OPTIMAL TIME ALLOCATION IN COMPLEX TASKS,

Abstract

The thesis studies the problem-solving situation from an input-output point of view, with emphasis on the following two aspects. First, the expected payoff is related to the time spent analyzing the various components of the problem. Second, the optimal allocation of a finite amount of time among m independent problems is derived in terms of the complexities of the problems, and the correlation between each problem and the problem solver's ability and experience. By alluding to the stochastic theory of learning it is shown that, in general, the expected payoff in a problem-solving situation is a concave function of time satisfying four conditions. Justifications are given for the approximation of the payoff by an exponential function of time. The theory of hypothesis testing in the presence of white Gaussian noise is also used to verify these assumptions about the functional form of the expected payoff. (Author)

Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1968
Accession Number
AD0673891

Entities

People

  • Ronald R. Yager

Organizations

  • New York University Tandon School of Engineering

Tags

DTIC Thesaurus Topics

  • Exponential Functions
  • Functions (Mathematics)
  • Gaussian Noise
  • Learning
  • Mathematics
  • Noise

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