Cheatsheet

Primitives

Here you can find all functional building blocks at a glance!

Primitive	Type	Meaning	Examples
	SD	Accumulated quantities or values that build up or deplete over time Stores the current value at different calculation time steps Changes through inflows or outflows	Population, water level, energy content
	SD	Describes the rate at which a stock increases or decreases Changes the stock but does not store values itself Can depend on other variables	Birth rate, water inflow, energy consumption
	Common	Modern standard by the Open Geospatial Consortium (OGC) Provision and retrieval of geodata via web interfaces Datasets from Geoportal via the Hamburg Masterportal	Census data, district data, infrastructure
	Common	Variable influencing factors No value storage Influences other calculation variables	—
	Common	Performs a calculation, transformation, or derivation from other variables or constants Similar to a helper variable; does not store values but is recalculated at each simulation time step Graphically represents input–output relationships	—
	Common	Structural element that simplifies model organization Selected model parts can be collected, grouped, and moved	—
	ABM	Represents the current situation or configuration of an agent, influencing its behavior State changes are triggered by rules, probabilities, or environmental factors	At home, at work, in quarantine
	ABM	Represents transitions between states of an agent Defines when and under what conditions an agent should change states	Healthy → infected, undecided → convinced, moving to another city
	ABM	Concrete actions performed by an agent during a state or during a state transition Describe operational behavioral rules, e.g., moving, interacting with other agents, consuming resources	Get vaccinated, find the nearest bike station, influence other agents
	ABM	An autonomous entity with its own states, properties, and behavioral rules Can interact with its environment and other agents Can make independent decisions, act state-dependently, change over time, and interact locally	Car in traffic, consumer in a market model, machine in a factory
	ABM	The total set of agents of a certain type within a model Agents share common characteristics or behavioral types	Residents in a city, vehicles on a road, trees in a neighborhood

Interface

Here you can find all interface components at a glance!

Interface	Component	Functions
	Navibar	Return to home screen List my models List public models Legal documents Language switch (DE/EN) User account name Log out
	My Models	Search function Filter function Create new model Open model Edit model name & description Delete model
	Sidebar	Model information Scenario settings Version settings Model settings
	Model Settings	Simulation start time Simulation duration Time intervals Time unit Computation algorithm Globals & Macros
	Clone Model	New model name
	Share Model	Recipient email address Assigned role
	Publish Model	Version type Publish as model server endpoint Notes
	Activebar	Primitives Run simulation
	Primitives	11 functional building blocks Pin-to-screen function
	Simulation Results	Display results in line or scatter chart Pin-to-screen function Speed settings
	Canvas	Workspace for placing primitives Graphically connect primitives / create & delete ghost variables
	Primitive Settings Window	Settings for the respective primitive Interface & scenario settings Validation
	Scenario Settings	Parameter in primitive settings window Scenario settings in the sidebar

Glossary

Here you’ll find some important definitions at a glance!

Term	Definition
Model	A simplified, abstract representation of a real or imagined system that approximates certain aspects of the system depending on its purpose. Models can be physical, conceptual, or mathematical.
Model Structure	The formal organization of a model, consisting of equations, dependencies (SD) or states, rules, and interactions (ABM). Defines the functional logic of the system.
System Dynamics	A model-based approach to studying and describing complex, feedback-driven systems using stock and flow variables. Particularly suited for aggregated, long-term developments.
Agent-Based Modeling	Simulates systems through the interaction of many individual, autonomous agents, each following its own rules and goals. Suitable when individual behavior and interactions are key to system dynamics.
Causal Diagram	A visualization of cause–effect relationships, often used as a precursor or documentation for SD models. Shows qualitative system connections.
System Boundary	Defines the limits of the modeled system relative to its environment, determining which variables are included or excluded.
Simulation	Examining a system over time to analyze the behavior of a real or digital model under specific conditions.
Initialization	Defining the initial conditions of a model (e.g., start values, configurations). Essential for reproducibility and comparability.
Discretization	Converting continuous processes into discrete time steps (especially in SD models), affecting numerical accuracy and runtime.
Agent Interaction	In ABM: explicit coupling of agents via spatial proximity, rules, networks, or messaging. Leads to emergent behavior.
Rule-Based Modeling	Modeling approach where decisions or state transitions follow explicit rules. Common in ABM, especially for state transitions.
Dynamic Feedback	Circular causal relationships that evolve over time. In SD: positive feedback leads to growth, negative to stabilization.
Scenario Management	Methodical definition, execution, and evaluation of various future developments. In SD: via parameter combinations; in ABM: also via alternative rule sets.
Robust Decision Support	Identifying strategies that remain effective under high uncertainty and many possible futures (e.g., Robust Decision Making).
Calibration	Adjusting model parameters to empirical data to optimize the match to observed reality.
Verification	Technical check to ensure the model is implemented correctly — “Are we building the model right?” e.g., debugging, unit testing, source code review.
Validation	Assessing whether the model is consistent with the real system — “Are we building the right model?” e.g., expert feedback, data comparison, retrodiction.
Hybrid Modeling	Combining different modeling paradigms (e.g., SD+ABM) to capture both individual and systemic dynamics.
API (Application Programming Interface)	A standardized interface for software components to communicate. Defines how functions, data, or services are accessed and used by other applications without exposing internal structures.
Model Evaluation	Holistic assessment of a model in terms of assumptions, logic, results, plausibility, transparency, and applicability.
Behavioral Assumptions	Theoretically or empirically derived hypotheses about how actors make decisions or change states. Critical in ABM.
Versioning	Traceable storage of model development stages (e.g., via Git). Essential for reproducibility and collaboration.
Model Transparency	The degree to which a model, its structure, and assumptions are openly documented and made understandable — a foundation for trust and reuse.