Hierarchical Group-Based Sampling

2005 ◽  
pp. 120-132 ◽  
Author(s):  
Rainer Gemulla ◽  
Henrike Berthold ◽  
Wolfgang Lehner
Keyword(s):  
Hierarchical Group

Multi-agent deep reinforcement learning with type-based hierarchical group communication

2021 ◽  
Author(s):  
Hao Jiang ◽  
Dianxi Shi ◽  
Chao Xue ◽  
Yajie Wang ◽  
Gongju Wang ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Group Communication ◽  
Multi Agent ◽  
Hierarchical Group

Information Flow to Increase Support for Tidal Energy Development in Remote Islands of a Developing Country: Agent-Based Simulation of Information Flow in Flores Timur Regency, Indonesia

2021 ◽  
Author(s):  
Rohit Ramachandran ◽  
A. H. T. Shyam Kularathna ◽  
Hirotaka Matsuda ◽  
Ken Takagi
Keyword(s):  
Information Flow ◽  
Public Engagement ◽  
Local Community ◽  
Information Provision ◽  
Tidal Energy ◽  
Multinomial Probit ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Remote Islands ◽  
Hierarchical Group

Abstract BackgroundPublic awareness is crucial for successful deployment of tidal energy, a renewable energy source that can provide clean electricity to remote islands. However, considering public attitudes on tidal energy are not well known, especially in developing countries, a barrier exists in implementing public engagement strategies. This study aims to contribute by identifying strategies for information provision – the initial step in public engagement – and estimate how these can be engaged to enhance support for tidal energy among the local public in a remote area of a developing country, in this case, Flores Timur Regency, Indonesia, considering their socio-cultural background.MethodsIn this paper we employ statistical analyses using Multinomial Probit modelling to identify the key variables that shape information flow. The aptness of the variables is then verified using post-estimation techniques for their use as input parameters for simulation of the information-flow in the field study area. Agent-Based Simulation (ABS) is employed to replicate the actual conditions in Flores Timur regency, Indonesia and simulate the flow of information through the local community.ResultsAccording to the Multinomial Probit estimations, the people belonging to the top hierarchical group show a higher probability to support tidal energy compared to the members belonging to the lower groups. Understandably, it takes around twice as many information flow cycles to disseminate information to the members of the lowest hierarchical group, compared to the members of the top hierarchical group. Results also show that increasing the amount of available information has a positive impact on information dissemination.ConclusionsThis study found that information provision is highly effective with propagation of information that specifically highlights the individual benefits, rather than the community benefits of tidal energy. Additionally, savings in terms of cost, time, and effort can be realized if the most influential members of the local community are targeted initially before including all other stakeholders. The study also found that locals absorb more information and increase their support for tidal energy when additional data is made available. Finally, albeit long-term strategy, information provision becomes most effective when the local population gains higher educational capabilities.

Hierarchical group process representation in multi-agent activity recognition

2008 ◽  
Vol 23 (10) ◽  
pp. 739-753 ◽  
Author(s):  
Weidong Zhang ◽  
Feng Chen ◽  
Wenli Xu ◽  
Youtian Du
Keyword(s):  
Activity Recognition ◽  
Group Process ◽  
Process Representation ◽  
Multi Agent ◽  
Hierarchical Group
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