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Related Experiment Video

Updated: Mar 27, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Exploring stochasticity and imprecise knowledge based on linear inequality constraints.

Sam Subbey1,2, Benjamin Planque3,4, Ulf Lindstrøm4

  • 1Institute of Marine Research, PB-1870, 5817, Bergen, Norway. samuels@imr.no.

Journal of Mathematical Biology
|January 10, 2016
PubMed
Summary

This study models food web dynamics using ordinary differential equations with uncertain parameters. Numerical experiments show how the Hit-and-Run algorithm captures system stochasticity and uncertainty, offering a simple approach for complex biosystems.

Keywords:
FoodwebHit-and-RunLinear inequalityPolytopeStochastic

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Last Updated: Mar 27, 2026

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Area of Science:

  • Ecology
  • Mathematical Biology
  • Systems Biology

Background:

  • Ecological systems exhibit complex dynamics influenced by species interactions.
  • Modeling these systems often involves uncertainty in parameter values.
  • Network models provide a framework for understanding food web structures.

Purpose of the Study:

  • To explore the stochastic dynamics of a simple food web system using a network model.
  • To represent the system using ordinary differential equations with uncertain parameters and linear inequality constraints.
  • To demonstrate a method for capturing system stochasticity and uncertainty under vague knowledge.

Main Methods:

  • Developed a network model for a simple food web system.
  • Described the system using ordinary differential equations with real-valued uncertain parameters.
  • Applied the Hit-and-Run algorithm to sample the solution space (a bounded convex polytope) to capture stochasticity and uncertainty.

Main Results:

  • The system's dynamics are constrained within a bounded convex polytope defined by linear inequalities.
  • Numerical experiments successfully captured system stochasticity and uncertainty via polytope sampling.
  • The Hit-and-Run algorithm proved effective for exploring the solution space.

Conclusions:

  • A parsimonious approach to modeling complex biosystems with vague knowledge is presented.
  • Stochastic dynamics in food webs can be effectively modeled using constrained ordinary differential equations.
  • Network models combined with probabilistic sampling offer a robust method for ecological system analysis.