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Moreover, the system's sum utility is maximized when the optimal reward value is adopted.
We show that an optimal reward scheme exists under quite general conditions.
In their approach, rewards and costs are accumulated in the environment where offenders seek optimal reward and targets seek least cost (due to potential of being victimized).
Reference [18] discusses correlated equilibrium and achieves it by no-regret learning; that is, minimizing the gap between the current reward and optimal reward.
Method of constrained viscosity solution is used to characterize the dynamics governing the optimal reward function and the associated boundary conditions.
On the other hand, and according to the Nash bargaining solution, when the optimal reward value is employed, the best relay strategy involves forwarding the same fraction of data originated from the source.
Similar(41)
In other words, the same behavioral policy satisfies optimal reward-seeking as well as optimal homeostatic maintenance.
We note that most of the behavioral adapations that linearly relate to threat level might map to optimal reward-maximizing strategies predicted by standard economic theory.
Despite exhibiting near-optimal reward rates, all subjects feature small deviations from optimality.
It will be interesting to see whether participants are able to achieve near-optimal reward bias effects under such conditions, and if so to understand how such effects are implemented mechanistically.
Therefore, close-to-optimal reward rates do not necessarily predict the pattern we observe.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com