CausalLoopAgile system dynamics modeling with quantitative causal loop diagrams (CLD+) 
Information level for CLDlike modeling 

Information level for CLDmapping 

Evaluation of a stock on a [0,1] scale 

Directly set information using a ramp input profile 

Directly influencing a stock to keep it at setpoint value 

Directly influencing a stock to keep it at setpoint value 

Exponential growth or decline process 

Process of growth or decline at given rate 

Sshaped logistic growth process 

Fractional rate of change is proportional to that of the influencing stock 

Process driven by elasticity and additional exponential delay 

Rate of change is proportional to that of the influencing stock 

Process driven by proportionality with additional exponential delay 

Flow is obtained as closing the gap to input value 

Flow is obtained as closing the gap to input stock value 

Exponential delay of input flow 

Fixed or pure delay of input flow 

Interaction with linear and nonlinear terms 

Interaction with linear and nonlinear terms 

Splitting a flow into n flows 

Positively sloping sshaped lookup 

Negatively sloping sshaped lookup 

Positively sloping sshaped lookup (centered at origin) 

Negatively sloping sshaped lookup (centered at origin) 

Tablebased lookup with manual input of the interpolation table 

Tablebased lookup given on file 

Generalized mean function for multiple stock information inputs 

Provide amount in stock information 

Provide rate of net flow information 

Generalized mean function for multiple information inputs 

Sum all inputs 

Multiply all inputs 

Loopindicator for clockwise feedback loop 

Loopindicator for counter clockwise feedback loop 

Directional indicator for material flows in a diagram 

Directional indicator for information flows in a diagram 

Class to be used for comments or sketch variables 
This information is part of the Business Simulation Library (BSL). Please support this work and ► donate.
This package contains highlevel classes (i.e., wrappers) that allow to quickly set up dynamic models in a Causal Loop Diagram (CLD) fashion. Unlike conventional CLD such models can immediately be simulated and analyzed.
A typical workflow in an "Agile Dynamic Modeling" setup would be to quickly establish a first working model using the most relevant variables as starting points and consider them to be stocks/levels. Exogenous processes of change and/or control next to simplified relations between the variables—defined by elasticities or proportionalities—will then give rise to dynamic behavior.
Such a preliminary model can be made more expressive by introducing lookups to modify constant coefficients (e.g., factors of proportionality) thus making the models more nonlinear. As van Zijderveld [24] has shown, such models may even suffice in situations of tight budgets or lower stakes.
True to the Agile paradigm, we may start out with a preliminary, working model and replace any of its parts by more elaborate models as needed. Such a gradual approach to modeling is the sweet spot of objectoriented modeling in Modelica.
Since not all component names are visible in the diagram display for CausalLoop
classes the following table shows the defaultComponentName
for the main classes of the package:
defaultComponentName  Explicit Name  Classes 
s  Stocks (aka states)  SimpleInformationLevel, 
p  Processes (exogenous change ~)  ExponentialChange, 
r  Relations (aka links)  Elasticity, 
d  Delays  ExponentialDelay, 
c  Controls  InputControl, 
b  Blocks (i.e., information processing)  Lookup_ type,Aggregation, 
f  Flows  UnidirectionalFlow, 
L  Loop indicators  LoopIndicator_CW, 
lab  Labels (i.e., flow indicators)  MatFlowIndicator, 
To see the components of this package at work, have a look at these example models:
A short video presentation (12 min) introducing quantitative causal loop diagramming can be found here.
Copyright © 2021 Guido Wolf Reichert
Licensed under the EUPL1.2 or later