Multiplex Network Structure Enhances the Role of Generalized Reciprocity in Promoting Cooperation

In multi-agent systems, cooperative behavior is largely determined by the network structure which dictates the interactions among neighboring agents. These interactions often exhibit multidimensional features, either as relationships of different types or temporal dynamics, both of which may be modeled as a "multiplex" network. Against this background, here we advance the research on cooperation models inspired by generalized reciprocity, a simple pay-it-forward behavioral mechanism, by considering a multidimensional networked society. Our results reveal that a multiplex network structure can act as an enhancer of the role of generalized reciprocity in promoting cooperation by acting as a latent support, even when the parameters in some of the separate network dimensions suggest otherwise (i.e. favor defection). As a result, generalized reciprocity forces the cooperative contributions of the individual agents to concentrate in the dimension which is most favorable for the existence of cooperation.Comment: Extended abstract of "The Role of Multiplex Network Structure in Cooperation through Generalized Reciprocity

Meeting the challenges of decentralized embedded applications using multi-agent systems

International audienceToday embedded applications become large scale andstrongly constrained. They require a decentralized embedded intelligencegenerating challenges for embedded systems. A multi-agent approach iswell suited to model and design decentralized embedded applications.It is naturally able to take up some of these challenges. But somespecific points have to be introduced, enforced or improved in multiagentapproaches to reach all features and all requirements. In thisarticle, we present a study of specific activities that can complementmulti-agent paradigm in the ”embedded” context.We use our experiencewith the DIAMOND method to introduce and illustrate these featuresand activities

Organisational Intelligence and Distributed AI

The analysis of this chapter starts from organisational theory, and from this it draws conclusions for the design, and possible organisational applications, of Distributed AI systems. We first review how the concept of organisations has emerged from non-organised "blackbox" entities to so-called "computerised" organisations. Within this context organisational researchers have started to redesign their models of intelligent organisations with respect to the availability of advanced computing technology. The recently emerged concept of Organisational Intelligence integrates these efforts in that it suggests five components of intelligent organisational skills (communication, memory, learning, cognition, problem solving). The approach integrates human and computer-based information processing and problem solving capabilities.<br/

Analyzing the Effects of Role Configuration in Logistics Processes using Multiagent-Based Simulation: An Interdisciplinary Approach

Current trends like the digital transformation and Industry 4.0 are challenging logistics management: flexible process development and optimization has been a primary concern in research in the last two decades. However, flexibility is limited by its underlying distribution of action and task knowledge. Thus, our objective is to develop an approach to optimize performance of logistics processes by dynamic (re-) configuration of knowledge in teams. One of the key assumptions for that approach is, that the distribution of knowledge has impact on team‘s performance. Consequently, we propose a formal specification for representing active resources (humans or smart machines) and distribution of action knowledge in multiagent-based simulation. In the second part of this paper, we analyze process quality in a psychologically validated laboratory case study. Our simulation results support our assumption, i.e., the results show that there is significant influence of knowledge distribution on process quality

Emergence of social networks via direct and indirect reciprocity

Many models of social network formation implicitly assume that network properties are static in steady-state. In contrast, actual social networks are highly dynamic: allegiances and collaborations expire and may or may not be renewed at a later date. Moreover, empirical studies show that human social networks are dynamic at the individual level but static at the global level: individuals' degree rankings change considerably over time, whereas network-level metrics such as network diameter and clustering coefficient are relatively stable. There have been some attempts to explain these properties of empirical social networks using agent-based models in which agents play social dilemma games with their immediate neighbours, but can also manipulate their network connections to strategic advantage. However, such models cannot straightforwardly account for reciprocal behaviour based on reputation scores ("indirect reciprocity"), which is known to play an important role in many economic interactions. In order to account for indirect reciprocity, we model the network in a bottom-up fashion: the network emerges from the low-level interactions between agents. By so doing we are able to simultaneously account for the effect of both direct reciprocity (e.g. "tit-for-tat") as well as indirect reciprocity (helping strangers in order to increase one's reputation). This leads to a strategic equilibrium in the frequencies with which strategies are adopted in the population as a whole, but intermittent cycling over different strategies at the level of individual agents, which in turn gives rise to social networks which are dynamic at the individual level but stable at the network level