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Reputation In Artificial Societies discusses the role of reputation in the achievement of social order. The book proposes that reputation is an agent property that results from transmission of beliefs about how the agents are evaluated with regard to a socially desirable conduct. This desirable conduct represents one or another of the solutions to the problem of social order and may consist of cooperation or altruism, reciprocity, or norm obedience. Reputation In Artificial Societies distinguishes between image (direct evaluation of others) and reputation (propagating metabelief, indirectly acquired) and investigates their effects with regard to both natural and electronic societies. The interplay between image and reputation, the processes leading to them and the set of decisions that agents make on their basis are demonstrated with supporting data from agentbased simulations.
This monograph addresses the worlds of social science theory and artificial intelligence AI. The book examines the interaction of individual cognitive factors and social influence on human action and discusses the implications for developments in artificial intelligence.; This book is intended for graduate and research level artificial intelligence and social science theory including sociology, economics, psychology.
An exploration of the implications of developments in artificial intelligence for social scientific research, which builds on the theoretical and methodological insights provided by "Simulating societies".; This book is intended for worldwide library market for social science subjects such as sociology, political science, geography, archaeology/anthropology, and significant appeal within computer science, particularly artificial intelligence. Also personal reference for researchers.
This book argues that societies are complex dynamical systems that can be understood through the concept of emergence.
An Application Science For Multi-Agent Systems addresses the complexity of choosing which multi-agent control technologies are appropriate for a given problem domain or a given application. Without such knowledge, when faced with a new application domain, agent developers must rely on past experience and intuition to determine whether a multi-agent system is the right approach, and if so, how to structure the agents, how to decompose the problem, and how to coordinate the activities of the agents, and so forth. This unique collection of contributions, written by leading international researchers in the agent community, provides valuable insight into the issues of deciding which technique to apply and when it is appropriate to use them. The contributions also discuss potential trade-offs or caveats involved with each decision. An Application Science For Multi-Agent Systems is an excellent reference for anyone involved in developing multi-agent systems.
This book constitutes the thoroughly refereed post-conference proceedings of the 17th International Workshop on Multi-Agent-Based Simulation, MABs 2016, held in Singapore, in May 2016. The workshop was held in Conjunction with the 15th International Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2016. The 10 revised full papers included in this volume were carefully selected from 15 submissions. The topic of the papers is about modeling and analyzing multi-agent systems and applying agent-based simulation techniques to real-world problems, focusing on the confluence of socio-technical- natural sciences and multi-agents systems with a strong application/empirical vein. Special emphasis is given on exploratory agent-based simulation as a principled way of undertaking scientific research in the social sciences and on using social theories as an inspiration to new frameworks and developments in multi-agent systems.
The Portuguese Association for Arti cial Intelligence (APPIA) has been re- larly organising the Portuguese Conference on Arti cial Intelligence (EPIA). This ninth conference follows previous ones held in Porto (1985), Lisboa (1986), Braga (1987), Lisboa (1989), Albufeira (1991), Porto (1993), Funchal (1995) and Coimbra (1997). Starting in 1989, the conferences have been held biennially (alternating with an APPIA Advanced School on Arti cial Intelligence) and become truly international: English has been adopted as the o cial language and the proceedings are published in Springer’s LNAI series. The conference has recon rmed its high international standard this year, largely due to its progra...
This book presents selected extended and reviewed versions of the papers accepted for the First International Workshop on Regulated Agent Systems: Theory and Applications, RASTA 2002, held in Bologna, Italy, in July 2002, as part of AAMAS 2002. In addition, several new papers on the workshop theme are included as well; these were submitted and reviewed in response to a further call for contributions. The construction of artificial agent societies deals with questions and problems that are already known from human societies. The 16 papers in this book establish an interdisciplinary community of social scientists and computer scientists devoting their research interests to exploiting social theories for the construction and regulation of multi-agent systems.
The present book describes the methodology to set up agent-based models and to study emerging patterns in complex adaptive systems resulting from multi-agent interaction. It offers the application of agent-based models in demography, social and economic sciences and environmental sciences. Examples include population dynamics, evolution of social norms, communication structures, patterns in eco-systems and socio-biology, natural resource management, spread of diseases and development processes. It presents and combines different approaches how to implement agent-based computational models and tools in an integrative manner that can be extended to other cases.
Agent-based modelling on a computer appears to have a special role to play in the development of social science. It offers a means of discovering general and applicable social theory, and grounding it in precise assumptions and derivations, whilst addressing those elements of individual cognition that are central to human society. However, there are important questions to be asked and difficulties to overcome in achieving this potential. What differentiates agent-based modelling from traditional computer modelling? Which model types should be used under which circumstances? If it is appropriate to use a complex model, how can it be validated? Is social simulation research to adopt a realist ...