Maximization of an Asymmetric Utility Function by the Least Squares
DOI:
https://doi.org/10.7494/dmms.2014.8.1.5Keywords:
individual behavior, inverse problems, simultaneous equations, optimizationAbstract
This note points out that a utility maximization procedure proposed in an earlier paper may be reduced to the least squares. The utility function is asymmetric in the sense that for each cue its ideal value and the permissible range are assigned in such a way that the ideal is not necessarily at the center of the range, like “a beer of 350 ml would be ideal, but acceptable if within [100, 500]”. A practical consequence of the observation is that very little programming will be needed to deploy the utility maximization since software for the least squares is widely available.
References
Hastie R. & Dawes R.M. (2009). Rational Choice in an Uncertain World: The Psychology of Judgment and Decision Making. 2nd Edition. Thousand Oaks, CA: SAGE Publications. ISBN: 978-1-4129-5903-2.
R Core Team. (2013). R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. URL: https://www.R-project.org/ [21.08.2026].
Turi D. (2001). Category Theory Lecture Notes. Edinburgh: Laboratory for Foundations of Computer Science, University of Edinburgh. URL: http://www.dcs.ed.ac.uk/home/dt/CT/categories.pdf [21.08.2026].
Yoneda K. & Celaschi W. (2013). A utility function to solve approximate linear equations for decision making. Decision Making in Manufacturing and Services, 7(1), pp. 5–18. DOI: http://doi.org/10.7494/dmms.2013.7.1.5.
