Game theory framework for agent decision-making in communication constrained environments

2021 ◽  
Author(s):  
Steven Dennis ◽  
Frederick E. Petry ◽  
Donald Sofge ◽  
Daniel Yang
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Constrained Environments ◽  
Theory Framework

scholarly journals Clickbait news and algorithmic curation: A game theory framework of the relation between journalism, users, and platforms

2021 ◽  
pp. 146144482110271
Author(s):  
Juliane A Lischka ◽  
Marcel Garz
Keyword(s):  
Machine Learning ◽  
Game Theory ◽  
Decision Making ◽  
User Interaction ◽  
Supervised Machine Learning ◽  
Media Environment ◽  
Competitive Environments ◽  
Social Media Platforms ◽  
Editorial Decision ◽  
Theory Framework

Algorithmic curation of social media platforms is considered to create a clickbait media environment. Although clickbait practices can be risky especially for legacy news outlets, clickbait is widely applied. We conceptualize clickbait content supply as a revision game with an unknown threshold. Combining supervised machine learning with time series analysis of Facebook posts and Twitter messages of 37 German legacy news outlets over 54 months, we observe outlets’ behavior following algorithm changes. Results reveal (1) an infrequent use of clickbait with few heavier-using outlets and (2) turning points of clickbait performance as clickbait supply and user interaction form a reversed U-shaped relationship. News outlets (3) collectively adjust toward an industry clickbait standard. While we (4) cannot prove that algorithmic curation increases clickbait, (5) Facebook’s regulative intervention to decrease clickbait disperses heterogeneous tendencies in clickbait supply. We contribute to an understanding of editorial decision-making in competitive environments facing platforms’ regulative intervention.

An evidential game theory framework in multi-criteria decision making process

2014 ◽  
Vol 244 ◽  
pp. 783-793 ◽  
Author(s):  
Xinyang Deng ◽  
Xi Zheng ◽  
Xiaoyan Su ◽  
Felix T.S. Chan ◽  
Yong Hu ◽  
...  
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Decision Making Process ◽  
Multi Criteria Decision Making ◽  
Theory Framework

scholarly journals A Game Theory-Based Approach for Modeling Autonomous Vehicle Behavior in Congested, Urban Lane-Changing Scenarios

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1523
Author(s):  
Nikita Smirnov ◽  
Yuzhou Liu ◽  
Aso Validi ◽  
Walter Morales-Alvarez ◽  
Cristina Olaverri-Monreal
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Urban Traffic ◽  
Road User ◽  
Lane Change ◽  
Traffic Light ◽  
User Interactions ◽  
Lane Changing

Autonomous vehicles are expected to display human-like behavior, at least to the extent that their decisions can be intuitively understood by other road users. If this is not the case, the coexistence of manual and autonomous vehicles in a mixed environment might affect road user interactions negatively and might jeopardize road safety. To this end, it is highly important to design algorithms that are capable of analyzing human decision-making processes and of reproducing them. In this context, lane-change maneuvers have been studied extensively. However, not all potential scenarios have been considered, since most works have focused on highway rather than urban scenarios. We contribute to the field of research by investigating a particular urban traffic scenario in which an autonomous vehicle needs to determine the level of cooperation of the vehicles in the adjacent lane in order to proceed with a lane change. To this end, we present a game theory-based decision-making model for lane changing in congested urban intersections. The model takes as input driving-related parameters related to vehicles in the intersection before they come to a complete stop. We validated the model by relying on the Co-AutoSim simulator. We compared the prediction model outcomes with actual participant decisions, i.e., whether they allowed the autonomous vehicle to drive in front of them. The results are promising, with the prediction accuracy being 100% in all of the cases in which the participants allowed the lane change and 83.3% in the other cases. The false predictions were due to delays in resuming driving after the traffic light turned green.

scholarly journals Entangled and correlated photon mixed strategy for social decision making

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shion Maeda ◽  
Nicolas Chauvet ◽  
Hayato Saigo ◽  
Hirokazu Hori ◽  
Guillaume Bachelier ◽  
...  
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Mixed Strategy ◽  
Evolutionary Game ◽  
Slot Machines ◽  
Decision Strategy ◽  
Social Infrastructure ◽  
Positive Effects ◽  
Social Decision Making ◽  
The Given

AbstractCollective decision making is important for maximizing total benefits while preserving equality among individuals in the competitive multi-armed bandit (CMAB) problem, wherein multiple players try to gain higher rewards from multiple slot machines. The CMAB problem represents an essential aspect of applications such as resource management in social infrastructure. In a previous study, we theoretically and experimentally demonstrated that entangled photons can physically resolve the difficulty of the CMAB problem. This decision-making strategy completely avoids decision conflicts while ensuring equality. However, decision conflicts can sometimes be beneficial if they yield greater rewards than non-conflicting decisions, indicating that greedy actions may provide positive effects depending on the given environment. In this study, we demonstrate a mixed strategy of entangled- and correlated-photon-based decision-making so that total rewards can be enhanced when compared to the entangled-photon-only decision strategy. We show that an optimal mixture of entangled- and correlated-photon-based strategies exists depending on the dynamics of the reward environment as well as the difficulty of the given problem. This study paves the way for utilizing both quantum and classical aspects of photons in a mixed manner for decision making and provides yet another example of the supremacy of mixed strategies known in game theory, especially in evolutionary game theory.

Optimized Electric Vehicle Charging Pricing Method Using Game Theory

2014 ◽  
Vol 986-987 ◽  
pp. 400-403 ◽  
Author(s):  
Kuan Lu ◽  
Wen Shan Gao ◽  
Jing Li ◽  
Wan Lei Xue ◽  
Wen Xue Sun
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Cost Analysis ◽  
Benefit Cost Analysis ◽  
Charging Station ◽  
Electric Vehicle Charging ◽  
Main Factors ◽  
Benefit Cost ◽  
Behavior Models ◽  
Ev Charging

First, behavior models of the station operator and consumer are established considering all the main factors. Second, an optimized EV charging pricing method using Game Theory is introduced based on benefit-cost analysis. The calculation of government subsidies is also proposed in order to reach an equilibrium charging price. Finally, actual data of Hebei is used and results are shown to give supports to local government and charging station operator’s decision making.

scholarly journals A game theory framework for ISP streaming traffic management

2011 ◽  
Vol 68 (11) ◽  
pp. 1162-1174 ◽  
Author(s):  
V. Bioglio ◽  
R. Gaeta ◽  
M. Grangetto ◽  
M. Sereno ◽  
S. Spoto
Keyword(s):  
Game Theory ◽  
Traffic Management ◽  
Streaming Traffic ◽  
Theory Framework
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