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Management Review Quarterly
https://doi.org/10.1007/s11301-021-00250-9
1 3
Ambiguity aversion: bibliometric analysis andliterature
review ofthelast 60years
ChristophBühren2 · FabianMeier1· MarcoPleßner1
Received: 5 May 2021 / Accepted: 17 November 2021
© The Author(s) 2021
Abstract
We conduct a bibliometric analysis and review the literature of the last six decades
on ambiguity aversion. Comparing trends in theoretical, experimental, and empirical
contributions, our study presents the main aspects that are discussed in this litera-
ture. We show the increasing relevance of ambiguity aversion for decision-making
research and discuss factors influencing attitudes on ambiguity. Our literature review
reveals unsolved problems in the research on ambiguity and gives an outlook on new
ventures for future research.
Keywords Ambiguity aversion· Bibliometric analysis· Literature review· Ellsberg
paradox
1 Introduction
Daily, we decide under uncertainty. In some of these decisions, we know the objec-
tive probabilities of the underlying alternatives (decisions under risk) or can at least
assess subjective probabilities (decisions under uncertainty in the narrow sense)
(Knight 1921). But sometimes, we have to decide under ambiguity – without hav-
ing a clue on the probabilities of the outcomes. Typically, people feel uncomfort-
able deciding under ambiguity and try to avoid these decisions, they are ambigu-
ity-averse. This can lead to systematic biases (Ellsberg 1961) – especially to the
* Fabian Meier
fabian.meier@pdp-holding.de
* Marco Pleßner
Marco.Plessner@hshl.de
Christoph Bühren
buehren@uni-kassel.de
1 Hamm-Lippstadt University ofApplied Science: Hochschule Hamm-Lippstadt, Hamm,
Germany
2 Department ofEconomics, University ofKassel, Kassel, Germany
C.Bühren et al.
1 3
violation of the independence axiom, which demands that rational decisions should
be independent of outcomes that all alternatives have in common.
The research field of ambiguity aversion has experienced a boom of publications
in the past two decades. It all began with the seminal paper of Ellsberg (1961), fol-
lowed by both theoretical and experimental contributions. Recent theoretical appli-
cations of the decision-theoretic models are, for instance, Gao and Driouchi (2018),
Bergen etal. (2018), and Dicks and Fulghieri (2019). Ryall and Sampson (2017)
and Anderson (2019) are examples of recent empirical studies on ambiguity aver-
sion. Furthermore, the corona pandemic yields several examples for decisions under
ambiguity (Durodié 2020; Gassman etal. 2021; Kishishita etal. 2021). The prob-
abilities of the effects of anti-corona measures are very hard to assess. Thus, deci-
sions on these measures are likely to be prone to biases. Also, several exogenous
factors influence decisions in pandemic management that are even harder to asses,
such as time pressure on decision-makers or ambiguity about personal vaccination
decisions (Courbage and Peter 2021; Lipscy 2020).
Since 1961, ambiguity aversion has been inspiring several strands of theoretical
and experimental literature in decision theory, economics, psychology, and behav-
ioral economics. The goals of our paper are (1) to show the development of this
literature (Sect.2.1), (2) to present an overview of its essential findings (Sects.3 and
4), and (3) to identify research gaps and room for future research (Sects.5 and 6.1).
Literature reviews on ambiguity aversion have already been published. Camerer
and Weber (1992) review the early literature on ambiguity aversion, while Traut-
mann and van de Kuilen (2015) and Al-Najjar and Weinstein (2009) discuss more
recent contributions. Etner etal. (2012) review the literature on ambiguity aversion
in decision theory and Guidolin and Rinaldi (2013) in asset pricing. Our contri-
bution is to provide an overall overview of both theoretical and empirical papers.
Furthermore, we cover the entire period of 60years of research on ambiguity aver-
sion. Finally, we are the first to publish a bibliometric analysis on ambiguity aver-
sion. Recent bibliometric studies on other streams of literature, for instance, Keding
(2021) and Ozturk (2021) in the field of strategic management, show the fruitfulness
of this analysis.
We follow Block and Fisch (2020) in conducting a reproducible, standardized lit-
erature search with a specified research goal and providing a map of the research
field. Our study starts with the bibliometric analysis on the development of research
on ambiguity aversion based on 556 publications. In line with Pleßner (2017), we
extract the documents from the search platform EBSCOhost. We present the devel-
opment of the number of publications and authors over time and analyze the ratings
of these publications. Moreover, we distinguish six clusters of publications (e.g.,
experimental vs. theoretical) and study the co-occurrence of keywords. The analysis
highlights the most important papers and authors in the field.
Our data set used for the bibliometric analysis (Sect.2) also serves as the basis
for our literature overview (Sects. 3–5). In this review, however, we focus on 91
papers from the bibliometric analysis that are ranked as A and B in JOURQUAL3.
To reduce the probability that we miss relevant papers from journals without rank-
ing, from journals ranked worse than B, or from discussion paper series, we manu-
ally add further 40 titles. We separate these 131 publications into theoretical and
1 3
Ambiguity aversion: bibliometric analysis andliterature…
experimental/empirical contributions. First, we describe the Subjective Expected
Utility (SEU), Choquet Expected Utility (CEU), Maxmin Expected Utility (MEU),
and Smooth Ambiguity Model (SAM) as well as theoretical applications of these
models. Distinct to Machina and Siniscalchi (2013), we limit our review to the four
most cited theoretical models in the Business Source Premier database in EBSCO-
host (see Appendix F for an overview). Second, we consider the evidence for ambi-
guity aversion concerning (1) laboratory experiments testing the models, (2) gains
and losses with varying probabilities of the risky alternative, (3) ambiguity premi-
ums, and (4) experimental and empirical applications.
2 Bibliometric analysis
Figure1 describes the procedure of our literature review and bibliometric analy-
sis. We follow Shaffril etal. (2019) and Det Udomsap and Hallinger (2020) in
applying the PRISMA standard for our literature search. As the resource, we use
noitacifitnedIScreening
EligibilityIncluded
Records idenfied by
EBSCOhost (n = 524)
Duplicate records are removed (n=8)
Records clustered for
literature review (n=131)
Records screened (n=556)
n=91 records are included aer a detailed analysis
of publicaons ranked A-B according to the
JOURQUAL3 ranking. We read every document and
created excerpts that summarized the document‘s
contents and theories. With the same procedure, we
added relevant literature manually (n=40).
Full-text arcles assessed for eligibility (n=456)
Records clustered for
bibliometric analysis
(n=456)
n=131 records are used for the literature review.
Both the A-B ranked and the manually added
documents serve as the database for the literature
review.
n=100 records with only an associated relaon to
the topic are excluded. The resulng 456 documents
have a direct relaon to the topic and serve as the
data set for the bibliometric analysis (papers
associated with ambiguity aversion are those in
which the term "ambiguity" is not menoned in the
abstract but in the text. In papers with a direct
relaon, “ambiguity” is already used in the abstract).
EBSCOhost includes six databases: the eBook
Collecon, APA Psychinfo, APA PsycArcles, APA
PsycBooks, Regional Business News, Business Source
Premier. In all databases, we used the search term
„Ambiguity Aversion“.
Fig. 1 Methodological steps of our bibliometric analysis and our literature review
C.Bühren et al.
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524 publications in the bibliometric analysis. For our analysis, we mainly focus
on the title, abstract, and year of publication. We conduct the literature search
on the EBSCOhost platform using six databases (the eBook Collection, APA
Psychinfo, APA PsycArticles, APA PsycBooks, Regional Business News, and
Business Source Premier) with the search term “ambiguity aversion’’. EBSCO-
host has also been used by Pleßner (2017) for a bibliometric analysis on the dis-
position effect. We apply the same search key string for all six databases. Most of
the 524 found documents are journal articles (471), others are monographs (24),
dissertations (17), and articles in anthologies (12).
In the screening stage, we had to remove 8 duplicates from the data basis (e.g.,
working papers that became journal articles). This results in 516 documents. In
the literature review, we mainly focus on sources published in journals with a
high ranking (91 titles published in journals with the ratings A + to B according
to the JOURQUAL3 ranking). On the one hand, our sample is, thus, likely to be
of high quality and impact. On the other hand, the focus on these journals can
lead to aretrieval bias in the selection of the literature (Cooper etal. 2018). We
try to reduce this bias in the screening stage by manually adding relevant litera-
ture that are not necessarily part of the JOURQUAL ranking. As the search for
“ambiguity aversion’’ does not automatically find all relevant papers, we added
40 additional documents from the literature review in Sects.3 and 4. We identi-
fied the additional documents by reading all 91 documents carefully. Afterward,
we created excerpts that summarized the papers’ main contents. We integrated
documents that were mentioned frequently and that were not already part of our
data set. This results in 131 documents that are used for the literature review.
Nevertheless, the possibility that some publications are still missing cannot be
ruled out. After the screening stage, we end up with 556 documents (524 minus 8
duplicates plus 40 manually added documents).
These 556 articles enter the eligibility stage. Here, we divide the papers into a
group with direct relation (82%) and a group with associated relation to ambiguity
aversion (18%). Papers associated with ambiguity aversion are those in which the
term “ambiguity’’ is not mentioned in the abstract but in the text. These documents
are excluded from further analysis. In papers with a direct relation, “ambiguity”
is already used in the abstract. Finally, we use 456 documents for the bibliometric
analysis and 131 documents (91 + 40) for the literature review.
Based on a content analysis, we cluster the publications into “decision-theoreti-
cal model’’, “experimental study’’, “empirical study”, “survey”, “model application
(without data)” and “comment”. The cluster decision-theoretical model includes all
publications that either develop a new model or further develop an existing model.
An empirical paper that is based on quantitative analyses is labeled empirical study.
A publication containing an experiment is categorized as an experimental study.
The category model application without data encompasses publications that either
apply a model to a theoretical problem or include theoretical analyses without any
experimental or empirical evidence. The comment cluster only contains comments
without own data or theory.
Figure 2 shows that the majority of publications with direct relation to ambi-
guity aversion can be assigned to the clusters model application (without data),
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Ambiguity aversion: bibliometric analysis andliterature…
experimental study, and empirical study. 5% of these publications belong to the cat-
egory decision-theoretical model. Especially the publications of the cluster model
application (without data) are connected to the cluster decision-theoretical model.
These applications are typically based on such a model. As an example, Altug etal.
(2020) analyze the cyclical dynamics of a real business cycle model with ambiguity-
averse consumers using the model of Klibanoff etal. (2005) (see Sect.3.4).
2.1 Development overtime
The number of publications on ambiguity remained relatively low until the begin-
ning of the 2000s (see Fig.3). After that, it rose sharply until the end of our obser-
vation period. The highest number of annual publications is recorded in 2018 with
43. In the years 1951–1999, an average of 0.76 papers are published. In the years
2000–2020, it is 20.2. This dynamic is in line with the development of behavio-
ral economics analyzed by Costa etal. (2019) showing an exponential increase of
publications between 2000 and 2015. Using the search terms “behavioral econom-
ics, “behavioral finance”, and “behavioral accounting”, Costa etal. (2019) observe
1–3 publications per year between 1967 and 1990, less than 30 publications per
year between 1991 and 2001, over 100 in 2008, 250 in 2012, and 346 publications
in 2015. Similarly, in the behavioral finance literature, Pleßner (2017) identifies
two papers on the disposition effect in 2000 but 26 in 2014 and Jain etal. (2021)
merely count one paper on behavioral biases in 1995 but 28 in 2019. In our data, the
7%
23%
60%
5% 2%
3%
Empirical Study Experimental Study
Model Applicaon (without Data) Decision-theorecal Model
Survey Comment
Fig. 2 “Direct Relation” publications by subcategory
C.Bühren et al.
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increasing trend of publications between 2000 and 2015 continues in the years 2016
and 2018. However, in 2017, the number of publications on ambiguity aversion
decreases compared to the previous year. This could be interpreted as the beginning
of a period of stagnation in the number of publications. The lower publication num-
bers in 2019 and 2020 compared to 2018 support this interpretation. Figure3 also
shows the distribution of our five clusters over time. In nearly every year, most of
the papers are model applications (Gao and Driouchi 2018; Lo 1998; Turocy 2008).
This does not correspond to the review by Goyal and Kumar (2021), who observe
86% empirical studies, 10% conceptual studies, 3% reviews, and 1% meta-analyses
on financial literacy from 2000 to 2019. But also in the field of ambiguity aver-
sion, empirical and experimental studies have been gaining ground from the 2000s
onwards (Dimmock etal. 2016b, 2016a; Koudstaal etal. 2016; Muthukrishnan etal.
2009; Sutter etal. 2013). The low proportion of qualitative studies is striking. The
proportion of comments is also low.
Table 1 analyzes the average number of authors per publication. In the early
development of the research field – from 1961 until 1990 – more than 40% of the
papers were single-authored (1.71 authors on average), in the recent development
– from 2011 until 2020 – around 20% (2.35 authors on average). This is in line with
the development of co-authorship in the literature of economics in general. Analyz-
ing the RePEc archive, e.g., Rath and Wohlrabe (2016) observe an increase from
1.56 authors per paper in 1991 to 2.23 authors in 2013. This reflects the general trend
0
5
10
15
20
25
30
35
40
45
50
Comment
Survey
Experimental Study
Empirical Study
Decision-theorecal Model
Model Applicaon (without Data)
Fig. 3 Number of publications with “Direct Relation” over time
Table 1 Average number of
authors by period Period Average number of
authors
Share (%)of
single-author
publications
2011–2020 2.35 20.43
2001–2010 2.01 31.46
1991–2000 1.90 25.00
1961–1990 1.71 41.18
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Ambiguity aversion: bibliometric analysis andliterature…
and increased importance of intra- and interdisciplinary collaboration in behavioral
economics among theorists, experimentalists, and empirics.
In addition to the number of publications, we examine the journal rankings of
the papers based on JOURQUAL3 (Henning-Thurau et al. 2004). The ranking
assesses the quality of the journals from the best category “A + ” to the worst “D”.
The overall ranking includes several sub-rankings, e.g., “General Business Studies”
or “Banking/Finance”. It should be noted that a large number of publications (321
of 456) are not part of the ranking. They are either listed in other rankings (e.g.,
SJR), or they do not have any ranking. These 321 publications are published in 125
different journals and 10 discussion paper series. The five journals in which these
papers are published most frequently (Journal of Risk and Uncertainty, Journal
of Economic Theory, Journal of Economic Behavior and Organization, Journal of
Mathematical Economics; 21.8papers on average per journal) have an average SJR
ranking of 0.129. The 20 journals with the most frequent publications on ambiguity
aversion outside JOURQUAL (8.4 papers on average per journal) have an average
ranking of 0.148.
Most of the 135 papers analyzed in Fig.4 are published in journals ranked “A + ”
or “A” in JOURQUAL. For decision-theoretical models and comments, the propor-
tion of publications rated “C” or worse is zero. 74.8% of the experimental studies,
empirical studies, and model applications (without data) are published in journals
rated “A + ”, “A”, or “B”. The share of model applications published in A + journals
(34.2%) is lower than that of experimental studies (41.4%).
According to Google Scholar,1 the clusters also differ in their citation frequency:
The average number of citations in the clusters decision-theoretic model (113.5) and
experimental study (100.0) is much higher than in model applications (without data)
(59.5) and comments (8.1). Empirical studies are cited 82.5 times on average. Web
0
10
20
30
40
50
60
70
80
Decision-theorec
Model
CommentExperimental Study Empirical Study Model Applicaon
(without Data)
A+ A B B/C C D n.R.
Fig. 4 JOURQUAL3-rankings by cluster
1 Data retrieved from Google Scholar only serves as additional information in our study because of the
database’s limitations concerning bibliometric research (Aguillo 2012; Harzing and Alakangas 2016).
C.Bühren et al.
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of Science also reports the highest citation numbers for decision-theoretic models
(47.1) and experimental studies (43.6), followed by empirical studies (26.5), model
applications (without data) (22.2), and comments (2.4). The Business Source Pre-
mier database (via EBSCOhost) indicates a lower number of citations per cluster.
According to this database, the citation frequencies of decision-theoretic models
(15.3) and experimental studies (11.2) are higher than that of model applications
(without data) (5.3) and comments (0.7). Empirical studies are cited nearly as much
as experimental studies, 10.4 times on average.
Figure5 shows the JOURQUAL journal rankings of papers on ambiguity aver-
sion over time. The absolute scale of the ordinal axis refers to the number of pub-
lications in the respective year, while the relative scale shows the proportion of
A + publications. Considering the whole period – including years in which there are
no publications in ranked journals – the average proportion of A + papers is 23.9%.
Excluding years with no ranked publications, the average share of A + journals is
52.6%. Thus, ambiguity aversion is very relevant and of general interest. The share
of top publications decreases after 2006 because of the increasing number of publi-
cations on ambiguity aversion. The absolute number of A + publications is highest in
2017 with seven papers.
Citation frequencies in Google Scholar also differ between journal rankings:
While publications in A + -ranked journals are cited 229.9 times on average, papers
are cited 135.2 times on average if they are published in an A-ranked journal. In
B-ranked journals, they are cited 17.2 times, and in C-ranked ones 6.5 times. Not-
ranked journals have a citation rate of 35.1 on average. Web of Science reports lower
levels of citation frequencies: A + papers are cited 85.9 times, A papers 53.6 times,
B publications 6.8 times, and C publications 2.8 times on average. Not-ranked jour-
nals have an average citation frequency of 15.5 in Web of Science. Using the Busi-
ness Source Premier database, EBSCOhost finds 26.9 citations for papers in journals
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0
2
4
6
8
10
12
14
16
18
A+ A B B/C C D n.R. Share A+
Fig. 5 JOURQUAL3-rankings over time
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Ambiguity aversion: bibliometric analysis andliterature…
ranked A + and 16.4 in those ranked A. B-ranked articles are cited 1.5 times and
C-ranked ones 1.2 times. Papers that are not ranked in JOURQUAL are cited 3.9
times on average according to EBSCOhost.
2.2 Important papers, authors, andkeywords
Table2 shows that the most prominent publication in the field of ambiguity aver-
sion is the paper that started this line of research. According to Google Scholar,
Ellsberg (1961) has already been cited by 9411 papers, EBSCOhost finds 831
citations from the Business Source Premier database. Moreover, the theoretical
models of Gilboa and Schmeidler (1989), Schmeidler (1989), and Klibanoff etal.
Table 2 Important publications in the field
Citation counts were retrieved on October 2, 2021
Source Google scholar cita-
tions
Web of science cita-
tions
Business source
premier citations
Ellsberg (1961) 9411 3210 885
Gilboa and Schmeidler (1989) 5036 1854 732
Schmeidler (1989) 3776 1490 502
Heath and Tversky (1991) 1979 657 253
Klibanoff etal (2005) 1860 687 280
Savage (1951) 1560 NA NA
Judge etal. (1999) 1524 474 196
Hansen and Sargent (2001) 1214 NA 168
Chen and Epstein (2002) 1128 433 2
Ghirardato etal. (2004) 992 390 173
Abdellaoui etal. (2011) 553 193 63
Sutter etal. (2013) 530 215 88
Table 3 Top-7 authors in the field
Citation counts from Google Scholar and Business Source Premier were retrieved on May 3, 2021. Cita-
tion Counts from Web of Science were retrieved on October 2, 2021
Author Google Scholar Cita-
tions
Web of Science Cita-
tions
Business Source
Premier Cita-
tions
Ellsberg, Daniel 9413 3226 831
Schmeidler, David 8812 3344 491
Gilboa, Itzhak 5379 1854 707
Marinacci, Massimo 4509 1453 306
Epstein, Larry G 3812 1754 153
Tversky, Amos 2746 657 249
Klibanoff, Peter 2209 696 278
C.Bühren et al.
1 3
(2005) (see Sects.3.2–3.4) are cited very frequently. Heath and Tversky (1991),
Judge etal. (1999), and Chen and Epstein (2002) are connected to these models.
Table2 and Fig.3 reveal that theoretical papers set the basis of the research field
of ambiguity aversion and are most cited up to now. Since 2000, a lot of experi-
ments and empirical papers have been testing these models (Eichberger et al.
2012; Halevy 2007; Hey etal. 2010, see also Sect.4). At least by now,however,
these publications have been cited less often. Table3 complements this finding by
showing the five most-cited authors in the field. Besides the top-cited author Ells-
berg, some prominent theorists co-author many theoretical applications or experi-
mental tests of their models.
Furthermore, we conduct a co-occurrence analysis of the papers’ keywords. Fig-
ure6 presents the mapping of the most frequently used keywords. We set the mini-
mum threshold for an appearance on the map at 12. This criterion is met by 77 key-
words. The size of the nodes in the figure indicates the frequency of the keywords.
We see at least three topic areas. The left one shows an application-related focus on
financial risk management, portfolio decision problems, and investments in general.
Especially the literature we discuss in Sects.3.5 and 4.4 is represented in this field,
e.g., Anderson (2019; keyword: economic models), Vardas and Xepapadeas (2015;
keyword: expected utility), or Dicks and Fulghieri (2019; keyword: risk aversion).
The area on the right shows a decision-oriented focus (ambiguity tolerance, choice
behavior, risk-taking). The literature located in this topic area can be found in par-
ticular in Sects.3.2, 3.3, and 3.4 (e.g., Trojani and Vanini 2004 with the keyword
decision making). The category in the middle bottom of Fig.6represents behavioral
and experimental economics in general (e.g., Peysakhovich and Naecker 2017 use
the keyword behavioral economics, see Sect.6.1). The keyword Ellsberg paradox
(in the middle on top) combines these three categories. It is for example used by
Application-related focus
Behavioral/experimental economics in general
Decision-oriented focus
Ellsberg paradox
Fig. 6 Co-occurrence of keywords (Created using VOS Viewer)
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Ambiguity aversion: bibliometric analysis andliterature…
Klibanoff etal. (2005), who present a new decision-theoretic model of ambiguity
aversion. The most used keywords and the keywords with the most connections to
others are (of course) ambiguity aversion, decision making, uncertainty, aversion,
risk aversion, and probability theory. We can see from the interconnection of the
categories that the keywords are centered around decision theory (building or apply-
ing models to describe human decision processes under uncertainty and ambiguity
theoretically) and behavioral applications (theoretical or experimental and empirical
analyses of how ambiguity aversion affects economic decision making). In Appen-
dix A–E, we split the data set into five periods showing the development of the key-
words’ co-occurrence. This analysis indicates a trend of the research field from theo-
retical contributions to experimental and empirical applications.
2.3 Interpretation anddiscussion ofbibliometric results
Our bibliometric analysis shows a significant rise in the number of publications
during the last six decades, especially a boom of papers from the 2010s onwards.
Simultaneously, the average number of authors per publication rises, which speaks
for the increasing importance of collaboration. The development of the research
field is in line with the results of other bibliometric research in economics and espe-
cially behavioral economics (Rath and Wohlrabe 2016; Pleßner 2017; Costa et al.
2019; Jain etal. 2021). The topics of the publications in the field of ambiguity aver-
sion cluster around decision theory (Schmeidler 1989; Klibanoff etal. 2005; Etner
etal. 2012) and behavioral applications (Dimmock etal. 2016a; Stahl 2014; Ellsberg
1961). Most of the papers are either theoretical model applications or empirical and
experimental studies that test the theoretical propositions. Few publications are ded-
icated to the development of theoretical models. We observe a high but decreasing
proportion of A + and A publicationsand an increase of publications with a lower
ranking. Contributions on new theoretical models have the highest share of A + pub-
lications. Ellsberg’s (1961) seminal paper on his paradox and further behavioral
theories build the basis of research on ambiguity aversion and have been cited most
up to now, followed by model applications (Gao and Driouchi 2018), experimental
papers (Stahl 2014), and empirical research (Anderson 2019). In recent years, the
majority of papers have been data-driven (Altug etal. 2020; Anderson 2019; Bren-
ner and Izhakian 2018).
Researchers may find our bibliometric analysis useful to get an overview of the
important developments and trends in this stream of literature and to discover the
potential for future research publishable in high-quality journals. Nevertheless,
our approach of extracting and screening the papers for our literature review (see
Fig.1) might lead to biases, as we had to manually add literature to the bibliometric
analysis based on excerpts. In the next two sections, we review the basic theories
on ambiguity aversion as well as their theoretical applications (Sect.3) and present
experimental and empirical evidence based on these models (Sect.4).
C.Bühren et al.
1 3
3 Theory
Figure7 summarizes the timeline of theoretical models connected to the Ellsberg
(1961) paradox. The Ellsberg experiment is not the beginning but seemingly the
turning point in the development of theoretical research on ambiguity (see also
Tables2 and 3). We limit our review to the most cited theoretical models on ambigu-
ity aversion in the Business Source Premier database (see Appendix F). Our review
differs from Machina and Siniscalchi (2013) as we combine more recent literature
(Agliardi etal. 2016; Gilboa and Marinacci 2016; Zheng etal. 2015) with the most
relevant theoretical approaches from the previous literature.
3.1 Subjective expected utility andambiguity
In Ellsberg‘s (1961) seminal paper, he questiones the Sure Thing Principle of
Savage‘s (1951) Subjective Expected Utility (SEU) theory with a thought experi-
ment. SEU assumes individuals to assess subjective probabilities to alternatives
of decision problems under uncertainty if these cannot be objectively quantified
(see the Expected Utility theory of von Neumann and Morgenstern 1944). Ells-
berg (1961) presents two experimental designs. The first design comprises two
urns with 100 balls. Urn A contains 50 white and 50 black balls, urn B has an
unknown distribution of white and black balls. An ambiguity-averse decision-
maker prefers the known probability in urn A. The preference for urn A, no
(P. Klibanoff)
1951 1961 19721989 2005
Subjective Expected
Utility
Ellsberg experiment Hurwicz Criterion Choquet Expected
Utility
Smooth Ambiguity
Model
Von Neumann-
Morgenstern Utility
(J. v. Neumann and
O. Morgenstern)
1947
(I. Gilboa and
D. Schmeidler)
Maxmin Expected
Utility
(L. J. Savage) (D. Ellsberg) (K. J. Arrow and
L. Hurwicz) (D. Schmeidler)
Fig. 7 Timeline of theoretical models linked to ambiguity aversion
Table 4 Numerical example for
the three-color experimental
design in Ellsberg (1961)
30 60
Red Blue Yellow
f100 0 0
g0 100 0
f’ 100 0 100
g’ 0 100 100
1 3
Ambiguity aversion: bibliometric analysis andliterature…
matter if the decision-maker is paid when (1) a black or (2) a white ball is drawn,
leads to a contradiction. The first decision indicates the belief that urn B contains
fewer black balls than urn A, the second decision implies the belief that urn B
contains more black balls than urn A.
The second experimental design is based on an urn with 90 balls – 30 red balls
and 60 blue or yellow balls. In the first decision, the decision-maker receives a
payoff if the color is drawn on which he or she betted (either red or blue), in the
second decision the decision-maker additionally receives a payoff if a yellow ball
is drawn. Table4 provides an example.
An ambiguity-averse decision-maker prefers action
f
over
g
(thus he or she
would win $100 if a red ball is drawn) and action
g′
over
f′
(thus winning $100 if
either a blue or a yellow ball is drawn). Again, this leads to the contradiction that
the decision-maker seems to believe that the urn contains less than 30 blue balls
when deciding for
f
but more than 30 blue balls when deciding for
g′
:
f ≿ g implies:
f’ ≿ g’ implies:
After Ellsberg (1961) introduced the experiment, many applications and exten-
sions of the design were published (Di Mauro and Maffioletti 2004; Du and Budescu
2005; Halevy 2007; Moore and Eckel 2006; Yates and Zukowski 1976, see also
Sect.4).
3.2 Choquet expected utility
The Choquet Expected Utility (CEU) model developed by Schmeidler (1989) is
based on capacity
v
, which reflects the degree of uncertainty experienced by the
decision-maker and his or her attitude towards uncertainty (Agliardi etal. 2016).
The capacity represents a set of non-additive probability distributions, and the util-
ity is calculated with the Choquet integral (Etner etal. 2012). Schmeidler (1989)
describes the decision criterion as follows:
If
S
=
{
s
1
,s
2
,…,s
n}
and f
(
s
i)
=x
i
,i=1, …,
n
with
xi≤xi+1
,
then
∫
Ch
u(f)dv =u(x1)+(u(x2)−u(x1))v({s2,s3,…,sn}) +⋯
+
(
u
(
x
i
+1
)
−u
(
x
i))
v
({
s
i+
1,…,s
n})
+⋯+
(
u
(
x
n)
−u
(
x
n−
1
))
v
({
s
n}).
p
R
u(100)+1−
[
p
R
u(0)
]
>p
B
u(100)+1−
[
p
B
u(0)
]
⇔p
R
>p
B
(
pB∨pG
)
u(100)+1−
(
pB∨pG
)
u(0)>(pR∨pG)u(100
)
+1−(p
R
∨p
G
)u(0)⇔p
B
∨p
G
>p
R
∨p
G
⇔p
B
>p
R
f≿gis equivalent to
�Ch
u(f)dv ≥
�Ch
u(g)
dv
C.Bühren et al.
1 3
Thus, a decision-maker first considers the worst outcome of an alternative and
continues with better ones until the whole outcome space is evaluated. In contrast to
Savage’s SEU, different utility functions can be applied for different actions (Agli-
ardi et al. 2016). Furthermore, the non-additivity of CEU can be used to model
ambiguity aversion. An individual is ambiguity-averse if his or her capacity
v
is con-
vex and the utility function concave or linear (Schmeidler 1989).
3.3 Maxmin expected utility
While Savage’s SEU assumes that an individual prefers a single alternative, the
Maxmin Expected Utility (MEU) model assumes that several alternatives can be
preferred simultaneously. In the model, the decision-maker chooses the option that
promises the maximum of the minimal expected utilities (Gilboa and Schmeidler
1989). Formally, an action
f
is preferred to an action
g
if and only if
(Etner etal. 2012).
Hence, MEU can model ambiguity aversion. An action
f
in a state
s
leads to a
prize
x
with a probability distribution of
f(s)
. Imagine an experiment in which the
decision-maker is allowed to choose between the draw from urn A or B (contain-
ing black and white balls each), urn B representing ambiguity. Formally, the pro-
cess can be described by
𝛼f+(1−𝛼)g
with
𝛼∈[0, 1]
. If state
s
occurs, then the
prize the decision-maker wins (outcome) depends on the lottery
𝛼f(s)+(1−𝛼)g(s)
(Segal 1990).
𝛼
represents the probability of choosing action
f
,
1−𝛼
the probability
of choosing action
g
, and
f(s)
the probability distribution of the lottery. Accord-
ing to MEU,
𝛼f(s)+(1−𝛼)g(s)≿f
applies (Gilboa and Schmeidler 1989). As an
example,
f
can denote the bet on a white ball in urn B, while
0.5f(s)+0.5g(s)
repre-
sents the bet on white in urn A (
g
is the draw of a black ball). Under ambiguity, the
decision-maker tries to find the option that maximizes his or her utility of the worst
outcome (Gilboa and Schmeidler 1989), which means minimizing the possibility of
drawing a black ball from one of the urns in our example. This leads to an aversion
to non-quantifiable probabilities and a preference for urn A.
Several studies refer to MEU (Kochov 2015; Maccheroni etal. 2006; Gilboa and
Marinacci 2016), apply it (Bidder and Dew-Becker 2016; Zheng etal. 2015; Trojani
and Vanini 2004), or extend it (Ghirardato etal. 2004; Li etal. 2016; Hansen and
Sargent 2001; Chen and Epstein 2002; Miao and Wang 2011).
3.4 Smooth ambiguity model
The two models described so far are based on a two-stage decision-making process.
Before betting on an action, the decision-maker mentally estimates the probabili-
ties of the actions. The Smooth Ambiguity Model (SAM) does not need the mental
reduction of two-stage lotteries (Lang 2017).
min
p∈CE
p
u(f)≥min
p∈CE
p
u(g)
1 3
Ambiguity aversion: bibliometric analysis andliterature…
Segal (1987, 1990) proposes two axioms, the Reduction of Compound Lotteries
Axiom and the Compound Independence Axiom, which provide a new explanation
for the Ellsberg paradox. Klibanoff etal. (2005) develop these further and designed
the SAM. In this model, the decision criterion is based on a real-value function on
a state space
S
. The decision-maker’s utility function
u
follows von Neumann and
Morgenstern (1944) and includes the risk attitude. The decision-maker’s attitude
towards ambiguity is captured by
𝜙
.
𝜇
is the subjective prior over
Δ
, the set of possi-
ble probabilities over the state space, and
𝜋
a probability measure on the state space.
The criterion results in
A concave function of
𝜙
defines ambiguity aversion, while a convex function rep-
resents ambiguity seeking. When
𝜙
is linear, the decision-maker is ambiguity neu-
tral and acts as an SEU maximizer. Assuming ambiguity aversion, Klibanoff etal.
(2005) give the following example of the decision maker’s thinking:”My best guess
of the chance that the return distribution [of an investment decision] is ‘
𝜋
’ is 20%.
However, this is based on ‘softer’ information than knowing that the chance of a
particular outcome in an objective lottery is 20%. Hence, I would like to behave
with more caution with respect to the former risk.”
After the introduction of SAM, many other papers followed the approach (Bat-
tigalli etal. 2015; Battigalli etal. 2016; Ju and Miao 2012; Maccheroni etal. 2013;
Strzalecki 2013; Thimme and Völkert 2015; van de Kuilen and Wakker 2011; Wong
2015).
3.5 Applications ofthemodels
Models on ambiguity aversion have been used in various theoretical applications.
Gao and Driouchi (2018), for instance, apply CEU to model the influence of ambi-
guity aversion on outsourcing decisions. Some papers study the role of ambiguity
aversion in the design of auctions. Salo and Weber’s (1995) decision-makers, e.g.,
are characterized by CEU, Lo (1998) and Turocy (2008) use SEU with multiple pri-
ors as a framework, and the recent work by Koçyiğit etal. (2020) is based on MEU.
Analyzing investment decisions, Dow and Werlang (1992) use the CEU model
for portfolio choices. Likewise, Berger etal. (2013) model the propensity of invest-
ments and portfolio compositions considering ambiguity aversion and learn-
ing. Escobar et al. (2015) investigate portfolio management for financial deriva-
tives modeling ambiguity-averse investors. Using a similar approach, Bergen etal.
(2018) study portfolios composed of derivatives and equities. Furthermore, Vardas
and Xepapadeas (2015) analyze ambiguity aversion in portfolio selection using the
Robust Portfolio Choice Theory.
Chateauneuf etal. (2000) apply the CEU model to the analysis of market equilib-
ria and identify the decision-maker’s tendency to diversify risk. Rigotti and Shannon
(2005) use CEU to study factors influencing ambiguity aversion in the market with a
V
(f)=∫
Δ
𝜙
∫
s
u(f)d𝜋)
d𝜇=E𝜇𝜙
E𝜋u⋅f
.
C.Bühren et al.
1 3
model developed by Bewley (2002). Mukerji and Tallon (2001) attribute the imper-
fection of financial markets to ambiguity aversion utilizing the CEU model (see
Rinaldi 2009 for a similar analysis with SAM). Chau and Vayanos (2008) analyze
a market model in which some individuals have insider knowledge. Vitale (2018)
extend this approach and apply it specifically to the insurance market. Also, Epstein
and Wang (1994), Chen and Epstein (2002), and Liu (2011) study the impact of
ambiguity aversion on market equilibria.
Epstein and Schneider (2008) model the valuation of assets for which decision-
makers have access to information of different quality – assuming that low-quality
information is associated with a high degree of uncertainty. Leippold etal. (2008)
follow the same approach and extend the assumptions of Epstein and Schneider
(2008) by learning of the decision-maker. The theoretical analysis of Dicks and Ful-
ghieri (2019) implies that ambiguity aversion accelerated the financial crises. How-
ever, Condie (2008) argues that ambiguity aversion has very limited explanatory
power for the long-term price development of assets.
4 Evidence
In this section, we cluster the analyzed experimental and empirical literature into
five categories discussing different forms of evidence on ambiguity aversion. The
first subsection presents laboratory experiments testing the theoretical models from
Sect.2. The next subsection looks deeper into behavior in the domains of gains and
losses, followed by evidence on ambiguity premiums. The last subsection reviews
experimental and empirical applications on behavior under ambiguity.
4.1 Testing themodels
Halevy (2007) analyzes how far his experimental data can be explained by SEU,
MEU, Recursive Non-Expected Utility, or Recursive Expected Utility. None of these
models universally represents all the students’ preferences. Similarly, Eichberger
etal. (2012) do not find evidence for ambiguity-aversion when not only probabilities
but also the stake sizes are ambiguous. However, Ahn etal. (2014) reveal that most
of their subjects express SEU preferences and ambiguity aversion in line with MEU
and CEU. Hey etal. (2010) are better able to explain behavior in their experiment
with relatively simple models like the MEU compared to more sophisticated models
like the CEU.
Stahl (2014) examines the preferences of decision-makers in a two-urn design.
The subjects can decide to bet on a risky urn with a 50% probability to win $10
or on an ambiguous urn leading to a win of $10, $12, or $15. Most of the subjects
prefer the risky over the $ 10 ambiguous urn. A large number of subjects are indif-
ferent between the risky and the $12 ambiguous urn, and the majority prefer the $15
ambiguous over the risky urn. Stahl (2014) categorizes the subject pool into three
groups: (1) subjects who behave according to SEU (12%), (2) subjects who behave
according to MEU (26%), and (3) subjects whose decisions cannot be assigned to a
1 3
Ambiguity aversion: bibliometric analysis andliterature…
theoretical model (60%). In line with his findings, most of the subjects in Charness
etal. (2013) cannot be classified as ambiguity-averse.
4.2 Gains versuslosses
Curley and Yates (1989) let subjects choose between a risky bet (with a 25% prob-
ability of winning) and a draw from an urn representing ambiguity with 5 winning,
55 losing, and 40 unknown balls. In contrast to the Ellsberg paradox, most of the
subjects prefer the ambiguous urn, either because of the rather low winning proba-
bility in the risky bet or because of optimism (see also Dimmock etal. 2013, 2016b;
Kahn and Sarin 1988).
Cohen etal. (1987) compare decisions on potential gains with those on poten-
tial losses. Similar to Kahnemann and Tversky (1979), the authors observe that the
majority of decision-makers are ambiguity-averse in the domain of gains but indif-
ferent in the domain of losses. Likewise, Friedl etal. (2014) assess the willingness
to pay (WTP) for insurance policies and observe no differences in the preferences
for risky or ambiguous options in the domain of losses. For comparable results with
low winning probabilities (in the range from 0.1% to 30%) of the risky alternative
in the domain of losses see Curley and Yates (1985), Einhorn and Hogarth (1986),
Di Mauro and Maffioletti (1996), Lara Resende and Wu (2010) and Tymula et al.
(2012). With moderate (30% to 60%) winning probabilities, subjects tend to decide
in favor of the ambiguous option (Baillon etal. 2018b, 2018a; Chakravarty and Roy
2009; Ho etal. 2002; Liu and Onculer 2017). In line with the Ellsberg paradox,
higher winning probabilities lead to ambiguity aversion (Abdellaoui etal. 2011).
4.3 Ambiguity premium
The ambiguity premium is comparable to the risk premium and reflects the differ-
ence between the WTPs of the risky and ambiguous option. Grou and Tabak (2008)
observe that business and economics students in Brazil (in contrast tostudents from
the University of Chicago) do not want to pay a premium to reduce ambiguity. Trau-
tmann and van de Kuilen (2015) review several studies on ambiguity premiums and
suppose that the magnitude of the premiums depends on the valuation method, the
stake size, and the incentive method. They leave a systematic analysis of the hetero-
geneity of ambiguity premiums to future research.
Analyzing the Standard & Poor’s (S&P) 500 index, Brenner and Izhakian (2018)
find evidence for an ambiguity premium in the stock market. They assume that the
equity premium in asset pricing theory contains both a risk and an ambiguity pre-
mium. Jeong et al. (2015) develop a method for separately measuring premiums
for risks and ambiguity. They apply this method to the S&P 500 index and confirm
that investors pay an ambiguity premium. Although there have been publications on
ambiguity premiums, research on their causes and the factors that may increase or
decrease these premiums is still pending.
C.Bühren et al.
1 3
4.4 Experimental andempirical applications
Sarin and Weber (1993) compare the behavior of students with bank executives’
choices in sealed-bid auctions and double oral auctions. They find evidence for
ambiguity aversion in both samples. Koudstaal etal. (2016) observe that entrepre-
neurs express a similar level of ambiguity aversion compared to employees and
managers. Furthermore, Sutter etal. (2013) show that even children between 10 and
18years are ambiguity-averse.
In a lab in the field experiment in Ethiopia, Akay etal. (2012) find that farmers
do not differ in their level of ambiguity aversion from Dutch students in a classroom
experiment. Likewise, Engle-Warnick etal. (2007) investigate the decision-making
behavior of Peruvian farmers about the use of new cultivation technologies. Com-
bining survey data with a lab in the field experiment, they observe a positive correla-
tion between ambiguity aversion and conservative choices concerning new technolo-
gies. Similarly, Ross etal. (2012) find that ambiguity-averse farmers from Laos have
a lower propensity to use new rice varieties.
Dimmock etal. (2016a, 2016b) confirm ambiguity aversion for US and Dutch
households using online experiments implemented in representative surveys. Dim-
mock etal. (2016b) conduct five experiments with subjects of the “RAND Ameri-
can Life Panel” on household portfolio choice puzzles. They show a negative associ-
ation of ambiguity aversion with stock market participation, the fraction of financial
assets, and foreign stock ownership. Wakker etal. (2007) experimentally analyze
the WTP for insurances with a representative sample from the general Dutch public.
Using in-depth individual interviews to gain more information about the decision-
maker’s choices, they find evidence for ambiguity seeking rather than aversion.
Muthukrishnan et al. (2009) analyze the tendency of customers to prefer well-
known over unknown brands. They observe that ambiguity-averse subjects prefer
established brands, even if the product specifications are inferior compared to the
unknown brands. Liu and Colman (2009) examine the long-term change in prefer-
ences for marketing strategies with a repeated experiment. They find that the inten-
sity of ambiguity aversion decreases with an increasing number of repetitions. The
authors assess decisions on marketing strategies as a control for classical Ellsberg
urn decisions.
Moreover, Berger etal. (2013) investigate patients’ decisions regarding vaccina-
tion against swine flu. They find that medical advice intended to support patients’
decisions does not consider their ambiguity aversion. Courbage and Peter (2021)
extend this study by examining the influence of ambiguity on personal vaccina-
tion decisions. Hoy etal. (2014) show that patients are reluctant to use free genetic
tests due to ambiguity aversion. Segal and Stein (2006) find evidence that ambigu-
ity-averse defendants in court tend to prefer bench trials over jury trials whenever
their acquittal chances are substantial. Otherwise, they prefer jury trials. Further-
more, Ryall and Sampson (2017) see that contract partners tend to assure themselves
against ambiguous actions in joint contracts.
Analyzing data of mutual fund investors, Li et al. (2017) argue that investors
seem to place greater weight on the worst signal when confronted with information
of ambiguous quality. Breuer etal. (2016) analyze the level of capital reserves held
1 3
Ambiguity aversion: bibliometric analysis andliterature…
by companies. They find that managers reduce capital reserves when their inves-
tors are more ambiguity-averse. Similarly, Antoniou etal. (2015) observe that an
increase of ambiguity in the stock market yields outflows from equity funds. Finally,
Anderson (2019) examines the behavior of investors during the financial crisis in
2008. She finds that market outcomes can change more abruptly under ambiguity
than under risk.
5 Interpretation anddiscussion ofreview results
The overview of the models in Sect.3 shows that the theoretical literature on ambi-
guity aversion focuses on two main topics: decision theory and its application to
economic behavior (see also Sect.2.3 for the interpretation and discussion of the
bibliometric results). Remarkably, the application-oriented publications frequently
refer to financial economics. Not often, the models are tested for their robustness in
other economic contexts. This may explain why some researchers in this field sug-
gest focusing more on natural experiments (see Sect.6.1).
We see that the discussed theoretical models are frequently criticized for not
delivering a realistic picture of human decision processes. Research on ambiguity
aversion started with the criticism of SEU. Even Savage violated “his” Sure Thing
Principle in the experiment of Allais and Hagen (1979) and the Ellsberg experi-
ment. However, in Foundations of Statistics (1954) he shows a representation of
Allais’ paradox with which he wants to affirm his axiom (similar to our Table4).
Tversky and Slovic (1974) conduct an experiment in which subjects are allowed to
retract their decisions if theyhave violated the axiom. Yet most of them stick to their
first intuitive decision. Epstein (2010), Baillon etal. (2012), and Halevy and Ozde-
noren (2008) criticize SAM by Klibanoff etal. (2005) for distinguishing between
different forms of ambiguity that are not perceived differently by decision-makers.
Klibanoff et al. (2012) respond that Epstein’s (2010) thought experiments rather
support their model than MEU. Furthermore, Machina (2009, 2014), l’Haridon and
Placido (2010), and Baillon etal. (2011) criticize CEU by Schmeidler (1989) for
contradicting robust experimental evidence.
Ellsberg (2011) notes that large parts of the literature misinterpret his paper in
1961 and that ambiguity aversion may not be a robust phenomenon: „…I repeatedly
mentioned that some subjects deliberately and consistently chose the more ambigu-
ous alternative, rather than choosing to ‚avoid ambiguity ‘…My long-term com-
plaint is not about the mischaracterization of my own exposition but about the gen-
eral failure to explore this phenomenon in subsequent experiments and analysis.“
While we agree that ambiguity aversion should not be considered in isolation, we
disagree on a general failure to explore this aversion experimentally. Section4 pre-
sents robust evidence on ambiguity aversion and little evidence on ambiguity seek-
ing. It should be investigated which factors lead decision-makers to behave more
ambiguity-averse (or ambiguity-seeking).
In contrast to the criticism of the models described in Sect. 3, experimental
results on ambiguity aversion are less often discussed. One exemption is the general
C.Bühren et al.
1 3
criticism of the artificial environment of laboratory experiments, which again speaks
for more field or natural experiments (Trautmann and van de Kuilen 2015).
6 Conclusion
6.1 Future research
The bibliometric analysis of our paper can be used as a basis for further research.
We aim to set a starting point for identifying research trends and potential for
future research (Sects. 2.2 and 2.3). Appendices A–E show the keyword co-
occurrence by period. It can be seen that the topic areas in research on ambigu-
ity aversion diversified over the years. In the period 1961–2000, the keywords
ambiguity, probability theory, and decision making are predominant. In the fol-
lowing decades, new fields of applications emerged, e.g., financial markets (in
2006–2010) and assets (accounting), investments, as well as portfolio manage-
ment (in 2011–2015). This development indicates a trend towards application-ori-
ented research on ambiguity aversion, which is likely to continue.
Some of the papers reviewed in our study suggest a high potential for future
research. For instance, Gilboa and Marinacci (2016) see the opportunity to
applythe ambiguity research to Akerlof ‘s (1970) “Market for Lemons”. A gen-
eral advice is to focus more on natural experiments (Camerer and Weber 1992;
Ellsberg 2011; Heath and Tversky 1991; Trautmann and van de Kuilen 2015).
Baillon etal. (2018a) present a method for the experimental evaluation of natu-
ral decision problems under ambiguity. They distinguish two indices for ambigu-
ity attitudes: ambiguity aversion and ambiguity perception. Further applications
and critical examinations of this method concerning its validity are still needed.
Moreover, future research could investigate factors influencing ambiguity premi-
ums. Several real-life problems are not yet addressed by this literature stream,
such as medical or consumption decisions.
Al-Najjar and Weinstein (2009) question numerous developments in theoretical
and experimental research on ambiguity aversion. They demand a clearer deline-
ation of theoretical models from each other – especially concerning descriptive
versus normative models. Descriptive models should describe experimentally
verifiable decision-making processes, while normative models are based on the
rationality hypothesis. Also, they call for a more critical examination of the entire
research on ambiguity aversion. Al-Najjar and Weinstein (2009) argue that the
empirical results are not only explained by the models presented in Sect.3 but
also by models on heuristics (Samuelson 2001).
Ellsberg (2011) himself calls for a reorientation of decision-theoretical
research and a less narrow investigation of ambiguity aversion. He recommends
an examination of ambiguity – regardless of whether the decision-maker prefers
or rejects it. Based on our results, we still see the demand for testing different
models on ambiguity aversion experimentally. In line with Ellsberg (2011), one
can question how far ambiguity aversion is a real phenomenon. We did not iden-
tify a lot of applications of the Ellsberg experiment outside the laboratory. Since
1 3
Ambiguity aversion: bibliometric analysis andliterature…
the global economic situation in 2020 and 2021 is characterized by great uncer-
tainty concerning future market developments, especially due to the Covid 19
pandemic, new opportunities for natural experiments emerge.
Information technology research has paid special attention to the development
of artificial intelligence. The characteristics of ambiguity aversion in such arti-
ficially created, intelligence-based systems could be examined concerning simi-
larities and differences to previous findings. Peysakhovich and Naecker (2017)
provide a first approach, searching for alternative models of machine learning
that could evaluate models of decision-making under risk and ambiguity. Moreo-
ver, research on ambiguity aversion could benefit from agent-based simulations
(Georgalos 2018). Another promising tool to analyze if people are indeed ambi-
guity-averse is functional magnetic resonance imaging (fMRI) from neuroeco-
nomics (Camerer etal. 2007; Hall etal. 2021).
7 Summary
The results of our bibliometric analysis indicate a research boom on ambiguity aver-
sion, especially in the last two decades. This research is published in highly ranked
journals. The co-occurrence analysis of keywords reveals a focus on two specific
main categories: the modeling of the decision process affected by ambiguity and the
analysis of the effects of ambiguity aversion on economic decision problems, such
as portfolio choices or investment decisions. The trend of the number of publica-
tions suggests that many more insights on ambiguity aversion can be gained in the
coming years.
Our literature review shows that the theoretical models on ambiguity can only
partly be verified experimentally or empirically. One of the main reasons for this is
the complexity of the models. Furthermore, the subjects’ thought processes are dif-
ficult to trace. However, we can expect new contributions from agent-based models,
in which the impact of different preferences can be directly tested, or neuroeconomic
approaches, which try to open the black box of decision making.
One of the experimental findings is that the probability of the risky alternative
influences ambiguity attitudes in Ellsberg paradoxes – from ambiguity seeking with
low probabilities to ambiguity aversion with moderate or high probabilities. The
mixed evidence for different models on ambiguity aversion encouraged a discus-
sion on the robustness of the phenomenon (Baillon etal. 2012; Epstein 2010; Kli-
banoff etal. 2012; Machina 2009). l’Haridon and Placido (2010), e.g., show that the
“Machina paradoxes” of CEU also apply to other models like MEU or SAM.
Our review reveals that there is still a large potential for future research, for
instance on the heterogeneity of preferences under ambiguity – in the lab and espe-
cially in the field. Thus, research designs should emancipate from the Ellsberg-urn
design and develop new methods for exploring ambiguity aversion. Anderson’s
(2019) study on the role of ambiguity during the financial crisis may serve as an
example. The corona crisis provides further potential for studying natural experi-
ments on behavior under ambiguity.
C.Bühren et al.
1 3
Appendix
A. Key‑Word Co‑Occurrence (1961‑1999)
B. Key‑Word Co‑Occurrence (2000–2005).
1 3
Ambiguity aversion: bibliometric analysis andliterature…
We set the minimum threshold for occurrence of the keywords at 5. 7 of the 201
keywords met this criterion.
C. Key‑Word Co‑Occurrence (2006–2010).
We set the minimum threshold for occurrence of the keywords at 5. 22 of the 530
keywords met this criterion.
C.Bühren et al.
1 3
D. Key‑Word Co‑Occurrence (2011–2015).
We set the minimum threshold for occurrence of the keywords at 5. 71 of the 1167
keywords met this criterion. We had to remove 3 keywords manually.
1 3
Ambiguity aversion: bibliometric analysis andliterature…
E. Key‑Word Co‑Occurrence (2016–2020).
We set the minimum threshold for occurrence of the keywords at 5. 55 of the 1291
keywords met this criterion. We had to remove 7 keywords manually.
F. Citation numbers oftheoretical models
Source Google Scholar
Citations
Web of Science
Citations
Business Source
Premier Cita-
tions
Gilboa and Schmeidler (1989) 5036 1854 707
Schmeidler (1989) 3776 3776 491
Klibanoff etal. (2005) 1860 687 286
Maccheroni etal. (2006) 1170 NA 195
Bewley (2002) 937 NA NA
Segal (1987a) 447 183 63
Cerreia-Vioglio, Maccheroni, Mari-
nacci, (2011)
Montrucchio (2011) 283 9 46
Ergin and Gul (2009) 214 NA 38
Siniscalchi (2009) 176 NA 35
Chateauneuf, Faro (2009) 151 64 27
C.Bühren et al.
1 3
Source Google Scholar
Citations
Web of Science
Citations
Business Source
Premier Cita-
tions
Ahn (2008) 152 55 24
Gul and Pesendorer (2013) 1 30 0
Citation counts from Google Scholar and Business Source Premier were retrieved on May 3, 2021. Cita-
tion Counts from Web of Science were retrieved on October 2, 2021.
Funding Open Access funding enabled and organized by Projekt DEAL.
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ses/ by/4. 0/.
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