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QUANTIFICATION OF MOTIVATION FOR BEHAVIOR MODELING: A PIECEWISE SPECIFICATION FOR DECISION ANALYSIS

Kiyoshi Yoneda

Pesquisa Operacional2026https://doi.org/10.1590/0101-7438.2026.046.00301429article
AJG 1
Weight
0.50

What the paper says

An autonomous agent's behavior may be modeled by specifying an objective function such as utility function it attempts to optimize by adjusting a variable representing its current status. A historical insight in economics has been that the agent adjusts its current status based on marginal utility rather than utility, which applies equally well to the loss function interpreted as the negative utility function. This leads to a method to specify the derivative of loss function by quantifying the level of the agent's motivation to improve the current status as compared to the level at the least relevant value of the variable. Integrating the derivative specified recovers the loss function, which in turn defines the corresponding maximum entropy distribution enabling probability prediction of actions the agent will take.

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https://doi.org/https://doi.org/10.1590/0101-7438.2026.046.00301429

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@article{kiyoshi2026,
  title        = {{QUANTIFICATION OF MOTIVATION FOR BEHAVIOR MODELING: A PIECEWISE SPECIFICATION FOR DECISION ANALYSIS}},
  author       = {Kiyoshi Yoneda},
  journal      = {Pesquisa Operacional},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1590/0101-7438.2026.046.00301429},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.