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Updated: Jun 19, 2026

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

TIME DELAY SYSTEMS WITH DISTRIBUTION DEPENDENT DYNAMICS.

H T Banks1, Sava Dediu, Hoan K Nguyen

  • 1Center for Research in Scientific Computation, Box 8205, North Carolina State University, Raleigh, N.C. 27695-8205 USA.

Annual Reviews in Control
|September 28, 2011
PubMed
Summary
This summary is machine-generated.

This study explores probability measure dependent dynamics in general delay dynamical systems, particularly in biological contexts. It surveys a functional analytic framework for analyzing system properties and computational approximations.

Related Experiment Videos

Last Updated: Jun 19, 2026

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

Area of Science:

  • Mathematical Biology
  • Dynamical Systems Theory
  • Probability Theory

Background:

  • Delay dynamical systems are crucial for modeling phenomena with time lags.
  • Incorporating uncertainty via probability measures presents significant analytical challenges.
  • Biological systems often exhibit complex dynamics influenced by time delays and inherent randomness.

Purpose of the Study:

  • To introduce and survey a functional analytic framework for probability measure dependent delay dynamical systems.
  • To address key analytical aspects including well-posedness, inverse problems, and sensitivity analysis.
  • To explore computational approximations for these complex systems.

Main Methods:

  • Functional analysis techniques are employed to define and analyze the system dynamics.
  • Methods for investigating existence, uniqueness, and continuous dependence of solutions are surveyed.
  • Approximation techniques for probability measures are discussed for computational tractability.

Main Results:

  • The functional analytic framework provides a rigorous approach to studying these systems.
  • The framework facilitates the analysis of inverse problems and sensitivity of the system to measure variations.
  • Computational methods for approximating the probability measures are presented.

Conclusions:

  • The surveyed framework offers a robust methodology for understanding uncertain delay dynamical systems.
  • This approach is particularly relevant for complex biological modeling where uncertainty is prevalent.
  • Further research can leverage this framework for advanced computational and analytical studies.