# Systems-of-Systems: In Need of a Paradigm Shift?

Jakob Axelsson

► To cite this version:

Jakob Axelsson. Systems-of-Systems: In Need of a Paradigm Shift?. 2026. hal-05484824

HAL Id: hal-05484824

https://hal.science/hal-05484824v1

Preprint submitted on 30 Jan 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
# Systems-of-Systems:
In Need of a Paradigm Shift?

Jakob Axelsson
Mälardalen University
jakob.axelsson@mdu.se

2026-01-29

## Abstract

The systems-of-systems (SoS) field has, over the last few decades, progressed from initial problem awareness, through conceptualization and characterization, into maturation and codification in engineering standards. This has resulted in knowledge that helps in resolving practical complex systems problems. However, research challenges remain that appear difficult to handle within the current practice. This paper looks at the development of SoS engineering through the lens of Kuhn's theory of scientific revolutions. It summarizes some of the key features of the current SoS engineering paradigm and identifies a number of anomalies in it. Based on these anomalies, a new SoS paradigm is suggested, which builds extensively on systems theory, cybernetics, and complexity theory. It views an SoS as a sociotechnical situation, and its elements as agents with different world models. The agents interact in dynamic heterarchical networks, and the SoS is engineered from the inside by its participating agents. The paper thus contributes by pointing out a potential direction in which the field could develop to provide enhanced methods suitable for a broader range of complex SoS applications.

## 1 Introduction

Systems-of-systems (SoS) can informally be described as groups of independent constituent systems (CS) that interact to achieve things they cannot achieve on their own [1]. Although the term was already used in the 1950s [2, 3, 4], the SoS concept in its present meaning emerged in the 1990s. This was due to an increasing possibility of information exchange driven by advances in telecommunication and the massive expansion of the Internet. Initial SoS applications were primarily within defense and aerospace, such as network-based defense and coalition warfare. Other applications followed, including transportation, energy, crisis management, construction, infrastructure, healthcare, and smart cities [5, 6, 7, 8].

Within the defense sector, systems engineering (SE) plays a key role in the development of requirements, concepts, and architectures, and as a basis for acquisition [9]. Recognizing that SoS differ from the integrated products for which SE was originally conceived, specialized SoS engineering (SoSE) approaches were developed. These were based on SE, but adapted to the evolutionary development and distributed management and control present in SoS.

The challenges found in emerging SoS applications also stimulated the foundation of an SoS(E) research field around the year 2000. A decade later, the field had evolved into its own discipline and community, with dedicated publication venues.

### 1.1 Objective and Contribution

Given that SoS(E) has been an established research field for several decades and has expanded into many new application domains, this is a good time to step back and investigate whether the basic underpinnings of the field are still valid or if they should be modified. Such an evolution of a research topic is not unusual, and has been investigated in detail in Kuhn's theory of scientific revolutions [10].

In this essay, I investigate possible future advances in the SoS(E) research field by applying Kuhn's theory to its development, starting with the initial conception of the field through to its current status. Based on an identification of weaknesses in the current SoS(E) paradigm, a main contribution of the paper is an attempt to formulate an updated paradigm that builds more on systems theory, cybernetics,
and complexity theory than on SE. This opens up a number of new perspectives and research questions that can drive further advancement in the field.

## 1.2 Paper Overview

In the next section, Kuhn's theory about the progress of science is summarized. Then, in Section 3, this theory is used to frame the evolution of the SoS(E) field and its current paradigm. In Section 4, weaknesses and anomalies in the current paradigm are discussed. This leads to the tentative formulation of an updated paradigm in Section 5, including the identification of several research topics based on the new paradigm, which can stimulate further development. In the final section, the conclusions are summarized.

# 2 Scientific Paradigms and Revolutions

Kuhn's seminal work, first published in 1962 and enlarged in 1970 [10], provides a theory about the nature of progress and development in science. His study is based on historical accounts of events in the natural sciences. It models scientific evolution as episodic and discontinuous rather than cumulative, as it had previously been described. In this section, the main ideas of his theory are summarized as a foundation for applying it to the SoS(E) field.

## 2.1 Key Concepts and Phases

A key concept in Kuhn's work is the *paradigm*, which is described as the set of vocabulary, theories, rules, methods, techniques, and values that are shared by a certain scientific *community*. Most of the time, the community works within the paradigm, carrying out *normal science*. In this phase, researchers attempt to solve various problems that can be expressed within the paradigm, and their solutions lead to minor adjustments and extensions of the paradigm.

Over time, phenomena might be discovered that are difficult to reconcile with the paradigm. Such *anomalies* can result in a *crisis*, which leads to the formulation of multiple new theories that attempt to better explain the anomalies. These new theories challenge the current paradigm and compete against each other. If one of them gains traction in the community, it can lead to a *revolution*, where a new paradigm is adopted. This then allows the community to move into a new phase of normal science, but now with a different focus.

The main phases of Kuhn's model are illustrated in Figure 1. It also includes the pre-science phase, where a research field and community are starting to form but are not yet established. Often, the pre-science phase takes place within other, existing communities from which the new field spins off.

## 2.2 The Nature of Paradigms and Revolutions

Several of the concepts mentioned in the theory require further discussion. At the center is the paradigm, which goes far beyond just a theory. It is a certain way of looking at the world, and is based on a specific conceptualization and language. It thereby shapes what can be expressed in theories and reasoned about. It also determines what a scientist observing the world will be searching for, and what methods and instruments are used. Alternative paradigms are usually incommensurable, meaning that they involve fundamentally different worldviews and cannot be objectively compared.

Normal science is an activity where open questions within a given paradigm are explored, including the determination of what facts are significant, the matching of facts and observations to theory, and the articulation of a more complete theory. However, the basic premises of the paradigm are not questioned, and hence, normal science rarely identifies novel insights, concepts, or phenomena. Rather, as Kuhn phrases it, normal science solves puzzles with a given set of pieces. The choice of paradigm and concepts

<figure><img src="image_2.png" /><figcaption>Figure 1: Main phases in Kuhn's model of scientific revolutions.</figcaption></figure>
entails a concentration on certain aspects, which is beneficial, since it makes normal science efficient and focused. However, it is also detrimental since it ignores other aspects.

Larger breakthroughs start with the identification of anomalies, i.e., observations that disagree with predictions made by the models produced by normal science. Often, there is initially a resistance to changing a paradigm that has proven useful, leading to a tendency to ignore the anomalies. The paradigm must again and again prove to be insufficient before a serious search for other alternatives starts. Once this happens, an initial step is to try to isolate the anomalies, make them more precise, and give them structure.

When a proposal for a new paradigm appears, researchers in the community have to choose between them. The new paradigm needs to attract a sufficient number of researchers and open up new interesting problems for investigation. It must explain some of the anomalies of the previous paradigm, but not necessarily cover all observations from the start.

The transition to a new paradigm also requires a discussion about what problems are worthy of attention and resolution. This question cannot be resolved within the scope of normal science, since it includes a notion of subjective value. How the community ends up adopting or rejecting a certain paradigm is largely a question of social dynamics and consensus-making.

## 2.3 Tangible Signs of Communities and Phases

The phases in the evolution of a field are reflected in publication patterns and are thus evident in bibliometric data. While the area is forming, original research articles provide new evidence and lead to the formulation of the paradigm. Conferences and journals emerge as venues for the dissemination of results and discussions. Key researchers can be identified based on their publications and citation patterns, which also reveal collaborations. Changes in venues or publications can, in retrospect, indicate when the transition between phases occurred, although transitions are normally gradual over several years.

Once the paradigm is established, textbooks start to appear. These books, rather than the original articles, become the basis for introducing new members to the community and make the paradigm accessible to practitioners. The textbooks further articulate the paradigm by proposing exemplary solutions to different concrete problems. These can be used by students and practitioners as starting points for solving new problems.

# 3 Current SoS(E) Paradigm

I will now turn to an analysis of how the current SoS(E) paradigm developed, from its origins in systems engineering (SE) until today. The first purpose of this is to test whether Kuhn's theory applies to this field. The exposition therefore follows the main phases of the theory as illustrated in Figure 1. This also contributes to the second purpose, which is to show how the historical development led up to the current paradigm being formulated as it was, with an indication of some alternative choices that could have been made. The final purpose is to establish the main traits of the current SoS(E) paradigm, which will be the basis for the following parts of the paper.

As Kuhn recommends, bibliometric data is used as a major source of information, as are the scientific venues where the subject is discussed. Such data is presented in the first subsection, followed by subsections on the progress from a pre-science within SE, through crisis and revolution, to the establishment of a new paradigm for SoS(E) normal science.

## 3.1 Bibliometric Analysis

The main sources for the bibliometric analysis were the Scopus and Web of Science (WoS) databases, which index many of the most relevant publication venues. In addition, data from Wiley was included, since the other databases do not include publications at the INCOSE International Symposium, which is a significant event in the community. The search string was simply "system-of-systems" (and its variants with respect to spelling and number) within title, abstract, or keywords. This resulted in 8,555 publications from Scopus, 4,819 from WoS, and 205 from Wiley, as of September 2025. After removal of duplicates, the total number was 10,220.

With a broad search string, there is a risk that some non-relevant sources are included. To assess the magnitude of this, a manual review was performed on a randomly selected subset of 100 papers. Of these, 14 papers were deemed outside the scope of the study, and it can be confidently estimated that
<figure><img src="image_3.png" /><figcaption>Figure 2: Number of system-of-systems publications in the Scopus, Web of Science, and Wiley databases per region and year for the period 1990-2024.</figcaption></figure>

7-21% of all papers in the set are faulty. However, since the objective here is to paint a broad picture of the development of the SoS field, those false inclusions were assumed to have little effect on the overall conclusions and were therefore kept.

Figure 2 shows the publication frequency over time. The graph also indicates the major regions from which the publications originate. The regions shown are North America (mainly the USA), Europe (with major contributions from the UK, Germany, France, Italy, Sweden, Austria, and the Netherlands), and Asia (dominated by China). Whereas the USA was the epicenter in the early years, during the past decade, Europe has contributed around half the publications each year, with the USA and China having about a quarter each.

## 3.2 Pre-Science Phase

There were only scattered SoS(E) publications before the early 2000s, but still some key references emerged during the 1990s. Many of the results appeared at the INCOSE International Symposium, indicating that the discussions on the subject took place within the SE community. This is a community with an extensive exchange between practitioners and researchers, and this spirit of close relation between science and applications has remained within SoS(E).

A major theme of the phase, as seen in the publications, was an emerging insight that an SoS differs enough from an integrated system to require a specific concept. However, it was not questioned whether SE is the right approach to deal with SoS, but the focus was on how to perform SE for SoS. This matches Kuhn's description of the pre-science phase, where a research community is starting to form, but an independent field is not yet established.

During this phase, the publications were predominantly from the US and from the defense sector. An example is Eisner et al., who discuss the rationale for SoSE based on separately acquired but interdependent systems [11]. The main recommendation is to focus on integration-oriented activities of the SE process. Another example, illustrating the topics of the time, describes network-centric warfare challenges [12].

The one contribution from this period that has really stood the test of time is Maier's work on architECTING principles for SoS [13, 14]. The papers' two main contributions have remained the cornerstones of the SoS(E) paradigm. The first is the characterization of SoS, stating that CS have operational and managerial independence, and that the SoS shows emergence, evolutionary development, and geographical distribution. The second is the classification of SoS into the types directed, collaborative, and virtual, based on centrality of control and agreement of objectives among CS.
## 3.3 Crisis and Revolution

In the years around 2003 to 2007, several signs indicate that SoS(E) is attempting to separate itself from its roots in ordinary SE. This can be seen in the bibliometrics, with a sharp increase in the number of publications, and the establishment of dedicated venues, such as the IEEE System of Systems Engineering Conference in 2005 and the InderScience International Journal of System of Systems Engineering in 2008. At the same time, established SE venues such as the INCOSE symposia and journals retained SoSE within their scopes.

Compared to Kuhn's description, this phase is characterized by the identification of anomalies that are difficult to reconcile with the current paradigm. This leads to the formulation of multiple new theories that attempt to better explain the anomalies. These new theories challenge the current paradigm and compete against each other. If one of them gains traction in the community, it becomes the one defining the new normal science. Also, there is in this phase a discussion about what research topics are worthy of investigation.

These characteristics can also be recognized in SoS(E) during the period. The discussion transforms from how to apply SE to SoS, to now focusing on the differences between SoSE and SE. This appears to indicate a spreading insight that it is difficult to apply SE to SoS, due to fundamental differences. These differences can be seen as the Kuhnian anomalies, and an early attempt to enlist them was provided by Keating et al. [15]. They highlighted that SoSE is about multiple integrated complex systems rather than single systems; the objective and expectation is a satisficing initial response rather than an optimized solution; the problem is emergent with pluralistic goals, rather than well-defined with unitary goals; and the boundaries are fluid. All this indicates that the approach should be more of a methodology than a process.

Another influential contribution at the time was the SoS characterization of Boardman and Sauser [16], which focuses on autonomy, belonging, connectivity, diversity, and emergence. Although these characteristics have some similarity and overlap with those of Maier, the focus is more on understanding SoS than on engineering them. The approach is thus closer to systems theory and thinking than SE.

As predicted by Kuhn, the anomalies lead to different reactions among the researchers. Some argued that the SoS concept makes no sense and that the ordinary system concept is sufficient [17]. Others acknowledged the problems encountered, but proposed somewhat different and competing approaches and terminology [18]. An example of this is ultra-large-scale systems [19], with many similarities to SoS but focusing more on highly distributed, societal and sociotechnical situations.

The community also engaged in discussions on what topics are worthy of research. As an example, Maier provided research challenges for collaborative SoS, including sociotechnical interactions based on people's preferences; dealing with description and analysis of the SoS macrolevel; and handling sequential decision-making under severe uncertainty [20].

## 3.4 Normal Science Phase

From 2008 onwards, the publication rate in SoS has stabilized, but there are still interesting patterns to be observed. The initial US dominance was broken, and more contributions started to come from Europe, partly due to the establishment of several large EU-funded research projects. A bit later, there was also a significant increase in publications from China. While China and, to some extent, the USA still focus on defense, the application spectrum in Europe is much broader and includes many commercial sectors governed by market forces rather than acquisition. This indicates that just as digitalization and communication technologies transform defense, they also have similar effects on business models [21].

Kuhn characterizes this phase as working within a paradigm, where the topics of research are largely pre-determined. Activities can lead to minor adjustments of the paradigm, but the foundation is not questioned. The paradigm provides the lens through which researchers look at reality, and their vision is restricted to what appears in that particular view.

There are also in this phase of SoS(E) progress clear connections to what Kuhn describes. There are plenty of adjustments and extensions made to the paradigm, as exemplified by the identification of an acknowledged prototype [22] that complements Maier's directed, collaborative, and virtual archetypes.

Textbooks also appear, where the earliest were edited by Jamshidi [23] and Luzeaux and Ruault [24]. However, to some extent, these books are more snapshots of the ongoing research rather than a single, comprehensive view of the field, as they are compilations of chapters by separate authors.

As the literature body grows, the need appears to consolidate previous work in the form of literature reviews. An early example is by Gorod et al. [25], summarizing the historical development of the field to
that point. Later, SoS subtopics such as architecture were summarized in secondary [26] and eventually tertiary [27] systematic literature reviews.

Major consolidating efforts also appeared outside academic publications, which illustrates the applied nature of the field. Starting from 2011, Henshaw and Dahmann have been editors of an SoSE section of the SE Body of Knowledge [28], and its content has evolved with the progress of the field. A lasting achievement was the subsequent publication of a set of ISO/IEC/IEEE standards on SoSE in 2019 [29, 30, 31].

## 3.5 Prevailing SoS(E) Paradigm

The SoSE standards are based on the generic 15288 standard for lifecycle processes of a system-of-interest [32], a standard which explicitly assumes an SE approach. Among the SoSE standards, 21839 applies 15288 with an individual CS as the system-of-interest [29], whereas the system-of-interest of 21840 is the SoS as a whole [30]. The third standard, 21841, provides a common taxonomy for SoS [31]. The content of these standards is also summarized in INCOSE's official description of SoSE [1].

Given the rigorous process of establishing standards, they are authoritative sources of what can be considered the current SoS(E) paradigm. In particular, 21840 provides a fairly rich and succinct summary of the key research results on which it is based and gives a comprehensive account of the creators' view on SoS(E). The standards can be summarized by a few of their key definitions:

**System-of-systems:** a set of systems and system elements that interact to provide a unique capability that none of the CS can accomplish on its own.

**Capability:** a measure of capacity and the ability of an entity (system, person, or organization) to achieve its objectives.

**Constituent system:** an independent system that forms part of an SoS.

**System-of-systems engineering:** a process of planning, analyzing, organizing, developing, and integrating the capabilities of a mix of existing and new systems, including inter-system infrastructure, facilities, and overarching processes into an SoS capability that is greater than the sum of the capabilities of the CS.

The standards also rely heavily on Maier's characteristics and the commonly used SoS archetypes [13, 22].

# 4 Anomalies in the Current Paradigm

In the previous section, it was described how a full circle in Kuhn's model, as depicted in Figure 1, led to the establishment of SoS(E) as a new normal science partly separated from its roots in SE, and eventually manifested in a set of standards. A part of this development was the identification of anomalies in applying standard SE practices to SoS.

As with every paradigm, choices were made about what to include or not, as governed by the fundamental viewpoint taken, which for SoS(E) was firmly based on SE. Therefore, some characteristics identified already from the outset were not included since they did not align with the chosen worldview, which also means that certain anomalies remain.

Kuhn's model predicts that further crises will occur and lead to the questioning of the prevailing paradigm, and such revisions are natural ingredients of science. In this section, some of the anomalies and issues that may indicate the need for reconsidering the established SoS(E) paradigm are discussed.

## 4.1 Mindset

Despite SoS(E) having been established as a discipline in its own right, the current paradigm, as manifested in the standards, is best described as the application of SE to SoS. This is obvious from the approach taken, namely, to base the standards on the SE-oriented 15288. However, it is in stark contrast to some of the learnings from the formative years, such as Keating et al. [15], who emphasize that SoS problems need to be addressed with a methodology rather than a process. The 15288 is fundamentally about processes, and thus appears to be a questionable foundation. This is also supported by work in the soft systems field, indicating a fundamental difference in approaches between engineered systems and complex sociotechnical systems, where the latter requires an open and investigative approach [33, 34].
To deal with these anomalies, a new mindset is necessary, where the SoS is not looked upon primarily as an engineering problem for which a solution is to be designed following a specific process. Rather, a systems methodology is needed that can support decision-making under severe uncertainty [20]. This methodology needs to leverage methods also from systems thinking to appropriately understand the SoS [16].

This does not mean that engineering is useless, but it has its place mainly at the microlevel, in the design and adaptation of CS to function within the SoS. Finding effective approaches to integrating CS in an SoS remains a pain point [35]. On the macrolevel, other approaches are needed, but the paradigm does not clarify what principles should be used [35].

## 4.2 Conceptualization

As Kuhn points out, the conceptualization is a decisive part of a paradigm, since it sets boundaries for what can be expressed and reasoned about. The lack of conceptualization has also been pointed out as a weakness of the SoSE paradigm [36].

The key concepts defined in the standards, as described in Section 3.5 above, have two main issues. The first is that there are very few concepts, and this is insufficient for capturing complex real-life SoS situations. More concepts are needed to deal with different roles, relations, structures, and drivers over the lifecycle. The second issue is the terminology itself, where each item's definition is disputable when it comes to its applicability to SoS in general. These ingredients will now be dissected in detail.

### 4.2.1 SoS Concept

The SoS definition is neglecting the key aspect that the notion of a system is subjective [37]. The subjective nature of systems is acknowledged in the 15288 standard, which introduces the term system-of-interest and thereby makes the observer explicit [32]. In the SoSE standards, a certain SoS is assumed to be given. This can be justified for directed and acknowledged SoS where there is a strong central authority that can impose its view of the SoS onto other actors. However, in a general situation, each participant can have a different view on the purpose of the SoS. Some may not even think of it as an SoS, or may be part of several SoS simultaneously. They can also have different views on the SoS boundaries, and these can be fluent [15].

### 4.2.2 Capability Concept

The SoS definition emphasizes the purpose of achieving a unique capability. This is colored by military jargon and would not be considered the natural expression in commercial sectors. Also, the whole idea of an agreed-upon purpose is against the definition of virtual SoS [31]. It is not difficult to find situations where systems choose to collaborate within the SoS just for their own individual microlevel benefit, rather than to provide some macrolevel capability [16].

The term capability also suffers from a lack of precision. It is an abstract concept that is useful for a high-level understanding of what a system can do, but using it operationally is far from trivial. Often, a capability is thought of dichotomously—either you can do something or you cannot. However, in reality, what a system can do depends on external circumstances, the state of the system itself, available resources, and the details of what is to be achieved [38].

### 4.2.3 CS Concept

A CS is defined as an independent part of the SoS. As has previously been described, the SoS boundary is open to individual interpretation, and what it means to be part of an SoS is hence less obvious than for a physical, integrated system. In fact, parthood is a relation between the CS and the SoS, and this relation resides in how the CS views the SoS.

The focus on CS hides the question of whether there can also be other parts of the SoS. Although the standards mention (in a note) infrastructure and system elements to facilitate interactions between CS (sometimes referred to as mediators [39]), this topic is not discussed in much detail, and proper definitions are not provided [29].

There are also elements in the SoS that are social in their nature, namely the owners, developers, and operators of both different CS and of the SoS as a whole [40]. Sometimes, these actors form organizational constellations, such as committees or boards, that act together in management and governance. The paradigm does not clarify whether these actors are elements of the SoS or external to it.
The standards' definition focuses on the CS as a part, but the evolution of the SoS implies that participation is not static. Rather, a system can at different times have varying relations to the SoS. This includes being ignorant of the SoS, being prepared to join, having joined but not currently participating, or being an active participant [41]. The relations can change in either direction, and a CS may also leave the SoS. The onboarding of new CS has been studied from a technical perspective [42], but there are also questions related to business and legal aspects.

The term independent is also problematic and not clearly defined. In fact, independence is not a property of an entity, but a relation between two entities. Thus, it is insufficient to say that a CS is independent, but it must be made explicit what the other entity of the relation is. The most natural other entity would be a central controller that is the tangible representative of the SoS as a whole. However, only directed and acknowledged SoS have such central controllers, and it remains an open question what independence means in a collaborative or virtual SoS. In addition, the independence or autonomy of the CS is balanced with the choice to belong and connect to other CS [16], which means that there is a mix of independence and interdependence whose details must be grasped to properly understand the SoS and its dynamics.

### 4.2.4 SoSE Concept

As discussed in Section 4.1, it can be questioned if engineering is at all the right term for describing how an SoS is conceived and evolves. This also goes for the fundamental element of the definition, which states that it is a process. As pointed out by Keatings et al., it is more useful to think about methodologies than processes to deal with the complex, sociotechnical situations that SoS represent [15]. In the words of Jackson and Keys, SoS are systemic and pluralist, meaning that they are complex and with many decision-makers. For this, soft systems methods are needed rather than hard engineering [34].

In conclusion, a revision is needed of the language used to speak about SoS. It requires adjustments to current terminology as well as finding a richer set of concepts with clear definitions. Those concepts should include a broader range of SoS element types, including social ones, and be more precise about the nature of relations between the elements.

## 4.3 Classification

In the current paradigm, the Maier-Dahmann archetypes [13, 22] are the basis for classifying different types of SoS. These archetypes are based on the centrality of control and agreement on objectives among the CS. However, it is difficult to apply them to a specific SoS and arrive at a clear conclusion about which prototype applies [43]. This is not surprising, given that SoS have fluent boundaries and pluralistic goals [15]. Depending on how you choose to define the boundaries and what abstractions you choose to describe the SoS and its constituents, you are likely to end up with different classifications. The delimitation of the system is fundamentally a subjective choice, and is dictated by the interests of the observer [37].

The centrality of control of an SoS relates closely to how governance and management structures and processes are defined. However, centrality of control is an insufficient characterization since it is not uncommon that there are several different control structures in place in the same SoS, dealing with different aspects. Characterizing SoS governance is thus an anomaly of the current paradigm [36], and finding effective collaboration patterns remains a pain point [35].

Other approaches to classifying SoS have been proposed, such as the review by Cook and Pratt [44]. By incorporating no less than nine dimensions, each with 3-5 possible values, they acknowledge that a much richer framework is needed for capturing the heterogeneity of an SoS.

A further problem is that the classifications do not provide much support for how to act. Ideally, a classification should lead to an identification of some routes ahead, such as design patterns to be used to achieve particular effects [45, 46], but such guidance is largely absent in the paradigm.

## 4.4 Modeling and Analysis

To understand what actions are needed in an SoS context, both during development and operation, a proper description of the situation is needed, which makes modeling a key activity. A clear conceptualization, as discussed in Section 4.2 above, is a prerequisite, and it supports human understanding of the SoS. However, the models must also be suitable for qualitative and quantitative analyses that answer important questions for deciding on appropriate actions. For this, the current paradigm does not offer much support.
A major aspect of SoS modeling is the interplay between the macrolevel and microlevel, possibly with inclusion of various mesolevel structures. This points to the need for multi-level modeling approaches [36]. At the macrolevel, it is a recognized challenge to describe and analyze the SoS [20]. Similarly, at the microlevel, there is a lack of understanding of the consequences of CS interactions [36]. The simultaneous autonomy and interdependence of CS is a challenge to address [35], as are the emergent effects it has on the macrolevel [36].

In many cases, the modeling and analysis focus on the operational phase where the SoS has reached a mature steady state. This is reasonable when the SoS already exists, and the objective is to improve its functioning or performance. However, it does not address the creation of new SoS, such as during digitalization. In those cases, the SoS goes through phases of establishment, growth, and eventually reaches the steady state [47]. During those phases, the characteristics change considerably as the size of the network expands with more CS. This is due to effects such as Metcalf’s law, where the value of collaboration opportunities grows faster than linearly with the number of participants [48].

Many suggestions have been given for SoS analysis techniques, and some of these are detailed in [49]. However, the conceptualization provided in the SoS(E) paradigm is insufficient to support analysis, and many of the methods and tools need to make further, arbitrary assumptions. There is no consensus on what the standard analysis techniques should be within the paradigm, and this can be seen as an indication that the community has not agreed upon what questions are worth studying. The key question to ask is: Who will use the results of SoS modeling and analysis, and for what actions?

## 4.5 Application Bias

As discussed in Section 3, the SoS(E) paradigm grew out of SE, primarily as practiced in the US defense sector. Many of the results that made their way into the paradigm are thus biased towards defense applications. This bias appears in the emphasis of acquisition and a requirements-driven approach to development. It also appears in the sometimes tacit assumptions of centralized control and common goals, which presuppose a dominant keystone actor in the SoS.

Although defense remains an active SoS(E) research area, in modern days, a much broader range of applications is considered, in particular in European research. In commercial applications, other business models than those based on acquisition are used, and development is increasingly carried out using agile principles. These SoS are better characterized as collaborative or even virtual than directed or acknowledged, as is the case in defense. To make SoS(E) work in this broader context, it cannot be based on assumptions derived only from one application domain, but the characteristics of SoS in general have to be embraced.

It is also questionable if the current paradigm is sufficient even for defense. A survey among practitioners primarily in the US defense sector elicited seven "pain points" in applying SE to SoS [35]. The results touch upon many of the anomalies discussed earlier in this section. A further aspect is the extension of defense beyond the military. In a total defense doctrine, all activities preparing society for war are considered, both military and civilian. Numerous agencies, local authorities, civil organizations, companies, and individuals contribute, and the defense effort becomes by necessity more collaborative than directed.

The problem of application bias and the anomalies it leads to is connected to Kuhn's discussion about what questions are important to address and how this governs the choice of paradigm. In the defense sector, for which the prevailing paradigm was largely developed, it seems natural to stick to the current normal science with minor evolutions within it. This is also a sector where funding and resources are increasing. If, on the other hand, you see it as important to also treat other societal problems as SoS, questioning the paradigm becomes natural, although the funding and resources are more scattered.

## 4.6 Sociotechnical Aspects

One of the least studied aspects of SoS is the human, social, and sociotechnical perspective [50]. Sources show that these issues were recognized well before the establishment of the current SoS(E) paradigm [20, 19], yet they have failed to make any significant impact on it. Some authors go as far as calling this the "elephant in the room" of SoS, indicating that everyone is aware of the issues but no one wants to discuss them [36]. That, if anything, must be seen as an anomaly of the paradigm. It is likely to be caused by the attempt to engineer something which is in reality more of a social than a technical situation.
To some extent, the SoS(E) elephant has been inherited from the SE ancestors. An organization that performs engineering is also a sociotechnical system, but this fact is not studied much in SE either [51]. However, in SoS(E), the issue is more severe due to the independence of the CS.

As mentioned in Section 4.2 above, the independence is not given a clear definition in the standards, but a reasonable interpretation is that it allows the CS to decide on how to use their own capabilities [38]. These decisions are either taken directly by humans, for CS that include human operators, or by autonomous technical systems. In the latter case, the decisions are controlled by the design and input to those systems, which ultimately also come from humans.

It is thus necessary to understand who the actors are, what their priorities are, and what differences they have in how they understand and reason about the SoS context they are in. This requires acknowledging that SoS must be looked upon from multiple perspectives and that there are pluralistic goals that need to be satisfied [15]. These goals stem from humans and organizations that ultimately decide on what action should be taken, even when carried out by an automated system. The need for multiple perspectives is a cornerstone of Checkland's Soft Systems Methodology [52] and Jackson's Critical Systems Thinking approach [53], which can inspire enhanced SoS(E) methodologies.

# 5 Towards a New SoS Paradigm

Based on some of the anomalies presented in the preceding section, and building on insights from applying SoS(E) in several non-defense application areas, I have, together with research associates, gradually developed an extended SoS conceptualization. It includes ways of describing dynamic SoS substructures as constellations [41], an improved understanding of what independence of CS means [38], ontologies for capabilities and missions [54], leading up to a basic model of CS as agents [55, 56]. All these results have been formulated within the current SoS(E) paradigm, but they are also to some extent challenges to it. In combination, they can thus serve as a basis for formulating a new paradigm.

In this section, the suggested new paradigm will be described as a series of short subsections. Each subsection starts with a succinct description of the conceptualization relevant to a particular aspect of the paradigm. Then, some remarks are made to provide more details and implications of the conceptualization, motivations for the choices made, and comparisons to the current paradigm, including its anomalies. Finally, some suggestions are made on research topics that can be investigated in relation to the particular aspect of the paradigm. This last part is essential because the value of a new paradigm lies in its ability to frame more or different research questions than the current one.

Table 1 summarizes how the anomalies discussed in Section 4 are addressed by the new paradigm's concepts.

## 5.1 Sociotechnical Agents are the Defining Elements of an SoS

### 5.1.1 Conceptualization

An agent is a physical entity that has agency, i.e., that can decide how to use its capabilities to take actions. Agents can be humans, organizations, artificial entities, and combinations thereof. A capability is something an agent can do. It represents a state-transforming process parameterized by available resources and priorities.

### 5.1.2 Remarks

The agents can represent the CS of the current paradigm, but the agent concept also covers other actors that currently have no clear conceptualization. This includes organizational agents that develop, own, or operate technical systems or that manage the SoS as a whole. It also includes mediators and infrastructures that facilitate interactions. The reason for calling them agents is that the agency captures important aspects of independence that are not clearly defined in the current paradigm.

### 5.1.3 Research Topics

Agent-centric methods for SoS modeling and analysis are needed, not only for agent-based simulations, but also for descriptive characterizations of the situation based on the nature of the agents therein.

The capability concept in the current paradigm requires increased precision. By viewing it as a process, it is clarified that it has a duration and that the result is a state change. The capability may be provided at various levels of performance depending on the current resources available to the agent.
Also, it can be parameterized to express different ways of achieving the same outcome. These details of the capability concept are essential for planning actions and predicting their results.

## 5.2 SoS are Spatially Situated

### 5.2.1 Conceptualization

An SoS is a description of a physical situation comprising multiple independent, interdependent, and heterogeneous agents as well as other physical entities. The physical entities, including agents, are distributed in space. They have states that can change over time through physical interactions between them. The characteristics of the space and the position of entities within it influence what physical interactions take place.

### 5.2.2 Remarks

In the current paradigm, the geographical distribution of the CS is highlighted. However, the proposed formulation makes the spatial distribution more explicit and allows for a deeper analysis of the environment in which a particular SoS is situated. The position of an agent can have fundamental implications on what capabilities are available to it and on what information it bases its decisions about how to use the capabilities. Spatial information thus becomes essential and interesting to exchange among agents.

### 5.2.3 Research Topics

Spatial modeling is largely absent from existing SoS(E) methodologies and is left to the specific application domain or case. Research is needed on general approaches for representing various types of spaces, based on an improved understanding of what characteristics of spaces are relevant. This could include 2D or 3D representations, but also other approaches, such as networks for representing places and routes between them.

A particular challenge is to evaluate the behavior of an SoS, e.g., through simulations, without knowing the exact physical environment in which it will operate. Methods are therefore needed to generate synthetic environments from spatial characteristics, for the evaluation of the SoS using methods such as Monte Carlo simulations across multiple environments.

## 5.3 Agents are Independent and Interdependent

### 5.3.1 Conceptualization

Independence means that an agent decides for itself how to use its own capabilities. Interdependence means that an agent is affected by the actions of other agents directly through physical interactions or indirectly through objects in the physical space where they co-exist.

### 5.3.2 Remarks

The duality of independence and interdependence is fundamental to understanding the dynamics in an SoS. By anchoring it in physical rather than abstract interactions, more types of relations can be clarified.

### 5.3.3 Research Topics

The degrees of independence and interdependence are primary drivers of SoS dynamics and behavior. Enhanced analysis methods are needed for understanding the balance between the two and how it impacts microlevel and macrolevel properties in the situation. A better understanding is also needed of mechanisms that can be employed in the SoS to control this balance.

## 5.4 An Agent Perceives its Environment and Acts upon it

### 5.4.1 Conceptualization

An agent has perception, by which a part of its internal state changes as a function of the state of its environment. The agent has actuation, by which a part of its internal state can affect the state of its environment.
<table><caption>Table 1: Summary of anomalies in the current SoS(E) paradigm and how the proposed new paradigm addresses these.</caption><thead><tr><td>Anomaly in current paradigm</td><td>Elements in proposed paradigm</td></tr></thead><tbody><tr><td>Engineering process rather than systems thinking methodology</td><td>Systems thinking-based analysis to identify intervention strategies in a systemic situation</td></tr><tr><td>SoS boundaries assumed to be agreed upon rather than fluid</td><td>Each agent has its own world model, which includes the boundaries of the SoS</td></tr><tr><td>SoS aims at achieving a capability</td><td>Agents have partly intersubjective notions of value which guide their actions</td></tr><tr><td>Capability concept lacks precision</td><td>Actual capability is parameterized based on available resources and agent decisions on usage</td></tr><tr><td>Non-CS elements, such as mediators and infrastructures, are undefined</td><td>Mediators and infrastructures are seen as agents or physical entities</td></tr><tr><td>Positioning of social elements vis-à-vis the SoS boundary is unclear</td><td>Social elements, such as owners, developers, and operators, are agents within the SoS situation</td></tr><tr><td>Dynamic structures of the SoS are not captured</td><td>Dynamic structures, such as constellations, are considered as composed agents</td></tr><tr><td>Independence treated as a CS property rather than a relation</td><td>An agent is dependent on another if represented in the agent's world model</td></tr><tr><td>Fixed archetypes based on the degree of centrality of control and goal agreement</td><td>SoS are classified by combining patterns</td></tr><tr><td>Multilevel modeling not supported</td><td>Composed agents can represent higher levels whose behavior and properties emerge from its constituents</td></tr><tr><td>Analysis of independence and interdependence interplay lacking</td><td>Independence and interdependence framed as a game theory problem involving agents' notion of value and world models</td></tr><tr><td>SoS evolution not analyzed</td><td>SoS evolution modeled based on individual agent states and decisions</td></tr><tr><td>Acquisition-driven development inadequate for commercial applications</td><td>Organizational actors are represented as agents, allowing a more precise description of specific business relations</td></tr><tr><td>Sociotechnical aspects not considered</td><td>An SoS is a sociotechnical situation where agents can represent humans, organizations, and technical systems, or combinations thereof</td></tr></tbody></table>
### 5.4.2 Remarks

In general, agency requires the ability to act as a cybernetic controller, which presupposes both perception and actuation [57]. The internal states representing perception and actuation allow agent reasoning and decision-making to be expressed only in terms of its own internal state, without direct access to the actual state of the environment. The benefit of this is that an agent can cognitively evaluate alternative plans of action, where the perception and actuation are imagined rather than actual, before actually acting.

### 5.4.3 Research Topics

Perception and actuation are the interfaces between the physical world and the cognition of the agent. Analysis methods are needed for understanding how different degrees of perception in agents affect the overall SoS macrolevel behavior. Such analyses could be based on cybernetic principles, such as requisite variety [57].

## 5.5 World Models Represent Agents' Situation Understanding

### 5.5.1 Conceptualization

An agent possesses a world model, which is an internal state not directly interacting with the states of other physical entities. The agent's world model captures how it understands the situation. Agents construct their world models based on their perception and what information they need to decide on what actions to take. World models are not static, but are updated both as a consequence of perceived changes external to the agent and based on the agent's learning.

### 5.5.2 Remarks

The presence of an internal world model in an agent is consistent with the Good Regulator Theorem [58]. An agent may have several models of the same part of the world, either because they are useful for different purposes or due to uncertainty about what is the best representation, which could lead to several alternative hypotheses.

### 5.5.3 Research Topics

As pointed out in [59], every model has a purpose, and the purpose of the world model is to provide the information needed for the agent's actions. Therefore, the models must be able to predict the outcome of alternative actions and make plans for sequences of actions leading to future effects. How these models are represented and evolve is essential for understanding the resulting behavior on both microlevel and macrolevel in real complex SoS situations.

## 5.6 Systems Exist in the Agents' World Models

### 5.6.1 Conceptualization

Systems are concepts represented in agent world models. This representation includes system boundaries, parts, properties, states, and relations. Systems can represent both physical entities, including agents, and abstract entities with no direct connection to the perception of the physical reality.

### 5.6.2 Remarks

Which systems are included in the world model is subjective and based on what the agent needs to support its decisions on how to act. The suggested paradigm thus explicitly adopts the constructive worldview on systems [37].

### 5.6.3 Research Topics

Systems are abstractions of material or non-material entities, and hence, which systems to represent becomes a question of what the key concepts are. This touches upon metaphysical and ontological questions that are absent in the current paradigm. There is also an aspect of complexity, and the principle of parsimony (Occam's razor) states that a simpler model is preferred over a more complex
one if both lead to the same conclusions. A better understanding is needed of how agents manage this
complexity.

## 5.7 Agents Decide Based on an Individual Notion of Value

### 5.7.1 Conceptualization

Agents decide their actions based on an internal and individual notion of value. The notion of value allows it to compare the desirability of different states in the situation. The agent strives to use its capabilities in ways that its world model predicts will increase its perceived value.

### 5.7.2 Remarks

The value that results for an agent depends on both its own actions and on the actions of other agents. This is because several agents may attach value to states that they can all affect, which creates interdependencies. As a consequence, an agent's world model also needs to include models of the other agents, including their capabilities, notions of value, etc. Otherwise, it cannot predict the outcome of their combined actions. Further, the agent needs to include a model of its own physical state and capabilities, since this is needed for planning actions. Finally, an agent can only attach different values to two situation states if it can perceive a difference between those states.

### 5.7.3 Research Topics

The field of game theory studies agents with different notions of value, where all agents' actions determine the outcome for each agent. Although initial attempts have been made to apply game theory to SoS, much more research is needed [60]. In particular, it is necessary to move away from the oversimplified games studied in the game theory literature and instead model realistic situations with many agents. These models include how agents make decisions, what their incentives are, what information they have available, and in what ways they can act. Also, mechanism design is of interest for the design of an SoS. It involves finding rules and institutions that ensure certain qualities of the SoS, and that can be implemented using mediators or shared information.

## 5.8 Relations Are Represented in the Agent's World Model

### 5.8.1 Conceptualization

An agent $A$ is related to another agent $A'$ if $A'$ is represented in the world model of $A$.

### 5.8.2 Remarks

Although the current paradigm rightfully emphasizes relations, such as the power relations and goal sharing that are the foundations of the archetypes, the nature of those relations is not detailed. An agent's actions are influenced by its world model, and if another agent is represented there, it exercises an influence on those actions. As an example, a power relation builds on the fact that the controlled agent has a representation of what the controlling agent wants to achieve. This could include what mission the agent should carry out.

Worth noting is that an agent can be related to another agent even if it is not able to perceive the other agent. The representation can be caused by an earlier perception of the other agent that persists in the world model. It could also be based on communication that indicates information about the represented agent, or it could even be an abducted hypothesis of that agent's existence and characteristics.

### 5.8.3 Research Topics

Given the fundamental importance of agent relations to an SoS, a more detailed conceptualization is necessary. In particular, the dynamic nature of relations needs to be embraced and explained. This includes questions about how, why, and when the relations are formed, if and when they are dissolved, and how they affect decisions and actions.
## 5.9 Agents Are Subjective and Intersubjective

### 5.9.1 Conceptualization

Separate agents can have different models of the SoS situation and use different abstractions. The degree to which two agents have similar world models represents their intersubjectivity.

### 5.9.2 Remarks

Through intersubjectivity, agents can coordinate their actions and avoid misunderstandings. The primary means of achieving intersubjectivity is communication, but it can also appear in other ways. Consider two agents of similar disposition, including perceptive abilities, capabilities, and notions of value. They could tend towards drawing similar conclusions about what is a sensible representation of the world and thus reach some intersubjectivity.

However, intersubjectivity is not always desired. In situations where the notions of value of two agents are conflicting, the agents may benefit from keeping parts of their world models to themselves.

### 5.9.3 Research Topics

The notion of intersubjectivity is crucial to understanding SoS, and the situation can often be improved by increasing it. Yet, it is a new topic not previously discussed within SoS, probably due to the absence of agent world models in the current paradigm. Hence, a refined theory is needed, which further clarifies what enhances intersubjectivity and how the level of intersubjectivity can be characterized.

## 5.10 Agents Communicate by Perceiving Actions

### 5.10.1 Conceptualization

Agents communicate by perceiving each other's actions. Actions can be performed solely for the purpose of communication.

### 5.10.2 Remarks

Communication can involve a physical entity, which the sending agent modifies and the receiving agent observes. Such a medium allows for the expression of distant communication over an infrastructure.

### 5.10.3 Research Topics

Communication has always been an important topic in SoS(E), but the focus has primarily been on communication technology and interoperability. However, agent communication needs to go beyond that to also cover decisions of agents to perform communication actions, and how conversational interactions between agents enhance their world models and intersubjectivity. The cybernetic conversation theory may be one possible starting point for this [61].

## 5.11 Constellations Organize Collaboration

### 5.11.1 Conceptualization

Constellations are systems of agents that together behave as an agent. They are dynamic mesolevel structures within an SoS. The constellation's behavior emerges from the behavior of the agents and their relations.

### 5.11.2 Remarks

An agent's world model typically contains structure and hierarchy that go beyond just a systemic macrolevel and a set of microlevel elements. Non-physical inter-agent relations are essential for the collaboration, and the members of a constellation need some relations with each other. The constellation is thus a social construct, based on the information in the world models of the agents. Agents can simultaneously participate in several constellations. The SoS should thus be seen as a heterarchical, dynamic network rather than a hierarchical part-based breakdown structure.
### 5.11.3 Research Topics

Constellations [41] are the workhorses of SoS, and the way they are organized has large implications on the efficiency and effectiveness of the collaboration. It would be valuable to identify design patterns for constellations, making it easier for actors to predict what the effects will be of organizing the collaboration in a certain way.

## 5.12 An SoS Has a Dynamic and Heterarchical Structure

### 5.12.1 Conceptualization

Membership in an SoS is dynamic, and agents can join or leave the SoS.

### 5.12.2 Remarks

The SoS can be seen as a dynamic and heterarchical network of agents and other objects, where both nodes and edges in the network change over time. The SoS itself can, in some situations, be considered an agent if it has goals to be achieved. In those situations, the SoS is a macrolevel constellation encompassing all relevant agents. However, in situations resembling the virtual or collaborative archetypes, it is not certain that such a common goal exists, and the SoS may not be acting as an agent.

### 5.12.3 Research Topics

New methods are needed to analyze multilevel, dynamic networks and to understand how network metrics indicate SoS properties. Possibly, more elaborate mathematical notations than plain graphs are needed, such as dynamic hypergraphs.

## 5.13 SoS Evolve from Within Through Agent Interventions

### 5.13.1 Conceptualization

SoS and CS owners, developers, operators, etc., are all agents in the SoS situation. The evolution of the SoS is a result of interventions by such agents to change its structure or behavior.

### 5.13.2 Remarks

In the current paradigm, an outside observer engineers the SoS using methods from SE, in an approach similar to first-order cybernetics. A key aspect of the new paradigm is that an SoS evolves by itself from within. This is in the spirit of second-order cybernetics, where an observer is inside the system it observes [62, 63, 64].

The properties and behavior of the SoS emerge from the behavior and relations of its independent agents, but each agent can only control its own behavior. An intervention by one agent to change the situation thus involves the agreement or at least acceptance of the other agents to modify their behaviors. The intervening agent must therefore have, in its world model, relevant representations of the incentives of the other agents.

An intervention is more of a social than an engineering activity, often requiring extensive negotiations, and hence the term SoS engineering is a bit of a misnomer. This does not mean that engineering methods are irrelevant in SoS situations. On the contrary, analysis methods are essential, and for modifications of the technical parts of a sociotechnical agent, an engineering approach is required in which SE plays an important role. However, SE methods are insufficient for the macrolevel and need to be replaced by systems thinking and cybernetic approaches.

Worth noting is that the SoS situation evolves on many time scales. Short-term evolution occurs through the dynamics of the structure, with agents joining and leaving and constellations being formed and dissolved. This dynamic does however not change the basic way in which the SoS is organized or behaves. Interventions to change the SoS happen in parallel to the ongoing operation, but on a much longer timescale since many agents are required to change.
### 5.13.3 Research Topics

To improve the functioning of an SoS, methods for designing successful intervention strategies are needed. These strategies must take into account different agents' incentives and willingness to change. In most cases, the intervention requires a roadmap detailing a sequence of steps over time, which need to be planned and adapted based on progress.

The governance structure of the SoS is a key enabler for change, but can also be conserving. Only little work exists on what makes SoS governance effective [65].

## 6 Conclusion

In this essay, I have analyzed the development of the system-of-systems (SoS) field using Kuhn's theory of scientific progress. It was shown that SoS engineering emerged out of mainstream systems engineering (SE) during a period around the year 2000, based on increasing concerns that the SE paradigm was insufficient for dealing with the characteristics of SoS. A new paradigm was formulated and gained traction through the formation of a new community specializing in SoS. Through a process of normal science, the new paradigm evolved and was codified in standards and textbooks, which form the current basis for the field.

However, new concerns were also quickly raised with the SoS(E) paradigm itself, and in this study, I have tried to find how such anomalies could be better addressed within a new paradigm. The main problem with the current SoS(E) is that the break from SE was only partial, and the engineering mindset remains the same.

The fundamental distinction in the proposed new paradigm is that an SoS is not an artifact being engineered by an outsider. Rather, it is a situation in which multiple agents intervene to shape the SoS from within. This view has strong support in the broader systems community, relying on theories like second-order cybernetics and practices from the soft systems field.

As Kuhn points out, a paradigm is not only a theory, but it also encompasses our fundamental way of viewing the world. In this respect, what I propose is worthy of being called a different paradigm, precisely because it changes the fundamental viewpoint of the SoS from the outside to within. It ought to be clear that this new way of seeing the situation allows the formulation of a broader set of important research questions.

### 6.1 Possible Routes Forward

Based on the issues identified here, several routes forward are possible. One option is that the SoS community gradually starts exploring and eventually adopts a new paradigm, the one suggested herein or another alternative. This would then lead to a new normal science phase where researchers try to apply the new theories. Kuhn calls this a revolution, but he also emphasizes that revolutions can be both large and small, so the term itself may make it sound more frightening and dramatic than it has to be.

The second option is to remain within the current paradigm, and if this happens, Kuhn predicts that eventually the field makes less and less progress, resulting in fading interest. This could lead to SoS(E) eventually merging back into mainstream SE, a merger which can proceed in two ways. Either minor adaptations are made to plain SE, and SoSE is viewed as a special case, which is basically how the situation is viewed today from the broader SE community. Alternatively, SE itself is reformed to make SoSE the new normal, and SE for integrated systems a special case, which could be seen as a paradigm shift within SE.

A third possibility is that a new paradigm breaks out of SoSE to form a separate field and community, possibly aligning itself closer to systems theory [62, 63, 64], soft systems [33, 34], or complexity theory [66]. Current SoSE would remain as a field of its own, or merge with SE as suggested earlier.

Which of these paths to follow is, as Kuhn also mentions, not possible to settle within the scope of a certain normal science. It involves, by necessity, subjective opinions by the researchers in the field, based on how they perceive the pros and cons of adopting an alternative paradigm. This essay provides my subjective contribution to that debate.

## References

[1] INCOSE. INCOSE Systems of Systems Primer. INCOSE Systems of Systems Primer, 2018.
[2] Kenneth E. Boulding. General Systems Theory - The Skeleton of Science. Management Science, 2(3):197-208, 1956.

[3] Brian J. L. Berry. Cities as systems within systems of cities. *Papers of the Regional Science Association*, 13(1):146-163, December 1964.

[4] Russell L. Ackoff. Towards a System of Systems Concepts. Management Science, 17(11):661-671, July 1971.

[5] Mo Jamshidi. System of systems engineering - New challenges for the 21st century. *IEEE Aerospace and Electronic Systems Magazine*, 23(5):4-19, May 2008.

[6] Claus Ballegaard Nielsen, Peter Gorm Larsen, John Fitzgerald, Jim Woodcock, and Jan Peleska. Systems of Systems Engineering: Basic Concepts, Model-Based Techniques, and Research Directions. *ACM Computing Surveys*, 48(2):1-41, September 2015.

[7] Jakob Axelsson, Joakim Fröberg, and Peter Eriksson. Architecting systems-of-systems and their constituents: A case study applying Industry 4.0 in the construction domain. *Systems Engineering*, 22(6):455-470, November 2019.

[8] Everton Cavalcante, Nélio Cacho, Frederico Lopes, and Thais Batista. Challenges to the Development of Smart City Systems: A System-of-Systems View. In *Proceedings of the XXXI Brazilian Symposium on Software Engineering*, SBES '17, pages 244-249, New York, NY, USA, September 2017. Association for Computing Machinery.

[9] David D. Walden and International Council on Systems Engineering, editors. *Systems Engineering Handbook: A Guide for System Life Cycle Processes and Activities*. Wiley, Hoboken, NJ, fifth edition edition, 2023.

[10] Thomas S. Kuhn. *The Structure of Scientific Revolutions*. International Encyclopedia of Unified Science. Foundations of the Unity of Science, v. 2, No. 2. University of Chicago Press, Chicago, [2d ed., enl edition, 1970].

[11] H. Eisner, J. Marciniak, and R. McMillan. Computer-aided system of systems (S2) engineering. In *Conference Proceedings 1991 IEEE International Conference on Systems, Man, and Cybernetics*, pages 531-537, Charlottesville, VA, USA, 1991. IEEE.

[12] William H. J. Manthorpe. The emerging joint system of systems: A systems engineering challenge and opportunity for APL. *Johns Hopkins APL Technical Digest*, pages 305-313, 1996.

[13] Mark W. Maier. Architecting Principles for Systems-of-Systems. INCOSE International Symposium, 6(1):565-573, July 1996.

[14] Mark W Maier. Architecting principles for systems-of-systems. Systems Engineering, 1(4):267-284, 1998.

[15] Charles Keating, Ralph Rogers, Resit Unal, David Dryer, Andres Sousa-Poza, Robert Safford, William Peterson, and Ghaith Rabadi. System of Systems Engineering. *Engineering Management Journal*, 15(3):36-45, September 2003.

[16] John Boardman and Brian Sauser. System of Systems - the meaning of of. In *2006 IEEE/SMC International Conference on System of Systems Engineering*, pages 118-123. IEEE, 2006.

[17] Nancy Leveson. The Drawbacks in Using The Term ‘System of Systems’. *Biomedical Instrumentation & Technology*, 47(2):115-118, April 2013.

[18] Dan Cocks. How Should We Use the Term “System of Systems” and Why Should We Care? INCOSE International Symposium, 16(1):427-438, 2006.

[19] Peter Feiler, Bill Pollak, and Carnegie Mellon University, editors. *Ultra-Large-Scale Systems: The Software Challenge of the Future*. Software Engineering Institute, Carnegie Mellon Univ, Pittsburgh, 2006.

[20] M.W. Maier. Research Challenges for Systems-of-Systems. In *2005 IEEE International Conference on Systems, Man and Cybernetics*, volume 4, pages 3149-3154, October 2005.
[21] Michael E Porter and James E Heppelmann. How smart, connected products are transforming competition. *Harvard business review*, 92(11):64–88, 2014.

[22] Judith S. Dahmann and Kristen J. Baldwin. Understanding the Current State of US Defense Systems of Systems and the Implications for Systems Engineering. In *2nd Annual IEEE Systems Conference*, pages 1–7. IEEE, April 2008.

[23] Mo Jamshidi, editor. *System of Systems Engineering: Innovations for the 21st Century*. Wiley, 1 edition, October 2008.

[24] Dominique Luzeaux and Jean-René Ruault, editors. *Systems of Systems*. Wiley, 1 edition, February 2013.

[25] A. Gorod, B. Sauser, and J. Boardman. System-of-Systems Engineering Management: A Review of Modern History and a Path Forward. *IEEE Systems Journal*, 2(4):484–499, December 2008.

[26] John Klein and Hans van Vliet. A systematic review of system-of-systems architecture research. In *Proceedings of the 9th International ACM Sigsoft Conference on Quality of Software Architectures*, page 13, New York, New York, USA, June 2013. ACM Press.

[27] Héctor Cadavid, Vasilios Andrikopoulos, and Paris Avgeriou. Architecting systems of systems: A tertiary study. *Information and Software Technology*, 118, 2020.

[28] SEBoK. Guide to the Systems Engineering Body of Knowledge (SEBoK). https://sebokwiki.org, May 2025.

[29] ISO/IEC/IEEE. Standard 21839 Systems and software engineering — System of systems (SoS) considerations in life cycle stages of a system. Technical report, ISO/IEC/IEEE, 2019.

[30] ISO/IEC/IEEE. Standard 21840 Systems and software engineering - Guidelines for the utilization of ISO/IEC/IEEE 15288 in the context of System of Systems Engineering. Technical report, ISO/IEC/IEEE, 2019.

[31] ISO/IEC/IEEE. Standard 21841 Systems and software engineering - Taxonomies of systems-of-systems. Technical report, ISO/IEC/IEEE, 2019.

[32] ISO/IEC/IEEE. Standard 15288 Systems and software engineering - System life cycle processes. Technical report, ISO/IEC/IEEE, 2015.

[33] Peter Checkland. *Systems Thinking, Systems Practice*. John Wiley and Sons, Chichester, England, 1993.

[34] M. C. Jackson and P. Keys. Towards a System of Systems Methodologies. *Journal of the Operational Research Society*, 35(6):473–486, June 1984.

[35] Judith Dahmann. System of Systems Pain Points. In *INCOSE International Symposium*, volume 24, pages 108–121, July 2014.

[36] Michael Henshaw, Carys Siemieniuch, Murray Sinclair, Vishal Barot, S Henson, Dr Ncube, Soo Ling Lim, Huseyin Dogan, Mo Jamshidi, and D DeLaurentis. The Systems of Systems Engineering Strategic Research Agenda. Technical Report TAREA-PU-WP5-R-LU-26, August 2013.

[37] Dov Dori, Hillary Sillitto, Regina M. Griego, Dorothy McKinney, Eileen P. Arnold, Patrick Godfrey, James Martin, Scott Jackson, and Daniel Krob. System Definition, System Worldviews, and Systemness Characteristics. *IEEE Systems Journal*, 14(2):1538–1548, June 2020.

[38] Jakob Axelsson and Pontus Svenson. On the Concepts of Capability and Constituent System Independence in Systems-of-Systems. In *2022 17th Annual System of Systems Engineering Conference (SOSE)*, pages 247–252, June 2022.

[39] Jakob Axelsson. Mediators in Systems-of-Systems and Ecosystems: A Systematic Literature Review and Conceptualization. In *12th ACM/IEEE International Workshop on Software Engineering for Systems-of-Systems and Software Ecosystems*, SESoS '24, pages 21–28, New York, NY, USA, August 2024. Association for Computing Machinery.
[40] Pontus Svenson and Jakob Axelsson. Initiation and Formation of Constellations in Systems of Systems. In *2022 17th Annual System of Systems Engineering Conference (SOSE)*, pages 340–345, June 2022.

[41] Jakob Axelsson. A Refined Terminology on System-of-Systems Substructure and Constituent System States. In *IEEE Systems of Systems Conference*, pages 31–36, Anchorage, Alaska, 2019.

[42] Silia Maksuti, Ani Bicaku, Mario Zsilak, Igor Ivkic, Bálint Péceli, Gábor Singler, Kristóf Kovács, Markus Tauber, and Jerker Delsing. Automated and Secure Onboarding for System of Systems. *IEEE Access*, 9:111095–111113, 2021.

[43] Mohamed Mogahed and Mo Mansouri. Navigating the Complexities of Systems of Systems: Analysis, Challenges, and a Novel Proposed Construct. In *2024 IEEE International Symposium on Systems Engineering (ISSE)*, pages 1–8, Perugia, Italy, October 2024. IEEE.

[44] Stephen C Cook and Jaci M Pratt. Typology dimensions for classifying SoSE problem spaces. In *11th System of Systems Engineering Conference*, pages 1–6. IEEE, June 2016.

[45] Jan Felix Niemeyer, Christopher Dormeier, and Mark Mennenga. Pattern-based System of Systems Engineering Approach for Resilient and Sustainable Production Networks – A Review and Framework Proposal. In *2024 19th Annual System of Systems Engineering Conference (SoSE)*, pages 10–17, June 2024.

[46] Jakob Axelsson. Systems-of-Systems Design Patterns: A Systematic Literature Review and Synthesis. In *2022 17th Annual System of Systems Engineering Conference (SOSE)*, pages 171–176, June 2022.

[47] Jakob Axelsson. Business Models and Roles for Mediating Services in a Truck Platooning System-of-Systems. In *IEEE Systems of Systems Conference*, pages 113–118, Anchorage, 2019.

[48] B. Briscoe, A. Odlyzko, and B. Tilly. *Metcalfe's Law Is Wrong*. *IEEE Spectrum*, 43(7):34–39, July 2006.

[49] Daniel A. DeLaurentis, Kushal Moolchandani, and Cesare Guariniello. *System of Systems Modeling and Analysis*. System of Systems Engineering Series. CRC Press, Boca Raton, FL, first edition edition, 2023.

[50] Gordon Baxter and Ian Sommerville. Socio-technical systems: From design methods to systems engineering. *Interacting with Computers*, 23(1):4–17, January 2011.

[51] Jakob Axelsson. Towards an Improved Understanding of Humans as the Components that Implement Systems Engineering. In *INCOSE International Symposium*, volume 12, pages 1137–1142, August 2002.

[52] Peter Checkland, John D. Poulter, and John Poulter. *Learning for Action: A Short Definitive Account of Soft Systems Methodology and Its Use for Practitioner, Teachers, and Students*. Wiley, Chichester, 2006.

[53] Michael C. Jackson. *Critical Systems Thinking: A Practitioner's Guide*. Wiley, 1 edition, June 2024.

[54] Joyce Martin, Jakob Axelsson, Jan Carlson, and Jagadish Suryadevara. Towards a Core Ontology for Missions and Capabilities in Systems of Systems. In *2023 18th Annual System of Systems Engineering Conference (SoSe)*, pages 1–7, June 2023.

[55] Jakob Axelsson. An agent-based ontology to support modeling of socio-technical systems-of-systems. *INCOSE International Symposium*, 34(1):1705–1718, 2024.

[56] Jakob Axelsson. Implications of Second-Order Cybernetics and Autopoiesis on Systems-of-Systems Engineering. *Systems*, 13(2):119, February 2025.

[57] W Ross Ashby. *An Introduction to Cybernetics*. Chapman and Hall Ltd., London, 1956.

[58] Roger C Conant and W. Ross Ashby. Every Good Regulator of a System Must Be a Model of that System. *Int. J. Systems Sci.*, 1(2):89–97, 1970.
[59] Herbert Stachowiak. *Allgemeine Modelltheorie*. Springer-Verlag, Wien, 1973.

[60] Jakob Axelsson. Game theory applications in systems-of-systems engineering: A literature review and synthesis. In *17th Annual Conference on Systems Engineering Research (CSER) - Procedia Computer Science* 153, pages 154-165, Washington, D.C., 2019. Elsevier.

[61] Thomas Manning. What is conversation theory? *Cybernetics and Human Knowing*, 30(1-2):47-65, August 2023.

[62] Ranulph Glanville. Second Order Cybernetics. In *Encyclopedia of Life Support Systems (EOLSS)*, volume Systems Science and Cybernetics. 2003.

[63] Bernard Scott. Second-order Cybernetics: An Historical Introduction. *Kybernetes*, 33:1365-1378, October 2004.

[64] Stuart Umpleby. Second-Order Cybernetics as a Fundamental Revolution in Science. *Constructivist Foundations*, 11:455-465, July 2016.

[65] Iohan Goncalves Vargas and Rosana T. Vaccare Braga. Understanding System of Systems Management: A Systematic Review and Key Concepts. *IEEE Systems Journal*, 16(1):510-519, March 2022.

[66] W. Brian Arthur. Foundations of complexity economics. *Nature Reviews Physics*, 3(2):136-145, January 2021.