Markus Schwaninger
Title
Prof. em. Dr.
Last Name
Schwaninger
First name
Markus
Email
markus.schwaninger@unisg.ch
Phone
+41 71 224 2382
196 results
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Item type:Publication, Model-based Governance: A Cybernetic Approach to Water Allocation ControlJust like calcium, magnesium, and iron, water is vital for life on our planet. Alarming evidence indicates that water is becoming scarce. In this critical situation, the management of hydric systems turns out to be a bottleneck. Established modes focus on regulation through laws and rulings. This paper offers a ethodological contribution to improving the governance of River Basin Organizations (RBOs): a cybernetic model is introduced for the diagnosis and (re-)design of water governance, which is new in this context: the iable System Model (VSM). This model embodies both a theory and a methodology, incorporating a heuristic cybernetic framework. The theory it provides is unique in its claim to specify the necessary and sufficient preconditions for the viability of any social system. It gives access to the deep structures that ensure the viability of social, and thereby socio-ecological and socio-technical systems. The VSM also provides a methodology that is highly beneficial in environmental, social, and economic terms. Using the VSM in the field of water governance is unorthodox and challenging. It has the potential to balance the need and supply of water, and even to reframe the system-in-focus, ensuring its viability and sustainability. The theoretical claim of the model is underpinned by an extensive case study from the Tocantins-Araguaia Hydrographic Region in Brazil—the largest hydrographic basin completely within Brazil. The insights gained from this account allow for cautious generalization and enable impactful applications.Type:journal articleJournal:Environmental ManagementVolume:75Issue:12Scopus© Citations 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Commentaries on "what is (quantitative) system dynamics modeling? Defining characteristics and the opportunities they create," by Naugle et al. (2024)(2024); Spano, MiriamWhat is (quantitative) system dynamics modeling? Defining characteristics and the opportunities they create. System Dynamics Review 40 this issue.Type:journal articleDOI:10.1002/sdr.1763Scopus© Citations 3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Dealing With Global Warming: Carbon Neutrality and BeyondA comment on “Analysis of Policy Measures and Feedback Effects on Industry Transformation Towards Carbon Neutrality” by Ahlfs and Kieckhäfer is provided, which triggers reflections about how to cope with climate change . A holistic concept is developed to enable society and humankind to deal effectively with global warming.Type:journal articleJournal:Systems Research and Behavioral ScienceVolume:41Issue:6Scopus© Citations 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Applying Integrative Systems Methodology: The Case of Health Care Organizations(2024-04-22); Klocker, JohannThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY Systems thinking has a lot to offer to the field of strategizing. Calls for a greater use of the Systems Approaches to management are increasingly being advanced in the world of organizations, public and private. Yet, practical implementation of such postulates is lagging. This article aims to demonstrate the potential of systems thinking in shaping strategy in management and organization. It builds on a framework known as Integrative Systems Methodology, a circular set of established components of strategic processes: all elements of the methodology are covered, from finding purpose and goals, modeling, and diagnosing to the design of strategies and change. Along this path, complementary methodologies are combined to cope with high environmental complexity. In the present case, these are the systems methodologies of Organizational Cybernetics (namely Viable System Model) and System Dynamics. The layout of the treatise is conceptual in nature. Nevertheless, long-term empirical evidence from a set of real-world organizations-one center and several peripheral healthcare entities-is presented to make the methodology tangible: theoretically grounded and experientially corroborated. The case study we are introducing is from health care, concretely: an oncological care system. Structural patterns are elicited, which are isomorphic, applicable to any health care system. From a conceptual-theoretical stance, crucial factors conducive to continuous organizational learning are elaborated. Professionals can benefit from acquiring new ways of coping with the ever-increasing complexity.Type:journal articleJournal:SystemsIssue:12(140)Scopus© Citations 3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Linear versus non-linear modelling: A systemic analysis with empirical data on credit and investmentThe purpose of this article is to shed light on the capabilities of linear versus non-linear forms of modelling complex systems. A comparative analysis is realized, which shows the differences between two established methodsregression analysis (RA) and system dynamics (SD). To realize that methodological comparison, we need a benchmark in substantive terms. Therefore, we embed our analysis in a realistic scenario. A set of hypotheses related to the banking and investment sector is tested on the basis of rich empirical macro-and firmlevel data. We examine (1) whether the central bank's monetary policy affects the lending rate of commercial banks and (2) whether the lending rate of the commercial banks affects non-financial firms' investment. A RA is conducted, to test our hypotheses on the assumption of linear relationships between the variables. To explore the non-linear relationships between the variables, and to test the robustness of our regression results, we then model the system under investigation using the SD method(ology). 1 The regression results show that the monetary policy rate has a positive and immediate effect on the lending rates and a negative but lagged effect on the bank loan supply. The long-term lending rates are negatively associated with corporate investment, whilst the bank loan supply has no direct effect on corporate investment. Although the regression results indicate that monetary policy rate has no direct effect on corporate investment, the SD model shows that the monetary policy rate exerts a negative effect on corporate investment. Hence, the two types of modelling can come to different results, given the distinct implications of the exogenous perspective (i.e. the exogenous condition of predictor variables typically required in RA) and the endogenous perspective (i.e. the predictor variables are endogenized, as in SD). Comparing the outcomes of the two methodologies, we conclude that they complement each other. RA is easier to model. SD enhances the RA method by enabling a better understanding of non-linear phenomena. 1 We conceive of method as 'a procedure or process for attaining an object', and of methodology as 'a body of methods, rules, and postulates employed by a discipline: a particular procedure or set of procedures' (Merriam-Webster Dictionary). SD embraces both concepts.Type:journal articleJournal:Systems Research and Behavioral ScienceScopus© Citations 3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, What is variety engineering and why do we need it?(2023); Ott, StefanManagement in systemic terms means to cope with complexity. Ross Ashby's law of requisite variety shows the way-maintaining the varieties of interacting systems in balance. To denote that process, we use the cybernetic concept of 'variety engineering', which we also formalize. It refers to processes of mutual complexity amplification and attenuation by interacting agents. The purpose of this contribution is to elicit ways of coping with complexity by means of variety engineering. The abstract concepts are illustrated by examples from ecological, social and economic contexts.Type:journal articleJournal:Systems Research and Behavioral ScienceVolume:2023Scopus© Citations 11 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Sustainable Amazon: A Systemic Inquiry with Native Populations(2023-05-04); Scopus© Citations 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, ¿Como vencer sin ganar? Un camino hacia la paz en el conflicto entre Rusia y Ucrania(2022) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Intelligent Organizations: A Systemic Approach(2022)Type:journal articleJournal:CXO Magazin, 5/2022 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Cybernetic Crisis Management in a Federal System – Insights from the Covid PandemicType:journal articleJournal:Systems Research and Behavioral ScienceVolume:39Scopus© Citations 7