Towards Modelling Organisational Dynamics for Large-Scale Multiagent Systems

2017 ◽  
pp. 245-248
Author(s):  
Bogdan Okreša Đurić
Keyword(s):  
Multiagent Systems ◽  
Large Scale ◽  
Organisational Dynamics

scholarly journals Design process robustness: a bipartite network analysis reveals the central importance of people

2018 ◽  
Vol 4 ◽  
Author(s):  
Sebastiano A. Piccolo ◽  
Sune Lehmann ◽  
Anja Maier
Keyword(s):  
Design Process ◽  
Large Scale ◽  
Bipartite Network ◽  
Design Processes ◽  
Process Robustness ◽  
Highly Sensitive ◽  
Scale Design ◽  
Biomass Power Plant ◽  
Organisational Dynamics ◽  
Random Failures

Design processes require the joint effort of many people to collaborate and work on multiple activities. Effective techniques to analyse and model design processes are important for understanding organisational dynamics, for improving collaboration, and for planning robust design processes, reducing the risk of rework and delays. Although there has been much progress in modelling and understanding design processes, little is known about the interplay between people and the activities they perform and its influence on design process robustness. To analyse this interplay, we model a large-scale design process of a biomass power plant with $100+$ people and ${\sim}150$ activities as a bipartite network. Observing that some people act as bridges between activities organised to form nearly independent modules, in order to evaluate process fragility, we simulate random failures and targeted attacks to people and activities. We find that our process is more vulnerable to attacks to people rather than activities. These findings show how the allocation of people to activities can obscure an inherent fragility, making the process highly sensitive and dependent on specific people. More generally, we show that the behaviour of robustness is determined by the degree distributions, the heterogeneity of which can be leveraged to improve robustness and resilience to cascading failures. Overall, we show that it is important to carefully plan the assignment of people to activities.

A Distributed Least-Squares Algorithm in Wireless Sensor Networks With Unknown and Limited Communications

2017 ◽  
Vol 8 (3) ◽  
pp. 15-36 ◽  
Author(s):  
Jing Wang ◽  
In Soo Ahn ◽  
Yufeng Lu ◽  
Tianyu Yang ◽  
Gennady Staskevich
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Least Squares ◽  
Multiagent Systems ◽  
Information Exchange ◽  
Large Scale ◽  
Wireless Sensor ◽  
Left Eigenvector ◽  
Mild Assumption ◽  
Least Squares Algorithm

In this article, the authors propose a new distributed least-squares algorithm to address the sensor fusion problem in using wireless sensor networks (WSN) to monitor the behaviors of large-scale multiagent systems. Under a mild assumption on network observability, that is, each sensor can take the measurements of a limited number of agents but the complete multiagent systems are covered under the union of all sensors in the network, the proposed algorithm achieves the estimation consensus if local information exchange can be performed among sensors. The proposed distributed least-squares algorithm can handle the directed communication network by explicitly estimating the left eigenvector corresponding to the largest eigenvalue of the sensing/communication matrix. The convergence of the proposed algorithm is analyzed, and simulation results are provided to further illustrate its effectiveness.

scholarly journals New modes of managing grand challenges: Cross-sector collaboration and the refugee crisis of the Asia Pacific

2019 ◽  
Vol 44 (4) ◽  
pp. 665-686 ◽  
Author(s):  
Annette Quayle ◽  
Johanne Grosvold ◽  
Larelle Chapple
Keyword(s):  
Significant Role ◽  
Large Scale ◽  
Collaborative Governance ◽  
Jel Classification ◽  
Asia Pacific ◽  
Refugee Crisis ◽  
Governance Framework ◽  
Grand Challenges ◽  
Large Scale Problems ◽  
Organisational Dynamics

Grand challenges are complex, large-scale problems requiring collaborative, multidisciplinary attention. Cross-sector collaboration can potentially play a significant role in addressing these challenges by capturing the diverse vision, experience, knowledge and resources of different sectors. Yet we still know little of the inter-organisational dynamics of how sectors work together to address grand challenges and the consequences of doing so. Our article contributes to the literature at the intersection of management and grand challenges by identifying how cross-sector collaborations can be used more effectively to address grand challenges. Drawing on a study of Australia’s offshore processing of refugees, we highlight the inter-organisational issues that emerge and develop a collaborative governance framework to overcome these problems and guide future cross-sector collaborations directed at grand challenges. JEL Classification: M5, H11

scholarly journals Multiagent Decision Making and Learning in Urban Environments

2019 ◽  
Author(s):  
Akshat Kumar
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Strategies ◽  
Multiagent Systems ◽  
Intelligent Agents ◽  
Large Scale ◽  
Urban Environments ◽  
Aggregate Level ◽  
Domain Models ◽  
Self Driving Cars

Our increasingly interconnected urban environments provide several opportunities to deploy intelligent agents---from self-driving cars, ships to aerial drones---that promise to radically improve productivity and safety. Achieving coordination among agents in such urban settings presents several algorithmic challenges---ability to scale to thousands of agents, addressing uncertainty, and partial observability in the environment. In addition, accurate domain models need to be learned from data that is often noisy and available only at an aggregate level. In this paper, I will overview some of our recent contributions towards developing planning and reinforcement learning strategies to address several such challenges present in large-scale urban multiagent systems.

Formal Stepwise Development of Scalable and Reliable Multiagent Systems

2012 ◽  
pp. 58-74
Author(s):  
Denis Grotsev ◽  
Alexei Iliasov ◽  
Alexander Romanovsky
Keyword(s):  
Multiagent Systems ◽  
Distributed System ◽  
Large Scale ◽  
Formal Method ◽  
System Management ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Dynamically Reconfigurable ◽  
Stepwise Development ◽  
Multi Agent

This chapter considers the coordination aspect of large-scale dynamically-reconfigurable multi-agent systems in which agents cooperate to achieve a common goal. The agents reside on distributed nodes and collectively represent a distributed system capable of executing tasks that cannot be effectively executed by an individual node. The two key requirements to be met when designing such a system are scalability and reliability. Scalability ensures that a large number of agents can participate in computation without overwhelming the system management facilities and thus allows agents to join and leave the system without affecting its performance. Meeting the reliability requirement guarantees that the system has enough redundancy to transparently tolerate a number of node crashes and agent failures, and is therefore free from single points of failures. The Event B formal method is used to validate the design formally and to ensure system scalability and reliability.

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