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Systems
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Systems Science
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Systems Science

A special issue of Systems (ISSN 2079-8954).

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 7264

Special Issue Editors

Dr. Javier Calvo-Amodio

E-Mail Website
Guest Editor
1. Associate Professor of Industrial and Manufacturing Engineering, School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University, 204 Rogers Hall, Corvallis, OR 97331, USA
2. Chair of the Systems Science Working Group at INCOSE, 7670 Opportunity Rd, Suite 220, San Diego, CA 92111-2222, USA
Interests: integration of systems science into industrial and systems engineering; engineering of organization cultures; systemology; engineering management; system architecture
Special Issues, Collections and Topics in MDPI journals
Dr. James Martin

E-Mail Website
Guest Editor
Aerospace Corporation, Chair of INCOSE Systems Science Working Group, El Segundo, CA, USA
Interests: systems science; systems engineering; system architecting

Special Issue Information

Every year, the Systems Science Working Group (SSWG) convenes at the INCOSE International Workshop and INCOSE International Symposium, where everyone with an interest in exploring, learning, and/or sharing their knowledge of how to integrate systems science into systems engineering practice convene. What makes contributions in this area fundamental to systems engineering and systems science is that systems engineering is informed by systems science while systems engineering validates and verifies the claims and practice recommendations of systems science knowledge. In other words, systems engineers are the systems science experimentalists.

This Special Issue aims at identifying and enhancing:

  1. Systemic definitions, concepts, principles, laws, and models that are scientifically meaningful and are general enough for systems engineering and systems science.
  2. Foundational terminology to enable more efficient collaboration between systems scientists and systems engineers.
  3. A structured set of scientific principles that can guide judgement making and action taking in systems engineering practice.
  4. A set of archetypes, prototypical systems processes, linkage propositions, and pathologies to enhance engineering methods and activities.
  5. Models to help us verify and validate our designs by mapping the logical coherence between theories and nature.

Dr. Javier Calvo-Amodio
Dr. James Martin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Systems is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • systems science
  • systemic definitions
  • fundamental contributions to systems engineering
  • decision making in systems engineering

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

28 pages, 43084 KiB  
Article
Application of Systems-Approach in Modelling Complex City-Scale Transdisciplinary Knowledge Co-Production Process and Learning Patterns for Climate Resilience
by Burnet Mkandawire, Bernard Thole, Dereck Mamiwa, Tawina Mlowa, Alice McClure, Jessica Kavonic and Christopher Jack
Systems 2021, 9(1), 7; https://doi.org/10.3390/systems9010007 - 22 Jan 2021
Cited by 1 | Viewed by 3753
Abstract
Literature shows that much research has been conducted on the co-production of climate knowledge, but it has neither established a standardized and replicable model for the co-production process nor the emergent learning patterns as collaborators transition from the disciplinary comfort-zone (disciplinary and practice [...] Read more.
Literature shows that much research has been conducted on the co-production of climate knowledge, but it has neither established a standardized and replicable model for the co-production process nor the emergent learning patterns as collaborators transition from the disciplinary comfort-zone (disciplinary and practice biases) to the transdisciplinary third-space. This study combines algorithmic simulation modelling and case study lessons from Learning Labs under a 4-year (2016–2019) climate change management project called Future Resilience of African CiTies and Lands in the City of Blantyre in Malawi. The study fills the research gap outlined above by applying a systems-approach to replicate the research process, and a Markov process to simulate the learning patterns. Results of the study make a number of contributions to knowledge. First, there are four distinct evolutionally stages when transitioning from the disciplinary comfort-zone to the transdisciplinary third-space, namely: Shock and resistance to change; experimenting and exploring; acceptance; and integration into the third-space. These stages are marked by state probabilities of the subsequent stages relative to the initial (disciplinary comfort-zone) state. A complete transition to the third-space is marked by probabilities greater than one, which is a system amplification, and it signifies that there has been a significant increase in learning among collaborating partners during the learning process. Second, a four-step decision support tool has been developed to rank the plausibility of decisions, which is very hard to achieve in practice. The tool characterizes decision determinants (policy actors, evidence and knowledge, and context), expands the determinants, checks what supports the decision, and then rates decisions on an ordinal scale of ten in terms of how knowledge producers and users support them. Third, for a successful transdisciplinary knowledge co-production, researchers should elucidate three system-archetypes (leverage points), namely: Salient features for successful co-production, determinant of support from collaborators, and knowledge co-production challenges. It is envisioned that academics, researchers, and policy makers will find the results useful in modelling and replicating the co-production process in a methodical and systemic way while solving complex climate resilience development problems in dynamic, socio-technical systems, as well as in sustainably mainstreaming the knowledge co-produced in policies and plans. Full article
(This article belongs to the Special Issue Systems Science)
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11 pages, 2247 KiB  
Article
Comparing Equation-Based and Agent-Based Data Generation Methods for Early Warning Signal Analysis
by Daniel Reisinger and Manfred Füllsack
Systems 2020, 8(4), 54; https://doi.org/10.3390/systems8040054 - 10 Dec 2020
Cited by 6 | Viewed by 2481
Abstract
Dynamical systems are known to exhibit sudden state transitions, with abrupt shifts from one stable state to another. Such transitions are widely observed, with examples ranging from abrupt extinctions of species in ecosystems to unexpected financial crises in the economy or sudden changes [...] Read more.
Dynamical systems are known to exhibit sudden state transitions, with abrupt shifts from one stable state to another. Such transitions are widely observed, with examples ranging from abrupt extinctions of species in ecosystems to unexpected financial crises in the economy or sudden changes in medical conditions. Statistical methods known as early warning signals (EWSs) are used to predict these transitions. In most studies to date, EWSs have been tested on data generated using equation-based methods that represent a system’s aggregate state and thus show limitations in considering the interactions of a system at the component level. Agent-based models offer an alternative without these limitations. This study compares the performance of EWSs when applied to data from an equation-based and from an agent-based version of the Ising model. The results provide a reason to consider agent-based modelling a promising complementary method for investigating the predictability of state changes with EWSs. Full article
(This article belongs to the Special Issue Systems Science)
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