Socionics: Sociological Concepts for Social Systems of Artificial (and Human) Agents

Socionics is an interdisciplinary approach with the objective to use sociological knowledge about the structures, mechanisms and processes of social interaction and social communication as a source of inspiration for the development of multi-agent systems, both for the purposes of engineering applications and of social theory construction and social simulation. The approach has been spelled out from 1998 on within the Socionics priority program funded by the German National research foundation. This special issue of the JASSS presents research results from five interdisciplinary projects of the Socionics program. The introduction gives an overview over the basic ideas of the Socionics approach and summarizes the work of these projects.Socionics, Sociology, Multi-Agent Systems, Artificial Social Systems, Hybrid Systems, Social Simulation

Can models of agents be transferred between different areas?

One of the main reasons for the sustained activity and interest in the field of agent-based systems, apart from the obvious recognition of its value as a natural and intuitive way of understanding the world, is its reach into very many different and distinct fields of investigation. Indeed, the notions of agents and multi-agent systems are relevant to fields ranging from economics to robotics, in contributing to the foundations of the field, being influenced by ongoing research, and in providing many domains of application. While these various disciplines constitute a rich and diverse environment for agent research, the way in which they may have been linked by it is a much less considered issue. The purpose of this panel was to examine just this concern, in the relationships between different areas that have resulted from agent research. Informed by the experience of the participants in the areas of robotics, social simulation, economics, computer science and artificial intelligence, the discussion was lively and sometimes heated

K-Implementation

This paper discusses an interested party who wishes to influence the behavior of agents in a game (multi-agent interaction), which is not under his control. The interested party cannot design a new game, cannot enforce agents' behavior, cannot enforce payments by the agents, and cannot prohibit strategies available to the agents. However, he can influence the outcome of the game by committing to non-negative monetary transfers for the different strategy profiles that may be selected by the agents. The interested party assumes that agents are rational in the commonly agreed sense that they do not use dominated strategies. Hence, a certain subset of outcomes is implemented in a given game if by adding non-negative payments, rational players will necessarily produce an outcome in this subset. Obviously, by making sufficiently big payments one can implement any desirable outcome. The question is what is the cost of implementation? In this paper we introduce the notion of k-implementation of a desired set of strategy profiles, where k stands for the amount of payment that need to be actually made in order to implement desirable outcomes. A major point in k-implementation is that monetary offers need not necessarily materialize when following desired behaviors. We define and study k-implementation in the contexts of games with complete and incomplete information. In the latter case we mainly focus on the VCG games. Our setting is later extended to deal with mixed strategies using correlation devices. Together, the paper introduces and studies the implementation of desirable outcomes by a reliable party who cannot modify game rules (i.e. provide protocols), complementing previous work in mechanism design, while making it more applicable to many realistic CS settings

Communication Between Process and Structure: Modelling and Simulating Message Reference Networks with COM/TE

Focusing on observable message signs and referencing structures, communication processes can be described and analysed as message reference networks which are characterized by dynamic pattern evolution. Computational simulation provides a way of obtaining insights into the factors driving such processes. Our paper describes a theoretical framework for communication-oriented modelling — the COM approach — that is centred around the notion of social visibility as a reputation mechanism. The approach contrasts with agent-based social networks on the one hand, and with bibliometric document networks on the other. In introducing our simulation environment COM/TE, typical properties of message reference networks are discussed in terms of a case study which deals with the impact of different media and styles of communication on emergent patterns of social visibility.Communication, Communication-Oriented Modelling, Message Sign, Dynamic Networks, Bottom-up Approach, Temporality, Social Visibility, Reputation, Socionics

Modeling the Evolution of Artifact Capabilities in Multi-Agent Based Simulations

Cognitive scientists agree that the exploitation of objects as tools or artifacts has played a significant role in the evolution of human societies. In the realm of autonomous agents and multi-agent systems, a recent artifact theory proposes the artifact concept as an abstraction for representing functional system components that proactive agents may exploit towards realizing their goals. As a complement, the cognition of rational agents has been extended to accommodate the notion of artifact capabilities denoting the reasoning and planning capacities of agents with respect to artifacts. Multi-Agent Based Simulation (MABS) a well established discipline for modeling complex social systems, has been identified as an area that should benefit from these theories. In MABS the evolution of artifact exploitation can play an important role in the overall performance of the system. The primary contribution of this dissertation is a computational model for integrating artifacts into MABS. The emphasis of the model is on an evolutionary approach that facilitates understanding the effects of artifacts and their exploitation in artificial social systems over time. The artifact theories are extended to support agents designed to evolve artifact exploitation through a variety of learning and adaptation strategies. The model accents strategies that benefit from the social dimensions of MABS. Realized with evolutionary computation methods specifically genetic algorithms, cultural algorithms and multi-population cultural algorithms, artifact capability evolution is supported at individual, population and multi-population levels. A generic MABS and case studies are provided to demonstrate the use of the model in new and existing MABS systems. The accommodation of artifact capability evolution in artificial social systems is applicable in many domains, particularly when the modeled system is one where artifact exploitation is relevant to the evolution of the society and its overall behavior. With artifacts acknowledged as major contributors to societal evolution the impact of our model is significant, providing advanced tools that enable social scientists to analyze their findings. The model can inform archaeologists, economists, evolution theorists, sociologists and anthropologists among others