Shortest Stochastic Path with Risk Sensitive Evaluation

2013 ◽  
pp. 371-382 ◽  
Author(s):  
Renato Minami ◽  
Valdinei Freire da Silva
Keyword(s):  
Risk Sensitive

scholarly journals Cooperative Stochastic Games with Mean-Variance Preferences

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 230
Author(s):  
Elena Parilina ◽  
Stepan Akimochkin
Keyword(s):  
Standard Deviation ◽  
Stochastic Games ◽  
Stochastic Game ◽  
Stochastic Variable ◽  
Expected Payoff ◽  
The Core ◽  
Risk Sensitive ◽  
Payoff Functions ◽  
Cooperative Solutions ◽  
Mean Variance

In stochastic games, the player’s payoff is a stochastic variable. In most papers, expected payoff is considered as a payoff, which means the risk neutrality of the players. However, there may exist risk-sensitive players who would take into account “risk” measuring their stochastic payoffs. In the paper, we propose a model of stochastic games with mean-variance payoff functions, which is the sum of expectation and standard deviation multiplied by a coefficient characterizing a player’s attention to risk. We construct a cooperative version of a stochastic game with mean-variance preferences by defining characteristic function using a maxmin approach. The imputation in a cooperative stochastic game with mean-variance preferences is supposed to be a random vector. We construct the core of a cooperative stochastic game with mean-variance preferences. The paper extends existing models of discrete-time stochastic games and approaches to find cooperative solutions in these games.

scholarly journals Risk sensitive optimal stopping

2021 ◽  
Vol 136 ◽  
pp. 125-144
Author(s):  
Damian Jelito ◽  
Marcin Pitera ◽  
Łukasz Stettner
Keyword(s):  
Optimal Stopping ◽  
Risk Sensitive

scholarly journals Nyström Minimum Kernel Risk-Sensitive Loss Based Seamless Control of Grid-Tied PV-Hybrid Energy Storage System

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1365
Author(s):  
Mukul Chankaya ◽  
Ikhlaq Hussain ◽  
Aijaz Ahmad ◽  
Irfan Khan ◽  
S.M. Muyeen
Keyword(s):  
Energy Storage ◽  
Reactive Power ◽  
Storage System ◽  
Energy Storage System ◽  
Voltage Source ◽  
Grid Voltage ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Hybrid Energy Storage System ◽  
Risk Sensitive

This paper presents Nyström minimum kernel risk-sensitive loss (NysMKRSL) based control of a three-phase four-wire grid-tied dual-stage PV-hybrid energy storage system, under varying conditions such as irradiation variation, unbalanced load, and abnormal grid voltage. The Voltage Source Converter (VSC) control enables the system to perform multifunctional operations such as reactive power compensation, load balancing, power balancing, and harmonics elimination while maintaining Unity Power Factor (UPF). The proposed VSC control delivers more accurate weights with fewer oscillations, hence reducing overall losses and providing better stability to the system. The seamless control with the Hybrid Energy Storage System (HESS) facilitates the system’s grid-tied and isolated operation. The HESS includes the battery, fuel cell, and ultra-capacitor to accomplish the peak shaving, managing the disturbances of sudden and prolonged nature occurring due to load unbalancing and abnormal grid voltage. The DC link voltage is regulated by tuning the PI controller gains utilizing the Salp Swarm Optimization (SSO) algorithm to stabilize the system with minimum deviation from the reference voltage, during various simulated dynamic conditions. The optimized DC bus control generates the accurate loss component of current, which further enhances the performance of the proposed VSC control. The presented system was simulated in the MATLAB 2016a environment and performed satisfactorily as per IEEE 519 standards.

Complex multi-kernel random Fourier adaptive algorithms under the complex kernel risk-sensitive p-power loss

2021 ◽  
pp. 103087
Author(s):  
Gangyi Huang ◽  
Minglin Shen ◽  
Tao Zhang ◽  
Fuliang He ◽  
Shiyuan Wang
Keyword(s):  
Power Loss ◽  
Adaptive Algorithms ◽  
Risk Sensitive

The Road Not Taken: How Early Landscape Learning and Adoption of a Risk-Averse Strategy Influenced Paleoindian Travel Route Decision Making in the Upper Ohio Valley

2020 ◽  
Vol 86 (1) ◽  
pp. 133-152
Author(s):  
Matthew P. Purtill
Keyword(s):  
Time Allocation ◽  
Selection Process ◽  
Sampling Bias ◽  
Ohio River ◽  
Risk Averse ◽  
Ohio Valley ◽  
The Road ◽  
Archaeological Data ◽  
Risk Sensitive ◽  
Path Distance

To evaluate a model of the travel-route selection process for upper Ohio Valley Paleoindian foragers (13,500–11,400 cal BP), this study investigates archaeological data through the theoretical framework of landscape learning and risk-sensitive analysis. Following initial trail placement adjacent to a highly visible escarpment landform, Paleoindians adopted a risk-averse strategy to minimize travel outcome variability when wayfaring between Sandy Springs, a significant Ohio River Paleoindian site, and Upper Mercer–Vanport chert quarries of east-central Ohio. Although a least-cost analysis indicates an optimal route through the lower Scioto Valley, archaeological evidence for this path is lacking. Geomorphic and archaeological data further suggest that site absence in the lower Scioto Valley is not entirely due to sampling bias. Instead, evidence indicates that Paleoindians preferred travel within the Ohio Brush Creek–Baker's Fork valley despite its longer path distance through more rugged, constricted terrain. Potential travel through the lower Scioto Valley hypothesizes high outcome variability due to the stochastic nature of the late Pleistocene hydroregime. In this case, perceived outcome variability appears more influential in determining travel-route decisions among Paleoindians than direct efforts to reduce energy and time allocation.

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