Introduction and Challenges of Environment Architectures for Collective Intelligence Systems

Musil, Juergen; Musil, Angelika; Biffl, Stefan

doi:10.1007/978-3-319-23850-0_6

Cited by 11 publications

(6 citation statements)

References 27 publications

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“…Understanding the characteristics of self-organizing service ecosystems is a significant research problem, requiring interdisciplinary research (Rai 2016). Here, we draw on insights from selforganization theory, which has already been applied in physics (Haken 1977), computer science (Musil et al 2015), management (Coleman 1999), and the social sciences (Fuchs 2006). Taking from literature dedicated to self-organization theory, Table 1 provides an overview of existing definitions of selforganizing systems.…”

Section: Characterizing Self-organizing Service Ecosystemsmentioning

confidence: 99%

Self-organizing Service Ecosystems: Exploring a New Concept for Service Science

Pena¹,

Breidbach²,

Turpin³

2018

Australasian Conference on Information Systems 2018

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The rapid advancements on digital technologies have positioned digital transformation as a central topic of interest to information systems (IS) researchers. However, our understanding of the nature, extent and dynamics of digital service ecosystems remains limited. This short paper contributes to IS and service science research by introducing the conceptualization of self-organizing service ecosystem as an analytical lens for understanding digital transformative phenomena in service ecosystems. To achieve this, we draw on the most recent narrative of value co-creation from service-dominant logic and on key definitions from the theory of self-organization. This paper also discusses future research directions emphasizing on the role and impact of technology in self-organizing service ecosystems.

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Section: Characterizing Self-organizing Service Ecosystemsmentioning

confidence: 99%

Self-organizing Service Ecosystems: Exploring a New Concept for Service Science

Pena¹,

Breidbach²,

Turpin³

2018

Australasian Conference on Information Systems 2018

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“…Well-known examples of such CIS include Facebook 1 , Wikipedia 2 , YouTube 3 , and Yelp 4 . CIS are socio-technical multi-agent systems that aim to harness the collective intelligence of interacting human actors by providing a web-based environment for sharing, distributing and retrieving topic-specific information in an efficient way (Musil et al, 2015a). A CIS posses a characteristic system model that is illustrated by Fig.…”

Section: Collective Intelligence Systemsmentioning

confidence: 99%

“…So far CIS approaches consider humans as essential entities in the critical feedback loop to be successful and effective (e.g., Yelp 10 in the domain of business ratings and reviews or Facebook 11 for creating a social network of friend relationships). But if humans were regarded as one of many variability points in a CIS architecture, they could be replaced by machines as for instance robots or CPPS to realize machine-to-machine configurations (Musil et al, 2015a). The integration of a CIS with machines that represent its actor base into a manufacturing environment enables the connection and communication of several groups of CPPS or single systems to support the machines to share their information and experiences among each other, which is illustrated by Fig.…”

Section: Collective Intelligence Systems As Enabler For Emergent Machine-to-machine Interactionsmentioning

confidence: 99%

“…To deal with uncertainty while planning, Mukherjee and Chaudhury (2013) explored (1) bottom-up biologically inspired continuous planning in order to adapt to changing environment and (2) Blackboard-based multi-agent coordination. Similar to this approach, CIS use the nature-inspired coordination mechanism of stigmergy (Heylighen, 2016) which enables bottom-up, environment-mediated coordination and indirect communication of agents via traces in the environment (Musil et al, 2015a). Thus the stigmergic process creates a positive feedback loop that promotes awareness among agents about the activities of others and stimulates further agent activities.…”

Section: Collective Intelligence Systems As Enabler For Emergent Machine-to-machine Interactionsmentioning

confidence: 99%

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Patterns for Self-Adaptation in Cyber-Physical Systems

Musil

Weyns

et al. 2017

Multi-Disciplinary Engineering for Cyber-Physical Production Systems

Self Cite

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Engineering Cyber-Physical Systems (CPS) is challenging, as these systems have to handle uncertainty and change during operation. A typical approach to deal with uncertainty is enhancing the system with self-adaptation capabilities. However, realizing self-adaptation in CPS, and consequently also in Cyber-Physical Production Systems (CPPS) as a member of the CPS family, is particularly challenging due to the specific characteristics of these systems, including the seamless integration of computational and physical components, the inherent heterogeneity and large-scale of such systems, and their open-endedness. In this chapter we survey CPS studies that apply the promising design strategy of combining different self-adaptation mechanisms across the technology stack of the system. Based on the survey results, we derive recurring adaptation patterns that structure and consolidate design knowledge. The patterns offer problem-solution pairs to engineers for the design of future CPS and CPPS with self-adaptation capabilities. Finally, the chapter outlines the potential of Collective Intelligence Systems for CPPS and their engineering based on the survey results.

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“…Indeed, social media has been considered as a crowdmediated for harnessing collective intelligence from a group of diverse individuals [12]. Referring to [13], wikis, social networks, and content-sharing platforms is considered as collective intelligence systems for collective knowledge creation and sharing processes. Question and Answer (Q&A) websites such as Quora and Yahoo!Answers can be considered as efficient approaches to using collective intelligence of crowds on the Internet to find proper solutions to some common problems in the real world [14].…”

mentioning

confidence: 99%

Collective intelligence in information systems

Nguyên

Szczerbicki

Trawiński

et al. 2019

IFS

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In recent years, advances in information technologies have also facilitated the use of distributed knowledge from different autonomous sources for finding solutions to some common problems in the real world. This approach can be considered as an efficient approach to tap into collective intelligence, which is often considered as the intelligence emerging from the collaboration and competition of many individuals in a group. This paper aims to present the application of collective intelligence in information systems briefly. Apart from these, we also introduce papers in this issue.

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Introduction and Challenges of Environment Architectures for Collective Intelligence Systems

Cited by 11 publications

References 27 publications

Self-organizing Service Ecosystems: Exploring a New Concept for Service Science

Self-organizing Service Ecosystems: Exploring a New Concept for Service Science

Patterns for Self-Adaptation in Cyber-Physical Systems

Collective intelligence in information systems

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