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.

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.

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 meta-belief, 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 agent-based simulations.

Social Order in Multiagent Systems provides an overview of current approaches, problems, and considerations related to the study of norms and institutions in the context of multiagent systems. The contributions in this volume share the assumption that norms and other social institutions are of vital importance for the development of multiagent systems and agent-mediated interaction. Both formal and computational models of norms and normative systems are presented, including formal analysis of normative concepts and foundational models of norms; agent and systems architectures for implementing norms; and implemented systems. Social Order in Multiagent Systems is an excellent reference for researchers in artificial intelligence and computer science, and can be used as text for advanced level courses in multiagent systems.

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 meta-belief, 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 agent-based simulations.

Social Order in Multiagent Systems provides an overview of current approaches, problems, and considerations related to the study of norms and institutions in the context of multiagent systems. The contributions in this volume share the assumption that norms and other social institutions are of vital importance for the development of multiagent systems and agent-mediated interaction. Both formal and computational models of norms and normative systems are presented, including formal analysis of normative concepts and foundational models of norms; agent and systems architectures for implementing norms; and implemented systems. Social Order in Multiagent Systems is an excellent reference for researchers in artificial intelligence and computer science, and can be used as text for advanced level courses in multiagent systems.

This book constitutes the thoroughly refereed post-workshop proceedings of the First International Workshop on Multi-Agent Systems and Agent-Based Simulation, MABS'98, held in Paris, France in July 1998 in conjunction with Agent World 1998. The 15 revised full papers presented together with an introduction by the volume editors were selected from a total of more than 50 submissions. Among the topics covered are multi-agent systems, social simulation, agent-based modelling, cognitive emergence, honey-bee colonies, artificial societies, economic aspects, cultural evolution, roles in agent systems, applications in various areas, etc.

In this book experts from quite different fields present simulations of social phenomena: economists, sociologists, political scientists, psychologists, cognitive scientists, organizational scientists, decision scientists, geographers, computer scientists, AI and AL scientists, mathematicians and statisticians. They simulate markets, organizations, economic dynamics, coalition formation, the emergence of cooperation and exchange, bargaining, decision making, learning, and adaptation. The history, problems, and perspectives of simulating social phenomena are explicitly discussed.

Norms are prescribed conducts applied by the majority of people. Getting across cultures and centuries, norms evolved to rule all human relationships, from the most formal to the most intimate. Impinging on any sphere of life, from religious to political, norms affect social, moral, and even aesthetical behaviours. They are enforced through centralized sanctions or distributed control, and originate through deliberate acts of issuing or from spontaneous interaction in informal settings. Despite ubiquity and universality, norms are still awaiting for a general comprehensive theory, simultaneously doing justice to three intuitions: that, under variable contents, norms correspond to a common notion; that, once brought about, norms feedback on their producers, affecting their conducts; and finally that before and in order to drive the behaviours of individuals, norms must affect their beliefs and goals: people must detect and accept norms before converting them into observable behaviours. This volume presents an unprecedented attempt to account for all the three intuitions at once, providing a systematic view of norms. Based on a unitary and operational notion of norms, as behaviours spreading thanks to and to the extent that the corresponding prescriptions spread as well, a cognitive architecture, EMIL-A, which is the main output of a research project on norm emergence, is described. EMIL-A is a BDI-like platform for simulation, endowed with modules for detecting, reasoning and deciding upon norms. Next, the EMIL-A platform is applied to generate norms in different simulated scenarios (from a multi-setting world to a virtual Wikipedia), through a complex bidirectional dynamics, i.e., the bottom-up emergence of norms thanks to a gradual, top-down process, denoted as immergence. As simulations results show, norms emerge while immerging in agents' minds, thanks to their detecting, reasoning, and deciding whether to respect them or not.