under certain conditions,
it is doubtful whether this suffices to explain how humans may have acquired
the deeply rooted emotions and behaviors related to interpersonal commit-
ment that have been empirically observed.
This is why de Vos and collaborators (de Vos et al., 2001; Zeggelink et al.,
2000; de Vos and Zeggelink, 1997) extended theoretical models with assump-
tions from evolutionary psychology (Cosmides, 1989; Cosmides and Tooby,
1993). According to evolutionary psychologists, the way our mind functions
today is the result of an extremely long evolutionary process during which
our ancestors were subject to a relatively stable (social) environment. Individ-
ual preferences for various outcomes in typical social dilemmas stabilized in
this ancestral environment and still influence the way we decide and behave
in similar dilemma situations today.
To model a stylized ancestral environment, de Vos and collaborators de-
signed a help exchange game in which members of a relatively small group
need the help of others to survive a situation of distress from time to time.
More precisely, in their model agents come into distress at random points in
time and then ask other members of the group for help. They compared two
major contestants in their simulations of the evolution of exchange strategies,
a strategy they called “keeping books balanced” (KBB) and a strategy called
“commitment”. KBB corresponds to a strategy of strict reciprocity that is will-
ing to help another actor but only as long as the favor is returned by the re-
cipient as soon as possible. Otherwise, KBB will exit the relationship and seek
new exchange partners. By contrast, commitment needs only a few successful
initial help exchanges with a specific partner to become unconditionally co-

2.1. Introduction 31
operative to its partner further on. Broadly, de Vos and collaborators found
that when both strategies need to compete against “cheaters” – i.e. actors who
are unwilling to help but accept help from others – commitment is more viable
than KBB under a large range of conditions. They conclude that in an environ-
ment where unpredictable hazards occur, KBB may be too quick to abandon
exchange partners who get into trouble a second time before first reciprocat-
ing. As a consequence, a KBB player may often end up with no one willing to
help it. A commitment player avoids this problem, because once committed
to a cooperative partner it will not leave the partner in times of need and thus
will benefit from future help from this partner when it experiences distress.
De Vos et al. tentatively conclude from their computational experiments
that under conditions of the human ancestral environment, selection pressures
may have shaped a tendency towards commitment and largely unconditional
cooperation that contemporary humans may still have, even when the pres-
sures that formed it are no longer present. However, it is clearly an important
limitation of these studies that only three possible strategies, KBB, commit-
ment and cheating, have been taken into account and confronted with each
other in a tournament approach. As Binmore (1998) has argued forcefully, the
outcome of computer tournaments and simulations of evolutionary dynamics
strongly depends on the set of strategies that are initially present in a popu-
lation. The small set of strategies used by de Vos and collaborators may hide
two potentially severe problems for the viability of the strategy of commit-
ment. The first problem is the unconditionality of the strategy’s willingness
to cooperate once it has been committed to a partner. This property obviously
makes commitment highly vulnerable to exploitation by strategies that try to
take advantage of its willingness to help. The second problem is that com-
mitment may lose out in competition against more tolerant modifications of
strict reciprocity. As the work by Kollock (1993) and others suggests, such
modifications may avoid the major weakness of strong reciprocity to disrupt
potentially cooperative exchanges too readily when problems occur. At the
same time, such strategies also are less exploitable than commitment, because
they eventually avoid being exploited by a partner who steadfastly fails to
reciprocate help.
To address whether and to what extent these two potential problems re-
duce the viability of commitment, we propose in this paper a method to con-
siderably and systematically enlarge the strategy set used in the original anal-
ysis of the help exchange dilemma. The core idea is to represent strategies
as a set of individual preference parameters, or traits with respect to possi-
ble exchange outcomes in a relationship. Agents in our model are boundedly
and subjectively rational in the sense that they take decisions to cooperate, de-
fect and change partners with the goal of maximizing utility from their pref-
erences. However, maximizing subjective utility based on individual prefer-
ence values in our model does not necessarily lead agents to optimal exchange

outcomes. We assume that individual preferences or strategies are subject to
evolutionary pressure that selects for successful strategies based on the ob-
jective fitness consequences of the behavior resulting from the strategy. This
approach is similar to the “indirect evolutionary approach” proposed by Güth
and Kliemt (1998).
Our approach allows systematic mapping of a range of individual vari-
ation in decision-making rules, e.g. variation in the extent of commitment or
strictness of reciprocity. With this, we can carry out a stronger test of the viabil-
ity of commitment than de Vos et al. (2001). We use our model to carry out two
sets of simulation experiments designed to assess the viability of commitment
in a larger set of potential competitors. For this, we take the original design
of de Vos et al. as a starting point but systematically relax the assumption of
unconditionality of cooperation in the first set of experiments. In the second
set of experiments, we introduce and compare various degrees of relaxed ac-
counting to reciprocity (“fairness”) strategies. In Section 2.2, we motivate and
describe the model and our extensions. In Section 2.3, the computational ex-
periments are reported. Section 2.4 contains conclusions and a discussion of
our findings.
2.2 Model
Our model is based on a delayed exchange dilemma game, which is very sim-
ilar to the one originally proposed by de Vos et al. (2001). The game is played
by n agents in successive rounds. In the first round all agents are endowed
with fi fitness points. In the beginning of each round, Nature selects a num-
ber of agents with a given individually independent probability Pd who ex-
perience distress and thus become in need of help from other agents in order
to preserve their fitness level. These agents who are struck by Nature are the
initiators of interactions. They ask others for help which is either provided
or not. Providing help costs fh fitness points. Moreover, assuming that help
giving is a time-consuming activity, each agent may only provide help once
during one round; and only agents who are not distressed themselves may
provide help. If a help request is turned down, the distressed agent may ask
another agent for help but may not ask more than m agents altogether in the
same round. If an agent does not manage to get help before the end of the
round, it experiences fd loss in fitness. If the fitness level of an agent falls be-
low a critical threshold fc, the agent “dies”, i.e. it is eliminated from the agent
society.

2.2. Model 33
2.2.1 Modeling strategies
Agents in our delayed exchange dilemma face two different types of decision
situations from time to time. If they are hit by distress, they have to select
an interaction partner whom they believe most likely to be willing and able
to help them. On the other hand, when they themselves are asked to provide
help they have to decide whether to provide it and in case of multiple requests,
whom to provide it to. Thus the mental model, or strategy, of an agent is
represented as a combination of two sub-strategies: one for asking help and
one for giving help.
In previous studies by de Vos and others, behavioral strategies of agents
were defined in natural language in terms of a collection of condition-action
rules (e.g. for agent ai: if agent aj helped me before when I asked, then help
him now) and then translated into a programming language. Even for simpler
strategies several such decision rules had to be formulated, and this inherent
arbitrariness limited the generalizability of the model.
Our most important addition to these models is that we integrate them
into a utility-based framework and provide in this way an efficient method to
cover a large range of different strategies. In our model, when an agent has
to make a decision, it calculates utilities based on some or all of the informa-
tion available to it without the ability to objectively assess the consequences
of the decision on its overall fitness2
. Moreover, we assume that actors are
boundedly rational in the sense of being myopic, they evaluate the utility of
an action only in terms of consequences in the very near future, i.e. the state of
the world that obtains right after they have taken the action. This excludes the
strategic anticipation of future behavior of other agents. Since different agents
calculate utility differently, there is variation in behavior. Some of the behav-
iors lead to better fitness consequences than others. In turn, more successful
agents have better chances of staying in the game and propagating their way
of utility calculus to other agents, while unsuccessful ones disappear.
Recent advances in psychological research into interpersonal relationships
point to the influence of subjective well-being experienced when making cer-
tain relationship-specific decisions (Karremans et al., 2003). Unlike many ap-
plications of evolutionary game theory, we define utility calculus such that
agents derive an emotional utility from features of a relationship, in addition
to materialistic costs and benefits of help exchanges. This emotional utility
can be interpreted as feelings and emotions, such as togetherness, belonging,
sense of safety, identity, pride, etc., and the lack of it as loneliness, insecurity,
shame, etc. We concentrate our modeling efforts on describing and analyzing
this additional utility as a function of the history of help exchanges in a rela-
tionship. One of our main goals is to determine whether utility calculus based
2In certain cases these objective consequences may actually be impossible to foresee for the
agents or even for the modeler.

on some form of commitment can lead to beneficial fitness consequences.
In our delayed exchange game, agents have a very focused set of informa-
tion available about their physical and social environment. They are aware of
the fact that they got into distress, they follow the rules of the game (e.g. ask
for help when in distress), and they remember previous encounters with other
agents. This means that they know who and how often helped or refused them
and who was helped or refused by them in previous rounds. The implicit as-
sumption we make is that information about interactions between third-party
agents is either not (reliably) available to the focal agent or is simply not taken
into account in decision-making.
We restrict the information available to agents from their earlier interac-
tions to the following situation-specific decision parameters of an agent ai for
each interaction partner aj (i 6= j):
Definition 1 (Situation-specific decision parameters).
EHij = number of times i helped j (ego helped),
ERij = number of times i refused j (ego refused),
AHij = number of times j helped i (alter helped),
ARij = number of times j refused i (alter refused)
As we mentioned above, agents face two different decisions situations.
Accordingly, we define two independently calculated subjective utilities that
agents use in these two decisions. The utility of donating that agent ai gains
from helping agent aj is defined as a function of the situation-specific param-
eters:
UD
ij = UD
m + ehD
i · EHij + erD
i · ERij + ahD
i · AHij + arD
i · ARij,
where UD
m expresses materialistic costs of the interaction; ehD
i , erD
i , ahD
i , arD
i
are agent-specific parameters (or traits) for donation of agent ai that determine
the weight of the situation-specific parameters in the total utility. In the actual
implementation, every time an agent has to make a decision, there is also a
probability Pe that the agent will make a completely random decision. This
random error models noise in communication, misperception of the situation
or simply miscalculation of the utility by the agent. Taking this random error
into account increases the robustness of our results to noise in general3
.
For simplicity, we define the utility as a linear combination of situation-
specific parameters weighted by agent-specific parameters.
The utility of seeking is defined in the same way, the only difference is that
agents may put different weights on the situation-specific decision parameters
than in the utility of donation:
3See more about the problems of involuntary defection in Fishman (2003) and agents getting
stuck in mutual defection in noisy environments in Monterosso et al. (2002)

2.2. Model 35
US
ij = US
m + ehS
i · EHij + erS
i · ERij + ahS
i · AHij + arS
i · ARij,
Before agents make a decision, be it help seeking or help giving, they calcu-
late the corresponding one of these two utilities for each possible help donor
or help seeker. In case of help giving, they choose a partner with the highest
utility, if that utility is above an agent-specific threshold Ut
i . If the utility of all
possible decisions falls below the threshold utility, no help is given to anyone.
Otherwise, if there is more than one other agent with highest utility, the agent
chooses randomly.
As an addition to this rule, if an agent ai is asked to donate by another
agent aj with whom ai has had no prior interaction (therefore all situation-
specific parameters are 0), ai assumes that AHij = 1. In other words, agents
behave as if a successful interaction has already taken place between them.
Suspicious (non-nice) strategies can be defined by choosing the utility thresh-
old (Ut
) parameter so that without any prior interaction the utility of seeking
or donation is lower than the threshold utility.
In the case of help seeking, agents also choose a partner with the highest
utility but there is no threshold, i.e. agents in distress will always ask someone
for help.
Using these rules, a strategy S in our model is described by the way utility
is calculated. In other words, a strategy can be fully described by the two times
four agent-specific parameters and the utility threshold. 4
By specifying ranges
for agent-specific parameters we can easily define classes of strategies which
correspond to basic personality types. For example, we classify a strategy
S as belonging to the group of Commitment-type strategies, if the fact that
previously help was received from a certain partner increases the utility an
agent derives from donating help to or seeking help from that partner:
Definition 2 (Commitment). ahD
, ahS
> 0 and er, ar = 0
This means that an agent ai of the Commitment-type derives more utility
from choosing an agent aj as an interaction partner, the more times aj has
helped ai in the past. This is true for choosing from both a group of help
seekers and from possible help givers. This also means that the cooperative-
ness of a Commitment type is unaffected by the fact whether their help was
previously refused by an interaction partner.
If the utility threshold Ut
is zero or negative and ahD
> Ut
, the strategy
starts by cooperating, if able to.5
Otherwise, it behaves as “Suspicious Com-
mitment”, i.e. it starts with defecting but after some cooperative moves of
alter, it becomes cooperative.
4Since we are interested in the perception of the strength of a relationship between agents rather
than the perception of objective costs, we assume that UD
m and US
m are constant in all interactions.
5Note, that for the sake of simplicity in explaining the behavior of strategy classes, we will
assume that the probability of a decision making error Pe = 0 throughout this section.

In the remainder of this section, we show how a range of further strategies
can be defined with our method. For simplicity, we assume a utility thresh-
old of zero, if not mentioned otherwise. The strategy type of Defection can
be modeled with the assumption that it derives zero or negative utility from
donation under all conditions:
Definition 3 (Defection). ehD
, erD
, ahD
, arD
5 0 and
min(ehD
, erD
, ahD
, arD
) < 0
If the utility threshold Ut
is positive and ahD
< Ut
the strategy always
starts by defecting. Otherwise, it only starts defecting after some initial rounds
of cooperation.
In general, we say that a strategy is a cooperator if at least one of its do-
nation parameters is positive. In all other cases the strategy is a variant of the
Defection type. Such a subset of Defection is AllD, which never helps others
but when it is in need it randomly chooses others to ask for help:
Definition 4 (AllD).
Donation: ehD
, erD
, ahD
, arD
< 0
Seeking: ehS
= erS
= ahS
= arS
= 0
A much discussed strategy (type), especially in the experimental eco-
nomics literature (see e.g. Fehr and Schmidt, 1999; Fehr et al., 2002) is
Fairness.6
This is based on the observation that people may be willing to
invest in cooperation initially but will require reciprocation of these invest-
ments before they are willing to cooperate further. On the other hand people
following the fairness principle are also sensitive to becoming indebted,
therefore they will be inclined to reciprocate if they are in debt. In other
words, their most important aim is to have balanced relationships. Again,
translating this strategy class into our framework is straightforward.
Definition 5 (Fairness).
Donation: ehD
< 0, erD
> 0, ahD
> 0, arD
< 0
Seeking: ehS
> 0, erS
< 0, ahS
< 0, arS
> 0
Agents belonging to the Fairness class deduce more negative utility from
helping if they helped their partner in the past or if the partner refused them
before, and will deduce more positive utility from helping if the partner
helped them or if they refused to help the partner earlier. The twist here is
that it is actually the ones that are most likely to be selected for giving help
to that are different from those that are most likely to be selected for asking.
Note moreover, that in case of a “Truly Fair” strategy, we would make the
additional assumption about absolute values of traits such that |eh| = |ah|
and |er| = |ar|.
6Social psychologists also refer to this type of behavior as equity (cf. Smaniotto, 2004).

2.2. Model 37
Suppose, for example, that an agent ai receives two help requests at the
same time, one from aj, whom ai has helped twice before but from whom
ai received help already three times. The other help request comes from a
partner ak who helped ai three times and received help three times. A truly
fair-minded person should in this situation help aj and not ak, and this is
exactly what follows from our implementation, because in this case UD
ij =
UD
ik − ehD
and ehD
< 0.
Without making the assumption about absolute values, however, we are
able to examine a larger class of “Fairness-type” strategies, such as “Tolerant
Fairness” which increases credits (ah, er) more than it increases debts (ar, eh).
Note that Ut
must be negative or zero for Objective Fairness, otherwise
it requires more cooperation from its partner than it is willing to perform it-
self. Another way of relaxing the strictness of Objective Fairness is to decrease
Ut
, which allows an asymmetry in favor of alter, in the amount of required
reciprocation.
For analyzing the individual rationality of cooperation we also define a
trigger strategy, Grim Trigger. This strategy is the strictest form of cooperation,
in that it permanently retaliates after its partner or itself defected and never
cooperates again.
Definition 6 (Grim Trigger).
Donation: ehD
= ahD
= 0, erD
< 0, arD
< 0
Obviously, our approach allows generation of a much larger range of
strategies than we discussed above. For our present analysis, it suffices to
use these strategy templates but we will explore a larger variety of possible
behavioral rules in future work.
2.2.2 Evolutionary dynamic
The heart of our model is an evolutionary dynamic that ensures the selection
of objectively successful strategies (preferences). The dynamic we apply in
our simulation is based on the replicator dynamics (Taylor and Jonker, 1978).
Broadly, the replicator dynamics dictate that after a generation of genotypes
(strategies) replicates itself, each different genotype will be represented in the
next generation according to its relative success compared to other genotypes
in the current generation. This way, unfeasible or self-harming preferences
gradually become less widespread in the population, and give way to more
“rational” preferences (see also Güth and Kliemt, 1998).
To ensure that the size of the group remains constant throughout a simula-
tion run, we apply the replicator dynamics in the following way. Whenever an
agent dies, we create a new agent whose probability of belonging to a strategy
S is equal to the proportion of collective fitness that is held by the group of
agents belonging to S at the time of the new agent’s birth.

The evolutionary dynamic of our model is a strong simplification of the
actual genetic reproduction that could have taken place in human evolution-
ary history. One argument for this simplification is to avoid the unnecessary
overparameterization of our model. The central assumption we make is that
better exchange outcomes of a strategy type translate into better chances for
the propagation of that strategy. To capture this, there is no need to include
individual level variables such as average and maximum number of children,
age at giving birth etc., which are actually irrelevant for answering our re-
search questions. Thus the great advantage of the replicator dynamics for our
purposes is that it keeps the model of reproduction on the macro level. This
also means that we only model the evolutionary selection of strategies but not
mutation (see more under Discussion and Conclusions).
With our explicit model of evolution we improve upon previous work of
de Vos et al. (2001) in a number of ways. In their study they did not explicitly
model a replication dynamic but instead linked independent tournaments to
each other in order to map evolutionary trajectories. More precisely, the au-
thors assumed that in a sequence of evolution the final average distribution
of strategies at the end of one generation taken across a series of replications
of that generation would also be the initial distribution in all replications in
the next generation. This reduces repeatedly the distribution of individual
populations to its average trajectory, which may entail a biased picture of the
eventual distribution that arises. For example, unlike de Vos et al. (2001), we
consider in our analysis also those simulation runs in which the entire popu-
lation becomes extinct before a generation ends. These runs were originally
disregarded by De Vos et al. This may have biased their results towards an
overestimation of the survival chances of Commitment because only replica-
tions in which Commitment survived could have reached the end of a gener-
ation. Moreover, unlike previous work, our model does not suffer from the
specification of a “cut-off” parameter, i.e. there is no fixed number of rounds
after which we stop our simulations.7
In this way, we can be sure that the
evolutionary dynamic reaches an equilibrium state where the population is
homogeneous, and avoid biasing our results towards strategies that may be
only initially successful.
2.3 Results
De Vos et al. (2001) examined two cooperative strategies, Commitment and
Keeping Books Balanced (KBB), both playing against defectors. They showed
that Commitment, which is largely unconditionally cooperative to those pre-
vious interaction partners who gave help at least once, had better evolution-
7To be precise, a simulation run ends when agents of a strategy have completely pushed out
their opponents from population.

2.3. Results 39
ary success than the strictly reciprocal KBB under a large range of conditions.
They tentatively interpret this result as evidence for the advantages of being
unconditionally cooperative in an environment with scarce and uncertain op-
portunities to receive help. We argue that another conclusion may also be
possible. It is plausible that being so unconditionally cooperative still makes
Commitment more exploitable in comparison with conditional cooperators.
The relative success of Commitment in comparison with KBB may rather be
a result of KBB’s disadvantageous feature to disrupt relationships too readily
when some mishap occurs. In order to test this possibility, we first conducted
a simulation experiment to assess to what extent it makes a difference in Com-
mitment’s success against defectors, when various degrees of unconditionality
are compared. We did this by comparing four different types of Commitment
each playing against Defection.
We then turned to the possibility that a more tolerant, fairness-based strat-
egy may be more successful against defectors than Commitment or strict fair-
ness (KBB), in an uncertain environment. To test this possibility, we compared
Commitment with more tolerant versions of Fairness, and we also compared
fairness strategies that vary in their degree of tolerance to each other.
Finally, while De Vos et al. measured and compared the individual success
of cooperative strategies playing against defectors, they did not consider the
possibility of an actual evolutionary invasion against Commitment by a condi-
tional cooperator that is less vulnerable to exploitation by smart cheaters. We
also provide results of this type below.
2.3.1 Simulation setup
Our goal with the simulation experiments was to compare different cooperative
strategies with each other in terms of viability when there are initially some
defectors in society. The most important indicator of a cooperative strategy’s
viability was its success in resisting this invasion of defectors under evolution-
ary pressures of selection and reproduction. More precisely, within each simu-
lation run, we started out with a group of multiple strategies. We allowed this
mixed group to play the game for an extended period under an evolutionary
dynamic, until only one strategy was present in the population. Within one ex-
periment, we independently repeated such simulation runs from their initial
state n times8
, until standard errors of measured variables became sufficiently
small in order to be meaningfully interpreted.
At first, we kept all environmental and model parameters constant and
varied only strategy parameters from experiment to experiment. Even us-
ing our compact way of representing strategies (see section 2.2.1), we need
to define strategies in a 9-dimensional space (using two times four weight
parameters and a threshold parameter). Assuming that parameters and the
8n = 2000 independent runs were usually sufficient.

threshold can only take 5 possible values (i.e. -2, -1, 0, 1, 2), we are left with
a strategy space of 59
= 1953125 individual strategies. Fortunately, vast parts
of this strategy-space yield similar behavior and thus can be classified under
common concepts such as Defection, Fairness, Commitment, Trigger, etc. (see
strategy types described above). For example, multiplying all traits and the
utility threshold of a strategy S with a positive value will yield a strategy
S0
that behaves identically to S. More generally, as long as a transformation
on the trait parameters does not shift the level of utility below or above the
threshold for any given situation-specific parameter and does not modify the
ordering of alters, it has no effect on behavior. Therefore, in the analysis that
follows we will not vary absolute values of single traits, only traits in propor-
tion to each other.9
To assess the robustness of results derived from the simulation experi-
ments we conducted exhaustive sensitivity tests for all sensibly variable pa-
rameters. We report interesting deviations from typical results in section 2.3.4
below. For a list of all parameters see Appendix B.
Initial parameters
To determine interesting initial parameters for the simulation experiments and
to reduce the parameter space that must be explored, we conducted a game
theoretical analysis of a simplified version of the dilemma. Our goal was to
identify the set of conditions that makes the choice for agents between pur-
poseful defection and (conditional) cooperation as difficult as possible. If co-
operation places an excessively high burden on agents, or conversely, if coop-
eration entails no real sacrifices, the model would hardly yield any interesting
insights.
To approximate the conditions under which cooperation is rational at all in
the delayed exchange dilemma, we calculated expected payoffs in a simplified
version of the game using trigger strategies. A trigger strategy behaves so that
as soon as its interaction partner or itself defects, it falls back into a period of
unconditional defection. The most severe version of trigger strategies is Grim
Trigger, which never switches back to cooperation after its partner or itself de-
fected. Even after its own unintended defection (i.e. due to being unable to
help), Grim Trigger applies the most severe punishment possible in the game,
permanent retaliation. If the sanction imposed by Grim Trigger cannot deter a
rational player from unilateral defection, then no cooperative strategy can do
so. As a consequence, there exists no Nash equilibrium – that is: a rational out-
come – in which both players choose a conditionally cooperative strategy (see
Abreu, 1988). The simplifications we make for the sake of the formal analysis
9In other words, we are not covering exhaustively the entire parameter space, only a very large
part of it. It may still be possible that there is a specific trait combination that is superior under
some conditions to the ones examined.

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