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Related Experiment Video

Updated: May 2, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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Satisficing versus optimality: criteria for sustainability.

Michel De Lara1, Vincent Martinet, Luc Doyen

  • 1CERMICS (ENPC), Université Paris-Est, 6-8 Avenue Blaise Pascal, Cité Descartes, 77455, Marne-la-Vallée, France, delara@cermics.enpc.fr.

Bulletin of Mathematical Biology
|March 13, 2014
PubMed
Summary

This study bridges economic analysis and viability theory, showing viability is equivalent to intertemporal optimization problems. This offers a new framework for sustainability, risk, and long-term decision-making in economics and environmental science.

Related Experiment Videos

Last Updated: May 2, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

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Area of Science:

  • Economics, Sustainability Science, Decision Theory

Background:

  • Economic analysis uses discounted expected utility for risk and long-term issues, focusing on optimality.
  • Viability theory emphasizes satisfying sustainability constraints over time for feasibility.

Purpose of the Study:

  • To bridge economic analysis and viability theory by establishing their equivalence.
  • To explore the application of viability theory in deterministic and stochastic contexts.
  • To propose viability theory as a framework for environmental and climate change issues.

Main Methods:

  • Establishing the equivalence between viability and degenerate intertemporal optimization problems.
  • Analyzing the deterministic case, connecting the viability kernel to the minimum time of crisis.
  • Developing stochastic viability, introducing viable scenarios and maximal viability probability.

Main Results:

  • Viability is shown to be equivalent to a set of degenerate intertemporal optimization problems.
  • Deterministic viability links the viability kernel to crisis timing.
  • Stochastic viability introduces concepts of viable scenarios and maximal viability probability.

Conclusions:

  • The conceptual results align viability theory with economic approaches, particularly for stochastic efficiency.
  • Viability theory offers a robust framework for addressing biodiversity conservation, ecosystem management, and climate change challenges.