Sensitivity Analysis of Optimal Commodity Decision Making with Neural Networks: A Case for COVID-19
Mathematics, 2023 The COVID-19 pandemic caused a significant disruption to food demand, leading to changes in household expenditure and consumption patterns. This paper presents a method for analyzing the impact of such demand shocks on a producer’s decision to sell a ...
Nader Karimi +3 more
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Prospect theory: A parametric analysis of functional forms in Brazil
RAE: Revista de Administração de Empresas, 2017 This study aims to analyze risk preferences in Brazil based on prospect theory by estimating the risk aversion parameter of the expected utility theory (EUT) for a select sample, in addition to the value and probability function parameter, assuming ...
Robert Eugene Lobel +3 more
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Developing Prospect Theory with Multiple Reference Points in Decision Making [PDF]
مدیریت صنعتی, 2022 Objective: Multi-attribute utility theory is one of the approaches in multi-attribute decision-making. It is developed based on the logic of the expected utility theory. The main difference between the methods derived from the expected utility theory and
Zahra Nemati +2 more
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Fair Allocation in Crowd-Sourced Systems
Games, 2023 In this paper, we address the problem of fair sharing of the total value of a crowd-sourced network system between major participants (founders) and minor participants (crowd) using cooperative game theory.
Mishal Assif +2 more
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Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learning [PDF]
arXiv.org, 2017 Recent advances in combining deep neural network architectures with reinforcement learning techniques have shown promising potential results in solving complex control problems with high dimensional state and action spaces.
Seyed Sajad Mousavi +3 more
semanticscholar +1 more source
Does Amount of Information Support Aesthetic Values?
Frontiers in Neuroscience, 2022 Obtaining information from the world is important for survival. The brain, therefore, has special mechanisms to extract as much information as possible from sensory stimuli.
Norberto M. Grzywacz +3 more
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Feedback Stabilization of the Two-Dimensional Navier–Stokes Equations by Value Function Approximation [PDF]
Applied Mathematics and Optimization, 2019 The value function associated with an optimal control problem subject to the Navier–Stokes equations in dimension two is analyzed. Its smoothness is established around a steady state, moreover, its derivatives are shown to satisfy a Riccati equation at ...
T. Breiten, K. Kunisch, Laurent Pfeiffer
semanticscholar +1 more source
Interplanetary transfers via deep representations of the optimal policy and/or of the value function [PDF]
Annual Conference on Genetic and Evolutionary Computation, 2019 A number of applications to interplanetary trajectories have been recently proposed based on deep networks. These approaches often rely on the availability of a large number of optimal trajectories to learn from.
D. Izzo, Ekin Öztürk, Marcus Märtens
semanticscholar +1 more source
Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition [PDF]
Journal of Artificial Intelligence Research, 1999 This paper presents a new approach to hierarchical reinforcement learning based on decomposing the target Markov decision process (MDP) into a hierarchy of smaller MDPs and decomposing the value function of the target MDP into an additive combination of ...
Thomas G. Dietterich
semanticscholar +1 more source
Global $C^{1}$ regularity of the value function in optimal stopping problems [PDF]
The Annals of Applied Probability, 2018 We show that if either the process is strong Feller and the boundary point is probabilistically regular for the stopping set, or the process is strong Markov and the boundary point is probabilistically regular for the interior of the stopping set, then ...
T. Angelis, G. Peskir
semanticscholar +1 more source