System Dynamics Group<\/h1><\/div>The UNISG System Dynamics Group (SDG) is a research group at the Institute of Management and Strategy of the University of St.Gallen, Switzerland. It is dedicated to the modeling, simulation and management of complex, dynamic systems.<\/div><\/div><\/div><\/div>![](\"https:\/\/ifb.unisg.ch\/wp-content\/uploads\/2018\/07\/Studierende-in-\u00abtheCo\u00bb-2018-24.jpg\" "\\"Studierende")
<\/span><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/span><\/a><\/span><\/div>About us<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/span><\/a><\/span><\/div>Our Team<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/span><\/a><\/span><\/div>Methodology<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/span><\/a><\/span><\/div>Research<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/span><\/a><\/span><\/div>Conferences<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/span><\/a><\/span><\/div>Publications<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>About us<\/h2><\/div><\/div><\/div>\n<\/div><\/div>System Dynamics<\/h4><\/div><\/div><\/div>\nSystem Dynamics is a discipline for the modeling, simulation and steering of complex, dynamic systems. It provides a sophisticated, generic methodology to model the structure of such systems and to simulate their behavior. This methodology was developed at the Massachusetts Institute of Technology (MIT) by Prof. Jay Forrester. It is used worldwide today.<\/p>\n
System Dynamics is unmatched in dealing with dynamic complexity in organizations of all kinds. It can provide deep insights into how they work and enable the development of powerful strategies. The methodology is applicable to dynamic issues in general. It can yield astonishing learning effects. That is why System Dynamics is not only used for questions in organizations, but also for the modeling and simulation of \u201cbig\u201d problems \u2013 ecological, social and economic.<\/p>\n<\/div><\/div><\/div>
UNISG System Dynamics Group<\/h4><\/div><\/div><\/div>\nThe UNISG System Dynamics Group (SDG) is a group of researchers at the Institute of Management and Strategy of the University of St. Gallen, Switzerland. SDG is dedicated to the modeling, simulation and governance of complex, dynamic systems. It goes back to pertinent research and teaching activities since the middle of the eighties.<\/p>\n
The System Dynamics methodology distinguishes itself from other methodologies for the analysis of complex issues. The distinction is in the modeling of feedback structures, and in capturing the nonlinearities of a system\u2019s behavior, which these structures produce. In this context, the inclusion and simulation of temporal delays as well as probabilistic functions play important roles.<\/p>\n
The SDG aims at making substantial contributions:<\/p>\n<\/div>
- <\/i><\/span>\n
to progress in the state of modeling and simulation of complex dynamic systems with an emphasis on social systems, namely companies and other organizations (public organizations, non-profit-organizations, social initiatives)<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
to the application of System Dynamics in research and practice<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
to the education of advanced students and users of the System Dynamics methodology<\/p>\n<\/div><\/li><\/ul><\/div><\/div><\/div><\/div>
<\/div><\/div><\/div>Our Team<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/div>Markus Schwaninger, Prof. em. Dr.<\/h4><\/div><\/div><\/div>\nInstitute of Management & Strategy at the University of St. Gallen
\nDufourstrasse 40a
\nCH-9000 St. Gallen<\/p>\n
T +41 71 224 2382
\nmarkus.schwaninger@unisg.ch<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div>Stefan Groesser, Prof. Dr.<\/h4><\/div><\/div><\/div>\nProfessor of Strategy
\nat Bern University of Applied Sciences
\nand Lecturer in Management at HSG<\/p>\n
School of Management
\nBr\u00fcckenstrasse 73
\nCH-3005 Bern
\nT +41 31 848 34 54
\nstefan.groesser@bfh.ch<\/a><\/p>\nLink to the profile page of BFH<\/a><\/p>\n<\/div><\/div><\/div>Evgenia Ushakova, Dr.oec. UNISG<\/h4><\/div><\/div><\/div>\nResearch Associate<\/p>\n
T +41 79 4338784
\ne.ushakova@yahoo.com<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div>Markus Schwenke, Dr. oec. UNISG<\/h4><\/div><\/div><\/div>\nResearch Associate<\/p>\n
markus.schwenke@posteo.de<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>Methodology<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/div>System Dynamics is a methodology for dealing with dynamic complexity by means of modeling, simulation and governance. This methodology is based on the idea that any complex dynamic system can be described by two kinds of variables only: stocks and flows. This is visualized in Figure 1. Please note that the flow variables in this diagram either originate from an environment (inflow to the left) or flow into an environment (outflow to the right). This relationship is not analyzed further here; in other words, the boundary of the system is where the structure of the system represented meets the environment.<\/p>\n<\/div><\/div><\/div>
![](\"https:\/\/ifb.unisg.ch\/wp-content\/uploads\/2023\/01\/Figure1-1-1024x418.png\")
<\/a><\/span><\/div>Figure 1: System Dynamics \u2013 Stock and Flow variables<\/p>\n<\/div><\/div><\/div><\/div><\/div>
The loop shown in the picture consists of auxiliary variables, which feed back on the inflow. Suppose the stock is a population, the births are the inflows and the auxiliary variables represent the economic production of the respective system and the resultant wealth (prosperity), which thereupon reduces the birth rate. Note that the diagram does not contain any proposition if the level of wealth induces a reduction of life span and consequently the size of the population. The diagram also shows parameters, \u2013 which in the cited case would be fractional birth and death rates. The closed loop is an image of a system state, which retroacts causally onto itself. This causal process includes delays, as shown by the crossbars.<\/p>\n<\/div><\/div><\/div>
![](\"https:\/\/ifb.unisg.ch\/wp-content\/uploads\/2023\/01\/Figure2-2-1024x677.png\")
<\/a><\/span><\/div>Figure 2: System Dynamics \u2013 Causal circuits (\u201cloops\u201d)<\/p>\n<\/div><\/div><\/div><\/div><\/div>
A system model usually consists of several loops of this kind, which are connected among one another. The stock-and-flow diagram of a clearly organized macroeconomic model, built by one of our young researchers is shown in Figure 3.<\/p>\n<\/div><\/div><\/div>
![](\"https:\/\/ifb.unisg.ch\/wp-content\/uploads\/2018\/05\/system-dynamic-group_Methodik_Abbildung-3.png\")
<\/a><\/span><\/div>Figure 3: Macroeconomic Model \u2013 Stock-and-flow diagram<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Figure 3: Macroeconomic Model \u2013 Stock-and-flow diagram<\/p>\n<\/div><\/div><\/div>
![](\"https:\/\/ifb.unisg.ch\/wp-content\/uploads\/2018\/05\/system-dynamic-group_Methodik_Abbildung-4.png\")
<\/a><\/span><\/div>Figure 4: Non-linearity \u2013 simulated versus real values of interest (in%)<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Besides the nonlinear behavior of the focal variable, the diagram also shows that the simulated values only deviate little from the real values. That speaks for a good quality of the model.<\/p>\n
The insights from a simulation project can be carved out meaningfully. For this purpose, the CLDs \u2013 Causal Loop Diagrams \u2013 have proved their worth. These diagrams are also frequently useful in modeling, \u2013 for a phase of model conceptualization or qualitative modeling, which takes place before quantitative modeling. Figure 5 shows the CLD as a tool, by which the model just demonstrated was sketched out, conceptually.<\/p>\n
The circular arrows at the center of the loops indicate their polarities. In this case, all loops are of the balancing type (designated with \u201c-\u201c in Figure 3 and \u201cB\u201d in Figure 5). The polarity results from a multiplication of the signs on the arrows along the loop. A result of \u201c+\u201d or \u201cR\u201d would denote a self-reinforcing loop.<\/p>\n<\/div><\/div><\/div>
<\/span><\/div><\/div><\/div><\/div><\/div>About us<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/span><\/a><\/span><\/div>Our Team<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/span><\/a><\/span><\/div>Methodology<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/span><\/a><\/span><\/div>Research<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/span><\/a><\/span><\/div>Conferences<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/span><\/a><\/span><\/div>Publications<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>About us<\/h2><\/div><\/div><\/div>\n<\/div><\/div>System Dynamics<\/h4><\/div><\/div><\/div>\nSystem Dynamics is a discipline for the modeling, simulation and steering of complex, dynamic systems. It provides a sophisticated, generic methodology to model the structure of such systems and to simulate their behavior. This methodology was developed at the Massachusetts Institute of Technology (MIT) by Prof. Jay Forrester. It is used worldwide today.<\/p>\n
System Dynamics is unmatched in dealing with dynamic complexity in organizations of all kinds. It can provide deep insights into how they work and enable the development of powerful strategies. The methodology is applicable to dynamic issues in general. It can yield astonishing learning effects. That is why System Dynamics is not only used for questions in organizations, but also for the modeling and simulation of \u201cbig\u201d problems \u2013 ecological, social and economic.<\/p>\n<\/div><\/div><\/div>
UNISG System Dynamics Group<\/h4><\/div><\/div><\/div>\nThe UNISG System Dynamics Group (SDG) is a group of researchers at the Institute of Management and Strategy of the University of St. Gallen, Switzerland. SDG is dedicated to the modeling, simulation and governance of complex, dynamic systems. It goes back to pertinent research and teaching activities since the middle of the eighties.<\/p>\n
The System Dynamics methodology distinguishes itself from other methodologies for the analysis of complex issues. The distinction is in the modeling of feedback structures, and in capturing the nonlinearities of a system\u2019s behavior, which these structures produce. In this context, the inclusion and simulation of temporal delays as well as probabilistic functions play important roles.<\/p>\n
The SDG aims at making substantial contributions:<\/p>\n<\/div>
- <\/i><\/span>\n
to progress in the state of modeling and simulation of complex dynamic systems with an emphasis on social systems, namely companies and other organizations (public organizations, non-profit-organizations, social initiatives)<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
to the application of System Dynamics in research and practice<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
to the education of advanced students and users of the System Dynamics methodology<\/p>\n<\/div><\/li><\/ul><\/div><\/div><\/div><\/div>
<\/div><\/div><\/div>Our Team<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/div>Markus Schwaninger, Prof. em. Dr.<\/h4><\/div><\/div><\/div>\nInstitute of Management & Strategy at the University of St. Gallen
\nDufourstrasse 40a
\nCH-9000 St. Gallen<\/p>\n
T +41 71 224 2382
\nmarkus.schwaninger@unisg.ch<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div>Stefan Groesser, Prof. Dr.<\/h4><\/div><\/div><\/div>\nProfessor of Strategy
\nat Bern University of Applied Sciences
\nand Lecturer in Management at HSG<\/p>\n
School of Management
\nBr\u00fcckenstrasse 73
\nCH-3005 Bern
\nT +41 31 848 34 54
\nstefan.groesser@bfh.ch<\/a><\/p>\nLink to the profile page of BFH<\/a><\/p>\n<\/div><\/div><\/div>Evgenia Ushakova, Dr.oec. UNISG<\/h4><\/div><\/div><\/div>\nResearch Associate<\/p>\n
T +41 79 4338784
\ne.ushakova@yahoo.com<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div>Markus Schwenke, Dr. oec. UNISG<\/h4><\/div><\/div><\/div>\nResearch Associate<\/p>\n
markus.schwenke@posteo.de<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>Methodology<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/div>System Dynamics is a methodology for dealing with dynamic complexity by means of modeling, simulation and governance. This methodology is based on the idea that any complex dynamic system can be described by two kinds of variables only: stocks and flows. This is visualized in Figure 1. Please note that the flow variables in this diagram either originate from an environment (inflow to the left) or flow into an environment (outflow to the right). This relationship is not analyzed further here; in other words, the boundary of the system is where the structure of the system represented meets the environment.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 1: System Dynamics \u2013 Stock and Flow variables<\/p>\n<\/div><\/div><\/div><\/div><\/div>
The loop shown in the picture consists of auxiliary variables, which feed back on the inflow. Suppose the stock is a population, the births are the inflows and the auxiliary variables represent the economic production of the respective system and the resultant wealth (prosperity), which thereupon reduces the birth rate. Note that the diagram does not contain any proposition if the level of wealth induces a reduction of life span and consequently the size of the population. The diagram also shows parameters, \u2013 which in the cited case would be fractional birth and death rates. The closed loop is an image of a system state, which retroacts causally onto itself. This causal process includes delays, as shown by the crossbars.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 2: System Dynamics \u2013 Causal circuits (\u201cloops\u201d)<\/p>\n<\/div><\/div><\/div><\/div><\/div>
A system model usually consists of several loops of this kind, which are connected among one another. The stock-and-flow diagram of a clearly organized macroeconomic model, built by one of our young researchers is shown in Figure 3.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 3: Macroeconomic Model \u2013 Stock-and-flow diagram<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Figure 3: Macroeconomic Model \u2013 Stock-and-flow diagram<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 4: Non-linearity \u2013 simulated versus real values of interest (in%)<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Besides the nonlinear behavior of the focal variable, the diagram also shows that the simulated values only deviate little from the real values. That speaks for a good quality of the model.<\/p>\n
The insights from a simulation project can be carved out meaningfully. For this purpose, the CLDs \u2013 Causal Loop Diagrams \u2013 have proved their worth. These diagrams are also frequently useful in modeling, \u2013 for a phase of model conceptualization or qualitative modeling, which takes place before quantitative modeling. Figure 5 shows the CLD as a tool, by which the model just demonstrated was sketched out, conceptually.<\/p>\n
The circular arrows at the center of the loops indicate their polarities. In this case, all loops are of the balancing type (designated with \u201c-\u201c in Figure 3 and \u201cB\u201d in Figure 5). The polarity results from a multiplication of the signs on the arrows along the loop. A result of \u201c+\u201d or \u201cR\u201d would denote a self-reinforcing loop.<\/p>\n<\/div><\/div><\/div>
Our Team<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/span><\/a><\/span><\/div>Methodology<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/span><\/a><\/span><\/div>Research<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/span><\/a><\/span><\/div>Conferences<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/span><\/a><\/span><\/div>Publications<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>About us<\/h2><\/div><\/div><\/div>\n<\/div><\/div>System Dynamics<\/h4><\/div><\/div><\/div>\nSystem Dynamics is a discipline for the modeling, simulation and steering of complex, dynamic systems. It provides a sophisticated, generic methodology to model the structure of such systems and to simulate their behavior. This methodology was developed at the Massachusetts Institute of Technology (MIT) by Prof. Jay Forrester. It is used worldwide today.<\/p>\n
System Dynamics is unmatched in dealing with dynamic complexity in organizations of all kinds. It can provide deep insights into how they work and enable the development of powerful strategies. The methodology is applicable to dynamic issues in general. It can yield astonishing learning effects. That is why System Dynamics is not only used for questions in organizations, but also for the modeling and simulation of \u201cbig\u201d problems \u2013 ecological, social and economic.<\/p>\n<\/div><\/div><\/div>
UNISG System Dynamics Group<\/h4><\/div><\/div><\/div>\nThe UNISG System Dynamics Group (SDG) is a group of researchers at the Institute of Management and Strategy of the University of St. Gallen, Switzerland. SDG is dedicated to the modeling, simulation and governance of complex, dynamic systems. It goes back to pertinent research and teaching activities since the middle of the eighties.<\/p>\n
The System Dynamics methodology distinguishes itself from other methodologies for the analysis of complex issues. The distinction is in the modeling of feedback structures, and in capturing the nonlinearities of a system\u2019s behavior, which these structures produce. In this context, the inclusion and simulation of temporal delays as well as probabilistic functions play important roles.<\/p>\n
The SDG aims at making substantial contributions:<\/p>\n<\/div>
- <\/i><\/span>\n
to progress in the state of modeling and simulation of complex dynamic systems with an emphasis on social systems, namely companies and other organizations (public organizations, non-profit-organizations, social initiatives)<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
to the application of System Dynamics in research and practice<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
to the education of advanced students and users of the System Dynamics methodology<\/p>\n<\/div><\/li><\/ul><\/div><\/div><\/div><\/div>
<\/div><\/div><\/div>Our Team<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/div>Markus Schwaninger, Prof. em. Dr.<\/h4><\/div><\/div><\/div>\nInstitute of Management & Strategy at the University of St. Gallen
\nDufourstrasse 40a
\nCH-9000 St. Gallen<\/p>\n
T +41 71 224 2382
\nmarkus.schwaninger@unisg.ch<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div>Stefan Groesser, Prof. Dr.<\/h4><\/div><\/div><\/div>\nProfessor of Strategy
\nat Bern University of Applied Sciences
\nand Lecturer in Management at HSG<\/p>\n
School of Management
\nBr\u00fcckenstrasse 73
\nCH-3005 Bern
\nT +41 31 848 34 54
\nstefan.groesser@bfh.ch<\/a><\/p>\nLink to the profile page of BFH<\/a><\/p>\n<\/div><\/div><\/div>Evgenia Ushakova, Dr.oec. UNISG<\/h4><\/div><\/div><\/div>\nResearch Associate<\/p>\n
T +41 79 4338784
\ne.ushakova@yahoo.com<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div>Markus Schwenke, Dr. oec. UNISG<\/h4><\/div><\/div><\/div>\nResearch Associate<\/p>\n
markus.schwenke@posteo.de<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>Methodology<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/div>System Dynamics is a methodology for dealing with dynamic complexity by means of modeling, simulation and governance. This methodology is based on the idea that any complex dynamic system can be described by two kinds of variables only: stocks and flows. This is visualized in Figure 1. Please note that the flow variables in this diagram either originate from an environment (inflow to the left) or flow into an environment (outflow to the right). This relationship is not analyzed further here; in other words, the boundary of the system is where the structure of the system represented meets the environment.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 1: System Dynamics \u2013 Stock and Flow variables<\/p>\n<\/div><\/div><\/div><\/div><\/div>
The loop shown in the picture consists of auxiliary variables, which feed back on the inflow. Suppose the stock is a population, the births are the inflows and the auxiliary variables represent the economic production of the respective system and the resultant wealth (prosperity), which thereupon reduces the birth rate. Note that the diagram does not contain any proposition if the level of wealth induces a reduction of life span and consequently the size of the population. The diagram also shows parameters, \u2013 which in the cited case would be fractional birth and death rates. The closed loop is an image of a system state, which retroacts causally onto itself. This causal process includes delays, as shown by the crossbars.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 2: System Dynamics \u2013 Causal circuits (\u201cloops\u201d)<\/p>\n<\/div><\/div><\/div><\/div><\/div>
A system model usually consists of several loops of this kind, which are connected among one another. The stock-and-flow diagram of a clearly organized macroeconomic model, built by one of our young researchers is shown in Figure 3.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 3: Macroeconomic Model \u2013 Stock-and-flow diagram<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Figure 3: Macroeconomic Model \u2013 Stock-and-flow diagram<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 4: Non-linearity \u2013 simulated versus real values of interest (in%)<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Besides the nonlinear behavior of the focal variable, the diagram also shows that the simulated values only deviate little from the real values. That speaks for a good quality of the model.<\/p>\n
The insights from a simulation project can be carved out meaningfully. For this purpose, the CLDs \u2013 Causal Loop Diagrams \u2013 have proved their worth. These diagrams are also frequently useful in modeling, \u2013 for a phase of model conceptualization or qualitative modeling, which takes place before quantitative modeling. Figure 5 shows the CLD as a tool, by which the model just demonstrated was sketched out, conceptually.<\/p>\n
The circular arrows at the center of the loops indicate their polarities. In this case, all loops are of the balancing type (designated with \u201c-\u201c in Figure 3 and \u201cB\u201d in Figure 5). The polarity results from a multiplication of the signs on the arrows along the loop. A result of \u201c+\u201d or \u201cR\u201d would denote a self-reinforcing loop.<\/p>\n<\/div><\/div><\/div>
Methodology<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/span><\/a><\/span><\/div>Research<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/span><\/a><\/span><\/div>Conferences<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/span><\/a><\/span><\/div>Publications<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>About us<\/h2><\/div><\/div><\/div>\n<\/div><\/div>System Dynamics<\/h4><\/div><\/div><\/div>\nSystem Dynamics is a discipline for the modeling, simulation and steering of complex, dynamic systems. It provides a sophisticated, generic methodology to model the structure of such systems and to simulate their behavior. This methodology was developed at the Massachusetts Institute of Technology (MIT) by Prof. Jay Forrester. It is used worldwide today.<\/p>\n
System Dynamics is unmatched in dealing with dynamic complexity in organizations of all kinds. It can provide deep insights into how they work and enable the development of powerful strategies. The methodology is applicable to dynamic issues in general. It can yield astonishing learning effects. That is why System Dynamics is not only used for questions in organizations, but also for the modeling and simulation of \u201cbig\u201d problems \u2013 ecological, social and economic.<\/p>\n<\/div><\/div><\/div>
UNISG System Dynamics Group<\/h4><\/div><\/div><\/div>\nThe UNISG System Dynamics Group (SDG) is a group of researchers at the Institute of Management and Strategy of the University of St. Gallen, Switzerland. SDG is dedicated to the modeling, simulation and governance of complex, dynamic systems. It goes back to pertinent research and teaching activities since the middle of the eighties.<\/p>\n
The System Dynamics methodology distinguishes itself from other methodologies for the analysis of complex issues. The distinction is in the modeling of feedback structures, and in capturing the nonlinearities of a system\u2019s behavior, which these structures produce. In this context, the inclusion and simulation of temporal delays as well as probabilistic functions play important roles.<\/p>\n
The SDG aims at making substantial contributions:<\/p>\n<\/div>
- <\/i><\/span>\n
to progress in the state of modeling and simulation of complex dynamic systems with an emphasis on social systems, namely companies and other organizations (public organizations, non-profit-organizations, social initiatives)<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
to the application of System Dynamics in research and practice<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
to the education of advanced students and users of the System Dynamics methodology<\/p>\n<\/div><\/li><\/ul><\/div><\/div><\/div><\/div>
<\/div><\/div><\/div>Our Team<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/div>Markus Schwaninger, Prof. em. Dr.<\/h4><\/div><\/div><\/div>\nInstitute of Management & Strategy at the University of St. Gallen
\nDufourstrasse 40a
\nCH-9000 St. Gallen<\/p>\n
T +41 71 224 2382
\nmarkus.schwaninger@unisg.ch<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div>Stefan Groesser, Prof. Dr.<\/h4><\/div><\/div><\/div>\nProfessor of Strategy
\nat Bern University of Applied Sciences
\nand Lecturer in Management at HSG<\/p>\n
School of Management
\nBr\u00fcckenstrasse 73
\nCH-3005 Bern
\nT +41 31 848 34 54
\nstefan.groesser@bfh.ch<\/a><\/p>\nLink to the profile page of BFH<\/a><\/p>\n<\/div><\/div><\/div>Evgenia Ushakova, Dr.oec. UNISG<\/h4><\/div><\/div><\/div>\nResearch Associate<\/p>\n
T +41 79 4338784
\ne.ushakova@yahoo.com<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div>Markus Schwenke, Dr. oec. UNISG<\/h4><\/div><\/div><\/div>\nResearch Associate<\/p>\n
markus.schwenke@posteo.de<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>Methodology<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/div>System Dynamics is a methodology for dealing with dynamic complexity by means of modeling, simulation and governance. This methodology is based on the idea that any complex dynamic system can be described by two kinds of variables only: stocks and flows. This is visualized in Figure 1. Please note that the flow variables in this diagram either originate from an environment (inflow to the left) or flow into an environment (outflow to the right). This relationship is not analyzed further here; in other words, the boundary of the system is where the structure of the system represented meets the environment.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 1: System Dynamics \u2013 Stock and Flow variables<\/p>\n<\/div><\/div><\/div><\/div><\/div>
The loop shown in the picture consists of auxiliary variables, which feed back on the inflow. Suppose the stock is a population, the births are the inflows and the auxiliary variables represent the economic production of the respective system and the resultant wealth (prosperity), which thereupon reduces the birth rate. Note that the diagram does not contain any proposition if the level of wealth induces a reduction of life span and consequently the size of the population. The diagram also shows parameters, \u2013 which in the cited case would be fractional birth and death rates. The closed loop is an image of a system state, which retroacts causally onto itself. This causal process includes delays, as shown by the crossbars.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 2: System Dynamics \u2013 Causal circuits (\u201cloops\u201d)<\/p>\n<\/div><\/div><\/div><\/div><\/div>
A system model usually consists of several loops of this kind, which are connected among one another. The stock-and-flow diagram of a clearly organized macroeconomic model, built by one of our young researchers is shown in Figure 3.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 3: Macroeconomic Model \u2013 Stock-and-flow diagram<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Figure 3: Macroeconomic Model \u2013 Stock-and-flow diagram<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 4: Non-linearity \u2013 simulated versus real values of interest (in%)<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Besides the nonlinear behavior of the focal variable, the diagram also shows that the simulated values only deviate little from the real values. That speaks for a good quality of the model.<\/p>\n
The insights from a simulation project can be carved out meaningfully. For this purpose, the CLDs \u2013 Causal Loop Diagrams \u2013 have proved their worth. These diagrams are also frequently useful in modeling, \u2013 for a phase of model conceptualization or qualitative modeling, which takes place before quantitative modeling. Figure 5 shows the CLD as a tool, by which the model just demonstrated was sketched out, conceptually.<\/p>\n
The circular arrows at the center of the loops indicate their polarities. In this case, all loops are of the balancing type (designated with \u201c-\u201c in Figure 3 and \u201cB\u201d in Figure 5). The polarity results from a multiplication of the signs on the arrows along the loop. A result of \u201c+\u201d or \u201cR\u201d would denote a self-reinforcing loop.<\/p>\n<\/div><\/div><\/div>
Research<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/span><\/a><\/span><\/div>Conferences<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/span><\/a><\/span><\/div>Publications<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>About us<\/h2><\/div><\/div><\/div>\n<\/div><\/div>System Dynamics<\/h4><\/div><\/div><\/div>\nSystem Dynamics is a discipline for the modeling, simulation and steering of complex, dynamic systems. It provides a sophisticated, generic methodology to model the structure of such systems and to simulate their behavior. This methodology was developed at the Massachusetts Institute of Technology (MIT) by Prof. Jay Forrester. It is used worldwide today.<\/p>\n
System Dynamics is unmatched in dealing with dynamic complexity in organizations of all kinds. It can provide deep insights into how they work and enable the development of powerful strategies. The methodology is applicable to dynamic issues in general. It can yield astonishing learning effects. That is why System Dynamics is not only used for questions in organizations, but also for the modeling and simulation of \u201cbig\u201d problems \u2013 ecological, social and economic.<\/p>\n<\/div><\/div><\/div>
UNISG System Dynamics Group<\/h4><\/div><\/div><\/div>\nThe UNISG System Dynamics Group (SDG) is a group of researchers at the Institute of Management and Strategy of the University of St. Gallen, Switzerland. SDG is dedicated to the modeling, simulation and governance of complex, dynamic systems. It goes back to pertinent research and teaching activities since the middle of the eighties.<\/p>\n
The System Dynamics methodology distinguishes itself from other methodologies for the analysis of complex issues. The distinction is in the modeling of feedback structures, and in capturing the nonlinearities of a system\u2019s behavior, which these structures produce. In this context, the inclusion and simulation of temporal delays as well as probabilistic functions play important roles.<\/p>\n
The SDG aims at making substantial contributions:<\/p>\n<\/div>
- <\/i><\/span>\n
to progress in the state of modeling and simulation of complex dynamic systems with an emphasis on social systems, namely companies and other organizations (public organizations, non-profit-organizations, social initiatives)<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
to the application of System Dynamics in research and practice<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
to the education of advanced students and users of the System Dynamics methodology<\/p>\n<\/div><\/li><\/ul><\/div><\/div><\/div><\/div>
<\/div><\/div><\/div>Our Team<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/div>Markus Schwaninger, Prof. em. Dr.<\/h4><\/div><\/div><\/div>\nInstitute of Management & Strategy at the University of St. Gallen
\nDufourstrasse 40a
\nCH-9000 St. Gallen<\/p>\n
T +41 71 224 2382
\nmarkus.schwaninger@unisg.ch<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div>Stefan Groesser, Prof. Dr.<\/h4><\/div><\/div><\/div>\nProfessor of Strategy
\nat Bern University of Applied Sciences
\nand Lecturer in Management at HSG<\/p>\n
School of Management
\nBr\u00fcckenstrasse 73
\nCH-3005 Bern
\nT +41 31 848 34 54
\nstefan.groesser@bfh.ch<\/a><\/p>\nLink to the profile page of BFH<\/a><\/p>\n<\/div><\/div><\/div>Evgenia Ushakova, Dr.oec. UNISG<\/h4><\/div><\/div><\/div>\nResearch Associate<\/p>\n
T +41 79 4338784
\ne.ushakova@yahoo.com<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div>Markus Schwenke, Dr. oec. UNISG<\/h4><\/div><\/div><\/div>\nResearch Associate<\/p>\n
markus.schwenke@posteo.de<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>Methodology<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/div>System Dynamics is a methodology for dealing with dynamic complexity by means of modeling, simulation and governance. This methodology is based on the idea that any complex dynamic system can be described by two kinds of variables only: stocks and flows. This is visualized in Figure 1. Please note that the flow variables in this diagram either originate from an environment (inflow to the left) or flow into an environment (outflow to the right). This relationship is not analyzed further here; in other words, the boundary of the system is where the structure of the system represented meets the environment.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 1: System Dynamics \u2013 Stock and Flow variables<\/p>\n<\/div><\/div><\/div><\/div><\/div>
The loop shown in the picture consists of auxiliary variables, which feed back on the inflow. Suppose the stock is a population, the births are the inflows and the auxiliary variables represent the economic production of the respective system and the resultant wealth (prosperity), which thereupon reduces the birth rate. Note that the diagram does not contain any proposition if the level of wealth induces a reduction of life span and consequently the size of the population. The diagram also shows parameters, \u2013 which in the cited case would be fractional birth and death rates. The closed loop is an image of a system state, which retroacts causally onto itself. This causal process includes delays, as shown by the crossbars.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 2: System Dynamics \u2013 Causal circuits (\u201cloops\u201d)<\/p>\n<\/div><\/div><\/div><\/div><\/div>
A system model usually consists of several loops of this kind, which are connected among one another. The stock-and-flow diagram of a clearly organized macroeconomic model, built by one of our young researchers is shown in Figure 3.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 3: Macroeconomic Model \u2013 Stock-and-flow diagram<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Figure 3: Macroeconomic Model \u2013 Stock-and-flow diagram<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 4: Non-linearity \u2013 simulated versus real values of interest (in%)<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Besides the nonlinear behavior of the focal variable, the diagram also shows that the simulated values only deviate little from the real values. That speaks for a good quality of the model.<\/p>\n
The insights from a simulation project can be carved out meaningfully. For this purpose, the CLDs \u2013 Causal Loop Diagrams \u2013 have proved their worth. These diagrams are also frequently useful in modeling, \u2013 for a phase of model conceptualization or qualitative modeling, which takes place before quantitative modeling. Figure 5 shows the CLD as a tool, by which the model just demonstrated was sketched out, conceptually.<\/p>\n
The circular arrows at the center of the loops indicate their polarities. In this case, all loops are of the balancing type (designated with \u201c-\u201c in Figure 3 and \u201cB\u201d in Figure 5). The polarity results from a multiplication of the signs on the arrows along the loop. A result of \u201c+\u201d or \u201cR\u201d would denote a self-reinforcing loop.<\/p>\n<\/div><\/div><\/div>
Conferences<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/span><\/a><\/span><\/div>Publications<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>About us<\/h2><\/div><\/div><\/div>\n<\/div><\/div>System Dynamics<\/h4><\/div><\/div><\/div>\nSystem Dynamics is a discipline for the modeling, simulation and steering of complex, dynamic systems. It provides a sophisticated, generic methodology to model the structure of such systems and to simulate their behavior. This methodology was developed at the Massachusetts Institute of Technology (MIT) by Prof. Jay Forrester. It is used worldwide today.<\/p>\n
System Dynamics is unmatched in dealing with dynamic complexity in organizations of all kinds. It can provide deep insights into how they work and enable the development of powerful strategies. The methodology is applicable to dynamic issues in general. It can yield astonishing learning effects. That is why System Dynamics is not only used for questions in organizations, but also for the modeling and simulation of \u201cbig\u201d problems \u2013 ecological, social and economic.<\/p>\n<\/div><\/div><\/div>
UNISG System Dynamics Group<\/h4><\/div><\/div><\/div>\nThe UNISG System Dynamics Group (SDG) is a group of researchers at the Institute of Management and Strategy of the University of St. Gallen, Switzerland. SDG is dedicated to the modeling, simulation and governance of complex, dynamic systems. It goes back to pertinent research and teaching activities since the middle of the eighties.<\/p>\n
The System Dynamics methodology distinguishes itself from other methodologies for the analysis of complex issues. The distinction is in the modeling of feedback structures, and in capturing the nonlinearities of a system\u2019s behavior, which these structures produce. In this context, the inclusion and simulation of temporal delays as well as probabilistic functions play important roles.<\/p>\n
The SDG aims at making substantial contributions:<\/p>\n<\/div>
- <\/i><\/span>\n
to progress in the state of modeling and simulation of complex dynamic systems with an emphasis on social systems, namely companies and other organizations (public organizations, non-profit-organizations, social initiatives)<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
to the application of System Dynamics in research and practice<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
to the education of advanced students and users of the System Dynamics methodology<\/p>\n<\/div><\/li><\/ul><\/div><\/div><\/div><\/div>
<\/div><\/div><\/div>Our Team<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/div>Markus Schwaninger, Prof. em. Dr.<\/h4><\/div><\/div><\/div>\nInstitute of Management & Strategy at the University of St. Gallen
\nDufourstrasse 40a
\nCH-9000 St. Gallen<\/p>\n
T +41 71 224 2382
\nmarkus.schwaninger@unisg.ch<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div>Stefan Groesser, Prof. Dr.<\/h4><\/div><\/div><\/div>\nProfessor of Strategy
\nat Bern University of Applied Sciences
\nand Lecturer in Management at HSG<\/p>\n
School of Management
\nBr\u00fcckenstrasse 73
\nCH-3005 Bern
\nT +41 31 848 34 54
\nstefan.groesser@bfh.ch<\/a><\/p>\nLink to the profile page of BFH<\/a><\/p>\n<\/div><\/div><\/div>Evgenia Ushakova, Dr.oec. UNISG<\/h4><\/div><\/div><\/div>\nResearch Associate<\/p>\n
T +41 79 4338784
\ne.ushakova@yahoo.com<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div>Markus Schwenke, Dr. oec. UNISG<\/h4><\/div><\/div><\/div>\nResearch Associate<\/p>\n
markus.schwenke@posteo.de<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>Methodology<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/div>System Dynamics is a methodology for dealing with dynamic complexity by means of modeling, simulation and governance. This methodology is based on the idea that any complex dynamic system can be described by two kinds of variables only: stocks and flows. This is visualized in Figure 1. Please note that the flow variables in this diagram either originate from an environment (inflow to the left) or flow into an environment (outflow to the right). This relationship is not analyzed further here; in other words, the boundary of the system is where the structure of the system represented meets the environment.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 1: System Dynamics \u2013 Stock and Flow variables<\/p>\n<\/div><\/div><\/div><\/div><\/div>
The loop shown in the picture consists of auxiliary variables, which feed back on the inflow. Suppose the stock is a population, the births are the inflows and the auxiliary variables represent the economic production of the respective system and the resultant wealth (prosperity), which thereupon reduces the birth rate. Note that the diagram does not contain any proposition if the level of wealth induces a reduction of life span and consequently the size of the population. The diagram also shows parameters, \u2013 which in the cited case would be fractional birth and death rates. The closed loop is an image of a system state, which retroacts causally onto itself. This causal process includes delays, as shown by the crossbars.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 2: System Dynamics \u2013 Causal circuits (\u201cloops\u201d)<\/p>\n<\/div><\/div><\/div><\/div><\/div>
A system model usually consists of several loops of this kind, which are connected among one another. The stock-and-flow diagram of a clearly organized macroeconomic model, built by one of our young researchers is shown in Figure 3.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 3: Macroeconomic Model \u2013 Stock-and-flow diagram<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Figure 3: Macroeconomic Model \u2013 Stock-and-flow diagram<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 4: Non-linearity \u2013 simulated versus real values of interest (in%)<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Besides the nonlinear behavior of the focal variable, the diagram also shows that the simulated values only deviate little from the real values. That speaks for a good quality of the model.<\/p>\n
The insights from a simulation project can be carved out meaningfully. For this purpose, the CLDs \u2013 Causal Loop Diagrams \u2013 have proved their worth. These diagrams are also frequently useful in modeling, \u2013 for a phase of model conceptualization or qualitative modeling, which takes place before quantitative modeling. Figure 5 shows the CLD as a tool, by which the model just demonstrated was sketched out, conceptually.<\/p>\n
The circular arrows at the center of the loops indicate their polarities. In this case, all loops are of the balancing type (designated with \u201c-\u201c in Figure 3 and \u201cB\u201d in Figure 5). The polarity results from a multiplication of the signs on the arrows along the loop. A result of \u201c+\u201d or \u201cR\u201d would denote a self-reinforcing loop.<\/p>\n<\/div><\/div><\/div>
Publications<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>About us<\/h2><\/div><\/div><\/div>\n<\/div><\/div>System Dynamics<\/h4><\/div><\/div><\/div>\nSystem Dynamics is a discipline for the modeling, simulation and steering of complex, dynamic systems. It provides a sophisticated, generic methodology to model the structure of such systems and to simulate their behavior. This methodology was developed at the Massachusetts Institute of Technology (MIT) by Prof. Jay Forrester. It is used worldwide today.<\/p>\n
System Dynamics is unmatched in dealing with dynamic complexity in organizations of all kinds. It can provide deep insights into how they work and enable the development of powerful strategies. The methodology is applicable to dynamic issues in general. It can yield astonishing learning effects. That is why System Dynamics is not only used for questions in organizations, but also for the modeling and simulation of \u201cbig\u201d problems \u2013 ecological, social and economic.<\/p>\n<\/div><\/div><\/div>
UNISG System Dynamics Group<\/h4><\/div><\/div><\/div>\nThe UNISG System Dynamics Group (SDG) is a group of researchers at the Institute of Management and Strategy of the University of St. Gallen, Switzerland. SDG is dedicated to the modeling, simulation and governance of complex, dynamic systems. It goes back to pertinent research and teaching activities since the middle of the eighties.<\/p>\n
The System Dynamics methodology distinguishes itself from other methodologies for the analysis of complex issues. The distinction is in the modeling of feedback structures, and in capturing the nonlinearities of a system\u2019s behavior, which these structures produce. In this context, the inclusion and simulation of temporal delays as well as probabilistic functions play important roles.<\/p>\n
The SDG aims at making substantial contributions:<\/p>\n<\/div>
- <\/i><\/span>\n
to progress in the state of modeling and simulation of complex dynamic systems with an emphasis on social systems, namely companies and other organizations (public organizations, non-profit-organizations, social initiatives)<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
to the application of System Dynamics in research and practice<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
to the education of advanced students and users of the System Dynamics methodology<\/p>\n<\/div><\/li><\/ul><\/div><\/div><\/div><\/div>
<\/div><\/div><\/div>Our Team<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/div>Markus Schwaninger, Prof. em. Dr.<\/h4><\/div><\/div><\/div>\nInstitute of Management & Strategy at the University of St. Gallen
\nDufourstrasse 40a
\nCH-9000 St. Gallen<\/p>\n
T +41 71 224 2382
\nmarkus.schwaninger@unisg.ch<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div>Stefan Groesser, Prof. Dr.<\/h4><\/div><\/div><\/div>\nProfessor of Strategy
\nat Bern University of Applied Sciences
\nand Lecturer in Management at HSG<\/p>\n
School of Management
\nBr\u00fcckenstrasse 73
\nCH-3005 Bern
\nT +41 31 848 34 54
\nstefan.groesser@bfh.ch<\/a><\/p>\nLink to the profile page of BFH<\/a><\/p>\n<\/div><\/div><\/div>Evgenia Ushakova, Dr.oec. UNISG<\/h4><\/div><\/div><\/div>\nResearch Associate<\/p>\n
T +41 79 4338784
\ne.ushakova@yahoo.com<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div>Markus Schwenke, Dr. oec. UNISG<\/h4><\/div><\/div><\/div>\nResearch Associate<\/p>\n
markus.schwenke@posteo.de<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>Methodology<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/div>System Dynamics is a methodology for dealing with dynamic complexity by means of modeling, simulation and governance. This methodology is based on the idea that any complex dynamic system can be described by two kinds of variables only: stocks and flows. This is visualized in Figure 1. Please note that the flow variables in this diagram either originate from an environment (inflow to the left) or flow into an environment (outflow to the right). This relationship is not analyzed further here; in other words, the boundary of the system is where the structure of the system represented meets the environment.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 1: System Dynamics \u2013 Stock and Flow variables<\/p>\n<\/div><\/div><\/div><\/div><\/div>
The loop shown in the picture consists of auxiliary variables, which feed back on the inflow. Suppose the stock is a population, the births are the inflows and the auxiliary variables represent the economic production of the respective system and the resultant wealth (prosperity), which thereupon reduces the birth rate. Note that the diagram does not contain any proposition if the level of wealth induces a reduction of life span and consequently the size of the population. The diagram also shows parameters, \u2013 which in the cited case would be fractional birth and death rates. The closed loop is an image of a system state, which retroacts causally onto itself. This causal process includes delays, as shown by the crossbars.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 2: System Dynamics \u2013 Causal circuits (\u201cloops\u201d)<\/p>\n<\/div><\/div><\/div><\/div><\/div>
A system model usually consists of several loops of this kind, which are connected among one another. The stock-and-flow diagram of a clearly organized macroeconomic model, built by one of our young researchers is shown in Figure 3.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 3: Macroeconomic Model \u2013 Stock-and-flow diagram<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Figure 3: Macroeconomic Model \u2013 Stock-and-flow diagram<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 4: Non-linearity \u2013 simulated versus real values of interest (in%)<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Besides the nonlinear behavior of the focal variable, the diagram also shows that the simulated values only deviate little from the real values. That speaks for a good quality of the model.<\/p>\n
The insights from a simulation project can be carved out meaningfully. For this purpose, the CLDs \u2013 Causal Loop Diagrams \u2013 have proved their worth. These diagrams are also frequently useful in modeling, \u2013 for a phase of model conceptualization or qualitative modeling, which takes place before quantitative modeling. Figure 5 shows the CLD as a tool, by which the model just demonstrated was sketched out, conceptually.<\/p>\n
The circular arrows at the center of the loops indicate their polarities. In this case, all loops are of the balancing type (designated with \u201c-\u201c in Figure 3 and \u201cB\u201d in Figure 5). The polarity results from a multiplication of the signs on the arrows along the loop. A result of \u201c+\u201d or \u201cR\u201d would denote a self-reinforcing loop.<\/p>\n<\/div><\/div><\/div>
About us<\/h2><\/div><\/div><\/div>\n<\/div><\/div>System Dynamics<\/h4><\/div><\/div><\/div>\nSystem Dynamics is a discipline for the modeling, simulation and steering of complex, dynamic systems. It provides a sophisticated, generic methodology to model the structure of such systems and to simulate their behavior. This methodology was developed at the Massachusetts Institute of Technology (MIT) by Prof. Jay Forrester. It is used worldwide today.<\/p>\n
System Dynamics is unmatched in dealing with dynamic complexity in organizations of all kinds. It can provide deep insights into how they work and enable the development of powerful strategies. The methodology is applicable to dynamic issues in general. It can yield astonishing learning effects. That is why System Dynamics is not only used for questions in organizations, but also for the modeling and simulation of \u201cbig\u201d problems \u2013 ecological, social and economic.<\/p>\n<\/div><\/div><\/div>
UNISG System Dynamics Group<\/h4><\/div><\/div><\/div>\nThe UNISG System Dynamics Group (SDG) is a group of researchers at the Institute of Management and Strategy of the University of St. Gallen, Switzerland. SDG is dedicated to the modeling, simulation and governance of complex, dynamic systems. It goes back to pertinent research and teaching activities since the middle of the eighties.<\/p>\n
The System Dynamics methodology distinguishes itself from other methodologies for the analysis of complex issues. The distinction is in the modeling of feedback structures, and in capturing the nonlinearities of a system\u2019s behavior, which these structures produce. In this context, the inclusion and simulation of temporal delays as well as probabilistic functions play important roles.<\/p>\n
The SDG aims at making substantial contributions:<\/p>\n<\/div>
- <\/i><\/span>\n
to progress in the state of modeling and simulation of complex dynamic systems with an emphasis on social systems, namely companies and other organizations (public organizations, non-profit-organizations, social initiatives)<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
to the application of System Dynamics in research and practice<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
to the education of advanced students and users of the System Dynamics methodology<\/p>\n<\/div><\/li><\/ul><\/div><\/div><\/div><\/div>
<\/div><\/div><\/div>Our Team<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/div>Markus Schwaninger, Prof. em. Dr.<\/h4><\/div><\/div><\/div>\nInstitute of Management & Strategy at the University of St. Gallen
\nDufourstrasse 40a
\nCH-9000 St. Gallen<\/p>\n
T +41 71 224 2382
\nmarkus.schwaninger@unisg.ch<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div>Stefan Groesser, Prof. Dr.<\/h4><\/div><\/div><\/div>\nProfessor of Strategy
\nat Bern University of Applied Sciences
\nand Lecturer in Management at HSG<\/p>\n
School of Management
\nBr\u00fcckenstrasse 73
\nCH-3005 Bern
\nT +41 31 848 34 54
\nstefan.groesser@bfh.ch<\/a><\/p>\nLink to the profile page of BFH<\/a><\/p>\n<\/div><\/div><\/div>Evgenia Ushakova, Dr.oec. UNISG<\/h4><\/div><\/div><\/div>\nResearch Associate<\/p>\n
T +41 79 4338784
\ne.ushakova@yahoo.com<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div>Markus Schwenke, Dr. oec. UNISG<\/h4><\/div><\/div><\/div>\nResearch Associate<\/p>\n
markus.schwenke@posteo.de<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>Methodology<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/div>System Dynamics is a methodology for dealing with dynamic complexity by means of modeling, simulation and governance. This methodology is based on the idea that any complex dynamic system can be described by two kinds of variables only: stocks and flows. This is visualized in Figure 1. Please note that the flow variables in this diagram either originate from an environment (inflow to the left) or flow into an environment (outflow to the right). This relationship is not analyzed further here; in other words, the boundary of the system is where the structure of the system represented meets the environment.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 1: System Dynamics \u2013 Stock and Flow variables<\/p>\n<\/div><\/div><\/div><\/div><\/div>
The loop shown in the picture consists of auxiliary variables, which feed back on the inflow. Suppose the stock is a population, the births are the inflows and the auxiliary variables represent the economic production of the respective system and the resultant wealth (prosperity), which thereupon reduces the birth rate. Note that the diagram does not contain any proposition if the level of wealth induces a reduction of life span and consequently the size of the population. The diagram also shows parameters, \u2013 which in the cited case would be fractional birth and death rates. The closed loop is an image of a system state, which retroacts causally onto itself. This causal process includes delays, as shown by the crossbars.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 2: System Dynamics \u2013 Causal circuits (\u201cloops\u201d)<\/p>\n<\/div><\/div><\/div><\/div><\/div>
A system model usually consists of several loops of this kind, which are connected among one another. The stock-and-flow diagram of a clearly organized macroeconomic model, built by one of our young researchers is shown in Figure 3.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 3: Macroeconomic Model \u2013 Stock-and-flow diagram<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Figure 3: Macroeconomic Model \u2013 Stock-and-flow diagram<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 4: Non-linearity \u2013 simulated versus real values of interest (in%)<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Besides the nonlinear behavior of the focal variable, the diagram also shows that the simulated values only deviate little from the real values. That speaks for a good quality of the model.<\/p>\n
The insights from a simulation project can be carved out meaningfully. For this purpose, the CLDs \u2013 Causal Loop Diagrams \u2013 have proved their worth. These diagrams are also frequently useful in modeling, \u2013 for a phase of model conceptualization or qualitative modeling, which takes place before quantitative modeling. Figure 5 shows the CLD as a tool, by which the model just demonstrated was sketched out, conceptually.<\/p>\n
The circular arrows at the center of the loops indicate their polarities. In this case, all loops are of the balancing type (designated with \u201c-\u201c in Figure 3 and \u201cB\u201d in Figure 5). The polarity results from a multiplication of the signs on the arrows along the loop. A result of \u201c+\u201d or \u201cR\u201d would denote a self-reinforcing loop.<\/p>\n<\/div><\/div><\/div>
System Dynamics<\/h4><\/div><\/div><\/div>\nSystem Dynamics is a discipline for the modeling, simulation and steering of complex, dynamic systems. It provides a sophisticated, generic methodology to model the structure of such systems and to simulate their behavior. This methodology was developed at the Massachusetts Institute of Technology (MIT) by Prof. Jay Forrester. It is used worldwide today.<\/p>\n
System Dynamics is unmatched in dealing with dynamic complexity in organizations of all kinds. It can provide deep insights into how they work and enable the development of powerful strategies. The methodology is applicable to dynamic issues in general. It can yield astonishing learning effects. That is why System Dynamics is not only used for questions in organizations, but also for the modeling and simulation of \u201cbig\u201d problems \u2013 ecological, social and economic.<\/p>\n<\/div><\/div><\/div>
UNISG System Dynamics Group<\/h4><\/div><\/div><\/div>\nThe UNISG System Dynamics Group (SDG) is a group of researchers at the Institute of Management and Strategy of the University of St. Gallen, Switzerland. SDG is dedicated to the modeling, simulation and governance of complex, dynamic systems. It goes back to pertinent research and teaching activities since the middle of the eighties.<\/p>\n
The System Dynamics methodology distinguishes itself from other methodologies for the analysis of complex issues. The distinction is in the modeling of feedback structures, and in capturing the nonlinearities of a system\u2019s behavior, which these structures produce. In this context, the inclusion and simulation of temporal delays as well as probabilistic functions play important roles.<\/p>\n
The SDG aims at making substantial contributions:<\/p>\n<\/div>
- <\/i><\/span>\n
to progress in the state of modeling and simulation of complex dynamic systems with an emphasis on social systems, namely companies and other organizations (public organizations, non-profit-organizations, social initiatives)<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
to the application of System Dynamics in research and practice<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
to the education of advanced students and users of the System Dynamics methodology<\/p>\n<\/div><\/li><\/ul><\/div><\/div><\/div><\/div>
<\/div><\/div><\/div>Our Team<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/div>Markus Schwaninger, Prof. em. Dr.<\/h4><\/div><\/div><\/div>\nInstitute of Management & Strategy at the University of St. Gallen
\nDufourstrasse 40a
\nCH-9000 St. Gallen<\/p>\n
T +41 71 224 2382
\nmarkus.schwaninger@unisg.ch<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div>Stefan Groesser, Prof. Dr.<\/h4><\/div><\/div><\/div>\nProfessor of Strategy
\nat Bern University of Applied Sciences
\nand Lecturer in Management at HSG<\/p>\n
School of Management
\nBr\u00fcckenstrasse 73
\nCH-3005 Bern
\nT +41 31 848 34 54
\nstefan.groesser@bfh.ch<\/a><\/p>\nLink to the profile page of BFH<\/a><\/p>\n<\/div><\/div><\/div>Evgenia Ushakova, Dr.oec. UNISG<\/h4><\/div><\/div><\/div>\nResearch Associate<\/p>\n
T +41 79 4338784
\ne.ushakova@yahoo.com<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div>Markus Schwenke, Dr. oec. UNISG<\/h4><\/div><\/div><\/div>\nResearch Associate<\/p>\n
markus.schwenke@posteo.de<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>Methodology<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/div>System Dynamics is a methodology for dealing with dynamic complexity by means of modeling, simulation and governance. This methodology is based on the idea that any complex dynamic system can be described by two kinds of variables only: stocks and flows. This is visualized in Figure 1. Please note that the flow variables in this diagram either originate from an environment (inflow to the left) or flow into an environment (outflow to the right). This relationship is not analyzed further here; in other words, the boundary of the system is where the structure of the system represented meets the environment.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 1: System Dynamics \u2013 Stock and Flow variables<\/p>\n<\/div><\/div><\/div><\/div><\/div>
The loop shown in the picture consists of auxiliary variables, which feed back on the inflow. Suppose the stock is a population, the births are the inflows and the auxiliary variables represent the economic production of the respective system and the resultant wealth (prosperity), which thereupon reduces the birth rate. Note that the diagram does not contain any proposition if the level of wealth induces a reduction of life span and consequently the size of the population. The diagram also shows parameters, \u2013 which in the cited case would be fractional birth and death rates. The closed loop is an image of a system state, which retroacts causally onto itself. This causal process includes delays, as shown by the crossbars.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 2: System Dynamics \u2013 Causal circuits (\u201cloops\u201d)<\/p>\n<\/div><\/div><\/div><\/div><\/div>
A system model usually consists of several loops of this kind, which are connected among one another. The stock-and-flow diagram of a clearly organized macroeconomic model, built by one of our young researchers is shown in Figure 3.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 3: Macroeconomic Model \u2013 Stock-and-flow diagram<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Figure 3: Macroeconomic Model \u2013 Stock-and-flow diagram<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 4: Non-linearity \u2013 simulated versus real values of interest (in%)<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Besides the nonlinear behavior of the focal variable, the diagram also shows that the simulated values only deviate little from the real values. That speaks for a good quality of the model.<\/p>\n
The insights from a simulation project can be carved out meaningfully. For this purpose, the CLDs \u2013 Causal Loop Diagrams \u2013 have proved their worth. These diagrams are also frequently useful in modeling, \u2013 for a phase of model conceptualization or qualitative modeling, which takes place before quantitative modeling. Figure 5 shows the CLD as a tool, by which the model just demonstrated was sketched out, conceptually.<\/p>\n
The circular arrows at the center of the loops indicate their polarities. In this case, all loops are of the balancing type (designated with \u201c-\u201c in Figure 3 and \u201cB\u201d in Figure 5). The polarity results from a multiplication of the signs on the arrows along the loop. A result of \u201c+\u201d or \u201cR\u201d would denote a self-reinforcing loop.<\/p>\n<\/div><\/div><\/div>
System Dynamics is a discipline for the modeling, simulation and steering of complex, dynamic systems. It provides a sophisticated, generic methodology to model the structure of such systems and to simulate their behavior. This methodology was developed at the Massachusetts Institute of Technology (MIT) by Prof. Jay Forrester. It is used worldwide today.<\/p>\n
System Dynamics is unmatched in dealing with dynamic complexity in organizations of all kinds. It can provide deep insights into how they work and enable the development of powerful strategies. The methodology is applicable to dynamic issues in general. It can yield astonishing learning effects. That is why System Dynamics is not only used for questions in organizations, but also for the modeling and simulation of \u201cbig\u201d problems \u2013 ecological, social and economic.<\/p>\n<\/div><\/div><\/div>
UNISG System Dynamics Group<\/h4><\/div><\/div><\/div>\nThe UNISG System Dynamics Group (SDG) is a group of researchers at the Institute of Management and Strategy of the University of St. Gallen, Switzerland. SDG is dedicated to the modeling, simulation and governance of complex, dynamic systems. It goes back to pertinent research and teaching activities since the middle of the eighties.<\/p>\n
The System Dynamics methodology distinguishes itself from other methodologies for the analysis of complex issues. The distinction is in the modeling of feedback structures, and in capturing the nonlinearities of a system\u2019s behavior, which these structures produce. In this context, the inclusion and simulation of temporal delays as well as probabilistic functions play important roles.<\/p>\n
The SDG aims at making substantial contributions:<\/p>\n<\/div>
- <\/i><\/span>\n
to progress in the state of modeling and simulation of complex dynamic systems with an emphasis on social systems, namely companies and other organizations (public organizations, non-profit-organizations, social initiatives)<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
to the application of System Dynamics in research and practice<\/p>\n<\/div><\/li>
- <\/i><\/span>\n
to the education of advanced students and users of the System Dynamics methodology<\/p>\n<\/div><\/li><\/ul><\/div><\/div><\/div><\/div>
<\/div><\/div><\/div>Our Team<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/div>Markus Schwaninger, Prof. em. Dr.<\/h4><\/div><\/div><\/div>\nInstitute of Management & Strategy at the University of St. Gallen
\nDufourstrasse 40a
\nCH-9000 St. Gallen<\/p>\n
T +41 71 224 2382
\nmarkus.schwaninger@unisg.ch<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div>Stefan Groesser, Prof. Dr.<\/h4><\/div><\/div><\/div>\nProfessor of Strategy
\nat Bern University of Applied Sciences
\nand Lecturer in Management at HSG<\/p>\n
School of Management
\nBr\u00fcckenstrasse 73
\nCH-3005 Bern
\nT +41 31 848 34 54
\nstefan.groesser@bfh.ch<\/a><\/p>\nLink to the profile page of BFH<\/a><\/p>\n<\/div><\/div><\/div>Evgenia Ushakova, Dr.oec. UNISG<\/h4><\/div><\/div><\/div>\nResearch Associate<\/p>\n
T +41 79 4338784
\ne.ushakova@yahoo.com<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div>Markus Schwenke, Dr. oec. UNISG<\/h4><\/div><\/div><\/div>\nResearch Associate<\/p>\n
markus.schwenke@posteo.de<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>Methodology<\/h2><\/div><\/div><\/div>\n<\/div><\/div><\/div><\/div>System Dynamics is a methodology for dealing with dynamic complexity by means of modeling, simulation and governance. This methodology is based on the idea that any complex dynamic system can be described by two kinds of variables only: stocks and flows. This is visualized in Figure 1. Please note that the flow variables in this diagram either originate from an environment (inflow to the left) or flow into an environment (outflow to the right). This relationship is not analyzed further here; in other words, the boundary of the system is where the structure of the system represented meets the environment.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 1: System Dynamics \u2013 Stock and Flow variables<\/p>\n<\/div><\/div><\/div><\/div><\/div>
The loop shown in the picture consists of auxiliary variables, which feed back on the inflow. Suppose the stock is a population, the births are the inflows and the auxiliary variables represent the economic production of the respective system and the resultant wealth (prosperity), which thereupon reduces the birth rate. Note that the diagram does not contain any proposition if the level of wealth induces a reduction of life span and consequently the size of the population. The diagram also shows parameters, \u2013 which in the cited case would be fractional birth and death rates. The closed loop is an image of a system state, which retroacts causally onto itself. This causal process includes delays, as shown by the crossbars.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 2: System Dynamics \u2013 Causal circuits (\u201cloops\u201d)<\/p>\n<\/div><\/div><\/div><\/div><\/div>
A system model usually consists of several loops of this kind, which are connected among one another. The stock-and-flow diagram of a clearly organized macroeconomic model, built by one of our young researchers is shown in Figure 3.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 3: Macroeconomic Model \u2013 Stock-and-flow diagram<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Figure 3: Macroeconomic Model \u2013 Stock-and-flow diagram<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 4: Non-linearity \u2013 simulated versus real values of interest (in%)<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Besides the nonlinear behavior of the focal variable, the diagram also shows that the simulated values only deviate little from the real values. That speaks for a good quality of the model.<\/p>\n
The insights from a simulation project can be carved out meaningfully. For this purpose, the CLDs \u2013 Causal Loop Diagrams \u2013 have proved their worth. These diagrams are also frequently useful in modeling, \u2013 for a phase of model conceptualization or qualitative modeling, which takes place before quantitative modeling. Figure 5 shows the CLD as a tool, by which the model just demonstrated was sketched out, conceptually.<\/p>\n
The circular arrows at the center of the loops indicate their polarities. In this case, all loops are of the balancing type (designated with \u201c-\u201c in Figure 3 and \u201cB\u201d in Figure 5). The polarity results from a multiplication of the signs on the arrows along the loop. A result of \u201c+\u201d or \u201cR\u201d would denote a self-reinforcing loop.<\/p>\n<\/div><\/div><\/div>
The UNISG System Dynamics Group (SDG) is a group of researchers at the Institute of Management and Strategy of the University of St. Gallen, Switzerland. SDG is dedicated to the modeling, simulation and governance of complex, dynamic systems. It goes back to pertinent research and teaching activities since the middle of the eighties.<\/p>\n
The System Dynamics methodology distinguishes itself from other methodologies for the analysis of complex issues. The distinction is in the modeling of feedback structures, and in capturing the nonlinearities of a system\u2019s behavior, which these structures produce. In this context, the inclusion and simulation of temporal delays as well as probabilistic functions play important roles.<\/p>\n
The SDG aims at making substantial contributions:<\/p>\n<\/div>
- <\/i><\/span>\n
to progress in the state of modeling and simulation of complex dynamic systems with an emphasis on social systems, namely companies and other organizations (public organizations, non-profit-organizations, social initiatives)<\/p>\n<\/div><\/li>
- <\/i><\/span>
\nto the application of System Dynamics in research and practice<\/p>\n<\/div><\/li>
- <\/i><\/span>
\nto the education of advanced students and users of the System Dynamics methodology<\/p>\n<\/div><\/li><\/ul><\/div><\/div><\/div><\/div>
<\/div><\/div><\/div>Our Team<\/h2><\/div>
<\/div><\/div>\n<\/div><\/div><\/div><\/div>Markus Schwaninger, Prof. em. Dr.<\/h4><\/div>
<\/div><\/div>\nInstitute of Management & Strategy at the University of St. Gallen
\nDufourstrasse 40a
\nCH-9000 St. Gallen<\/p>\nT +41 71 224 2382
\nmarkus.schwaninger@unisg.ch<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div>
Stefan Groesser, Prof. Dr.<\/h4><\/div>
<\/div><\/div>\nProfessor of Strategy
\nat Bern University of Applied Sciences
\nand Lecturer in Management at HSG<\/p>\nSchool of Management
\nBr\u00fcckenstrasse 73
\nCH-3005 Bern
\nT +41 31 848 34 54
\nstefan.groesser@bfh.ch<\/a><\/p>\nLink to the profile page of BFH<\/a><\/p>\n<\/div><\/div><\/div>
Evgenia Ushakova, Dr.oec. UNISG<\/h4><\/div>
<\/div><\/div>\nResearch Associate<\/p>\n
T +41 79 4338784
\ne.ushakova@yahoo.com<\/a><\/p>\nLink to Alexandria<\/a><\/p>\n<\/div><\/div><\/div>
Markus Schwenke, Dr. oec. UNISG<\/h4><\/div>
<\/div><\/div>\nResearch Associate<\/p>\n
markus.schwenke@posteo.de<\/a><\/p>\n
Link to Alexandria<\/a><\/p>\n<\/div><\/div><\/div><\/div><\/div>
<\/div><\/div><\/div>Methodology<\/h2><\/div>
<\/div><\/div>\n<\/div><\/div><\/div><\/div>System Dynamics is a methodology for dealing with dynamic complexity by means of modeling, simulation and governance. This methodology is based on the idea that any complex dynamic system can be described by two kinds of variables only: stocks and flows. This is visualized in Figure 1. Please note that the flow variables in this diagram either originate from an environment (inflow to the left) or flow into an environment (outflow to the right). This relationship is not analyzed further here; in other words, the boundary of the system is where the structure of the system represented meets the environment.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 1: System Dynamics \u2013 Stock and Flow variables<\/p>\n<\/div><\/div><\/div><\/div><\/div>
The loop shown in the picture consists of auxiliary variables, which feed back on the inflow. Suppose the stock is a population, the births are the inflows and the auxiliary variables represent the economic production of the respective system and the resultant wealth (prosperity), which thereupon reduces the birth rate. Note that the diagram does not contain any proposition if the level of wealth induces a reduction of life span and consequently the size of the population. The diagram also shows parameters, \u2013 which in the cited case would be fractional birth and death rates. The closed loop is an image of a system state, which retroacts causally onto itself. This causal process includes delays, as shown by the crossbars.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 2: System Dynamics \u2013 Causal circuits (\u201cloops\u201d)<\/p>\n<\/div><\/div><\/div><\/div><\/div>
A system model usually consists of several loops of this kind, which are connected among one another. The stock-and-flow diagram of a clearly organized macroeconomic model, built by one of our young researchers is shown in Figure 3.<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 3: Macroeconomic Model \u2013 Stock-and-flow diagram<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Figure 3: Macroeconomic Model \u2013 Stock-and-flow diagram<\/p>\n<\/div><\/div><\/div>
<\/a><\/span><\/div>Figure 4: Non-linearity \u2013 simulated versus real values of interest (in%)<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Besides the nonlinear behavior of the focal variable, the diagram also shows that the simulated values only deviate little from the real values. That speaks for a good quality of the model.<\/p>\n
The insights from a simulation project can be carved out meaningfully. For this purpose, the CLDs \u2013 Causal Loop Diagrams \u2013 have proved their worth. These diagrams are also frequently useful in modeling, \u2013 for a phase of model conceptualization or qualitative modeling, which takes place before quantitative modeling. Figure 5 shows the CLD as a tool, by which the model just demonstrated was sketched out, conceptually.<\/p>\n
The circular arrows at the center of the loops indicate their polarities. In this case, all loops are of the balancing type (designated with \u201c-\u201c in Figure 3 and \u201cB\u201d in Figure 5). The polarity results from a multiplication of the signs on the arrows along the loop. A result of \u201c+\u201d or \u201cR\u201d would denote a self-reinforcing loop.<\/p>\n<\/div><\/div><\/div>
- <\/i><\/span>
![](\"https:\/\/ifb.unisg.ch\/wp-content\/uploads\/2018\/05\/system-dynamic-group_Methodik_Abbildung-5.png\")
<\/a><\/span><\/div>![](\"https:\/\/ifb.unisg.ch\/wp-content\/uploads\/2023\/01\/Figure3-1024x685.png\"){kind=link}