Understanding Digital Economy Dilemmas

The contrasts captured above reveals different modes of thought that are complementary to seeking an explanation to any phenomenon. Given the current age has been christened the “Systems Age” (Ackoff, 1973; Mitroff et al., 1974; Tarasenko, 2020; van Gigch, 1984; Zandi, 2000), the use of systems thinking tools for understanding current digital transformation dilemmas is not only apt but also fits well with the current age.

Sustainability is humanity's attempt to right past wrongs to save the planet. Humanity faces environmental, economic, and social challenges under the megatrend of "sustainability" (Zander &
Mosterman, 2013). Following the failure of the Millennium Development Goals (see McCloskey,2015), a new international development agenda, the UN 2030 Agenda for Sustainable Development, was adopted in September 2015 (Johnston, 2016). The challenges humanity faces in achieving the 17 SDGs are less clear. The Circular Economy (CE) and Industry 4.0 are two emerging discourses that have the potential to contribute to the 17 SDGs. CE advocates for an economic system that separates environmental pressure from economic growth by switching from linear to circular production (Belmonte-Ureña et al., 2021; Sanguino et al., 2020). The current system is based on large-scale extraction, use, and disposal of materials, which is the root of intergenerational and inter-governmental issues such as waste disposal in natural areas, resource scarcity, and climate change (de Souza Junior et al., 2020). The CE paradigm is a "regenerative system that minimizes resource input and waste, emission, and energy leakage by slowing, closing, and narrowing material and energy loops". Maintenance, repair, reuse, remanufacturing, refurbishing, and recycling can all help (Geissdoerfer et al., 2017, p.759). Thus, CE has emerged as a strategic response to the challenges of a linear economy's sustainability, promoting a circular rationale centered on a "zero waste economy" (van Langen et al., 2021). But CE's theoretical, practical, and ideological foundations are questioned by critics. On the one hand, CE supporters envision sustainable futures based on planned circularity, circular modernism, bottom-up sufficiency, and peer-to-peer circularity; on the other hand, critics claim it is a myth that "paints" a "utopian" future (Bauwens et al., 2020; Corvellec et al., 2022). The evolution of Industry 4.0/5.0 is founded on the long-held belief that technological development and sustainable development are inextricably linked; despite criticism, the conceptualization of this link is neoliberal and Eurocentric in nature (Schelenz & Pawelec, 2022). In spite of this criticism, it is deemed essential to implement innovative technologies as a key factor in establishing a proactive, self configured, and automated system for achieving sustainability objectives. Industry 4.0 refers to the evolution of industries toward cyber-physical systems, and Society 5.0 refers to the evolution of society toward socio-cyber-physical systems (Friedenthal et al., 2021). Industry 4.0 seeks to optimize
production in productive sectors with its integrative and interconnected technologies (Berawi, 2019; Dantas et al., 2021; Ramakrishna et al., 2020). However, it remains unclear how to achieve integration and interconnection of Industry 4.0's core technologies (Hughes et al., 2022; Pourmehdi et al., 2022;
Suleiman et al., 2022). Consequently, although there is substantial evidence linking Industry 4.0 and sustainable development, the identified impediments create roadblocks to the agenda's realization,
posing difficult choices. The global community is increasingly being facilitated by Industry 4.0 technologies, resulting in higher levels of socio-political and economic interdependence (Friedenthal et al., 2021). The increased global interdependence subsequently incites the need for sharing of resources and the interconnection of systems (Interconnectivity) in global partnerships (Friedenthal et al., 2021). The interconnected world has seen the rise of new economic business models such as the "sharing economy", whose focus is on the sharing of unused or underutilized capacity. While there is evidence that the sharing economy exists, critics view it as a questionable phenomenon that threatens existing markets and institutions (Codagnone & Martens, 2016). In this "share-the-scraps-economy" (Srnicek, 2017, p. 254), what is emerging is a new "precariat" labor class that is flexible but without work security (Standing, 2018). The concept of "Digital Transformation" (DT) has emerged as a key metaphor for describing Industry 4.0/5.0. According to Bodrožić & S. Adler (2022), four competing and conflicting future trajectories of DT development are possible: digital authoritarianism, digital oligarchy, digital localism, and digital democracy (Figure 1). Because of the contradictory effects and benefits, this suggests a systemic dilemma. The desired scenario is a digital democracy, which combines a proactive system building regime with a management paradigm based on community and collaboration (Bodroi & S. Adler, 2022). However, most DT implementations are based on digital authoritarianism, digital localism, and digital oligarchies, which exacerbate social exclusion. The DT dilemmas envisaged are couched in the Schumpeterian notion of' ‘creative destruction,' which is linked to the instability and emergence of new forms of economic cycles embedded in these contradictory DT trajectories.

Figure 1: Four Scenarios for Digital Transformation (Source: Bodrožić & S. Adler, 2022)

Smartness entails the intentional development of devices, sociotechnical systems, and fully automated systems using Industry 4.0 technology. Smart systems can update their internal knowledge
up to date to make the best decisions(Romero et al., 2020). User mobility, or the ability of the (smart) environment to continuously provide access to computational tasks and resources, is a critical attribute of smart systems. To link the dilemmas associated with smart systems to flexible modularization, an understanding of the design principles of Industry 4.0 is critical. Hermann et al., (2016) identifies six critical principles for designing Industry 4.0: interoperability, virtualization, decentralization, real-time capability, service orientation and modularity. Incorporating these design principles for Industry 4.0 will give rise to smart systems (and its elements) with the following characteristics: communication capability, embedded knowledge, learning capability, reasoning capability, perception capability and control capability. However, these smart systems are not static, and as the systems elements interact to
exhibit the characteristics above, new characteristics and properties emerge. Contemporary methods to system design are still immersed in a "functional top-down design" paradigm founded on the false idea
that complex systems design can be "managed." Valckenaers et al., (2003) argue that control in the design of complex systems is illusionary and that 'object-oriented design' moored on 'synthetic'
reasoning is appropriate in VUCA situations. Adopting synthetic thinking in the design of complex smart CPS, based on Hermann et al., (2016)’s, systems design principles, is still a global dilemma. The
'Dilemma of Synthetic Design' describes the boundaries of existing smart system design ideas. The "Dilemma of Synthetic Design" challenges the limits of analytical thinking in designing complex
systems.

The horizontal axis depicts increasing complexity because of conflicting values and/or interests among problem stakeholders. The words "unitary," "pluralist," and "coercive" are used. Values, beliefs,
and interests are shared by "unitary" stakeholders. Values and beliefs clash in a "pluralist" relationship, but basic interests are compatible. Among "coercive" stakeholders, there are competing interests as well
as irreconcilable values and beliefs. Combining the dimensions of systems and stakeholders yields nine "ideal-type" problem contexts (Table 6). The "Ideal-Type" grid is then used to map aspects of the five previously identified digital economy dilemmas.
All identified digital dilemmas are volatile, uncertain, complex, and ambiguous (VUCA). Morin (2007) considers such dilemmas to be "general complexity" that must be managed by decision makers.
CSP, a CST multimethodology, seeks to comprehend dilemmas of "general complexity." CSP embraces systems thinking on the grounds that reductionist thought cannot deal with complexity (Jackson, 2021).
Jackson (2020) describes CSP using the acronym EPIC: Exploring the problem situation (E), Producing (P), Intervening flexibly (I), and Checking progress (C). Figure 3 summarizes and describes the EPIC
stages of the CSP. The systems thinking approach requires you to use the first stage (Explore) to identify the critical issues of a digital dilemma and the second stage (Produce) to develop an intervention
strategy for its resolution. The first stage of CSP demonstrates how to deconstruct a digital transformation dilemma to aid decision makers.

Figure 1:The four key EPIC Stages of CSP (Adopted from (Jackson, 2020a)

In its application for understanding complex problematical situations, CSP makes use of seven integrated and cohesive systemic perspectives that allow decision makers to make sense of specific
dilemmas: machine, organism, cultural, political, coercive system, environmental, interrelationships (Jackson, 2021). These seven, integrated into five perspectives (See Jackson, 2020a) are the lenses that decision makers can use to ‘structure’ the dilemmas that they face to foster greater understanding, the goal of systems thinking. To demonstrate, these five systemic perspectives are employed to offer a broad exploration to foster understanding of aspects of five digital economies dilemmas identified above. A similar approach can be used for the exploration of any other problematical dilemma that exhibit VUCA characteristics or ‘general complexity’.

4.1 Machine View: Exploring Goal Seeking Behavior

The machine perspective is used, exploratively, to identify the causes of faults in an existing problem situation and/or to design a better system (Jackson, 2020). According to this perspective, a problematical situation is like a complex machine made up of parts that work together to achieve a desired outcome. By focusing on how the problematic situation is like a "machine," it is possible to gain
insight into the machine perspective. For instance, Morgan (1998) invites us to consider the organization as a machine. Considering the organization as a machine may create valuable insights about how an
organization is structured to achieve predetermined results, though the human aspects may be ignored.
This mechanical mode of thinking helps decision makers to picture challenging circumstances as interlocking components or elements, each with a clearly defined role in the whole (Kohnen, 1999). This involves identifying the system's aim and its causal connections and effects. Consider the dilemma of the circular economy, whose potential to disrupt contemporary social institutions built on a linear rationale is being questioned by its detractors. In what ways can the circular economy be conceived using the machine metaphor? To demonstrate, we use Stahel's, (2016) conceptualization, which expands CE to be a performance economy in which items are marketed to consumers as services through rent, leasing, and sharing; while manufacturers and service providers retain ownership of the product and solutions and bear the cost of risks and waste. As a result, the performance economy emphasizes services over products and generates income through sufficiency, such as waste reduction, as well as design, reuse, and business model innovation to circular business models, or CBMs (Stahel, 2016). Using the machine metaphor, the emphasis should be on the structure of the CE to achieve sufficiency through design and business model innovations centred on sustainability yardsticks. Sustainability yardsticks are centred on a dynamic closed-loop system focusing on environmental protection, the full reuse of resources and the recycling of waste (Franco, 2019; Kazancoglu et al., 2021). Following Jackson (2020), such precision in determining “cause-effect” relationships is linked to
‘Hard Systems Thinking’ (HST) approaches, notably Systems Analysis (SA), Operations Research/Management Science (OR/MS) and Systems Engineering (SE). For these three ‘Hard Systems Thinking’ (HST) methodologies, a problematical situation falls within a “simple-unitary” complexity domain (Table 3) where the systems, though having multiple interactions, are “simple”; while the stakeholders agree as to the “goal” of the system. As illustrated above, the goal-oriented nature of the machine metaphor fits well with thinking that underlie HST. However, the machine perspective will only reveal an aspect of the problematical situation where both cases are true, that is, when there is goal agreement and the systems to be designed are ‘simple’ in the manner of a complete understanding of cause-effect relationship. Prior research demonstrates the application of such HST approaches to understand CE from a machine perspective. Such research is premised on the caveat that the goal of any CE system is sufficiency through product design and business model innovation strategies, with emphasis on quantifiable “cause-and-effect” relationships or factors. For instance, Choi et al.,(2020) synthesizes findings from several research focusing on the sharing and circular economy that employ the use of OR and SE techniques. The machine perspective can help reveal a "slice" of the problematical situation that other perspectives cannot. “Designing for Sufficiency” is fundamental to the machine metaphor.

4.2 Organism View: Exploring System Viability

Von Bertalanffy (1937), the founder of General Systems Theory (GST) and the open system concept, is credited with the inception of the organism/organismic perspective. He offered these ideas in opposition to the then-dominant mechanistic and reductionist models in biology and psychology (Hammond, 2010). The organismic perspective shifts from goal-seeking to viability by diagnosing
pathologies and suggesting system designs for survival (Jackson, 2020a). The underlying systems approach of the organismic perspective in the manner of von Bertalanffy is moored on the open system concept, in which he highlights the relationship between the organism and environment. The open system approach recognized that organisms are complex entities with many parts (or subsystems) that interact with each other and the environment. When thinking about the interaction of the various parts, the system boundary must be considered because it allows the exchange of materials, energy, and information with the external environment. The system boundary facilitates the exchange of materials, energy, and information with the external environment, according to the open system concept. As an open system that is dependent on its environment, the organism must maintain a dynamic equilibrium with the environment to survive and thrive (Jackson, 2020a). And the sub-systems that comprise the
organism only make sense in terms of the functions that the individual parts play in the whole system.
However, Sub-systems must have some autonomy or the "brain," which coordinates, will be overwhelmed. The organism perspective on digital economy dilemmas requires understanding of organizational
'life' images. After the mechanistic view's limitations were realized, the organism vocabulary permeated organizational life discussions. The corporation is a biological concept rooted in a corpus, a body or
organism (Ackoff, 1990). C-level executives have adopted organismic language. The CEO is the organization's "head." Other executives have adopted the 'head' vocabulary to represent various business
functions, such as 'Head' of Human Capital, CIO as 'head' of Information Systems, etc. The organism metaphor in organizational life confirms Morgan’s (1986) claim that "most modern organizational theorists look to nature to understand organizations" (p. 71). Following Morgan's (1986) claim, Prabhu (2022) observed a marked institutional shift on various dimensions (Table 7). The mechanistic
perspective seeks efficiency, while the organismic perspective seeks adaptability to the environment (thus a focus on effectiveness). These organismic ideas can be used to understand 'human' systems,
which have subsystems with their own purposes, unlike mechanistic systems.

Table 4: Mechanistic versus Organismic Perspective

Consider the integration and interconnection of cyber-physical systems in Industry 4.0 and Society 5.0. CPSs can integrate computation, networking, and physical processes. CPS integration is
complicated by heterogeneous components and interactions. While CPSs are promising systems for addressing societal and technical challenges, their success is dependent on a mindset shift to address
collaboration and integration of heterogeneous components. The dilemmas of integration and interconnection of CPSs emerge from various sources. For instance, Wang et al., (2022) points to the lack of focus on human beings on initial designs of CPSs, yet they are meant for humans. Instead, they propose a human-cyber-physical systems (HCPS) perspective that elevates the role of human beings to overcome limitations of contemporary CPSs. Bonci et al., (2019) 'zeroes in' on "web technology" as a limiting "language" for the modern design of CPSs and argues for a shared human-to-machine and machine-to-machine language to actualize such a human-centric viewpoint on the architecture of CPSs.
According to Potekhin et al., (2022), the viability of CPSs depends on how well physical objects are represented as digital twins to help manage large distributed systems. Using digital twins, it is possible
to monitor and improve networked services, physical products, machines, and devices (Stary et al., 2022). Because digital twins intertwined with CPSs, evolving open social and technical systems must maintain a dynamic equilibrium with the changing environment (Jackson, 2020a). Adaptability, as the ‘goal seeking’ behavior, therefore becomes core for these large distributed CPSs, based on the digital twin’s concept. Digital twins are seen as integrators of the digital and physical worlds, as well as of the process of creating value both internally and outside (Barth et al., 2020).

Figure 2: A Human-Cyber-Physical System (Source: Wang et al., 2022)

To overcome the dilemmas associated with distributed CPSs by supporting the proper theorization and design of digital twins to ensure CPSs are adaptable, the relevant systems approaches are Socio-Technical Systems (STS) techniques and the Viable Systems Model (VSM). In the manner of Jackson (2020), STSs and the VSM are associated with problematical situations, in which the systems are
“complex”, while the stakeholder perspective is “unitary”. Complexity is exacerbated due to the heterogeneity of the devices; while the stakeholder perspective is unitary as the discursive practice settles on the “digital twin” concept to characterize distributed CPSs. Exemplars of the use of these two systems thinking approaches include the use of the VSM to create a holistic vision of a system-of system based on the holon concept to simplify the design and implementation of a CPS architecture that exploits recursivity (Bonci et al., 2018) ; and the use of STS to incorporate the human-centric perspective in the design of distributed CPSs and to enhance the bi-directional and multi-directional control, aggregation and management of such systems (Anumba et al., 2022; Horváth, n.d.; Pessoa et al., 2022). Therefore, “Designing for Viability” remains a conundrum, even as CPSs infrastructures take hold in the evolving Industry 4.0/5.0 society.

4.3 Cultural/Political View: Exploring Inscribed Cultural Knowledge

The "sharing" economy, also called "platform capitalism," is gaining traction, revolutionizing product and service production and distribution (Sharif & Huang, 2019). However, “platform capitalism” has a darker side, christened the “share-the-craps-economy”(Reich, 2015), which continues to entrench the inequality loop linked to the self-reinforcing effect of digital and social exclusion (Ragnedda et al., 2022). Digital and social exclusion can be viewed through the prism of contemporary global society's dominant cultural and political narratives. However, it requires posing the question:
what is the dominant cultural/political representation in the current practice of platform capitalism? A specific value extraction model is the by-product of the dominant cultural and political representations
that are embedded in the digital turn towards platform capitalism. This model occurs when the technology surpasses its conventional connective and extractive functions (i.e., data as value) by translating cultural knowledge as a key business practice (Dal Maso et al., 2021). By extending the concept of “platform capitalism,” Dal Maso et al., (2021) contends that this is a form of “cultural platform capitalism”. That is, a Western paternalistic perspective underpins the platform business model, in which cultural representations in digital artifacts are based on a 'rentier capitalism' value extraction model.

Rentier capitalism entails creating and extracting value through the techno-economic expansion of ownership and/or control over assets, often due to artificial or natural scarcity, quality, or productivity(Birch, 2020). The hegemonic dominance of Western corporations in rentier capitalism is well established(Christophers, 2022; Klinge et al.,2022), so their 'cultural knowledge' is deeply embedded in the digital economy. Prior research shows that the hegemonic dominance of specific cultural representations via intermediary digital infr-astructures has increased digital and social isolation for large segments of the population, as evidenced by the digital divide (Figueiredo & Borges, 2021; Kwet, 2022; Mihelj et al., 2019; Peters, 2022). This form of rentier capitalism, in which digital monopolies create and extract value, is contradictory and has been criticized (Birch et al., 2022). As a result, Western cultural platform capitalism has now become the shared culture, but diversity and differences in human interests continue to fuel resistance, conflict, and contestation of the digital space.
Given that Western "cultural knowledge" dominates the digital monopolies shaping the trajectory of the sharing economy, the dilemma of rentier capitalism may be characterized as a "simple-pluralist" structure. Digital monopolies are "simple" because they follow a well-known cultural/political logic of their "cultural knowledge": a "Winner-Takes-All" approach in the design of the digital platform ecosystem. Considering that there are numerous parties involved in the digital platform ecosystem who hold various values, beliefs, and interests, the problem context for rentier capitalism is "pluralist." The cultural/political perspective emphasizes how a diverse groups of individuals become stablished, as they promote processes imprinted with their own interests, to become a “shared culture” (Jackson, 2020). "Winner Take It All" has become the shared cultural knowledge that anchors the sharing economy. To uncover assumptions that underpin such “shared cultures”, possible questions to ask include: What assumptions are inscribed in digital platforms due to Western-oriented cultural platform capitalism? How can these assumptions be 'surfaced' to re-design digital artifacts that reduce social and digital exclusion? Uncovering stakeholders' assumptions in system design emphasizes the 'pluralist' dimension of people complexity. Systems methodologies that align well with problem context of “Digital Exclusion through Rentier Capitalism” include Strategic Assumption Surfacing and Testing (SAST), Interactive Planning (IP), and Soft Systems Methodology (SSM).

4.4 Societal/Environmental View: Explore ‘Humanization’ of Systems

The discourses of humanity’s societal transition to Industry 4.0/5.0 are predominantly couched in the language of Digital transformation (DT). However, dilemmas associated with the pursuit of ‘undemocratic’ ideal type DT process point to outcomes that have exacerbated different manifestations of social exclusion (Figure 3). Prior research has determined that social exclusion is by design since
‘smart’ technologies, the bedrock of Industry 4.0/5.0, are currently being embedded into social systems that already have underlying inequalities (Park & Humphry, 2019). There is extensive support from
scholarship that reveal that social exclusion is by design (see Goldstraw & Herrington, 2021; Good Gingrich, 2010; Jensen, 2021; Park & Humphry, 2019; Sin, 2019). Smart' technologies and systems,
the cornerstone of Industry 4.0/5.0, are therefore being incorporated into social systems with underlying inequities. Addressing social exclusion through DT, which excludes certain segments of humanity, is a persistent problem in the transition to Industry 4.0/5.0. From an Industry 4.0/5.0 standpoint, understanding contemporary social inequities that perpetuate chronic social exclusion requires a systemic lens.

Figure 3:Social Exclusion (Source: Mayer et al., 1958)

The social/environmental approach may provide light on parts of the digital exclusion challenge, which is being addressed from a human-centric, sustainable, and resilient standpoint (Figure 4). The social/environmental perspective, as a systems thinking lens, fosters the understanding of discrimination and inequality in the context of ‘neglected’ stakeholders (Jackson, 2020). Social exclusion is a kind of hegemonic tyranny that reveals power dynamics. This kind of hegemonic tyranny dehumanizes by using coercive power via powerful 'digital intermediaries' to influence the DT process'trajectory and outcome. As Industry 5.0 takes shape, the de-humanizing ideas of Industry 4.0 are being disputed (Grabowska et al., 2022), and a new regime of 'humanization' is emerging in which the technological environment of Industry 4.0 is coupled with a human-centric approach to Industry 5.0. Humanism resists the authoritarian control of 'cultural platform capitalism' and strives to include human-centricity, resilience, and sustainability, which define modern debate for human survival. The most probable emancipatory systems lenses for understanding power dynamics in the design of simple, complicated, and complex systems under coercive stakeholder situations are Team Syntegrity (TS), Critical Systems Heuristics (CSH), and Liberating Systems Theory (LST).

Figure 4: Framework of Industry 5.0 (Source: Grabowska et al., 2022)

4.5 Interrelationship View: Exploring Causality in Systems

As the legitimacy and mobilization for Industry 4.0/5.0 takes hold, it will become clear that the raison d'être for the digital transformation process is the development and integration of smart systems as the core components of the digital economy. Smart systems are not static, but are dynamic, open, and complex in design, while contemporary approaches to design of systems are still steeped in a
‘functional top-down design’ paradigm premised on the unrealistic assumption that complex systems design can be ‘controlled. Further, smart systems, such as smart cities, smart buildings,and autonomous
cars, are each essentially a System-of-Systems (SoS), which are dynamically established as alliances among independent and heterogeneous software systems to offer complex functionalities due to constituent interoperability (Neto et al., 2018). However, recent implementations of smart systems have elicited various challenges that point to a dilemma in how such systems are designed. For instance Mohammadi et al., (2022) cites the difficulty of decision making in smart environments due to the high direct and indirect dimensional factors. Following Medina-Borja (2015)’s conceptualization of “smart service systems”, Alter, (2020) characterizes the smartness of systems into four categories with several dimensions: information processing (six dimensions), internal regulation (five dimensions), knowledge acquisition (six dimensions), and action in the world (six dimensions). Due to the high dimensionality of these smart systems, computational intensity is extremely high in dimensionality which consequently creates a data storage burden at every smart devices due to their different capabilities (Kamruzzaman, 2021). Also, with the broader push towards Industry 4.0/5.0, there are open security and privacy challenges that have heightened, with an increase in the frequency and sophistication of cyber-physical attacks posing significant threats to organizations globally(Williams et al., 2023). The intrinsic dilemma of designing smart systems arises from the high dimensionality of the concept of ‘smartness’ used to characterize to what extent a system is smart (Figure 5).

Figure 5: Dimensions of Smartness (Source: Alter, 2020)

Understanding how smart system dimensions interact can shed light on their nature and improve design. The aspects of interest would focus on causal linkages between the dimensions and how such
linkages influence the ‘smartness’ of the smart system. The inter-relationship perspective, a lens of systems thinking, can be an effective approach for structuring such causal linkages of smart systems.
The foundation of the interrelationship perspective is that all issues (factors, dimensions) identified in a problematical scenario are interconnected in chains of reciprocal causation, which suggest the
possibility of unintended consequences or serve as leverage points for improved design (Jackson, 2020). To understand the 'smartness' of systems, System Dynamics (SD) can simulate such known causal relationships. To diagnose smart systems or improve their design, prior research shows how SD may be utilized as a modeling tool. Prior research shows that SD can be used to diagnose or improve smart system design. Khalid Khan et al., (2022) used SD to model the cause-effect relationships and mechanisms of Connected and Autonomous Vehicles (CAVs) cybersecurity and system behavior to
create a road map for an optimized, self-regulating, and resilient cyber-safe CAV system. Recent contributions using SD include modeling of smart tourism ecosystems (Sedarati et al., 2022; Shafiee et al., 2022); understanding the relationship between information management barriers and collaborative technologies adoption factors, and sustainability-related policy analysis in the built environment (Amin et al., 2022; Francis & Thomas, 2022); and an exploration of factors that foster smart city success, as well as the cause-and-effect relationships among these determinants (Nunes et al., 2021). Although not exhaustive, these contributions show the proven usage of SD for modeling difficult diagnostic or prognostic cause-effect situations. The interrelationship perspective offered through SD makes it an ideal systems methodology for structuring such problematical situations with cause-effect relationships.