=Paper=
{{Paper
|id=Vol-3804/short5m
|storemode=property
|title=What does complex adaptive systems theory mean for modelling of organisational rules?
|pdfUrl=https://ceur-ws.org/Vol-3804/short5m.pdf
|volume=Vol-3804
|authors=Jöran Lindeberg,Martin Henkel,Eric-Oluf Svee
|dblpUrl=https://dblp.org/rec/conf/bir/LindebergHS24a
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==What does complex adaptive systems theory mean for modelling of organisational rules?==
https://ceur-ws.org/Vol-3804/short5m.pdf
What does complex adaptive systems theory mean for
modelling of organisational rules?⋆
Jöran Lindeberg1,* , Martin Henkel1 and Eric-Oluf Svee1
1
Department of Computer and Systems Sciences, Stockholm University, Box 7003, Kista, 16407, Sweden
Abstract
Organisational rules and regulations are vital components of a business. However, their growing numbers,
interdependencies, and often unpredictable interactions with social agents make them challenging to manage.
Enterprise modelling has proven an effective technique for sensemaking and creating a shared understanding of
organisational structures, such as rules and goals. However, what is captured or not in a model depends on the
theory used to examine the organisation, whether implicit or explicit. Particularly in healthcare, many scholars
view organisations from the lens of Complex Adaptive Systems (CAS), rather than General Systems Theory
(GST). This paper discusses how enterprise modelling of organisational rules grounded in CAS theory will have a
different focus than if grounded in GST. Four key themes for are identified: abstraction, rule-agent interaction,
emergence, and feedback channels. Each is discussed in light of privacy regulation and healthcare practice, and
proposals are made for future research directions in enterprise modelling.
Keywords
enterprise modelling, organisational rule, complex adaptive system, healthcare, privacy regulation, legal design
1. Introduction
Organisational rules and regulations are an essential part of an enterprise. They define what should be
done and how, being a powerful means of control. An organisational rule constrains the action space
of an organisational unit. Their effectiveness has contributed to their popularity, and their number
is growing. However, as these rule systems expand, intertwined with IT systems and in constant
interaction with social agents, managing them becomes more challenging. In fact, as early as 1893,
Emile Durkheim observed that "domestic law, from being originally simple, has become increasingly
complex" [1, p. 155]. In organisational life, there are many vague, conflicting, and suffocating rules. In
fact, Max Weber, otherwise a strong proponent of bureaucracy, cautioned that an iron cage of rules [2]
could be humanity’s inescapable faith [3].
In this paper, we continue with a definition of the concept of organisational rule from a previous,
forthcoming study [4, Introduction]. An organisational rule is:
an element of guidance that constrains the action space of an organisational unit. It refers
to the overall business rather than IT-systems. It is formalised, i.e., is written and has an
official standing in the organisation it applies to. An organisational rule can be both of
external and internal origin and can have any enforcement level, from rigid enforcement
to mere guidelines.
As mentioned above, managing these rules is challenging. Fortunately, enterprise modelling has
proven to be an effective technique for sensemaking and creating shared understanding of organisational
structures, such as rules and goals [5, p. v]. But all models are simplifications and will only show
BIR-WS 2024: BIR 2024 Workshops and Doctoral Consortium, 23rd International Conference on Perspectives in Business Informatics
Research (BIR 2024), September 11-13, 2024, Prague, Czech Rep.
*
Corresponding author.
$ joran@dsv.su.se (J. Lindeberg); martinh@dsv.su.se (M. Henkel); eric-sve@dsv.su.se (E. Svee)
https://www.su.se/english/profiles/jli6887-1.620851/ (J. Lindeberg); https://www.su.se/english/profiles/mhenk-1.182179/
(M. Henkel); https://www.su.se/english/profiles/ersv6598-1.188778/ (E. Svee)
0000-0001-7806-749X (J. Lindeberg); 0000-0003-3290-2597 (M. Henkel); 0000-0003-2218-8094 (E. Svee)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
certain parts of reality. What parts will be emphasised, or invisible, depends, among other things, on
the underlying theory of organisations of the modeler [6]. Indirectly, it also depends on the theories of
the modellers that made the artefacts that the modeler is using, such as modelling languages and tools.
Any artefact will embed a substantial portion of organisational theory from these design processes.
Organisational theory can be implicit (which could be referred to as a mental model) or explicit.
However, most traditional modelling efforts are based on positivist views [6]. In 1991, Orlikowski &
Baroudi [7] concluded that information systems (IS) research is dominated by positivist perspectives,
either explicit or implicit, while interpretivism, social constructivism, and critical studies were somewhat
absent. This is problematic since certain phenomena will not be effectively studied and explained if
only one set of philosophical assumptions is used. As put by Daft & Wiginton [8, p. 187] (cited in [7]):
"If complex organizational behaviors are modelled as if they are simple, well understood, deterministic
systems, or even as stochastic systems, then the resulting models will tend to be insignificant." We are
unaware of any more recent surveys similar to the work of Orlikowski & Baroudi, but our impression
is that the dominance of positivism in IS research remains.
A widespread organisational theory, particularly in healthcare, is the theory of complex adaptive
systems (CAS) [9, 10, 11, 12]. CAS theory can be contrasted with General Systems Theory (GST) [13].
While the philosophical standpoints of CAS theory resonate with social constructivism, GST is closer
to positivism. In short, organisational theory matters for enterprise modelling of organisational rules,
and alternatives to positivism and GST need to be explored. The research question is the following.
What are the key themes for modelling organisational rules if CAS theory is applied?
The remainder of the paper is structured as follows. Section 2 contrasts CAS theory with GST,
identifying four differences relevant to modelling organisational rules. Section 3 discusses the possible
implications of identified differences for the modelling of organisational rules. Section 4 concludes.
2. Complex adaptive systems theory and general systems theory
CAS theory originally comes from the field of biology, but has also been used to understand social
systems, including organisations [14]. This includes their organisational rules and the agents, such
as organisational units, that interact with them. CAS theory [13] emphasises that the world is messy
[15], fuzzy [10], non-linear [13], and non-deterministic [13], in other words, complex. The building
blocks of a CAS are agents and constraints (also known as rules). Agents interact according to their
constraints [16] and will adapt according to feedback [17] they perceive. In a CAS, it is difficult or
impossible in advance to know exactly what the results will be of a particular decision, such as a change
of rules. Thus, effective feedback channels are essential [18]. CAS theory also recognises that agents
have agency and are part of several systems simultaneously. It is therefore demanding to know how
they will interpret and implement a new rule.
CAS theory can be contrasted to General Systems Theory (GST) [13]. There is no consensus on
whether CAS theory is a specialisation of GST or whether it is something different. In this article, we
subscribe to the view of Turner & Baker [13], who argue that CAS theory (together with Chaos Theory)
is part of Complexity Theory, and that Complexity Theory differs from GST. However, despite this
separation, Turner & Baker also recognise that there is a common ground between CAS theory and GST.
The differences are more about emphasis than apparent dichotomies. We recognise that many theories
and models cannot be categorised as clearly built on either GST or CAS theory but are somewhere in
between. Yet, to simplify the following discussion, we will refer to them as two separable theories.
So, what are these differences in emphasis between CAS theory and GST? As the name implies, CAS
theory embraces dynamic complexity. Compared to GST, CAS theory would, therefore, be more inclined
to recognise that theoretical models of a system have been heavily simplified, at least if a complete
socio-technical system is to be understood. Another option would be to, on the contrary, focus on small
details of the complex reality, but from a CAS perspective this is an unattractive option since it forsakes
the interconnections and wholes.
GST would assume that most systems are, in the words of Simon [19], "nearly decomposable". In
contrast, a CAS has fuzzy and "folded" boundaries, meaning that its components tend to be parts of
other systems, or at least interact with others, making meaningful decomposition harder. When systems
overlap, rules collide [20]. There is usually more than one rule system at play in a particular situation,
including the internal rule system of the agents involved, each with their mental models and goals.
How an actor will decide to interpret and implement a specific rule depends not only on the rule, but
also on the actor and what other rule systems are at play in a particular situation. While GST would
tend to assume that rules are both given and followed, CAS theory would instead focus on how rules
are constantly recreated and modified by agents.
Although GST may be better suited to represent easily interpreted rules, CAS theory provokes more
interest in the dynamics of more complex rules. When an organisation strives to be legally compliant,
it may only know if it succeeded once a final court judgement says so, perhaps after months or years
of legal process. This uncertainty has been identified as a considerable obstacle to organisational
development in Swedish healthcare [21].
Compared to GST, CAS theory is even more geared towards describing the phenomenon of emergence:
how the whole can have properties beyond the aggregation of the parts. In the above, we have explained
why agents’ behaviour in a CAS is unpredictable. At the system level, the behaviour becomes even more
erratic. The unpredictability of outcomes makes effective feedback loops essential. Enterprise models
must also include feedback channels that ensure that the emergent and often unexpected consequences
of, for example, a rule change, are quickly brought to the attention of stakeholders, particularly decision
makers [18].
The above discussion can be distilled into the following focus areas that help in the differentiation of
CAS from GST:
1. Abstraction (to be able to represent wholes rather than details)
2. Rule-Agent interaction (rather than interaction just between rules)
3. Emergence (properties of system structure and behaviour rather than properties of individual
rules)
4. Feedback channels (learning what happens rather than trying to foresee what will happen)
When describing rules that govern a CAS, such as when creating enterprise models, it is essential to
cater to the above areas.
3. Organisational rules modelling grounded in complex adaptive
systems theory
In the previous section, we identified four focus areas for the sense-making of organisational rules
grounded in CAS theory. In this section, we will discuss what this means in practice for modelling
organisational rules.
3.1. Abstraction
Both GST and CAS theory subscribe to the notion that "all models are wrong, but some are useful" 1 ,
but from a CAS theory point of view capturing a high degree of system detail appears even more futile.
Instead, to be helpful, modelling grounded in CAS theory would focus on higher levels. In modelling,
abstraction is commonly achieved through hierarchies. However, Krogstie [6] has observed that how to
model rules as hierarchies needs to be studied more. Imagine, for example, the tremendous impact of the
General Data Protection Regulation (GDPR) on innumerable organisations in the European Union and
worldwide. GDPR has caused many other, more detailed, rules to be created, and it is also hierarchically
superior to many rules.
Thus, power-subjection and cause-effect are needed to model a CAS. An example of a power-subjection
relation is that the legislation of member states must comply with EU legislation, or that the rule of an
1
Quote attributed to the British statistician George E.P. Box.
organisational unit must align with company-wide rules. An example of cause-effect relation would be
that GDPR invites the member states to complement it with their own more detailed legislation. At
least in Sweden, this means that the parliament makes a law which is complemented by a government
ordinance, supplemented by national authorities’ regulations.
It should be noted that the hierarchies for abstraction that are discussed above do not necessarily
correspond to the principles for deciding about rule precedence. (One of these principles is lex specialis,
according to which the most detailed rule should be the one in effect. [22])
A concrete contribution to enterprise modelling in this area could be to design patterns for building
various types of hierarchies among organisational rules. Patterns could be used for representing
hierarchies between types of rules, for example, that a company policy must not contravene regulations
by public authorities, and hierarchies established between individual rules, e.g., if law X explicitly states
that law Y prevails in case of conflict. Patterns like this could give the modeler a toolbox of abstractions
to use when modelling rules.
3.2. Rule-agent interaction
As noted in the previous section, rules collide and will not always be followed. Also, rules are normative
models, and like any model, they are imperfect imperfect simplifications, unable to cope with reality in
all its complexity.
From a perspective of CAS theory, it is important to model how rules are constantly recreated through
how agents decide to interpret, implement, and enforce them (or not) in different situations. For example,
in a fourth-coming study, we modelled the legal enforcement of GDPR provisions in a Swedish Hospital.
The designated supervisory authority decided on a million euro administrative fine, partly confirmed
by a primary instance court but ultimately overruled by a secondary instance court. In other words, the
law’s wording was just the story’s start, and the actions of institutional agents wrote the rest. Defining
the rule is a first step, but, as seen from this example, interpreting and enforcing rules requires a whole
system of public authorities at different levels, who may not always agree with each other.
A tangible contribution to enterprise modelling in this area could be to support the modelling of not
only the rules themselves but also their relation to agents that create and enforce them.
3.3. Emergence
Emergence [23], is the phenomenon where patterns arise out of a multiplicity of simple interactions.
3.3.1. Structural emergence
Emergent structural properties of a rule system would be the patterns formed by the interconnections
between rules and between rules and agents. These structural properties may be more relevant than
the examination of individual rule to the sense-making of a complex system. Some of these patterns of
interconnections between rules may be formulated and effectively represented in a model. For example:
Who typically creates (owns) the rule? What type of rule is present (constitutional, legislation, industry
standard, company-wide internal rule, etc.)? What typicial citations between rules are there? Other
patterns of interconnections are also important but more complicated to define. For example: What
types of actors are regulated by the rule? In what situations? What goals does it support, or oppose?
Once these interconnections have been identified and represented in an enterprise model, they can
be translated into properties that describe the whole. In the context of GDPR and healthcare, one
relevant relation would be how rules cite each other. Other examples would be actors creating rules,
roles defined by these rules, actors having these roles, and actors enforcing the rules.
3.3.2. Behavioural emergence
An example of behavioural emergence is when several birds move as a flock. Behavioural emergence,
as explained by Juarrero [24] is the result of positive feedback loops. At some point, it becomes relevant
to describe the properties of the flock rather than each bird. In the context of GDPR and healthcare, it
would be interesting to represent how collaborative a system is. Rigid privacy regulations obviously
have the potential to make information exchange difficult. Another barrier to collaboration could be
the lack of monetary incentives. In Swedish healthcare, when caregivers report back to the national
authorities to receive funding, it has, for example, been observed that there is no code for "collaboration".
Enterprise modelling in this area could build on existing contributions within behavioural modelling
[6], e.g., state charts and system dynamics, as well as the studies of the dynamics of rule networks by
Zhu and Schulz [25]. For structural emergence, there could be a need of defining typical structures of
rules; this would make it easier to categorise and understand a rule system.
3.4. Feedback channels
The unpredictability of the outcomes makes effective feedback loops essential. Enterprise models must
therefore also include feedback channels ensuring that the emergent and often unexpected consequences
of, for example, rule change, are brought to the attention of the stakeholder, particularly the decision-
makers. For example, a study of general practitioners in the healthcare sector in the United Kingdom
concluded that it is nearly impossible for them to adhere to all applicable medical recommendations
[26, 27]. Only one of the recommendations, a routine for how general practitioners should act when
visited by an obese person, was calculated to, if thoroughly implemented, take up around 15 % of the
total work time of all general practitioners in the UK. It appears safe to assume that the practitioners
only partially followed this routine. It would probably be helpful if the decision-makers of such routines
learnt about their consequences. Of course, feedback could reach them in many ways, but the probability
of timely and correct feedback increases if feedback channels are consciously designed and represented
in enterprise models. Returning to the issue of GDPR, EU lawmakers could not foresee all consequences
of GDPR, and also within a small or medium-sized enterprise it can be difficult for a rule-maker to
foresee the impact of their decisions.
A concrete modelling contribution in this area could be to represent: a) what a rule change aims
to achieve, b) through which channel rule-makers expect to be informed about its consequences, c),
with what delay, and d) a comparison between the stated objectives and the actual consequences. Such
contribution could draw from goal modelling, scanning [28] and system dynamics [18].
4. Conclusion
In this paper, we have emphasised how enterprise modelling is grounded in organisational theory,
either implicit or explicit. We then identified several differences between GST and CAS theory. While
the former is associated with positivism, the latter is closer to social constructivism. We then discussed
what each identified difference could entail for modelling organisational rules, using examples of
how privacy regulation affects healthcare. In conclusion, there is a need for an increased focus on
(1) managing abstraction through modelling rule hierarchies, (2) how agents interact with rules, (3)
emergent, system-wide, properties, and (4) feedback channels. In these areas, there are opportunities
for contribution to enterprise modelling.
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