Catálogo de publicaciones - libros

The aim of the introduction is to provide insight into the interdisciplinary research program of Socionics and to clarify fundamental concepts like micro-macro linkage and scalability from the two different perspectives of Sociology and DAI&MAS research. Far away from the intention to offer final answers, the article rather tries to provide a framework to understand the contributions of the book as well as to relate their content to each other. The introduction also informs the reader about the scientific context of the interdisciplinary field of Socionics and deals with basic concepts and comments from the point of view of both Sociology and DAI&MAS research.

Quantitative and qualitative directions of an up-scaling of sociological and socionic models are discussed. In sociology, problems of up-scaling result from the fact that explanations of structural dynamics do not work with laws but with mechanisms. In contrast to scientific laws or simple correlations, a mechanism is a step-by-step analytical description of the social dynamics which bring about the respective structural effect. If models are up-scaled, the relations between their various independent and dependent variables become more and more ”fuzzy” and a tension can be identified between ”clean” mechanisms and ”dirty” models. Although sociological and socionic models are always constructed for specific cases, with all implications of ”dirtiness”, it will be argued that ”clean” mechanisms are not only helpful but indispensable: The ”dirtier” the models become with up-scaling, the ”cleaner” must be the mechanisms used in modelling to support scientific generalization.

In this paper, I discuss the most important aspects of a sociological foundation of holonic multiagent systems. Pierre Bourdieu’s habitus-field-theory forms the sociological basis for my arguments. With this theory I would like to consider the special quality of holons as autonomous and self-organising social entities with clear distinction to the simple coordination of social interactions. Holons are viewed as organisational fields, which are both “autonomous social fields” and “corporate agents”. To clarify the advantages of this approach, I introduce a matrix of mechanisms using delegation (task delegation and social delegation) as a central concept to define organisational relationships in task-assignment multiagent systems. Using the matrix of delegation as basic building block, I propose a new dimension of emergent system behaviour in a holonic multiagent system which allows new, qualitative forms of scalability in complex systems of distributed artificial intelligence.

Socionics attempts to release the architecture of multi-agent systems from the restrictive micro perspective viewpoint by the integration of the macro perspective in order to arrive at innovative agent systems. This paper shows how central research topics of sociology and computer science can be combined, in order to arrive at innovative agent systems. In the context of sociology the duality of micro and macro elements is relevant, while recursiveness of models appears in the perspective of computer science. These two elements are unified in our work to the socionic multi-agent architecture .

The formal model, on which the representation bases, is the recursive formalism of reference nets—an extension of Petri nets that permits to understand nets again as tokens. With the help of these nets first of all a compact implementation of the multi-agent architecture is designed, secondly it serves as a description language for the sociological model, which is the fundament of . The main result here is to present an architecture based on and allowing to cover the micro as well as the macro perspective in agent-oriented modelling. Doing so, we introduce a scalable model based on agent systems.

The concept of a “community” is often an essential feature of many existing scientific collaborations. Collaboration networks generally involve bringing together participants who wish to achieve some common outcome. Scientists often work in informal collaborations to solve complex problems that require multiple types of skills. Increasingly, scientific collaborations are becoming interdisciplinary—requiring participants who posses different skills to come together. Such communities may be generally composed of participants with complimentary or similar skills—who may decide to collaborate to more efficiently solve a single large problem. If such a community wishes to utilise computational resources to undertake their work, it is useful to identify metrics that may be used to characterise their collaboration. Such metrics are useful to identify particular types of communities, or more importantly, particular features of communities that are likely to lead to successful collaborations as the number of participants (or the resources they are sharing) increases.

In sociology and distributed artificial intelligence, researchers are investigating two different ways of scaling. On the one hand, there is qualitative scaling, meaning that (social) complexity is increased, introducing regular practices of action, institutions, new fields of social action and requiring new dimensions in perception and decision making. On the other hand, researchers are interested in investigating quantitative scalability, i.e. how goals can be achieved under the constraints imposed by a growing population.

Our argument is structured as follows: firstly, we want to establish that organizations and interorganizational networks are an important cornerstone for the analysis of qualitative scaling. Secondly, we show by empirical evaluation that an elaborate theoretical concept of such networks increases the quantitative scalability of multiagent systems.

We introduce an integrated approach to the conceptualisation, implementation and evaluation of a MAS (multi-agent system) which is based on sociological concepts of practical roles and organisational coordination via negotiations. We propose a middle level of scale, located between interaction and the overall organisational structure, as the starting point for MAS design, with formal and practical modes of coordination to be distinguished over all relevant levels of scale. In our contribution, we present the modelling principles of our MAS, the agent architecture and the implementation. In the next step the approach is extended to a methodology for the investigation of processes of hybridisation, which means the re-entering of artificial sociality in a real-world domain. The integrated approach is intended to contribute to a generalised understanding of the Socionics program, which in our view should be seen as the enrolment of independent, but subsequent steps in an overall interdisciplinary approach.

This work proposes a system theoretical framework for analyzing scalability and scaling processes. Our aim is to clarify the vocabulary used in the debate on scalability issues in multi-agent systems. We, therefore, refer to the terminology of Niklas Luhmann’s sociological system theory and general complexity science. To evaluate the heuristic strength of the analytical framework, it is applied to a particular socionic model of a scalable system. Finally, we introduce some proposals for the modelling of scalable multi-agent systems from a sociological point of view. More specifically and system theoretically seen, such a scalable system has to be conceptualized as an organized multi-system system.

This paper introduces “micro-scalability” as a novel design objective for social reasoning architectures operating in open multiagent systems. Micro-scalability is based on the idea that social reasoning algorithms should be devised in a way that allows for social complexity reduction, and that this can be achieved by operationalising principles of interactionist sociology. We first present a formal model of InFFrA agents called mInFFrA that utilises two cornerstones of micro-scalability, the principles of and . Then, we exemplify the usefulness of these concepts by presenting experimental results with a novel opponent classification heuristic that has been developed using the InFFrA social reasoning architecture. These results prove that micro-scalability deserves further investigation as a useful aspect of socionic research.

Today, the importance of trust to issues of social coordination seems to be largely accepted in Distributed AI and sociology. This paper suggests a sociological multi-level concept of trust to provide suitable solutions to problems of large-scale open multi-agent systems (MAS). For this purpose, we firstly analyze DAI concepts dealing with the notion of trust and examine effects of trust on the scalability of MAS. We argue that trust itself must be modeled as a social mechanism that allows the scaling up of agent coordination in open MAS. Secondly, we summarize sociological conceptions of trust and outline problems concerning the build-up and diffusion of trust from a sociological perspective. Finally, we introduce a multi-level approach to trust by referring to sociologist Pierre Bourdieu’s concept of the economy of symbolic goods including basic social mechanisms in order to cope with the coordination of large numbers of heterogeneous agents.