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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

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Published on: September 19, 2012

Adaptive walks and extreme value theory.

Johannes Neidhart1, Joachim Krug

  • 1Institute for Theoretical Physics, University of Cologne, 50937 Köln, Germany.

Physical Review Letters
|November 24, 2011
PubMed
Summary
This summary is machine-generated.

Biological evolution on high-dimensional genotype landscapes involves populations taking uphill walks toward fitness peaks. The study reveals average walk length logarithmically scales with beneficial mutations under rare mutations and strong selection.

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Last Updated: May 27, 2026

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

Area of Science:

  • Evolutionary biology
  • Theoretical biology
  • Computational biology

Background:

  • Biological evolution explores high-dimensional genotype spaces.
  • Populations adapt through sequences of mutations, often described as walks.
  • Understanding evolutionary trajectories under strong selection is crucial.

Purpose of the Study:

  • To analyze the evolutionary walk dynamics in high-dimensional genotype spaces.
  • To determine the factors influencing the length of adaptive evolutionary walks.
  • To investigate the impact of rare mutations and strong selection on evolutionary outcomes.

Main Methods:

  • Analytical derivation in a simplified model with fixed mutational neighborhoods.
  • Numerical simulations to confirm analytical findings.
  • Random assignment of fitness values to genotypes.

Main Results:

  • The mean length of an evolutionary walk is logarithmic with respect to the number of available beneficial mutations.
  • The prefactor of this logarithmic relationship is determined by the tail of the fitness distribution.
  • Evolutionary walks terminate at local fitness maxima.

Conclusions:

  • The study provides an analytical framework for understanding evolutionary walks.
  • The findings highlight the interplay between mutation rate, selection strength, and landscape topology.
  • The results offer insights into the efficiency of adaptation in complex biological systems.