Systems

Carlo Cipolla’s stupidity quadrant and his five laws of stupidity were proposed for the first time in 1976. Exposed in a humorous mood by the author, these concepts describe the interactions among human beings in terms of a semiquantitative model based on the gains and losses of the agents engaged in a process. Here, we propose a new interpretation of Cipolla’s ideas in a biophysical framework, using the well known “predator–prey”, or “Lotka–Volterra”, model. We find that there is indeed a correspondence between Cipolla’s approach, based on economics and biophysical economics. We propose a “sixth law of stupidity”, additional to the five proposed by Cipolla, stating that “humans are the stupidest species in the whole ecosystem” because of their tendency of overexploiting natural resources. Full article

Using Indian free-ranging dogs (FRD) as a case study, we propose a novel intervention of social integration alongside previously proposed methods for dealing with FRD populations. Our study subsumes population dynamics, funding avenues, and innovative strategies to maintain FRD welfare and provide societal benefits. We develop a comprehensive system dynamics model, featuring identifiable parameters customizable for any management context and imperative for successfully planning a widescale FRD population intervention. We examine policy resistance and simulate conventional interventions alongside the proposed social integration effort to compare monetary and social rewards, as well as costs and unintended consequences. For challenging socioeconomic ecological contexts, policy resistance is best overcome by shifting priority strategically between social integration and conventional techniques. The results suggest that social integration can financially support a long-term FRD intervention, while transforming a “pest” population into a resource for animal-assisted health interventions, law enforcement, and conservation efforts. Full article

This research aims to explore which kinds of metrics are more valuable in making investment decisions for a venture capital firm using machine learning methods. We measure the fit of developed companies to a venture capital firm’s investment thesis with a balanced scorecard based on quantitative and qualitative characteristics of the companies. Collaborating with the management team of Rose Street Capital (RSC), we explore the most influential factors of their balanced scorecard using their retrospective investment decisions of successful and failed startup companies. Our study employs six standard machine learning models and their counterparts with an additional feature selection technique. Our findings suggest that “planning strategy” and “team management” are the two most determinant factors in the firm’s investment decisions, implying that qualitative factors could be more important to startup evaluation. Furthermore, we analyzed which machine learning models were most accurate in predicting the firm’s investment decisions. Our experimental results demonstrate that the best machine learning models achieve an overall accuracy of 78% in making the correct investment decisions, with an average of 87% and 69% in predicting the decision of companies the firm would and would not have invested in, respectively. Our study provides convincing evidence that qualitative criteria could be more influential in investment decisions and machine learning models can be adapted to help provide which values may be more important to consider for a venture capital firm. Full article

The biology literature presents allometric relations for the specific metabolic rate (SMR_k) of an organ k of mass mk within the body of mass m_B: SMR_k ∝ m_B^fk (body mass allometry, BMA). Wang et al. used BMA, summed-up energy from all organs and validated Kleiber’s law of the whole body: SMR_M ∝ m_B^b’, b’ = −0.25. The issues raised in biology are: (i) why f_k and b’ < 0, (ii) how do the organs adjust f_k to yield b’? The current paper presents a “system” approach involving the field of oxygen deficient combustion (ODC) of a cloud of carbon particles and oxygen deficient metabolism (ODM), and provides partial answers by treating each vital organ as a cell cloud. The methodology yields the following: (i) a dimensionless “group” number G_OD to indicate extent of ODM, (ii) SMR_k of an organ in terms of the effectiveness factor; (iii) curve fitting of the effectiveness factor to yield the allometric exponents for the organ mass-based allometric laws (OMA); (iv) validation of the results with data from 111 biological species (BS) with m_B ranging from 0.0075 to 6500 kg. The “hypoxic” condition at organ level, particularly for COVID-19 patients, and the onset of cancer and virus multiplication are interpreted in terms of ODM and glycolysis. Full article

Data-centric models of COVID-19 have been attempted, but have certain limitations. In this work, we propose an agent-based model of the epidemic in a confined space of agents representing humans. An extension to the SEIR model allows us to consider the difference between the appearance (black-box view) of the spread of disease and the real situation (glass-box view). Our model allows for simulations of lockdowns, social distancing, personal hygiene, quarantine, and hospitalization, with further considerations of different parameters, such as the extent to which hygiene and social distancing are observed in a population. Our results provide qualitative indications of the effects of various policies and parameters, for instance, that lockdowns by themselves are extremely unlikely to bring an end to an epidemic and may indeed make things worse, that social distancing is more important than personal hygiene, and that the growth of infection is significantly reduced for moderately high levels of social distancing and hygiene, even in the absence of herd immunity. Full article

The Coronavirus pandemic of 2019–20 (COVID-19) affected multiple social determinants of health (SDH) across the globe, including in New Zealand, exacerbating health inequities. Understanding these system dynamics can support decision making for the pandemic response and recovery measures. This study combined a scoping review with a causal loop diagram to further understanding of the connections between SDH, pandemic measures, and both short- and long-term outcomes in New Zealand. The causal loop diagram showed the reinforcing nature of structural SDH, such as colonization and socio-economic influences, on health inequities. While balancing actions taken by government eliminated COVID-19, the diagram showed that existing structural SDH inequities could increase health inequities in the longer term, unless the opportunity is taken for socio-economic policies to be reset. Such policy resets would be difficult to implement, as they are at odds with the current socio-economic system. The causal loop diagram highlighted that SDH significantly influenced the dynamics of the COVID-19 impact and response, pointing to a need for purposeful systemic action to disrupt the reinforcing loops which increase health inequities over time. This will require strong systems leadership, and coordination between policy makers and implementation at local level. Full article

The ability to solve modern complex systems becomes a necessity of the 21st century. The purpose of this study is the development of an instrument that measures an individual’s perception toward solving complex problems. Based on literature and definitions, an instrument with four stages named perceived complex problem-solving (PCPS) was designed through exploratory and confirmatory stages. The instrument is validated and scaled through different models, and the final model is discussed. After completing validation and scale development of the PCPS instrument, the final model of the PCPS instrument was introduced to resolve the gap in the literature. The final model of the PCPS instrument is able to find and quantify the degree of perception an individual holds in dealing with complex problems and can be utilized in different settings and environments. Further research about the relationship between Systems Thinking and CPS revealed individuals with a high level of systems thinking have a better understanding of the characteristics of complex problems and so better perception of CPS. Full article

Tourism provides many advantages for Sweden and the whole world, as well as its travelers. Since almost all types of tourism are currently in crisis as a result of the current COVID-19 pandemic, information and communication technology is expected to play a role, not only during the crisis but also in the post-COVID-19 era. Thus, with no expectations from types of tourism, Sweden needs to broaden its digital tours. As a result, this letter aims to classify the transition readiness of industry clusters for this digitalization move. An extended version of the TOPSIS technique was formulated and validated, plus a new framework for measuring digitalization readiness for this purpose. Lastly, analysis of the collected data proves that business tourism could lead the change, though adventure and rural tourism are at the farthest point from being considered ready to change. Full article

This paper discusses the coordinated use of the Soft Systems Methodology (SSM) learning cycle with additional bodies of knowledge. This approach furthers focused understanding and appreciation for taking action within social systems. Adapting the SSM learning cycle extends the richness of the real-world situation understood from an analytic soft systems perspective to encompass the appreciation of a problematical situation using additional bodies of knowledge to explain and explore. Examples illustrate using SSM to foster learning and improve teaching in a research education practice, in a national level research project and, further, in professional in-service at advanced level education. Full article

Cyberspace is a new frontier, not just for hackers, but for engineers. It is a digital ecosystem, the next generation of Internet and network applications, promising a whole new world of distributed and open systems that can interact, self-organize, evolve, and adapt. These ecosystems transcend traditional collaborative environments, such as client-server, peer-to-peer, or hybrid models (e.g., web services), to become a self-organized, evolving, interactive environment. Understanding cyberspace as a system is critical if we are to properly design systems to exist within it. Considering it to be a digital ecosystem, where systems can adapt and evolve, will enable systems engineering to become more effective in the future of networks and the Internet. While most systems engineers have only anecdotal experience with large segments of this ecosystem, in today’s world all of them must come to understand it. Engineering any system, or portion of a system, begins with an understanding of the system. This paper presents two interrelated yet distinct foundational models of the ecosystem of cyberspace: a Systemigram to narrate the cyclical nature of cyber warfare, and a modified predator–prey model, as a mathematical model. Systems engineers can utilize these models to design digital “species” that function and adapt within this ecosystem. Full article

In this paper, we explore the need to improve systems awareness to support early-phase decision-making. This research uses the Norwegian energy industry as context. This industry deals with highly complex engineering systems that shall operate remotely for 25+ years. Through an in-depth study in a systems supplier company, we find that engineers are not sufficiently aware of the systems operational context and do not focus on the context in the early phase. We identified the lack of a holistic mindset and the challenge of balancing internal strategy and customers’ needs as the prevalent barriers. To support the concept evaluation, the subsea system suppliers need to raise systems awareness in the early phase. The study identifies four aspects that are important to consider when developing and implementing approaches to improve systems awareness in the early phase. Full article

In this paper, we discuss the problem of channel identification by using eight algorithms. The first three algorithms are based on higher-order cumulants, the next three algorithms are based on binary output measurement, and the last two algorithms are based on reproducing kernels. The principal objective of this paper is to study the performance of the presented algorithms in different situations, such as with different sizes of the data input or different signal-to-noise ratios. The presented algorithms are applied to the estimation of the channel parameters of the broadband radio access network (BRAN). The simulation results confirm that the presented algorithms are able to estimate the channel parameters with different accuracies, and each algorithm has its advantages and disadvantages for a given situation, such as for a given $SNR$ and data input. Finally, this study provides an idea of which algorithms can be selected in a given situation. The study presented in this paper demonstrates that the cumulant-based algorithms are more adequate if the data inputs are not available (blind identification), but the kernel- and binary-measurement-based methods are more adequate if the noise is not important ($SNR\ge 16$ dB). Full article

Since the migrant surge in 2015, social inclusion has become a crucial issue to be addressed effectively by the European Union, given that 39% of the population born outside of the EU member states faces the risk of poverty or social exclusion. Adding to that, the COVID-19 pandemic has severely affected migrant households worldwide, rendering migrant integration an urgent matter for national governments. Discrimination, racism, xenophobia, and radicalization are all societal threats emerging in periods of massive migrant flows and need appropriate policy measures to be employed in migrant host countries to tackle them. This paper suggests the integration of a multiple criteria decision analysis method, namely PROMETHEE, for policy making with regard to migrant social exclusion. In light of previous research findings and the recent release of the Migrant Integration Policy Index 2020, the authors argue that the method proposed could help policy makers to evaluate the effectiveness of the implemented policies, spot the discrepancies between policies and policy outcomes, and motivate knowledge sharing among the EU member states. The findings include a ten-year comparative list of the EU member states (2010–2019) driven by social inclusion indicators for the foreign-born (non-EU-born) population. The results are rather sensitive to changes in the data utilized but they provide an overall comparative picture of social inclusion policy effectiveness in the EU during the past decade. Full article

We discuss mathematical and physical arguments contrasting continuous and discrete, limitless discretization as arbitrary granularity. In this regard, we focus on Incomputable (lacking an algorithm that computes in finite time) Real Numbers (IRNs). We consider how, for measurements, the usual approach to dealing with IRNs is to approximate to avoid the need for more detailed, unrealistic surveys. In this regard, we contrast effective computation and emergent computation. Furthermore, we consider the alternative option of taking into account the properties of the decimal part of IRNs, such as the occurrence, distribution, combinations, quasi-periodicities, and other contextual properties, e.g., topological. For instance, in correspondence with chaotic behaviors, quasi-periodic solutions, quasi-systems, uniqueness, and singularities, non-computability represents and corresponds to theoretically incomplete properties of the processes of complexity, such as emergence and quantum-like properties. We elaborate upon cases of equivalences and symmetries, characterizing complexity and infiniteness as corresponding to the usage of multiple non-equivalent models that are constructively and theoretically incomplete due to the non-exhaustive nature of the multiplicity of complexity. Finally, we detail alternative computational approaches, such as hypercomputation, natural computing, quantum computing, and analog and hybrid computing. The reality of IRNs is considered to represent the theoretical incompleteness of complex phenomena taking place through collapse from equivalences and symmetries. A world of precise finite values, even if approximated, is assumed to have dynamics that are zippable in analytical formulae and to be computable and symbolically representable in the way it functions. A world of arbitrary precise infinite values with dynamics that are non-zippable in analytical formulae, non-computable, and, for instance, sub-symbolically representable, is assumed to be almost compatible with the coherence of emergence. The real world is assumed to be a continuous combination of the two—functioning and emergent—where the second dominates and is the norm, and the first is the locus of primarily epistemic extracts. Research on IRNs should focus on properties representing and corresponding to those that are detectable in real, even if extreme, phenomena, such as emergence and quantum phenomena. Full article

This article addresses the generic dynamic decision problem of how to achieve sustained market growth by increasing two interdependent organizational resources needed (1) to increase and (2) to sustain demand. The speed and costs of increasing each resource are different. Failure to account for this difference leads to policies that drive a quick increase of demand followed by decline. Three generic policies derived from the literature have been implemented in a system dynamics model. Simulation shows that they all can generate sustained exponential growth but differ in performance: even policies criticized in the literature for provoking overshoot and collapse can drive sustained growth. This leads to questions for further research regarding (1) the set of generic policies and its structure and (2) concerning the reasoning of human decision-makers when choosing between such policies and the salience of important but easily overlooked features of the decision situation. Full article

This article explores co-creative problem solving to support rapid learning of systems knowledge in the concept phase towards innovation. We introduce the term co-creative problem solving to describe the act of collective creation between systems engineers and stakeholders during problem solving. The context of this research is a mature Norwegian industry accustomed to efficiency and risk aversion, challenged by late validation of systems design due to poor utilization of systems knowledge. We have explored co-creation between systems engineers and stakeholders such as project managers, business developers, and subject-matter experts through a longitudinal in-depth industry case in the energy domain. The primary outcome is insights into how co-creative problem solving supports rapid learning of systems knowledge in the industry case. We propose a method building on the findings from the research results to support systems engineers in similar contexts facing similar challenges. Full article

The dynamics that lead to the spread of an infectious disease through a population can be characterized as a complex system. One way to model such a system, in order to improve preparedness, and learn more about how an infectious disease, such as COVID-19, might spread through a population, is agent-based epidemiological modelling. When a pandemic is caused by an emerging disease, it takes time to develop a completely new model that captures the complexity of the system. In this paper, we discuss adapting an existing agent-based model for the spread of measles in Ireland to simulate the spread of COVID-19. The model already captures the population structure and commuting patterns of the Irish population, and therefore, once adapted to COVID-19, it can provide important insight on the pandemic, specifically in Ireland. We first investigate the different disease parameters that need to be adjusted to simulate the spread of COVID-19 instead of measles and then run a set of experiments initially comparing the model output for our original measles model with that from the adjusted COVID-19 model. We then report on experiments on how the different values of the basic reproductive number, $R_0$, influence the simulated outbreaks, and find that our model behaves as expected: the higher the $R_0$, the more agents are infected. Then, we demonstrate how different intervention strategies, such as vaccinations and school closures, influence the spread of measles and COVID-19 and how we can simulate real pandemic timings and interventions in our model. We show that with the same society, environment and transportation components among the different disease components lead to very different results for the two diseases, and that our COVID-19 model, when run for Leitrim County, Ireland, predicts a similar outbreak length to a real outbreak in Leitrim County, Ireland, but the model results in a higher number of infected agents compared to the real outbreak. This difference in cases is most likely due to identifying all cases of COVID-19 in the model opposed to only those tested. Once an agent-based model is created to simulate a specific complex system or society, the disease component can be adapted to simulate different infectious disease outbreaks. This makes agent-based models a powerful tool that can be used to help understand the spread of new and emerging infectious diseases. Full article

This study utilized the application of authentic Virtual Reality (VR) to replicate the real-world complex system scenarios of a large retail supply chain. The proposed VR scenarios were developed based on an established systems thinking instrument that consists of seven dimensions: level of complexity, independence, interaction, change, uncertainty, systems’ worldview, and flexibility. However, in this study, we only developed the VR scenarios for the first dimension, level of complexity, to assess an individual’s Systems Thinking Skills (STS) when he or she engages in a turbulent virtual environment. The main objective of this study was to compare a student’s STS when using traditional ST instruments versus VR scenarios for the complexity dimension. The secondary aim was to investigate the efficacy of VR scenarios utilizing three measurements: Simulation Sickness Questionnaire (SSQ), System Usability Scale (SUS), and Presence Questionnaire (PQ). In addition to the three measures, NASA TLX assessment was also performed to assess the perceived workload with regards to performing the tasks in VR scenarios. The results show students’ preferences in the VR scenarios are not significantly different from their responses obtained using the traditional systems skills instrument. The efficacy measures confirmed that the developed VR scenarios are user friendly and lie in an acceptable region for users. Finally, the overall NASA TLX score suggests that users require 36% perceived work effort to perform the activities in VR scenarios. Full article

The implementation of information and communication technologies (ICT) in power systems increases the risks of cybersecurity threats, requiring protection measures that should reflect the multi-actor environment of the contemporary power systems. This paper provides a critical assessment of the cybersecurity practices of the transmission system operators (TSOs) from South East Europe (SEE) and the implementation of obligations for TSOs emerging from the complex set of cybersecurity and electricity legislation. The analyses of TSO cybersecurity practices are based on a survey conducted with the TSOs from SEE and show there is a lack of consistent cybersecurity policy at the TSO level. These analyses demonstrate that the differences between TSOs from the SEE region are not very significant with regards to implementation of technical protection and defense measures for critical infrastructures (CIs) and assets. The comprehensive analyses of electricity and cybersecurity legislation uncover the obligations of TSOs emerging from legislation and relate them to current TSO cybersecurity practices, confirming the necessity to boost existing practices. Considering the analyzed legislation and implemented practices, this paper presents a proposal for a cybersecurity framework for TSOs that should improve their organizational and operational response to the evolving cybersecurity challenges. Full article