The aim of this Special Issue (SI) is to connect the two main topics towards which Kybernetes is currently steering its focus, Systems Thinking and Cybernetics, exploring their potential impacts in addressing and analysing complex issues in Social, Environmental and Economic domains,
The expected contributions to this Special Issue are related, but not limited, to the BSLab-SYDIC Workshop 2017 (http://bslab-symposium.net/BSLab-Sydic-2017/BSLAB-SYDIC-WS-Rome-2017.htm) whose attempt was to aggregate various issues from the wide topic of Smart Model-based Governance and its perspective of applications to the present and the future complex organisations.
As both Governance and Self-organisation topics are highly-commented in the research environment, the Editors plan to join the previous research area, based on classical organisational approaches - including the use of methodologies like System Dynamics, Agent-based Modelling, Discrete Event modelling and simulation, as well as their “hybridizations” - with the research area on “data-driven” (or Smart-data) decision-making. The value of Smart Data is that many scenarios can be extrapolated from the past to facilitate and guide future decision-making. However, decision-making with Smart Data sometimes ignores the role played by other factors, such as intuition and personal experience, and simulation and modelling tools and techniques are increasingly used to further exploit the potentials of Smart data and/or overcome some of their limitations.
The rationale in connecting these two research “worlds” that, until today, have been working in the same direction but with two very different approaches represents the focus of this SI, providing original and innovative added value to decision-making processes in complex organisations.,
The main idea is that organisational knowledge-based models can leverage on the evidence arising from data of certain structural relationships, whereas data-driven approaches can leverage the value deriving from structural approaches that are able to suggest systemic behaviours in lieu of “just” a stochastically-inferred behaviour over time of certain relevant variables (as it mostly happens in visual analytics today, where the systems perspective is almost absent).
Overall, their joint use should support and facilitate new developments in the technologies as well as in organisational modelling and simulation approaches, enabling effective governance and self-organisation of the current society.
Moreover, due to the transdisciplinary topics that will be gathered, the SI scope offers an overview of cybernetic applications that can drive the society, creating new tools to manage and interpret new issues arising from the modern digital life.
Hence, we call for an in-depth examination of Systems Thinking-based and Cybernetics-based projects, case studies and theoretical analyses in the following areas:
- People, technology and governance for sustainability;
- Democracy, interactions, and organisation;
- Cyber-systemic thinking, modelling and epistemology;
- Data-Driven decision making vs Model-based decision making;
- Modelling and simulation with Big Data and Smart Data.
The SI aims to reach out both academics as well as practitioners interested in the addressed topics. Therefore, the SI is oriented to both academic based articles as well as some rigorous practice-based contributions.
University of Siena, Department of Business Studies and Law
P.za S. Francesco 7, 53100 Siena
National Interuniversity Consortium of Materials Science and Technology (INSTM), University of Florence, Chemistry Department.
Via della Lastruccia 3, 59013, Sesto Fiorentino, Italy
Submissions to this journal are through the ScholarOne submission system here: http://mc.manuscriptcentral.com/kyb
Please visit the author guidelines for the journal which gives full details. Please ensure you select this special issue from the relevant drop down menu on page four of the submission process.
Full papers are to be submitted by 31 October 2017.
Papers should be between 4000 and 7000 words in length.