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Ensure model reproducibility

Baptiste Lesquoy edited this page Apr 25, 2023 · 3 revisions

Ensure model's reproducibility

There has been a huge effort made in GAMA development in order to ensure the reproducibility of the simulations, i.e. when several simulations of the same models are launched with the same random generator seed and same parameter values, they are supposed to provide the same results.

Nevertheless, GAMA provides several ways to speed up simulations runs, e.g. by making parallel the execution of some agents' behaviors. The use of parallelism may destroy the reproducibility of the simulations. More generally, there are many sources of uncertainty which can break this reproducibility.

How to ensure reproducibility of a model?

If you aim at reproducibility, you need to reduce as much as possible all the sources of uncertainty.

  • Set the random number generator seed (explicitly set a value to the model's seed global attribute).
  • Reduce the parallel execution of agents' behaviors.
    • remove all the explicitly parallel execution, in particular remove / set to false all the parallel facets (e.g. in the loop, ask...).
    • Set all of GAMA's settings regarding parallelization to false. You can find them in the Preferences menu, then under the tab Execution at the section Parallelism to disable them globally, or you can set them to false only in your experiment with the corresponding variables as shown belown:
experiment 'any exp' {
  init {
	//Make grids schedule their agents in parallel
	gama.pref_parallel_grids <- false;
	//Make experiments run simulations in parallel
	gama.pref_parallel_simulations <- true;
	//Make species schedule their agents in parallel
	gama.pref_parallel_species <- false;
  }
}
  • Displays are computed independently of the simulation, and in parallel. Limit computation and model modifications in the aspects.
    • Remove any modification of the model in the aspects.
    • Do not use any random operators in the aspects (e.g. rnd, one_of, any ...).
  • The use of asynchronous communications (using network) with external applications, the use of files (in particular if they are changed externally) can also modify the behavior of simulations
  • As a safety measure, you can also set your random number generator to mersenne as others may not have been as much tested for reproducibility
  1. What's new (Changelog)
  1. Installation and Launching
    1. Installation
    2. Launching GAMA
    3. Updating GAMA
    4. Installing Plugins
  2. Workspace, Projects and Models
    1. Navigating in the Workspace
    2. Changing Workspace
    3. Importing Models
  3. Editing Models
    1. GAML Editor (Generalities)
    2. GAML Editor Tools
    3. Validation of Models
  4. Running Experiments
    1. Launching Experiments
    2. Experiments User interface
    3. Controls of experiments
    4. Parameters view
    5. Inspectors and monitors
    6. Displays
    7. Batch Specific UI
    8. Errors View
  5. Running Headless
    1. Headless Batch
    2. Headless Server
    3. Headless Legacy
  6. Preferences
  7. Troubleshooting
  1. Introduction
    1. Start with GAML
    2. Organization of a Model
    3. Basic programming concepts in GAML
  2. Manipulate basic Species
  3. Global Species
    1. Regular Species
    2. Defining Actions and Behaviors
    3. Interaction between Agents
    4. Attaching Skills
    5. Inheritance
  4. Defining Advanced Species
    1. Grid Species
    2. Graph Species
    3. Mirror Species
    4. Multi-Level Architecture
  5. Defining GUI Experiment
    1. Defining Parameters
    2. Defining Displays Generalities
    3. Defining 3D Displays
    4. Defining Charts
    5. Defining Monitors and Inspectors
    6. Defining Export files
    7. Defining User Interaction
  6. Exploring Models
    1. Run Several Simulations
    2. Batch Experiments
    3. Exploration Methods
  7. Optimizing Model Section
    1. Runtime Concepts
    2. Optimizing Models
  8. Multi-Paradigm Modeling
    1. Control Architecture
    2. Defining Differential Equations
  1. Manipulate OSM Data
  2. Diffusion
  3. Using Database
  4. Using FIPA ACL
  5. Using BDI with BEN
  6. Using Driving Skill
  7. Manipulate dates
  8. Manipulate lights
  9. Using comodel
  10. Save and restore Simulations
  11. Using network
  12. Headless mode
  13. Using Headless
  14. Writing Unit Tests
  15. Ensure model's reproducibility
  16. Going further with extensions
    1. Calling R
    2. Using Graphical Editor
    3. Using Git from GAMA
  1. Built-in Species
  2. Built-in Skills
  3. Built-in Architecture
  4. Statements
  5. Data Type
  6. File Type
  7. Expressions
    1. Literals
    2. Units and Constants
    3. Pseudo Variables
    4. Variables And Attributes
    5. Operators [A-A]
    6. Operators [B-C]
    7. Operators [D-H]
    8. Operators [I-M]
    9. Operators [N-R]
    10. Operators [S-Z]
  8. Exhaustive list of GAMA Keywords
  1. Installing the GIT version
  2. Developing Extensions
    1. Developing Plugins
    2. Developing Skills
    3. Developing Statements
    4. Developing Operators
    5. Developing Types
    6. Developing Species
    7. Developing Control Architectures
    8. Index of annotations
  3. Introduction to GAMA Java API
    1. Architecture of GAMA
    2. IScope
  4. Using GAMA flags
  5. Creating a release of GAMA
  6. Documentation generation

  1. Predator Prey
  2. Road Traffic
  3. 3D Tutorial
  4. Incremental Model
  5. Luneray's flu
  6. BDI Agents

  1. Team
  2. Projects using GAMA
  3. Scientific References
  4. Training Sessions

Resources

  1. Videos
  2. Conferences
  3. Code Examples
  4. Pedagogical materials
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