scholarly journals Market Competition and Lower Tier Incentives

2009 ◽  
Vol 9 (1) ◽  
Author(s):  
Bernd Theilen
Keyword(s):  
Market Competition ◽  
Single Agent ◽  
Incentive Pay ◽  
Incentive Payment ◽  
Agent Model ◽  
Lower Tier ◽  
Payment Schemes ◽  
Multi Agent ◽  
Firm Data ◽  
And Performance

The relationship between competition and performance–related pay has been analyzed in single–principal–single–agent models. While this approach yields good predictions for managerial pay schemes, the predictions fail to apply for employees at lower tiers of a firm's hierarchy. This paper describes a principal multi-agent model of incentive pay that analyzes the effect of changes in the competitiveness of markets on lower tier incentive payment schemes. The results explain why the payment schemes of agents located at low and mid tiers are less sensitive to changes in competition when aggregated firm data is used.

scholarly journals Multiagent Evaluation Mechanisms

2020 ◽  
Vol 34 (02) ◽  
pp. 1774-1781 ◽  
Author(s):  
Tal Alon ◽  
Magdalen Dobson ◽  
Ariel Procaccia ◽  
Inbal Talgam-Cohen ◽  
Jamie Tucker-Foltz
Keyword(s):  
Computational Complexity ◽  
Single Agent ◽  
Complete Characterization ◽  
Agent Model ◽  
Multi Agent ◽  
Feature Values

We consider settings where agents are evaluated based on observed features, and assume they seek to achieve feature values that bring about good evaluations. Our goal is to craft evaluation mechanisms that incentivize the agents to invest effort in desirable actions; a notable application is the design of course grading schemes. Previous work has studied this problem in the case of a single agent. By contrast, we investigate the general, multi-agent model, and provide a complete characterization of its computational complexity.

Research on multi-agent model based on track methodology of ontology building

2009 ◽  
Vol 29 (2) ◽  
pp. 412-415
Author(s):  
Qiang LU ◽  
Ming CHEN ◽  
Zhi-guang WANG
Keyword(s):  
Agent Model ◽  
Model Based ◽  
Multi Agent ◽  
Ontology Building

scholarly journals Multi-Agent Reinforcement Learning: A Review of Challenges and Applications

2021 ◽  
Vol 11 (11) ◽  
pp. 4948
Author(s):  
Lorenzo Canese ◽  
Gian Carlo Cardarilli ◽  
Luca Di Di Nunzio ◽  
Rocco Fazzolari ◽  
Daniele Giardino ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Mathematical Models ◽  
Learning Algorithms ◽  
Single Agent ◽  
Critical Issues ◽  
Multi Agent ◽  
Pros And Cons ◽  
Application Fields

In this review, we present an analysis of the most used multi-agent reinforcement learning algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the most critical issues that must be taken into account in their extension to multi-agent scenarios. The analyzed algorithms were grouped according to their features. We present a detailed taxonomy of the main multi-agent approaches proposed in the literature, focusing on their related mathematical models. For each algorithm, we describe the possible application fields, while pointing out its pros and cons. The described multi-agent algorithms are compared in terms of the most important characteristics for multi-agent reinforcement learning applications—namely, nonstationarity, scalability, and observability. We also describe the most common benchmark environments used to evaluate the performances of the considered methods.

scholarly journals A Coalitional Distributed Model Predictive Control Perspective for a Cyber-Physical Multi-Agent Application

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4041
Author(s):  
Anca Maxim ◽  
Constantin-Florin Caruntu
Keyword(s):  
Model Predictive Control ◽  
Communication Networks ◽  
Predictive Control ◽  
Technological Development ◽  
Distributed Model ◽  
Multi Agent Systems ◽  
Test Scenario ◽  
Distributed Model Predictive Control ◽  
Multi Agent ◽  
And Performance

Following the current technological development and informational advancement, more and more physical systems have become interconnected and linked via communication networks. The objective of this work is the development of a Coalitional Distributed Model Predictive Control (C- DMPC) strategy suitable for controlling cyber-physical, multi-agent systems. The motivation behind this endeavour is to design a novel algorithm with a flexible control architecture by combining the advantages of classical DMPC with Coalitional MPC. The simulation results were achieved using a test scenario composed of four dynamically coupled sub-systems, connected through an unidirectional communication topology. The obtained results illustrate that, when the feasibility of the local optimization problem is lost, forming a coalition between neighbouring agents solves this shortcoming and maintains the functionality of the entire system. These findings successfully prove the efficiency and performance of the proposed coalitional DMPC method.

Emergency clinician participation and performance in the CMS Merit‐based Incentive Payment System

2021 ◽  
Author(s):  
Cameron J. Gettel ◽  
Christopher R. Han ◽  
Michael A. Granovsky ◽  
Carl T. Berdahl ◽  
Keith E. Kocher ◽  
...  
Keyword(s):  
Payment System ◽  
Incentive Payment ◽  
And Performance

Otolaryngologist Performance in the Merit-Based Incentive Payment System in 2018

2021 ◽  
pp. 019459982110328
Author(s):  
Lauren E. Miller ◽  
Neil S. Kondamuri ◽  
Roy Xiao ◽  
Vinay K. Rathi
Keyword(s):  
Pay For Performance ◽  
Payment System ◽  
Incentive Payment ◽  
Cross Sectional ◽  
Medicare Data ◽  
And Performance ◽  
Value Based Care ◽  
Sectional Analysis ◽  
Median Change ◽  
Payment Models

In 2017, the Centers for Medicare and Medicaid Services transitioned clinicians to the Merit-Based Incentive Payment System (MIPS), the largest mandatory pay-for-performance program in health care history. The first full MIPS program year was 2018, during which the Centers for Medicare and Medicaid Services raised participation requirements and performance thresholds. Using publicly available Medicare data, we conducted a retrospective cross-sectional analysis of otolaryngologist participation and performance in the MIPS in 2017 and 2018. In 2018, otolaryngologists reporting as individuals were less likely ( P < .001) to earn positive payment adjustments (n = 1076/1584, 67.9%) than those participating as groups (n = 2802/2804, 99.9%) or in alternative payment models (n = 1705/1705, 100.0%). Approximately one-third (n = 1286/4472, 28.8%) of otolaryngologists changed reporting affiliations between 2017 and 2018. Otolaryngologists who transitioned from reporting as individuals to participating in alternative payment models (n = 137, 3.1%) achieved the greatest performance score improvements (median change, +23.4 points; interquartile range, 12.0-65.5). These findings have important implications for solo and independent otolaryngology practices in the era of value-based care.

scholarly journals A Novel Multi-Agent Model for Robustness with Component Failure and Malware Propagation in Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4873
Author(s):  
Biao Xu ◽  
Minyan Lu ◽  
Hong Zhang ◽  
Cong Pan
Keyword(s):  
Wireless Sensor ◽  
Component Reliability ◽  
Agent Based ◽  
Agent Model ◽  
Cross Domain ◽  
Malware Propagation ◽  
Network Topologies ◽  
Multi Agent ◽  
Agent Characteristics ◽  
Information Domain

A wireless sensor network (WSN) is a group of sensors connected with a wireless communications infrastructure designed to monitor and send collected data to the primary server. The WSN is the cornerstone of the Internet of Things (IoT) and Industry 4.0. Robustness is an essential characteristic of WSN that enables reliable functionalities to end customers. However, existing approaches primarily focus on component reliability and malware propagation, while the robustness and security of cascading failures between the physical domain and the information domain are usually ignored. This paper proposes a cross-domain agent-based model to analyze the connectivity robustness of a system in the malware propagation process. The agent characteristics and transition rules are also described in detail. To verify the practicality of the model, three scenarios based on different network topologies are proposed. Finally, the robustness of the scenarios and the topologies are discussed.

scholarly journals On-Demand Channel Bonding in Heterogeneous WLANs: A Multi-Agent Deep Reinforcement Learning Approach

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2789 ◽  
Author(s):  
Hang Qi ◽  
Hao Huang ◽  
Zhiqun Hu ◽  
Xiangming Wen ◽  
Zhaoming Lu
Keyword(s):  
Reinforcement Learning ◽  
Transmission Rate ◽  
Single Agent ◽  
Time Of Day ◽  
Action Space ◽  
Traffic Load ◽  
Traffic Demand ◽  
Channel Bonding ◽  
On Demand ◽  
Multi Agent

In order to meet the ever-increasing traffic demand of Wireless Local Area Networks (WLANs), channel bonding is introduced in IEEE 802.11 standards. Although channel bonding effectively increases the transmission rate, the wider channel reduces the number of non-overlapping channels and is more susceptible to interference. Meanwhile, the traffic load differs from one access point (AP) to another and changes significantly depending on the time of day. Therefore, the primary channel and channel bonding bandwidth should be carefully selected to meet traffic demand and guarantee the performance gain. In this paper, we proposed an On-Demand Channel Bonding (O-DCB) algorithm based on Deep Reinforcement Learning (DRL) for heterogeneous WLANs to reduce transmission delay, where the APs have different channel bonding capabilities. In this problem, the state space is continuous and the action space is discrete. However, the size of action space increases exponentially with the number of APs by using single-agent DRL, which severely affects the learning rate. To accelerate learning, Multi-Agent Deep Deterministic Policy Gradient (MADDPG) is used to train O-DCB. Real traffic traces collected from a campus WLAN are used to train and test O-DCB. Simulation results reveal that the proposed algorithm has good convergence and lower delay than other algorithms.

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