scholarly journals Improving the Social Capital of Trust-based Competitive Multi-Agent Systems by Introducing Meritocracy

2014 ◽  
Author(s):  
Antonello Comi ◽  
Lidia Fotia ◽  
Domenico Rosaci
Keyword(s):  
Social Capital ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
The Social ◽  
Multi Agent

Analysis and synthesis: multi-agent systems in the social sciences

2012 ◽  
Vol 27 (2) ◽  
pp. 123-136 ◽  
Author(s):  
Robert E. Marks
Keyword(s):  
Social Sciences ◽  
Computer Science ◽  
Common Source ◽  
Multi Agent Systems ◽  
Agent Based ◽  
Agent Systems ◽  
The Social ◽  
Analysis And Synthesis ◽  
Multi Agent ◽  
Computational Systems

AbstractAlthough they flow from a common source, the uses of multi-agent systems (or ‘agent-based computational systems’––ACE) vary between the social sciences and computer science. The distinction can be broadly summarized as analysis versus synthesis, or explanation versus design. I compare and contrast these uses, and discuss sufficiency and necessity in simulations in general and in multi-agent systems in particular, with a computer science audience in mind.

scholarly journals Electronic Social Capital for Self-Organising Multi-Agent Systems

2017 ◽  
Vol 12 (3) ◽  
pp. 1-25 ◽  
Author(s):  
Patricio E. Petruzzi ◽  
Jeremy Pitt ◽  
Dídac Busquets
Keyword(s):  
Social Capital ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent

scholarly journals Resource-bounded Norm Monitoring In Multi-agent Systems

2018 ◽  
Vol 62 ◽  
pp. 153-192 ◽  
Author(s):  
Natalia Criado
Keyword(s):  
Incomplete Information ◽  
Multi Agent Systems ◽  
Sociotechnical System ◽  
Agent Systems ◽  
Norm Violations ◽  
Norm Compliance ◽  
The Social ◽  
The World ◽  
Multi Agent ◽  
Bounded Norm

Norms allow system designers to specify the desired behaviour of a sociotechnical system. In this way, norms regulate what the social and technical agents in a sociotechnical system should (not) do. In this context, a vitally important question is the development of mechanisms for monitoring whether these agents comply with norms. Proposals on norm monitoring often assume that monitoring has no costs and/or that monitors have unlimited resources to observe the environment and the actions performed by agents. In this paper, we challenge this assumption and propose the first practical resource-bounded norm monitor. Our monitor is capable of selecting the resources to be deployed and use them to check norm compliance with incomplete information about the actions performed and the state of the world. We formally demonstrate the correctness and soundness of our norm monitor and study its complexity. We also demonstrate in randomised simulations and benchmark experiments that our monitor can select monitored resources effectively and efficiently, detecting more norm violations and fulfilments than other tractable optimization approaches and obtaining slightly worse results than intractable optimal approaches.

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