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This reaction has been modeled as a preference for fair outcomes, as in Levine (1998) and Rotemberg (2008), who show how the introduction of a reciprocal altruism term in the utility function can explain quite well the seemingly paradoxical evidence from ultimatum games (see also Fehr and Schmidt 1999 inter alia).
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A polynomial function can explain the relationship between UPV and FIT.
The von Neumann Morgenstern utility function can be used to explain risk-averse, risk-neutral, and risk-loving behaviour.
This utility function can be expressed as Eqs.
To achieve competitive fairness and high efficiency, the utility function can be formulated as (10).
The policyholders' expected utility function can be written: U=u+2theta yint_{0}^{1/2y} v-tx dx-P. (1).
The potential differences between patients' and physicians' impressions of the utility function can be well demonstrated by tanning bed counseling.
Other useful utility functions can also be solved by DCA-based algorithms (see [24]).
Therefore, different utility functions can be used to model the same preferences.
In the state-of-the-art literature, various proposals of utility functions can be found.
It follows that different utility functions can be used to accomplish differentiated rate allocation.
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