The soft underbelly of complexity science adoption in policymaking

Vol.:(0123456789)
Policy Sciences
https://doi.org/10.1007/s11077-024-09531-y
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RESEARCH ARTICLE
The soft underbelly of complexity science adoption
in policymaking: towards addressing frequently overlooked
non‑technical challenges
Darren Nel1
· Araz Taeihagh1
Accepted: 21 March 2024
© The Author(s) 2024
Abstract
The deepening integration of social-technical systems creates immensely complex envi-
ronments, creating increasingly uncertain and unpredictable circumstances. Given this
context, policymakers have been encouraged to draw on complexity science-informed
approaches in policymaking to help grapple with and manage the mounting complexity of
the world. For nearly eighty years, complexity-informed approaches have been promising
to change how our complex systems are understood and managed, ultimately assisting in
better policymaking. Despite the potential of complexity science, in practice, its use often
remains limited to a few specialised domains and has not become part and parcel of the
mainstream policy debate. To understand why this might be the case, we question why
complexity science remains nascent and not integrated into the core of policymaking. Spe-
cifically, we ask what the non-technical challenges and barriers are preventing the adoption
of complexity science into policymaking. To address this question, we conducted an exten-
sive literature review. We collected the scattered fragments of text that discussed the non-
technical challenges related to the use of complexity science in policymaking and stitched
these fragments into a structured framework by synthesising our findings. Our framework
consists of three thematic groupings of the non-technical challenges: (a) management, cost,
and adoption challenges; (b) limited trust, communication, and acceptance; and (c) ethical
barriers. For each broad challenge identified, we propose a mitigation strategy to facilitate
the adoption of complexity science into policymaking. We conclude with a call for action
to integrate complexity science into policymaking further.
Keywords Complexity science · Complexity theory · Systems thinking · Policymaking ·
Public policy · Challenges · Barriers
* Araz Taeihagh
spparaz@nus.edu.sg; araz.taeihagh@new.oxon.org
1
Lee Kuan Yew School of Public Policy, National University of Singapore, 469B Bukit Timah
Road, Li Ka Shing Building, Level 2, #02‑10, Singapore 259771, Singapore

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Introduction
As the reliance on technology grows, our socio-technical systems become increasingly
interconnected and complex. The resultant complexity makes behaviours of these sys-
tems harder to predict and possibly more vulnerable to unforeseen events. To counterbal-
ance these challenges, for nearly eight decades, decision makers have been encouraged to
make use of complexity science-informed approaches (including systems thinking, sys-
tems theory, cybernetics, and complexity theory) to understand and manage these complex
and dynamic settings (Cairney & Geyer, 2015; Colander & Kupers, 2014; Gerrits, 2012;
Haynes et al., 2020; Morçöl, 2012; Nel et al., 2018; Room, 2011; Taeihagh et al., 2009).
Numerous research groups worldwide increasingly recognise the promise of complexity
science, which is reflected in the frequent use of ’complex systems’ or ‘complexity’ as a
descriptive keyword (Li Vigni, 2021). Despite the overall increased uptake of complexity
science in policymaking (Barbrook-Johnson et al., 2019), it is yet to be fully integrated into
the policy debate and the everyday decision-making process or toolbox of policymakers
(Eppel, 2017). A point noted by Harrison and Geyer (2021a: 47), who state that “[t]o our
knowledge, there are no ‘complexity units’ at the heart of major governments. Although
there are some complexity-inspired policy documents, they are not the norm. At present,
evidence-based approaches continue to dominate.” Several authors express the sentiment
that, despite its potential, the adoption of complexity science into policymaking remains
limited (Astbury et al., 2023; Barbrook-Johnson et al., 2020; Eppel, 2017; Kwamie et al.,
2021; Nguyen et al., 2023). Often, complexity science is confined to the domain of analy-
sis, rather than offering solutions to problems (Head & Alford, 2015). Furthermore, where
complexity-informed approaches are adopted more widely, their use is primarily restricted
to a few specialised applications, such as traffic management and epidemiology, while
maintaining a limited scope (Wilkinson et al., 2013). Thus, this article aims to question
why policy debate has not fully integrated complexity science and to explore actions that
could facilitate its deeper integration into policy.
Many compelling works, including but not limited to those by Colander and Kupers
(2014), Gerrits et al. (2021), Geyer and Cairney (2015), Morçöl (2012, 2023), Room (2011,
2016), and Taeihagh et al. (2013), actively advocate for the necessity of using complex-
ity science in policymaking and planning. However, some suggest that complexity science
may not yet be ready for widespread use in policymaking. From a conceptual perspective,
critics have suggested that complexity science is often too vague in its definitions and rife
with ambiguities to be effectively used within policy (Finegood, 2021; Harrison & Geyer,
2021a; Haynes et al., 2020; Stewart & Ayres, 2001). While methodologically, others have
suggested that some of the dominant methods of complexity are not yet ready for full-
fledged adoption into policy and decision support. For example, Axtell and Shaheen (2021)
caution that agent-based models (ABM) are not yet mature enough to be effectively used
in policymaking (despite the approach being around for nearly 30 years). This sentiment
is also reflected by Loomis et al., (2008: 45–46), who state that “to some, ABM is not yet
ready to become a full-fledged decision support tool at this time. Perhaps in another decade
as modellers, other scientists and decision makers gain more experience with ABM, it will
become a well-accepted decision support tool", and effectively underscoring further that
ABM’s still require much more work before they can be reliably used for decision making.
However, it has been 16 years since this statement, and ABM, among many other complex-
ity science-informed approaches, are still not widely used, or accepted within policymak-
ing and decision support.

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This reality raises the question, is complexity science at risk of facing similar scepti-
cism and doubts as nuclear fusion,1
with predictions of its widespread use and availability
constantly being a decade or two away? Our view is that this need not be the case, as there
is evidence that complexity science is beginning to be taken more seriously and adopted
more frequently within policymaking (Bicket et al., 2020; Gerrits et al., 2021; Rhodes
et al., 2021). However, the question remains: why does complexity science (in one form or
another) remain nascent and not used extensively as part and parcel of mainstream policy-
making? What are the challenges, limits, or barriers to adopting complexity science in and
for policymaking? What actions can we take to address these challenges?
We undertook a detailed review of the complexity-policy literature to address our
research aims. In doing so, we note that the existing literature tends to focus on promot-
ing the adoption of complexity science in policymaking while providing little critique or
guidance on its implementation (Kwamie et al., 2021). It does so by generally taking on
more of an optimistic view of complexity science and pointing out the limits of current
approaches while highlighting what complexity science can offer as a remedy. While we
fully support these efforts to promote complexity science, we also note that the existing
literature offers a limited and mostly scattered discussion on the current barriers to imple-
menting complexity science in policymaking. Furthermore, when discussions explicitly
address the barriers to adopting complexity science, they tend to do so to a limited extent
(Harrison & Geyer, 2021a; San Miguel et al., 2012; Stewart & Ayres, 2001). Alternatively,
they frequently limit themselves to a single domain, like systems thinking (Haynes et al.,
2020; Loosemore & Cheung, 2015; Nguyen et al., 2023) or agent-based models (Axtell &
Shaheen, 2021; Elsawah et al., 2019; Levy et al., 2016), or confine themselves to specific
fields like critical infrastructure (Ouyang, 2014) or non-communicable disease prevention
(Astbury et al., 2023).
Thus, this article is our attempt to collect the scattered fragments of the literature dis-
cussing the challenges and barriers to adopting complexity science in policymaking and
attempting to stitch them together into a structured framework. Our goal is that the pro-
posed framework will help to highlight the current shortcomings within complexity sci-
ence and how it is used and communicated within policy. We hope that by doing so, we
might direct future research efforts and ease the pain of adopting complexity science while
speeding up the integration and use of the powerful methods and applications that com-
plexity science has to offer.
Given the scope and detail of the reviewed literature and the limited space, we have
selected to limit our findings and discussion within this article to the non-technical issues
identified. We define non-technical issues as challenges and barriers that pertain to issues
not directly related to the technical aspects of complexity science (modelling, data con-
straints, hardware, or software), such as the lack of awareness, resistance to change, cul-
tural barriers, and communication gaps. Non-technical challenges involve human and
organisational factors that can impede the successful integration of complexity science
into policymaking. Furthermore, we exclude theoretical and methodological challenges
from our definition of non-technical challenges. The challenges associated with technical
aspects, theory and methods tend to be more abstract or academic and are often aspects
that policymakers are less concerned about (Hamill, 2010). We make this distinction as
1
Nuclear fusion has been said to always be 20–30 years away since the 1950s, yet after over 70 years we
still do not have any functioning nuclear fusion power plants (Lefebvre and Morehouse, 2022). This point
has become so widely known that it is a common joke among nuclear physicist (Takeda et al., 2023).

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our evaluation of the literature points out that many of the technical, methodological, and
theoretical issues have been previously identified and discussed in detail (Axtell & Farmer,
2022; Elsawah et al., 2019; Heath et al., 2009), with many scholars working hard to over-
come some of the existing technical, methodological, and theoretical barriers. While we do
not diminish the immense challenges and work still needed to overcome these aspects of
complexity science, the non-technical facets, such as management and institutional barri-
ers; utility and trust; communication and reporting; and ethical considerations, appear to be
underrepresented, under-reported, and scattered within the literature, despite the apparent
hurdles they pose. As such, we focus our attention on the non-technical aspects to address
this literature gap.
The structure of the remaining article is as follows: Sect. 2 provides a brief overview of
complexity science. Section 3 details the scoping review-informed method we employed to
identify challenges to using complexity science in policymaking. Section 4 offers an over-
view of the literature review results. We then draw from these results in Sect. 5 to propose
a framework for non-technical challenges. Section 6 explores possible solutions to mitigate
these identified hurdles. Finally, Sect. 7 concludes by emphasising the contributions of our
study and advocating for further research to overcome barriers to adopting complexity sci-
ence in policymaking.
Complexity science
In this section, we present a concise overview of complexity science. Proponents of com-
plexity science consider it to be a paradigm shift in worldviews (Ackoff, 1994; Dent, 1999)
and a new scientific method (Mitchell, 2009). Complexity science challenges the reduc-
tionist worldview by focusing on holistic perspectives, studying relationships, and under-
standing non-linear interactions within systems. Scholars in this field explore how interac-
tions generate novel and emergent behaviours and patterns beyond individual parts’ actions
(Geyer & Harrison, 2021). Despite this shared view, a definition of complexity science
remains elusive (Mazzocchi, 2016; O’Sullivan, 2004). For example, Turner and Baker
(2019) identify 30 definitions of complex adaptive systems (CAS), a prominent branch
within complexity studies. This ambiguity arises from complexity science being an amal-
gamation of theories, frameworks, logics, mathematics, and methods rather than a unified
discipline (Harrison & Geyer, 2021a).
The science of complexity encompasses diverse approaches, classified here as ‘com-
plexity science’ or ‘complexity-informed approaches.’ This umbrella term includes, among
others, complexity theory, systems theory, cybernetics, network science, game theory, and
chaos theory (see the ‘Map of the Complexity Science’ by Castellani and Gerrits (2021)
for a visual overview). While the approaches have many commonalities, clear distinctions
exist, notably between systems theory and complexity theory.
Systems theory, forming the theoretical foundation for complexity science, studies
systems from the top down. Systems theory seeks to understand systems’ larger struc-
ture, interactions, and behaviours. Systems thinking, originating from systems theory
but distinct from it (Richmond, 1994), approaches problems holistically, emphasising
interconnections and feedback loops. Complexity theory, in contrast, takes a bottom-up
approach, focusing on individual components and their interactions to explore emergent
properties and unexpected phenomena (Mitchell, 2009). It prioritises understanding the
emergence of order from disorder and the self-organisation of complex systems (Geyer

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& Harrison, 2021). Both systems and complexity theories reject reductionism and
embrace holism, appreciating the complex interactions within systems (Manson, 2001).
Despite widespread discussion of complexity science in governance and policymak-
ing (Cairney et al., 2019; Colander & Kupers, 2014; Geyer and Cairney, 2015; Geyer
& Harrison, 2021; Innes & Booher, 2018; Morçöl, 2023; Room, 2011, 2016; Taeihagh
et al., 2013; Taeihagh, 2017b), its full embrace remains elusive (Barbrook-Johnson
et al., 2020; Eppel, 2017). The remainder of this article delves into the non-technical
barriers hindering the broader adoption of complexity science in policymaking.
Method
To understand the non-technical challenges, barriers, and constraints preventing com-
plexity science’s greater adoption into policymaking, we must undertake a detailed
review of the relevant literature. However, undertaking a literature review on com-
plexity science is no easy task. As noted above, complexity science is a collection of
approaches (Harrison & Geyer, 2021a). As a result, the field and its related literature are
fragmented into a multitude of disciplines and journals, with each a “continued evolu-
tion of the intuitive logics tradition and still emerging nature of complexity science”
(Wilkinson et al., 2013: 701). While our aim is not to present a review article, we lever-
age the research design associated with scoping reviews, as they provide a systematic
and structured procedure to identify and assess the existing knowledge base related to
our research aims (Grant & Booth, 2009). Unlike systematic reviews, scoping reviews
focus on examining evidence on a given topic’s extent, range, and nature. As such, the
methodological quality or risk of bias of individual sources is typically not appraised or
deemed optional (Tricco et al., 2018). Below is a brief overview of the method used to
identify and select the relevant literature and how the important aspects were extracted
and synthesised. We comprehensively describe the method in the supplemental mate-
rial, Sect. 1, titled Additional details on the method.
Literature inclusion and exclusion criteria
Using a scoping review approach, we developed a protocol to structure our inquiry. The
first step in the protocol began with using our research aims to guide the development of
a set of inclusion and exclusion criteria that we applied consistently to the literature iden-
tified through the search process (for details on the inclusion and exclusion criteria, see
supplemental material, Sect. 1.1. Inclusion and exclusion criteria). The inclusion criteria
included journal or review articles published in English; that use or discuss complexity
science (such as systems thinking, complexity theory, agent-based models, and network
theory); that discuss policy or have policy implications; and specifically discuss some form
of challenge or barrier to the use of complexity science. We excluded articles that did not
show sufficient engagement with the terms and concepts related to complexity science, that
simply made superficial reference to the concepts, or only recommended the use of com-
plexity science but did not discuss the challenges to its use in policy. Our inclusion and
exclusion criteria were applied during the screening phase to consistently identify the most
relevant studies while excluding those unrelated to our research aims.

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The search strategy and data sources
The second step in the protocol involved developing a search string to query the SCOPUS
database for relevant literature (see supplemental material, Sect. 1.2. Search strategy and
data sources). Our search string was developed collaboratively through multiple rounds of
discussion, brainstorming sessions, and workshops among the authors. We also consulted
with library specialists before we finalised the search string. Our search string consists of
three themes that aim to capture the relevant literature at the intersection of policy and
complexity science while also identifying articles that discuss potential barriers. The first
search theme consisted of 54 search terms and captured the concepts related to complexity
science. In many ways, the number of terms required to capture the scope of complex-
ity science reflects its broad and scattered nature and the challenges and ambiguities of
defining it. The second search theme relates to policy and policymaking and comprises
62 search terms. The final search theme comprised 72 search terms and covered keywords
related to challenges and issues. This search theme aimed to capture the breadth of the pos-
sible keywords to describe challenges, issues, or barriers. After applying the search sting
within the SCOPUS database, the identified studies’ bibliographic information was down-
loaded (including the study title, abstract, and authors). The bibliographic information was
loaded into PICO Portal (2023), a literature review platform that leverages artificial intel-
ligence (AI) to aid in the structured review and screening of the articles.
Data collection, extraction, and analysis
The third step in the protocol involved screening the abstracts and title of the identified
studies and applying our inclusion and exclusion criteria (details available in supplemen-
tal material, Sect. 1.3. Search strategy and data sources). The first author screened all
abstracts, while the second independently audited 10%. Any discrepancies were discussed
and resolved before moving on. With the abstracts filtered, we sourced full-text articles for
the selected titles and uploaded them to PICO Portal. The first author then screened these
full-texts against the inclusion and exclusion criteria. The second author audited 10% of
the full-text articles. The research team held bi-weekly meetings throughout the screening
process to discuss any uncertainties or conflicts in the full-text screening. For the proto-
col’s fourth step (Sect. 1.3. Data collection, extraction, and analysis of supplemental mate-
rial), we jointly developed and deployed a data extraction framework to obtain the relevant
information from the selected studies. Our framework included basic information on the
article, such as author(s), article title, publication year, and the type of challenge or issue
mentioned within the article. The first author conducted the data extraction, and the second
author audited 10% of the extracted texts. In the fifth step, the first author analysed the
extracted data through exploratory data analysis, visualisation, and thematic synthesis. The
first author conducted a thematic synthesis, identifying key themes and trends. The second
author reviewed these themes. We resolved any discrepancies collaboratively. Finally, we
combined our findings to solidify the core themes.
Limitations
Despite our efforts to capture the vast and fragmented literature on challenges associ-
ated with applying complexity science in policymaking, certain limitations require

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acknowledgement. The primary limitation stems from using a scoping review design.
This approach restricts identified articles to those containing specific keywords and
meeting our predefined inclusion criteria. This means that studies that do not explicitly
use our search terms get missed during the initial search phase (Arksey & O’Malley,
2005). To minimise this factor, we utilised a diverse range of terms within our search
strategy.
Similarly, during abstract and title screening, articles lacking an explicit mention of a
challenge related to using complexity science were excluded from the final selection. This
process of eliminating articles without reviewing their full text represents a limitation
inherent to any structured review, particularly for research exploring abstract or unconven-
tional areas. However, given the sheer volume of relevant research and manpower limita-
tions, no readily available alternative to a structured review process exists, at least not until
artificial intelligence applications can reliably screen articles and extract key information.
Furthermore, focusing on non-technical aspects precluded us from delving into the techni-
cal, theoretical, and methodological aspects that might influence the adoption of complex-
ity science in policy settings. Future research can explore these facets in greater depth and
assess their impact on utilising complexity science for and within policymaking.
Results
Overview of the dataset and key characteristics
Applying our search string within SCOPUS resulted in the identification of 9,943 studies.
Next, using the PICO Portal platform to aid in the screening process and leveraging its
machine learning algorithm to order the abstracts from most to least relevant, we screened
5,670 studies (57% of total studies identified in the search process). We halted the abstract
screening process after deciding that a saturation point in the screening had been reached,
meaning that we found no new relevant studies. Our decision was also validated by the
PICO Portal platform, which estimated that we had identified an estimated 98% of relevant
studies. This aligns with Agai and Qureshi (2023) and Qureshi et al. (2023), who studied
machine learning-assisted screening with PICO Portal and found its algorithms capable of
identifying over 90% of relevant articles after screening just 55–60% of abstracts. Indeed,
other research has shown that similar AI-assisted screening methods can speed up the
identification of relevant articles by more than 30% (Dijk et al., 2023; Hempel et al., 2012).
Given the supporting evidence, the authors agreed that identifying the remaining 2% of
relevant studies in the remaining 4,273 abstracts would likely not benefit the study. Using
our inclusion and exclusion criteria on the 5,670 articles screened, we identified 1,086
articles relevant to this study at the title and abstract level. At the full-text screening stage,
we excluded 750 studies while retaining 336 for final data extraction. Figure 1 summarises
the screening process and reasons for excluding studies.
All 336 articles we identified cited some form of obstacle or limitation to applying
complexity science in a policy context. These issues encompass but are not limited to
theoretical and methodological perspectives, social, institutional, and political roadblocks,
as well as technical factors related to, but not limited to, methods, tools, approaches, or
frameworks. In addition to the primary data extraction, we selected 56 articles out of
the 336 for detailed data extraction. These articles contained more detailed and nuanced
descriptions of the obstacles and challenges related to applying complexity science within

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policymaking that would be lost if the information was only captured within the primary
data extraction framework.
Overview of the challenges to applying complexity science in policy
As noted previously, this article focuses on the non-technical barriers related to the use
of complexity science in and for policymaking. However, we provide a brief overview of
all potential impediments identified. We distinguished 141 unique challenges and barri-
ers related to applying complexity science in and for policymaking from the literature.
Out of the 141 different challenges identified, 44% (62 issues) can be considered techni-
cal, typically related to data issues or aspects of modelling complex systems. As defined
in this study, non-technical challenges comprised 41.1% (58) of the total 141 challenges.
The remaining 14.9% (21) of the challenges were theoretical or methodological, including
challenges such as conceptualising system boundaries or the theory of complexity. Given
the nature of theoretical and methodological issues, which can be very abstract or closely
linked to technical challenges, attempting to describe these challenges in sufficient detail
would require a dedicated article to do the topic justice.
To better understand the prevalence of the various challenges identified across the 336
studies, we also analysed the frequency with which the challenges appear. To ensure a
fair and accurate representation of the relative importance of each challenge, we counted
each challenge only once per study, regardless of how often it was mentioned within that
study. This approach prevents overcounting common challenges and allows for a more bal-
anced view of the landscape. From our survey, the 141 challenges were reported 1,651
times across the 336 studies, with an average of 4.9 challenges per article. To better syn-
thesise the challenges, we grouped the 141 challenges into 16 main challenges that better
reflect their overall nature and present the frequency of their occurrence within the text (for
details, see the supplemental material Sect. 1.3.1. Data extraction and synthesis).
Fig. 1 A flow diagram of the
screening process showing
the database search, selection
process and reasons for the
exclusion of articles and data
extraction. Articles excluded
as ‘other’ include articles that
were not in English or whose
information was not complete

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The challenges most frequently reported (out of 1651) include modelling issues
(24.4%), conceptual and methodological challenges (12.5%) data related issues (9.6%),
utility and trust (9.3%), and management and institutional difficulties (8.2%). While
not as prevalent, other notable challenges included difficulties related to communica-
tion and reporting of complexity science (6.6%) and various barriers associated with
costs (6.1%). We also note that some aspects, such as ethical considerations (0.4%),
were not reported widely in the reviewed literature despite arguably being significant in
policymaking.
In addition to the collective number of issues identified in the literature, we assessed
the changes in challenges reported over time, as shown in Fig. 2. Specifically, focusing
on the non-technical challenges and including theoretical and methodological factors
in the assessment, conceptual and methodological considerations (frequency: 207)
are the most frequently discussed challenges in the literature over time. This is an
unsurprising result given the nature of complexity science in policymaking and that it
is a relatively young approach (compared to established approaches to policymaking),
rife with conceptual and methodological quandaries (Harrison & Geyer, 2021a). Since
2014, there has been an increase in articles reporting challenges related to management
and institutional (frequency: 136) aspects of the use of complexity science for policy.
Suggesting that complexity science is beginning to be explored more within the policy
sphere in the past decade.
Additionally, concerns about communication and reporting complexity (frequency:
109) science concepts and results have become more prevalent since 2003. Conversely, our
results show that theoretical challenges (frequency: 77) associated with complexity science
have been reported less frequently since around 2010, possibly indicating that these chal-
lenges are being addressed or are simply being discussed less in the literature. Similarly,
concerns about the utility, trust, and cost of complexity science (frequency: 153) have also
declined in frequency since 2008.
Fig. 2 Main non-technical challenges and issues identified by publication year. Methodological and
theoretical challenges have been included within the figure

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A framework of non‑technical barriers to complexity science’s use in policymaking
To interpret our findings coherently, we organised and consolidated the 141 unique chal-
lenges into a structured framework of 16 groups. To develop this framework, the first
author conducted several rounds of thematic synthesis of the unique challenges by identify-
ing, extracting, and grouping relevant challenges. The second author would independently
review the groupings after each round. After several iterations and extensive internal dis-
cussions, a consensus was reached about the groupings, which captured the thematic chal-
lenges related to using complexity science in policymaking. In this article, we report exclu-
sively on the non-technical challenges and barriers hindering the application of complexity
science in policymaking. Our thematic synthesis identified three non-technical challenges
hindering the use of complexity science in policymaking, outlined in Table 1. These over-
arching groups are management, cost, and adoption challenges (frequency: 295); limited
trust, communication, and acceptance (frequency: 279); and ethical barriers (frequency:
6). The text to follow will delve deeper into each group, providing detailed descriptions of
the specific and underlying challenges within each of the primary groupings.
Management, cost, and adoption challenges
This thematic group consists of challenges related to using complexity science within man-
agement and institutional settings (frequency: 136). Political and legal barriers are also
highlighted throughout the discussion. We also draw attention to other challenges, such
as the wide range of potential costs (frequency: 100) required for adopting complexity-
informed approaches. We conclude this section by summarising some of the barriers to
adopting and using complexity-based applications (frequency: 59).
Management and institutional barriers
The literature we surveyed suggests that a range of management and institutional barriers
has hindered the integration of complexity science into policymaking. Among the most
dominant of these is a concern that there is limited interest in or understanding of com-
plexity science and its related concepts and tools among policymakers and practitioners
(El-Jardali et al., 2014; Finegood, 2021; Otto, 2008). Compounding this challenge is the
difficulty of embedding complexity science into a field or institution, as this would require
a paradigm shift and a drastic departure from the conventional way of dealing with prob-
lems (De Greene, 1994b). Without this change in thinking, it becomes increasingly difficult
for policymakers to understand, accept and use complexity science within their work (Bale
et al., 2015; Collins et al., 2007).
Further exacerbating the above challenges is that complexity science has faced critiques
for being too ‘conceptual for policy’ and ill-suited for practical policy applications
(Kwamie et al., 2021). Here. Critics suggest that the language and concepts of complexity
science are unclear, ambiguous, and ill-suited for decision-makers and practical policy
applications (Bale et al., 2015; Loosemore & Cheung, 2015). This sentiment is reflected
by Stewart and Ayre (2001: 82), who observed that “the language and perspectives of these
[systems thinking] approaches has never been harmonised with that of the policy-maker.”
Stewart and Ayre argue that complexity science’s counterintuitive nature can be difficult to
grasp and can lead to ‘uncomfortable conclusions’ about the limitations of policymakers to

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Table
1 Framework
of
non-technical
challenges
and
barriers
to
the
use
of
complexity
science
in
policymaking.
The
Table
includes
the
frequency
that
the
challenges
were
identified
in
the
text
studied
Thematic
group
Main
challenges
Frequency
identified
Management,
cost,
and
adoption
challenges
Management
and
institutional
136
This
group
consists
of
challenges
related
to
management
and
institutional
difficulties,
costs
and
the
deployment
and
use
of
tools
or
methods
Cost
barriers
100
Adoption
and
usability
barriers
59
Trust,
communication,
and
acceptance
of
complexity
science
Covers
barriers
to
the
acceptance
and
use
of
complexity
science
and
its
tools
and
methods,
including
considerations
such
as
poor
or
limited
reporting
and
communication
of
complexity
science
and
the
results
from
models.
Linked
to
this
is
the
limited
utility
of
tools
and
the
trust
in
results
Limited
Trust
in
complexity
science
appli-
cations
and
methods
153
Understanding
and
acceptance
of
com-
plexity
science
17
Communication
and
reporting
109
Ethical
barriers
Describes
moral
implications
associated
with
complexity
science
within
the
context
of
policymaking
Ethical
barriers
6

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intervene within the complex systems they are tasked to govern. Authors such as Cairney
(2012) suggest that translating complexity science and its concepts to the policy domain
is incomplete, a sentiment echoed by Morçöl (2014, 2023). Harrison and Geyer (2021a)
suggest that among the reasons for this difficulty is that complexity is a relatively young
field in policy studies and that it, therefore, still lacks a unifying conceptualisation and
language of the approach.
The interplay between the knowledge gaps, limited interest, and misalignment between
the language of complexity and the policy domain creates a self-reinforcing cycle, further
discouraging the adoption of complexity-informed policies due to the constrained enthusi-
asm for building capacity, allocating resources, and creating opportunities to apply com-
plexity science (Bale et al., 2015; Haynes et al., 2020; Summers et al., 2015).
Conversely, there are concerns about when complexity-informed approaches are actu-
ally used in policymaking. As Levin et al. (2015) highlight, using complexity science for
policy development can lead to more holistic proposals but ultimately make them unattain-
able. Such proposals may exceed or fall outside of the implementing body’s institutional,
political, or financial purview or capability, resulting in implementation failure. Such issues
can be further compounded by bureaucracies’ tendency to incentivise efficiency, stand-
ardisation, linearity, compliance, and uniformity, further impeding complexity-informed
approaches that embrace change and uncertainty (Kwamie et al., 2021; Young, 2017).
While not discussed as frequently as other challenges in the studies we reviewed, some
authors have indicated that political considerations can be considerable hindrances to the
adoption of complexity science into policymaking (Hartman, 2016). Currie et al. (2018)
indicate that one such barrier is the incompatibilities between the time needed to build
a model or develop a complexity-informed proposal and the timelines of politicians and
public decision-making. Currie et al. suggest that such incompatibilities result in rushed
and oversimplified proposals and models, hindering their effectiveness. Additionally, Cur-
rie et al. also emphasise that potential issues may also arise due to conflicting goals of the
political process, which focuses on short-term outcomes (Barbrook-Johnson et al., 2019),
and that of complexity approaches and modelling, which seek long-term and holistic
solutions.
This dissonance is exacerbated further by the inherent incompatibility between com-
plexity science and policymakers’ traditional, reductionist worldview, who often seek to
simplify and control complex systems by eliminating complexity rather than embracing it
(Wilkinson et al., 2013). Harrison and Geyer (2021a) echo this sentiment but further high-
light that embracing complexity would require governments (and politicians, by extension)
to acknowledge their limited control and influence over the policy processes and outcomes
within the systems they govern. However, Harrison and Geyer also observe that embracing
a complexity approach and acknowledging limited control might reduce elected officials’
accountability. Loosemore and Cheung (2015) echoed a similar sentiment by suggest-
ing the potential for complexity-informed approaches to diffuse the allocation of risk and
responsibility, thereby reducing accountability when issues arise. They note that this is par-
ticularly relevant in high-risk high-responsibility domains, such as construction. To address
this concern, Harrison and Geyer (2021a) advocate for a balanced approach, stating that
“we need to combine a governmental acknowledgement of the limits to its powers with a
societal recognition of the complexity of the governance process and the need to still hold
elected policy actors to account in a meaningful way” (Harrison & Geyer, 2021a: 50).
Similarly, other studies have highlighted that most complexity-informed approaches
fail to account for power dynamics or the impact of human values in their frameworks
(Houchin & MacLean, 2005; Levy et al., 2016). For example, Lane and Oliva (1998) note

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a lack of social-political theory within systems dynamics, a subbranch of complexity sci-
ence. Haynes (2018) further suggests that one reason for neglecting values in complex-
ity-informed approaches within social and political science is the historical importation of
complexity science from the natural sciences, which typically disregard social norms and
values.
The literature also identifies potential barriers to integrating complexity science into
policymaking arising from institutional or legal considerations (Currie et al., 2018; Shep-
herd, 1997). Frohlich et al. (2018), for instance, describe how laws can sometimes impose
legal restrictions on adaptive management practices, an approach informed by complex-
ity science. They provide an example of legislation primarily focused on safeguarding an
individual species instead of protecting and managing the larger system. They also cite
several examples of legislation limiting adaptive management approaches, including regu-
latory fragmentation, spatial boundaries of jurisdiction, and the mismatch between legal
land ownership boundaries and ecosystem boundaries.
Linked to institutional challenges, the interdisciplinary nature of complexity science
presents significant challenges to its adoption in the policymaking process. Several authors
have reported a lack of collaboration between researchers, institutional departments, and
disciplines, hindering the effective implementation of complexity-informed approaches
(Schimel et al., 2015; Schlüter et al., 2014). Such challenges highlight the crucial need for
fostering interdisciplinary collaboration to bridge the gaps between disciplines and facili-
tate the effective integration of complexity science into policymaking.
Cost barriers
As with many things, certain costs are associated with adopting, using, or implementing
a proposal, approach, or tool. Because of its nature, complexity science might be more
vulnerable and disproportionately impacted by these costs compared to other established
approaches. From the literature studied, we identified a wide range of potential costs
associated with, among other things, the development of any tool or model, particularly
those using complexity science. The costs identified include general financial costs and
funding limitations (Druckenmiller et al., 2007; Lindkvist et al., 2020); financial cost of
data collection, creation, or purchasing (Burgess et al., 2020; Summers et al., 2015); time
needed to develop a model, collect data, or train users (Terzi et al., 2019); manpower and
expertise requirements (Astbury et al., 2023; Balajthy, 1988; Zhuo & Han, 2020); com-
putational costs to develop and run models (Pan et al., 2022; Wen & Li, 2021), which are
compounded by computational complexity (Nguyen et al., 2021; Taeihagh et al., 2014).
Adoption and usability barriers
From a more practical perspective, the literature points to several challenges related to the
limited adoption, utilisation, and deployment of complexity science and its related applica-
tions within organisations as an issue (Ligmann-Zielinska, 2009; Sharma-Wallace et al.,
2018). One reason for the limited uptake of complexity science is that many complexity-
informed applications are too domain-specific and cannot be used in broader contexts
(Moallemi et al., 2021; Taeihagh et al., 2014). While complexity-informed approaches
are not widely used in general policymaking, some limited exceptions exist where com-
plexity-based approaches are the mainstream, such as traffic simulations and epidemiol-
ogy (Wilkinson et al., 2013). Furthermore, Torrens et al. (2013) note that reductionist

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tools are easier to work with and understand than complexity-informed tools, even though
they do not accurately represent reality. The reluctance to adopt new complexity-informed
approaches and tools is likely further hindered because of existing tools, making users and
policymakers more reluctant to use different and potentially more complex tools (Cockerill
et al., 2007; Rich, 2020). This is especially true as many traditional tools (using statistical
analysis) can produce predictions with confidence intervals, encouraging additional trust in
the results (Maglio et al., 2014).
We also highlight a few other reasons for the limited uptake of complexity-informed
approaches or tools identified from the literature. These challenges include usability issues
of applications (Druckenmiller et al., 2007); time, effort, and data required to develop a
tool or model (Ecem Yildiz et al., 2020); mismatches between a model’s intended pur-
pose (teaching) and the way the model is used (decision support) in practice (Allison et al.,
2018); lack of a champion within an institution for complexity science or an application
(Shepherd, 1997). We also noted issues of trust in complexity-informed tools and methods,
which are linked to the challenges discussed here. However, issues of trust are discussed
in more detail in the next section. To summarise the silent challenges identified under the
broader thematic group of management, cost, and adoption challenges, a synopsis of the
main points has been provided in Table 2.
Trust, communication, and acceptance of complexity science
Within this thematic group, we highlight the barriers associated with trust, communication
and acceptance of complexity science and applications. We begin by discussing the chal-
lenges of building trust in complexity science and its methods and applications (frequency:
153). Linked to trust, we then discuss the barriers limiting the acceptance of complex-
ity science (frequency: 17). We conclude this section by highlighting the considerations
related to the challenges of communicating complexity science concepts and the results
from applications (frequency: 109).
Limited trust in complexity science and its methods and applications
From the non-technical issues we identified from the literature, challenges related to the
trust in complexity science and its applications and methods were among those cited
most frequently. Trust in an approach, method, or application is essential, especially for
policymakers (Lacey et al., 2018) who must make decisions that are often long-lasting,
non-reversible, and can impact many people. Thus, building and maintaining trust in com-
plexity science is essential before those making decisions are willing to embrace it and an
alternative approach.
Trust in complexity-informed approaches can be undermined in numerous ways. For
example, Ibrahim Shire et al. (2020) highlight, within the context of health care, that the
participants not involved in a model building exercise (notably the managers) were less
convinced of the model’s validity and were unwilling to claim ownership compared
to participants involved from the beginning of the process. Vermeulen and Pyka (2016)
reflect a similar sentiment by suggesting that the lack of policymakers’ involvement in
the modelling process erodes trust and understanding of the process and its outputs. As
noted by Levy et al. (2016), the challenge of model validation, particularly in ABM, also
contributes to the lack of trust in complexity-informed models. As a distinct issue related
to trust, Currie et al. (2018) identify that the problem may not lie with the complexity

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science application itself but rather how the application is used. Their study implies that
working in a multidisciplinary team would likely be most effective in some cases, such as
group system dynamics modelling.
Several authors (Kwamie et al., 2021; Wainwright & Millington, 2010) highlight
the scarcity of successful deployment of complexity-informed policies as a reason for
the hesitancy to adopt complexity into policymaking. Thus, decision-makers are reluc-
tant to put their trust in unproved systems or approaches and seek tangible examples of
the efficacy of modelling or an approach (Haynes et al., 2020; Maglio et al., 2014). To
gain acceptance, complexity and its modelling applications must showcase their value
through successful real-world applications (Haynes, 2018). However, Lorscheid et al.
(2019) highlight a compounding issue to this requirement: complexity applications,
like Agent-Based Modelling (ABM), need time to mature. Lorscheid et al. cite the time
Table 2 Summary of management, cost, and operational challenges
Challenges within the thematic group Additional details
Management & institutional Management challenges
- Low interest in complexity science among policymakers
- Limited capacity and resources for implementing complexity-
based approaches
- Difficulty integrating systems thinking and complexity science into
existing practices
- Developing targets outside of the institutional capability
- Vague and undefined language and concepts in complexity science
Policy and political challenges
- Time constraints and incompatibilities in decision-making lead to
rushed and oversimplified models
- Conflicting goals between the political process and modelling
- Disregard for power dynamics and human values in modelling
Legal and institutional challenges
- Legal restrictions on complexity-based practices, such as regula-
tory fragmentation and institutional boundaries
- Bureaucratic structures favouring efficiency and standardisation
hinder the adoption of complexity science
- Potential for complexity science to disperse risk and responsibility,
reducing accountability
- Lack of collaboration between researchers and practitioners
Cost barriers - Financial costs and funding limitations
- High cost of data collection, creation or purchasing
- Time-consuming process of model development, data collection,
and user training
- Manpower and expertise requirements
- Computational costs to develop and run models
Adoption and usability barriers Deployment and adoption barriers
- Limited applicability of domain-specific tools in broader contexts
- Reluctance to adopt complexity-based tools when traditional tools
provide confident predictions
- Hesitation due to the availability of simpler existing tools
Usability and resource challenges
- Difficulty and time investment required to use and develop com-
plexity science tools
- Mismatches between a model’s intended purpose and its practical
application
- Lack of institutional support or champion for the application of
complexity science

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required for calculus to be accepted as a historical parallel. From this point of view,
can it be that complexity science is still not yet ready for widespread use despite being
around for over 80 years? Lorscheid et al., however, also indicate that approaches such
as ABM are becoming more accepted within ecology and that there are indications that
the ABM method is starting to mature. Axtell and Shaheen (2021), while optimistic
about the future of ABMs, also caution that methods like ABMs are not yet ready to be
dependable tools for policymaking due to the technical challenges that still need to be
addressed (see Axtell and Shaheen (2021) and Levy et al. (2016) for a discussion of the
challenges related to ABM).
Beyond examples of success, Wainwright and Millington (2010) argue that models like
ABM need to demonstrate explanatory power before they will be accepted. However, the
literature often cites concerns regarding the results’ limited predictability and reliability
(Allison et al., 2018; Axtell & Shaheen, 2021; Burgess et al., 2020). While a technical
issue, the limited predictability of the models primarily stems from the impacts of sensi-
tivity to initial conditions, path dependencies, and stochasticity (Bale et al., 2015; Levy
et al., 2016). Additionally, as Whitfield (2013) notes, the increasing complexity and limited
reliability of predictions, especially for complex models like those associated with climate
modelling, hinder non-experts from understanding the models and results. This erosion of
trust and acceptance makes such models more open to politically motivated challenges like
those increasingly captured by climate change deniers. Consequently, using the outcomes
of such models is more difficult when used to inform climate policy. Astbury et al. (2023)
highlight several additional challenges related to modelling: a lack of trust in models as
evidence, model complexity that can alienate stakeholders, and stakeholder struggles with
interpreting complex results. Notably, they identify the "tension around model complex-
ity," where models must be complex enough to represent the system adequately while
remaining interpretable.
Mercure et al. (2016) and Banozic-Tang and Taeihagh (2022) suggest that rather than
focusing solely on the complexity or simplicity of models, greater emphasis needs to be
placed on the importance of the research-policy interface in relaying the results of models
and scientific research findings to policy circles, highlighting the challenges of communi-
cation and reporting (discussed in Sect. 5.2.3). These findings underscore the critical need
to bridge the gap between the research community and policymakers. Researchers must
effectively communicate complexity science’s value and limitations, while policymakers
must be open to embracing uncertainty and holistic perspectives to address complex soci-
etal challenges.
A different aspect of trust discussed across several studies was the lack of transparency
in models (Lindkvist et al., 2020; Taeihagh et al., 2014). For example, Torrens et al. (2013)
point out that ABMs have been critiqued for being ‘black-box’ models because the simula-
tion does not explicitly show the mechanisms generating emergent behaviours. This lack
of transparency can lead policymakers to question the internal validity of the model and
doubt its value and related policy recommendations (Vermeulen & Pyka, 2016). Ligmann-
Zielinska (2009) underscore the necessity for the transparency and availability of a model’s
code to address these concerns. However, Iwanaga (2021) contend that mere access to a
model’s code doesn’t equate to transparency. Additional contextual information about the
model is needed to assess its suitability regarding function and purpose (Burgess et al.,
2020; Schlüter et al., 2019). While transparency is a legitimate concern, decision-makers
must also consider security issues (Dorri et al., 2018). For example, Luck et al. (2004)
describe trust concerns about the safety of multi-agent systems because of agents’ self-
adaptive nature.

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In stark contrast to the challenges discussed above, some studies suggest that policy-
makers might readily accept the results of models without critical reflection on their pur-
pose, reliability, assumptions, or value (Whitfield, 2013). Allison et al. (2018) and Mercure
et al. (2016) note that some models initially designed to enhance social learning, under-
standing of a system, or built with unreliable data are used as decision-support systems to
inform policy. In these cases, the models are used outside of the modeller’s intention, with
factors such as the model limitations, reliability or predictability not considered. While no
research has explicitly been done on the topic that the authors are aware of, the reliance on
models not built for the purpose might also impact the long-term acceptance of complexity
science and its applications.
Understanding and acceptance of complexity science
Understanding and accepting complexity science is arguably one of the most critical factors
for its wider adoption within policymaking. However, as De Greene (1994b: 445) states,
“the basic challenge facing systems thinkers-and those policymakers, decision-mak-
ers, educators, and others who might benefit from systems advice – is not more data,
more information systems, more computers, more money, and so on. The challenge
is rather for all of us to restructure our very way of thinking.”
Despite its significance, the reviewed literature we studied seldom discusses the chal-
lenge of understanding or acceptance of complexity science, particularly within the con-
text of policy and decision support (only 17 instances were identified within the text stud-
ied). The limited discussion might be because complexity challenges the ontological and
epistemological assumptions of traditional ways of thinking (Morçöl, 2014). The limited
discourse on the topic might be because altering beliefs and paradigms towards a more
systemic approach is likely one of the most daunting barriers to the widespread adoption
of complexity science in policymaking (Mann & Sherren, 2018). Similarly, Cockerill et al.,
(2007: 39) indicate that if challenges to a person’s beliefs are presented, through a model
for example, then the output of such a model is more likely to be ignored than accepted.
This suggests, as noted above, that it might be best to include those who are sceptical of
complexity-informed approaches into the processes from the beginning to build trust and
understanding in the process and outcomes.
Other studies discussing the challenge of improving the understanding and acceptance
of complexity science have typically done so in the context of university teaching (York &
Orgill, 2020) or training settings (Haynes et al., 2020). These studies highlight the lack of
teaching material related to complexity science as a significant issue (Flynn et al., 2019).
Additionally, educating students to use complexity science effectively requires support
from educators, who themselves require training in complexity thinking before being able
to teach students (Schultz et al., 2021). Communicating complexity science concepts to
stakeholders and policymakers presents further challenges, similar to those faced in edu-
cation (Collins et al., 2007; Ibrahim Shire et al., 2020). Stakeholders and policymakers
involved in modelling processes can find the concepts or processes difficult and overly
complex, leading to information overload (Morais et al., 2021; Weeks et al., 2022). Even
well-educated professionals struggle to grasp certain complexity science concepts, such as
accumulation (Cronin et al., 2009; Sterman et al., 2015).

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A significant challenge to the broader adoption and application of complexity science
lies in the inability of policymakers to understand what complexity is and what exactly
it offers. While some may point the finger solely at policymakers for this lack of under-
standing, the blame cannot be laid entirely at their feet. Complexity science has been cri-
tiqued for its inherent ambiguity, terminology, and conceptual framework inconsistencies,
as noted by several authors. This ambiguity manifests in several ways, hindering both
comprehension and collaboration within the field, ultimately limiting its reach and impact.
Firstly, the absence of a unifying definition and theoretical framework within complex-
ity science creates a conceptual vacuum, leaving policymakers with little to grasp onto.
This issue, highlighted by Kok et al. (2021) and O’Sullivan, (2004), is further compounded
by the relative novelty of the field. Scholars are still grappling with its core concepts and
methodologies, as Cairney (2012: 352) observes: "the first difficulty with complexity the-
ory is that it is difficult to pin down when we move from conceptual to empirical analysis."
Secondly, the terminology employed within complexity science is often plagued by vague-
ness and inconsistency. As Finegood (2021) and Haynes et al. (2020) emphasise, this lack
of standardisation creates significant obstacles for researchers at all levels, hindering their
ability to engage effectively with the field. Houchin and MacLean (2005) further echo this
concern, critiquing the "variety of definitions, the doubts expressed as to whether it is a
theory, theories or a framework, and the different meanings given to the terminology asso-
ciated with complexity" as detrimental to the field’s coherence and credibility. Finally, the
terminology’s ambiguity extends beyond individual words, impacting the overall concep-
tual landscape of complexity science. As Teixeira de Melo et al. (2019) point out, different
approaches within the field can ascribe different meanings to the same concepts, leading
to misinterpretations and hindering effective communication and collaboration between
researchers from diverse backgrounds and disciplines. This lack of a shared language, as
Harrison and Geyer (2021a: 47) emphasise, further exacerbates the challenges, as "[com-
plexity] is not a unified field with a unifying interdisciplinary language." Given these argu-
ments, addressing the issues of ambiguity and inconsistency is crucial to unlocking the
potential of complexity science in policymaking.
The literature has also noted additional barriers to adopting complexity into policymak-
ing. These include policymakers’ lack of acceptance and willingness to use complexity-
informed models (Levy et al., 2016). Cockerill et al. (2007) suggest that policymakers’
limited willingness to use models is based on ‘intentional ignorance’, explaining that in
cases where models address controversial topics and provide meaningful insights, deci-
sion-makers might ignore the results and fail to address the issue, opting to maintain the
status quo, particularly if decision-makers already having existing models or tools (duel-
ling models). Policymakers are, therefore, unwilling to adopt new applications, especially
if existing applications suggest some form of certainty or predictability, which is harder to
achieve with dynamic and systems models (Cockerill et al., 2007).
Communication and reporting of complexity science.
Science communication is a significant challenge for many domains of study (Bucchi,
2019), with complexity science being no exception. However, complexity science faces a
more significant challenge than many other fields, as it can be challenging to understand
and produce unexpected or counter-intuitive results (Stewart & Ayres, 2001). As noted pre-
viously, ambiguous language and terminology impact the understanding of complexity sci-
ence. However, the same linguistic and conceptual inconsistencies, as well as the technical

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and complex nature of the concepts (Loosemore & Cheung, 2015), also hinder the ability
of those using complexity to communicate the concepts and the results effectively (Bale
et al., 2015).
Furthermore, Steger et al. (2021) and Taeihagh et al. (2013) highlight that without ade-
quate tools and visualisations, non-technical stakeholders and participants might be over-
whelmed by the amount of information required to use complexity-informed approaches.
Therefore, as Šucha (2017: 23) states, “there is a job to do in helping policy makers and
politicians to develop simple messages to persuade the public of the merits of the solutions
arrived at using complex science”, as even the best and most accurate models are of no
use if the results cannot be effectively communicated (Lehuta et al., 2016). However, the
results of complexity science approaches can be challenging to communicate effectively.
For example, describing how initial assumptions in a process can result in specific model
outcomes is challenging, particularly as models become more complex (Burgess et al.,
2020). Additionally, temporal disturbances, random perturbations, path dependencies,
agent learning, model initialisation, and various types of uncertainty add to the burden of
communication (Bale et al., 2015; Taeihagh, 2015; Vermeulen & Pyka, 2016).
Linked to the challenge of communication, some authors (Lehuta et al., 2016; Levy
et al., 2016) have noted the lack of standards for the evaluation, benchmarking, or imple-
mentation of various complexity science models, which hinders trust in the approach and
makes communication of a model and its results more challenging. Similarly, the lack of
clear guidance on actually applying complexity science-informed approaches in policy-
making is also an issue (Currie et al., 2018; Mora et al., 2012) and can produce messy or
confusing implementation of an application (Zukowski et al., 2019). For example, many
complexity-informed models allow policymakers to test various policy interventions. How-
ever, as Amagoh (2016: 3) states, such a “model gives little guidance as to which aspects
of the system should be manipulated to achieve policy objectives. In other words, it fails
to provide a way forward when constituents of a system are in conflict with each other”.
For complexity-informed methodologies to be taken seriously and utilised effectively in
policymaking, they must move beyond simply describing a system and its potential future
outcomes. There is a critical need to develop methods that provide concrete guidance on
how and where to intervene within a system to achieve desired policy goals while minimis-
ing unintended consequences and negative externalities.
Table 3 summarises the key sticking points related to the communication, implemen-
tation, and acceptance of complexity science in policymaking identified in the literature.
Addressing these challenges is crucial to unlocking the transformative potential of this field
and fostering its widespread application in policy development and implementation.
Ethical barriers
Ethical considerations reflect the moral implications of using complexity science and its
related applications. Ethical considerations were the literature’s least reported barriers
related to complexity science (identified 6 times in the literature studied). The limited dis-
cussion on ethical considerations related to complexity science might be because, as Fen-
wick (2009: 110) notes, complexity science “does not indicate what is desirable beyond the
survival of the system in some form”. Indeed, complexity science is a descriptive approach
to understanding the interactions and processing within complex systems. It focuses on
explaining how these systems work and change rather than developing specific, normative
outcomes.

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Table 3 Summary of the barriers related to communication, reporting, and acceptance of complexity
science
Limited trust in complexity science
applications and methods
Lack of transparency
- Limited inclusion of practitioners and users in the model building
process
- Lack of transparency on how models work and generate results.
I.e., too many black-box models
- A lack of documentation, reporting standards, and protocols
Limited predictability
- Limited predictability of models due to sensitivity to initial con-
ditions, path dependencies, and stochasticity
- Challenge validating models
- Complexity Science applications must demonstrate explanatory
power before they will be accepted
- Limited demonstration of value due to few examples of success
Challenges in the use and development of applications
- Inappropriate use of applications outside of their intended
purpose
- Approaches are too technical for non-experts to understand
- Security concerns
Understanding and acceptance of
complexity science
Changing beliefs and paradigm shift
- Difficulty of changing beliefs and a paradigm shift towards a
more systemic approach
- Applications that challenge user beliefs are more likely to be
ignored than accepted
Education and conceptual barriers
- Limited training in complexity science
- Complexity science concepts are difficult to understand making
them difficult to communicate
Willingness to embrace new applications
- Lack of acceptance by policy makers to use complexity-based
models if results are controversial
- Unwilling to adopt new applications if existing tools provide
more statistical certainty
Communication & reporting Communicating complexity concepts
- Ambiguous and confusing language and terminology
- Technical and complex nature of the concepts makes science less
accessible to people
Interpretation challenges
- Limited guidance on how to intervene to achieve desired goals
- Limited visualisation methods to explain results
- Non-technical participants can be overwhelmed by the amount of
information required
- Results can be counter-intuitive
Challenge to explain process and results
- Difficult to convey how inputs generate outputs
- Difficult to convey uncertainties in applications
Lack of standards and guidance
- The lack of standards for the evaluation, benchmarking, or
implementation
- Limited guidance can produce messy or confusing implementa-
tion

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However, we note that a limited number of studies did report on ethical and moral
matters related to complexity. These studies discussed the need for moral decision-
making when using complexity science with decision-support systems (Partanen,
2010). For example, Midgley (1992) suggested that researchers and modellers must
take moral responsibility when developing a model, including in the vision and use of
the model or decision-support tool. Additionally, it is also the moral responsibility of
the user or decision-maker not to accept the results of a decision-support tool or model
without question (de Greene, 1994a).
Choi and Park (2021) highlight a few additional ethical considerations relevant to
this debate. They indicated that careful consideration must be taken when modelling
society with artificial agents based on biased real-world data. The potential for preju-
dice also needs to be considered to avoid generating certain stereotypes of particu-
lar groups when modelling social groups, which might have significant implications,
as the results from such models might have real-world consequences. Choi and Park
emphasise that this is especially true for models created to control human behaviour,
a sentiment echoed by Anzola et al. (2022). Similarly, Leslie (2023) also notes sev-
eral ethical challenges. These include aspects of data privacy and protection, managing
user and modeller assumptions, erroneous data and the need to manage and mitigate
bias, such as sampling bias within data (social media data, for example) and the mis-
leading consequences or results generated from such data, the lack of transparency in
models and applications, which, as noted previously, also raises concerns about trust
(Lindkvist et al., 2020; Taeihagh et al., 2014). This lack of transparency raises ethical
concerns since it prevents evaluating bias and assumptions within the model’s inner
workings (Leslie, 2023). Table 4 provides a summary of the main ethical barriers and
considerations.
Discussion: Paths for overcoming non‑technical challenges.
Most studies examined fail to offer definitive solutions for the problems they highlight.
Among those providing solutions, they tended to promote adopting mixed methods
approaches to address the challenges (Alderete Peralta et al., 2022; Nikas et al., 2019),
or they predominantly concentrate on resolving technical difficulties rather than tackling
non-technical matters. While technical challenges pose distinct barriers, solutions for such
Table 4 Ethical and moral barriers and considerations for complexity science
Ethical barriers Need for moral responsibility
- Low reporting of ethical considerations
- Lack of normative guidance in complexity science
- Require moral responsibility in model development, users of tools,
and decision makers
Bias, fairness, and data privacy
- Issues of errors, bias, and prejudice in data and assumptions used
- Data privacy and protection
- Consider the real-world consequences of using complexity science
Transparency and trust
- Limited transparency and documentation mean that application
biases cannot be evaluated to address ethical concerns

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issues have already been discussed at length by many authors (Millington et al., 2017; Pan
et al., 2021; Rand & Stummer, 2021). To explore potential pathways for overcoming the
non-technical impediments identified in this study, we utilise the existing literature where
possible for solutions. We also draw from our experience working with complexity science
to suggest possible means to address or mitigate the barriers to using complexity science in
and for policymaking.
Addressing management, cost, and adoption barriers
Among the most formidable challenges to address are those related to management, insti-
tutional capacity, and cost. These significant barriers intersect with and exacerbate many
other potential obstacles, including technical hurdles, trust issues, communication difficul-
ties, and the general acceptance of complexity science.
The limited use of complexity-informed approaches in institutional settings, coupled
with concerns related to the limited understanding of complexity science and its applica-
tion (Finegood, 2021; Otto, 2008), underscores the importance of promoting the general
science of complexity at all levels. While fostering this understanding should start at the
school and university level, it must primarily focus on institutional and government settings
(King et al., 2012; York & Orgill, 2020). Additionally, demonstrating the tangible benefits
of using complexity science while simultaneously acknowledging the limits of existing
tools can build both trust and acceptance of complexity science within the policy commu-
nity (Cosens et al., 2021).
Addressing the challenges posed by the limited capacity, skills, and knowledge neces-
sitates investment in training and capacity-building initiatives tailored to policymakers and
decision-makers. As Zukowski et al. (2019) note, practitioners and policymakers should
engage with a range of different complexity-informed methods, learn from experienced
practitioners, and identify and distribute successful case studies, demonstrating the value
of using complexity-informed approaches. To achieve these outcomes, sustained funding
and support for the research community is essential, enabling them to develop materials
and conduct case study research. Additionally, the KISS (Keep it simple stupid) principle,
which argues that models should be kept as simple as possible, should be used whenever
possible (Johnson, 2015).
Policymakers should work closely with modellers when developing an application to
inform policy. Doing so helps to build a deeper understanding of the model and build trust
in the modelling process, thereby reducing policymakers’ scepticism about the model
(Balint et al., 2017). Furthermore, as indicated by Vermeulen and Pyka (2016), the model-
lers should be involved with the policy process as early as possible and not at the end to
validate the policy. Conversely, policymakers should assist modellers by making time and
resources available. Moreover, all assumptions, parameters, and processes should be well-
documented and maintained (Vermeulen & Pyka, 2016).
Gathering successful examples of similar applications can further convince policymak-
ers of the value of complexity-informed approaches and provide guidance on how com-
plexity science can be applied practically in policymaking and decision support (Finegood,
2021; Loosemore & Cheung, 2015). Furthering policymakers’ acceptance of complex-
ity science can also be aided by clarifying confusing and ambiguous terminology (Yang,
2021) and better aligning the language of systems thinking and complexity science with
that of policy (Stewart & Ayres, 2001). By clarifying the language and making complexity

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science more accessible for non-experts, critiques that ‘complexity is too vague or concep-
tual for policy’ (Kwamie et al., 2021) might be addressed.
Applying complexity-informed approaches might lead to politically uncomfortable
conclusions (Stewart & Ayres, 2001) or unattainable goals given political or institutional
structures and limits (Levin et al., 2015). While collaborative work with policymakers can
help navigate politically sensitive outcomes, legal and institutional barriers present a more
formidable challenge. Legislative changes can be complex and time-consuming, and insti-
tutional cultures often resist change (Čolić et al., 2022; Olsen, 2009). Consequently, imple-
menting a complexity-informed intervention may necessitate planning and working within
legal boundaries from the beginning. However, depending on the problem and the solution
identified, policymakers might be convinced to pursue legal and legislative changes to facili-
tate system-level interventions that would otherwise be impossible. Alternatively, where it
is infeasible to change legislation or work within spatial or institutional boundaries, inter-
governmental and inter-institutional collaboration across multiple levels of government can
go a long way to overcome the limits of a single institution and achieve goals that require
system-wide interventions (Morçöl, 2023; Sharma-Wallace et al., 2018). Such collaboration
allows for overcoming the limitations of single institutions and achieving goals that require
system-wide interventions (Finegood, 2021; Haynes et al., 2020) and promoting adaptive
governance (Cosens et al., 2021; Sharma-Wallace et al., 2018; Young, 2017).
While models are valuable tools for testing potential policy outcomes, other options
exist. Regulatory sandboxes, instruments allowing experimentation and testing of policies
with increased tolerance for error (Tan et al., 2023: 12), offer a promising alternative, pri-
marily when used with a complexity-informed approach. Although commonly used to test
new technology or services, their application can be expanded to pilot other policies. The
resulting data can be compared to model outputs or used to update models, further refining
their accuracy.
Policymakers are also encouraged to provide investment in research and training
to alleviate the time and cost burden associated with developing and implementing
complexity-based approaches. Additionally, crowdsourcing data can be one means of
collecting data at a high volume and lower cost (Taeihagh, 2017a). At the same time,
machine learning and natural language processing can aid in collecting and process-
ing information, speeding up the process of model formulation. Similarly, standardised
modelling protocols and platforms can reduce the time and financial cost required to
build models.
Addressing barriers to communication, reporting, and acceptance of complexity
science
Despite the broad scope and multifaceted nature of challenges surrounding communi-
cation, reporting, and acceptance of complexity science, steps can be taken to address
or mitigate some of the identified issues. Several authors have identified methods to
facilitate trust in complexity-informed methods. Beyond investing in stakeholders and
policymaker training and education (Flynn et al., 2019; York & Orgill, 2020), actively
engaging stakeholders and policymakers in the application’s development and deploy-
ment from the outset can be highly effective. This participatory approach builds trust
and ownership of the application (Ibrahim Shire et al., 2020; Vermeulen & Pyka, 2016).
Additionally, the collaborative development process can demonstrate the application’s

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practical value, further bolstering support, particularly if the application has undergone
rigorous validation (Kolkman et al., 2016). Trust and acceptance can also be enhanced
by showing examples of successful applications to policymakers (Maglio et al., 2014).
From an internal validity perspective, leveraging transparency and openness can
foster trust in complexity-informed applications. To further enhance trust, the code of
a model can be made open and available (Ligmann-Zielinska, 2009) or through clear
documentation, utilising protocols such as the Overview, Design Concepts and Details
(ODD) protocol (Grimm et al., 2006, 2020) or the expanded version, ODD + D, which
includes decision-making in the protocol (Müller et al., 2013). Misuse of tools can sig-
nificantly erode trust in complexity science and its applications. Enhancing transparency
and documentation serves not only to build trust but also to limit misuse (Iwanaga et al.,
2021). However, to the best of their ability, developers are responsible for ensuring their
work is used for its intended purpose.
Finally, there is a need to develop better means of communicating the concepts of com-
plexity science and the results of complexity-informed applications. Effective science
communication and promotion are crucial for gaining acceptance in the field. However,
without adequate means of communicating the results, users and participants can be easily
overwhelmed by the amount of information. Therefore, the ongoing development of com-
munication tools and visualisation methods specifically tailored to complexity science is
essential (Steger et al., 2021; Taeihagh, 2017b). These methods should aim to clearly com-
municate aspects such as uncertainties, model assumptions and rules, and their impact on
outcomes. Calenbuhr (2020) suggests that qualitative tools, visualisations, and metaphors
associated with complexity science, such as fitness landscapes, can be effective tools for
communication. Additionally, Taeihagh (2017b) indicates that network visualisations and
metrics or visualising and understanding policy interactions and supporting policy design.
Addressing ethical barriers
Ethical concerns in complexity science for policymaking can manifest in many ways.
Researchers and modellers must take moral responsibility when developing an applica-
tion, including in the vision and use of the application (Goodman, 2016). Clearly defining
the application’s goals and vision beforehand is crucial, including assessing how it will be
used. A thorough ethical evaluation of the proposed behaviours is required if the application
is intended for human manipulation, such as encouraging certain behaviours. Additionally,
the application must rely on reliable data that has been meticulously checked for errors and
manipulated in no way. Simultaneously, ensuring data privacy, confidentiality, and protection
is paramount (Leslie, 2023). Notably, Choi and Park (2021) highlight that real-world data is
often biased, necessitating active measures to ensure that the data used is fair and balanced.
Leslie (2023) emphasises that stakeholder engagement fosters transparency in both the
process and the outcome. Transparency is amplified further when the assumptions, algo-
rithms, and processes are clearly documented and accessible. Such transparency minimises
the possibility of unethical aspects being incorporated into the final product. These efforts
can be further strengthened by adopting ‘design for values’ (Helbing et al., 2021) or ‘eth-
ically aligned design’ (Van den Hoven et al., 2015) approaches, which aim to align the
development and use of applications with ethical considerations. These are critical consid-
erations, given that policy decisions can impact a vast number of people and have lasting
consequences. Table 5 summarises mitigation strategies for addressing non-technical chal-
lenges associated with the use of complexity science in policymaking.

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Table 5 Summary of mitigation strategies to address non-technical challenges for the use of complexity
science in policymaking
Thematic challenges Mitigation strategies
Management, cost, and adoption Management & institutional
- Promote complexity science at all levels, focusing on institutional and
government settings
- Involve modellers in the policy process early and not at the end
- Provide guidance on the use of complexity science in policymaking and
decision support
- Confusing and ambiguous terminology must be clarified early and
aligned with policy language
- Politically sensitive conclusions can be navigated through collaborative
work with policymakers
- Plan for and work within legal and legislative boundaries from the
beginning
- Inter-governmental and institutional collaboration to minimise legal
and institutional barriers and limitations
- Use regulatory sandboxes to test complexity-based model outcomes
and policies to build trust
Cost barriers
- Invest in training and capacity building
- Support for the research community to develop teaching materials and
conduct case study research
- Crowdsourcing to collect data at a high volume and lower cost
Adoption and usability
- Utilise the KISS (Keep it simple stupid) principle to keep models as
simple as appropriate
- Funding and support for research to improve development and imple-
mentation of applications
- Demonstrate the benefits of using complexity science and acknowledge
its limits
- Modelers should work with policymakers when developing a model to
help build an understanding and trust in the process and model
Communication, reporting, and
acceptance of complexity
science
Limited trust in complexity science applications and methods
- Collaborative building of applications between modelers and practition-
ers to demonstrate the utility of the application and build additional
support
- Trust and acceptance of complexity science can be enhanced through
examples of success
- Trust can be fostered through improved transparency by making code
available or through clear documentation such as the ODD or ODD+D
protocols
- Increasing transparency and documentation help to ensure applications
are used as intended
Understanding and acceptance of complexity science
- Providing more training and education to educators, stakeholders, and
policymakers
- Invest in research to develop complexity science and training material
- Involve stakeholders and policymakers in participatory development
and deployment of applications from the beginning to build trust,
understanding, and ownership

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Conclusion
Our investigation initially sought to answer a fundamental question: whydoes complexity
science remain largely absent from mainstream policymaking? Utilising a comprehensive
literature review, we embarked on a journey to uncover the challenges and barriers hinder-
ing complexity science’s wider adoption. Through our review and synthesis of the litera-
ture, our investigation yielded 141 unique challenges, which we subsequently consolidated
into three overarching themes: Management, cost, and adoption challenges; Trust, com-
munication, and acceptance of complexity science; and Ethical barriers. Our exploration
revealed a complex interplay between these themes, highlighting their interconnected and
interdependent nature. For instance, issues of trust and acceptance are frequently inter-
twined with the need for data transparency, model validation, and reliable outcomes, which
fall under the umbrella of technical challenges.
While the technical barriers are undoubtedly significant, our analysis underscores the
importance of addressing non-technical issues. Communication, trust, and understanding
are crucial for fostering acceptance and utilisation of complexity-informed approaches in
policymaking. Neglecting these non-technical aspects can confine the application of com-
plexity science to specialised domains and generate scepticism among non-experts. In
turn, this can exacerbate existing challenges related to management, institutional capac-
ity, and cost. We also note that much of the discussion related to the trust in complexity
science and its applications touches on the need for reliability, validity, and predictability
of applications, which are inherently technical challenges but have consequences for the
acceptance of complexity science. As such, there is an evident tension between technical
challenges and the continued persistence of some non-technical challenges, such as trust
and acceptance of complexity science. However, even with this relationship, we argue that
there is still no guarantee that if the technical barriers were overcome, the non-technical
challenges would also be addressed, as issues like trust are essential in technology adoption
(Bahmanziari et al., 2003) and potentially have more of a long-term impact on the ease of
Table 5 (continued)
Thematic challenges Mitigation strategies
Communication and reporting
- Standardise the language and concepts within complexity science
- Improve communication of complexity science concepts through
metaphors
- Develop communication and visualisation tools for complexity science
applications and their results
- Convey uncertainty, model assumptions and rules, and how they impact
the outcomes
Ethical barriers Ethical barriers
- Goals, vision, and the application’s use should be specified before
development
- Ethical evaluation of outcomes and processes is required
- Data reliability, use, processing and protection should be handled care-
fully
- Engage stakeholders to help build transparency in the process and
outcome
- All assumptions, algorithms, and processes should be well documented
and open to scrutiny
- Use ‘design for values’ or ‘ethically aligned design’ approaches

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adoption of complexity science in and for policymaking. Therefore, if considerations such
as trust, understanding, and communication are not addressed, then the utilisation of the
complexity science will likely remain limited to specialised and highly technical domains
while generating scepticism among non-experts, as seen in the climate debate (Whitfield,
2013). These factors highlight the interdependent nature of challenges like trust, as other
non-technical issues, such as those associated with management and institutional barriers,
are also linked to trust and the utility of complexity science.
Our research makes several key contributions to the body of knowledge. First, it offers a
comprehensive and systematic analysis of the non-technical challenges hindering the adop-
tion of complexity science in policymaking, drawing upon a broader range of literature
than previous studies. Second, our study highlights the interconnected nature of techni-
cal and non-technical challenges, emphasising the need for a multi-pronged approach to
address them. Third, this study identifies several gaps in the current literature, such as the
limited focus on the non-technical aspects of application and the ethical implications of uti-
lising complexity science for policymaking. While the ethical and moral concerns related
to policy are explored elsewhere (Brall et al., 2019; Marshall, 2017), there has been little
discussion on the broader ethical considerations related to the use of complexity science in
and for policymaking. This research gap must be addressed if complexity science is to gain
trust and be used effectively in policymaking.
This research also paves the way for addressing these challenges and promoting the
broader utilisation of complexity science in policymaking. We suggest a range of poten-
tial solutions, encompassing strategies for improved communication, enhanced training
and education, collaborative model development, and the adoption of ‘design for val-
ues’ approaches. Enhanced understanding equips policymakers to utilise the tools better
when they are more readily available. Similarly, improved communication of concepts
and results to policymakers and the public is crucial for building trust in the meth-
ods and outcomes (Banozic-Tang & Taeihagh, 2022; Whitfield, 2013). Moreover, we
emphasise the need for institutional and governance changes (Morçöl, 2023) to facilitate
cross-disciplinary collaboration and the implementation of system-level solutions (Cos-
ens et al., 2021; Young, 2017).
Much work is still required to make complexity science more accessible and palatable
to those within and outside policymaking. Those wishing to address the barriers
to adopting complexity into policy should not study the issues we have identified in
isolation. Instead, like the systems we seek to understand, addressing the myriad of
challenges will require a complexity perspective, as many issues are interconnected
and require more than one approach to solve. We also reinforce the work of others who
call for the need for a critical paradigm shift in policymaking by further integrating
complexity science into the science and art of policymaking (Cairney et al., 2019;
Gerrits, 2012; Geyer & Rihani, 2010; Harrison & Geyer, 2021b; Room, 2016; Taeihagh
et al., 2013; Taeihagh, 2017b). Embracing the complex, interconnected, and uncertain
nature of the challenges we face requires a shift from traditional, linear approaches to
more holistic and adaptive strategies. Complexity science, focusing on understanding
emergent phenomena and dynamic interactions, offers a powerful lens to navigate this
complex landscape. However, before complexity science can be fully adopted into
mainstream policymaking, the challenges identified in this study need to be addressed.
While we have highlighted a few means of addressing the challenges we identified,
much work is still required to understand their details within different contexts and how
to address them best. We hope that by addressing these challenges and embracing the

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transformative potential of complexity science, policymaking will be better equipped to
address the challenges of our time.
Supplementary Information The online version contains supplementary material available at https://doi.
org/10.1007/s11077-024-09531-y.
Author contribution Conceptualisation, A.T.; methodology, A.T. and D.N.; validation, A.T.; investigation,
D.N. and A.T.; resources, A.T.; data curation, D.N.; writing and editing D.N. and A.T.; supervision, A.T.;
project administration, A.T.; funding acquisition, A.T. All authors read and agreed to the published version
of the manuscript.
Funding This research is supported by Ministry of Education Singapore AcRF Tier 1 funding support
through NUS ODPRT Reimagine Grant.
Declarations
Conflict of interest The authors declare not having competing interests.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Com-
mons licence, and indicate if changes were made. The images or other third party material in this article
are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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