• Documentation
  • Core API
  • SmartOpenHamburg API
  • Model Components API
  • Common API

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    • MARS DSL Language
    • MARS Runtime System
      • Getting started with MARS
      • Basic Concepts
        • Multi-Agent-Simulation
        • Agents
        • Layers
        • Entities
        • Environments
          • SpatialHashEnvironment
          • GeoHashEnvironment
          • SpatialGraphEnvironment
          • CollisionEnvironment
        • Model Setup
      • Model Configuration
        • Agent and Entity Configuration
        • Layer Configuration
        • Output Filter
        • Simulation Configuration Options
        • Simulation Output Formats
      • Data Sources
        • Geospatial Data Sources
        • Geospatial Data Types
        • ASCII grid (.asc)
        • CSV
        • Time-series Data
      • Analysis and Visualization
        • Visualizing Agent Movement Trajectories
        • Simple Live Visualization
        • Analyzing Output Data
      • Tutorials
        • Creating a new MARS C# project
        • Creating a starting point for your model
        • Creating vector and raster layer files
        • Building your first model (wolf-sheep-grass)
        • Common problems and solutions
        • Acquire geo data for layers
        • Build and start your model in Kubernetes cluster
    • SmartOpenHamburg
      • Quick Start
      • Ready to use scenarios (boxes)
        • Ferry Transfer
        • Green4Bikes
        • Results
        • Result Schemas
      • Layer
        • Multimodal Layer
        • Modal Layer
        • Scheduling Layer
        • Vector Layer
      • Agents
        • Behaviour Model
        • Multimodal
        • Multi-Capable
        • Multi-Modality
        • Citizen
        • Traveler
      • Entities
        • Referencing
        • Street Vehicles
        • Bicycle Vehicles
        • Ferry

    Models: a general overview

    In this article, general concepts such as model and multi-agent system are described. A general understanding of these concepts is essential to being able to design effective models and work with the MARS systems.


    Model

    A model is a simplified representation of some specific aspects of reality. The goal is creating a model is typically to define and understand a concrete information system that closely resembles its real-world counterpart. When working with and running a model (in case of MARS, in the form of simulations), the obtained output data can be used to inform decisions in the real world.


    Tick-based simulation

    Tick-based simulation are in contrast to continuous simulations divided into steps. These steps symbolize a time-driven progress. Every tick stands for an equally large time frame, which is called Δt. This time frame is fixed for the whole simulation, so that for example a simulation is progressing in steps of 1 minute. Every scenario runs for a defined amount of ticks. In every tick all tick-based elements (agents and active layers) have the possibility to act. Time-referenced scenarios have a start and an end time-point and a step length (Δt). The amount of ticks is then inferred.


    Multi-Agent Systems

    Agent-based modelling derives from the field of artificial intelligence (AI). This simulation paradigm incorporates individuals, so-called agents, who interact with each other and their surroundings. The behavior is programmed on an individual level to follow a set of rules: The interactions between agents that occur as a result of individual behavior are studied to gain insights into collective behavior. Note that an agent is not restricted to be an individual but can also be a group, community, or other entity that acts and reacts to external conditions.

    The way of creating results bottom-up from an individual's actions leading to complex effect makes multi-agent modelling especially well-suited for research in the social sciences.

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