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Advances in Collective Intelligence 2011

herausgegeben von: Jörn Altmann, Ulrike Baumöl, Bernd J. Krämer

Verlag: Springer Berlin Heidelberg

Buchreihe : Advances in Intelligent and Soft Computing

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

Collective intelligence has become an attractive subject of interest for both academia and industry. More and more conferences and workshops discuss the impact of the users‘ motivation to participate in the value creation process, the enabling role of leading-edge information and communication technologies and the need for better algorithms to deal with the growing amount of shared data. There are many interesting and challenging topics that need to be researched and discussed with respect to knowledge creation, creativity and innovation processes carried forward in the emerging communities of practice.

COLLIN is on the path to become the flagship conference in the areas of collective intelligence and ICT-enabled social networking. We were delighted to again receive contributions from different parts of the world including Australia, Europe, Asia, and the United States. Encouraged by the positive response, we plan COLLIN 2012 to be held next year end of August at FernUniverstität in Hagen.

In order to guarantee the quality of the event, each paper went through a doubleblind review process. The reviews concentrated on originality, quality and relevance of the paper topic to the symposium. In addition, we invited a few renowned experts in the field to contribute to the success of the symposium with outstanding papers reporting on their most recent research. Our special thanks go to the authors for submitting their papers, to the international program committee members, and to numerous reviewers who did an excellent job in guaranteeing that the papers in this volume are of very high quality.

Inhaltsverzeichnis

Frontmatter

Enough Questions for Everybody

Abstract

While I was writing my book, The Smart Swarm, about collective intelligence in nature and society, I often felt like one of the bees depicted on the front cover, buzzing from one field of research to another to pick up the latest thinking. As I worked my way through the widespread and expanding landscape, I met biologists, physicists, computer scientists, sociologists, engineers, psychologists, economists, political scientists, network theorists, and neuroscientists, and I began to see broad connections between the problems they were tackling. Biologists were talking about self-organization in superorganisms, while economists were debating the self-correcting tendencies of markets. Physicists were modeling collective motion, while psychologists were measuring collective biases in decision-making. Sociologists were exploring the wisdom of crowds, while engineers were experimenting with smart teams of robots. Running through all these discussions was a common thread that seemed obvious even to a non-scientist like me: Groups in nature have evolved ways to squeeze intelligence from relatively simple ingredients, and if we could just figure out how they do it we might learn something useful.

Peter Miller

Understanding Collective Intelligence

Abstract

In this post dot com era the web is transforming. Newer web applications trust their users, invite them to interact, connect them with others, gain early feedback from them and then use the collected information to constantly improve the application. Web applications that take this approach develop deeper relationships with their users, provide more value to users as they return more often, and ultimately offer more targeted experiences for each user according to his or her personal need.

Satnam Alag

Predicting Asset Value through Twitter Buzz

Abstract

This paper describes early work trying to predict financial market movement such as gold price, crude oil price, currency exchange rates and stock market indicators by analyzing Twitter posts. We collected Twitter feeds for 5 months obtaining a large set of emotional retweets originating from within the US, from which six public opinion time series containing the keywords “dollar%_t”, “$%_t”, “gold%_t”, “oil%_t, “job%_t” and “economy%_t” were extracted. Our results show that these variables are correlated to and even predictive of the financial market movement. Except “$%_t”, all other five public opinion time series are identified by a Granger-causal relationship with certain market movements. It is demonstrated that daily changes in the volume of economic topic retweeting seem to match the value shift occurring in the corresponding market next day.

Xue Zhang, Hauke Fuehres, Peter A. Gloor

Adding Value with Collective Intelligence – A Reference Framework for Business Models for User-Generated Content

Abstract

Many web-based business models started to harnessing collective intelligence by integrating users and customers into the value-adding process. With common web applications users have the chance to be a significant provider of contents and can commit themselves to deliver. Today, more and more companies detect the commercial potential of this user-generated content (UGC). Amazon, for example, is one of the pioneers in integrating customers and users for providing comments and evaluations. More and more, business models started to evolve which build on UGC at their core. These UGC business models define a certain business model type which bases its primary service offering on UGC.

This paper describes and analyzes building blocks of business models for UGC to be found in the existing literature and in case studies. For that, the research method of multiple case study analysis is employed. As a result, a reference framework for business models for UGC is proposed, which can be used for the definition, development, comparison as well as the assessment of such business models.

Henrik Ickler, Ulrike Baumöl

Collective Intelligence Model: How to Describe Collective Intelligence

Abstract

A large number of scientific research exists, describing forms of collective intelligence (e.g. Wikipedia). But there are only few publications that describe how different forms of collective intelligence be described in general. In this paper, we therefore describe an approach how to characterise different forms of collective intelligence. We draw from existing research and build a comprehensive model and identify further characteristics to describe collective intelligence in a fine-grained manner. We propose a model with different characteristics, like form of cooperation, organisational pattern, and decision making process, which distinctively describe forms of collective intelligence and suggest possible attribute values.

Sandro Georgi, Reinhard Jung

The Participatory Roles Play Simulation in a Social and Collective Learning Context

Abstract

Knowledge building and behavioral change takes place by participation in collective learning process. Participatory learning methods and intellectual interaction between people are an effective part of professional development. This paper discusses the role of game play in engaging actively stakeholder in a collective learning. This paper intends to investigate the factors facilitating the learning capability while designing a participatory role play model process. In order to delineate a general model, we have conducted two experiments in different Norwegian organizations. The goal was to investigate the evidences of using role play game in a participatory as a suitable tool fostering collective learning.

Aurelie Aurilla Bechina, Tone Vold

A Complex Network Analysis of the Weighted Graph of the Web2.0 Service Network

Abstract

Service providers that own Web2.0 services allow Internet users not only to access their Web2.0 services but also to create new Web2.0 services (mashups) based on theirs. This creation of mashups generates the Web2.0 service network, in which a node represents a Web2.0 service and a link between two nodes represents a mashup using the two Web2.0 services linked. Since this Web2.0 service network is constructed without the control of a single entity (i.e., it is self-organizing), the network topology of the Web2.0 service network shows the scale-free characteristic. With respect of the weighting of those links, however, there are different approaches. Prior research either considered binary links or links that are weighted by summing up the number of mashups. Since the last approach might overestimate the strength of the link, we calculate the link weights according to Newman’s approach in this paper. Based on this weighted graph of the Web2.0 service network, we investigate the topology of the weighted graph and examine the pattern of Web2.0 service creations. Our results show that the Newman-based weighted graph of the Web2.0 service network shows the characteristics of a scale-free network and a small-world network.

Kibae Kim, Jörn Altmann

How Collective Intelligence Redefines Education

Abstract

While collective intelligence systems become ubiquitous for learning in knowledge industries, civic life and personal lives, they have yet to be embraced into formal schooling systems. Still, learning, knowledge and assessment protocols adhere, in large part, to the educational system’s logic of the industrial era. The temptation is to view schooling as falling behind with teacher retraining and curriculum revision leading the way. This article examines the underlying logic of both collective intelligence and formal education systems and traces education’s reluctance to it roots in an industrial era and the incentives prevailing in its structures. Embracing collective intelligence, then, will require a redefinition of schooling rather than a mere retooling.

Lynn Ilon

On Presence, Collective Performance and Assumptions of Causality

Abstract

The ways we assume, observe and model “presence” and its effects are the focus in this paper. Entities with selectively shared presences are the basis of any collective, and of attributions (such as “humorous”, “efficient” or “intelligent”). The subtleties of any joint presence can markedly influence potentials, perceptions and performance of the collective as demonstrated when a humorous tale is counterpoised with disciplined thought. Disciplines build on presences assumed known or knowable while fluid and interpretable presences pervade humor. Explorations in this paper allow considerations of collectives, causality and the philosophy of computing. Economics has long considered issues of collective action in ways circumscribed by assumptions about the presence of economic entities. Such entities are deemed rational but they are clearly not intelligent. To reach its potential, collective intelligence research needs more adequate considerations of alternate presences and their impacts.

Mark McGovern

Preservation of Enterprise Engineering Processes by Social Collaboration Software

Abstract

In design and engineering, it is important to preserve more than just the actual documents making up the product data. For knowledge-heavy industries it is of critical importance to also preserve the soft knowledge of the overall process, the so-called product lifecycle. The idea here is not only to send the designs into the future, but also the knowledge about processes, decision making, and people. In order to preserve this knowledge, it needs to be captured at content creation time, a process currently mostly independent from the act of preservation. This paper discusses how to make tools and applications used at content creation time, especially in design and engineering, but also, in general, preservation-aware by using the OpenConjurer approach and framework.

Dominic Heutelbeck

Univector Field Method Based Multi-robot Navigation for Pursuit Problem

Abstract

This paper introduces a new approach to solve the pursuit problem based on a univector field method. In our proposed method, a set of robots work together instantaneously to find suitable moving directions and follow the univector field to surround and capture a prey robot. In addition, a set of strategies is proposed to make the pursuit problem more realistic in the real world environment. This is a general approach based on univector field, and it can be extended for an environment that contains static or moving obstacles. Experimental results show that our proposed algorithm is effective for the pursuit problem.

Hoang Huu Viet, Sang Hyeok An, TaeChoong Chung

Harvesting Domain-Specific Data Resources for Enhanced After-Sales Intelligence in Car Industry

Abstract

Heterogeneous document landscapes in companies hold knowledge in the form of potential linkage between domain-specific documents of various document systems. To access this (hidden) knowledge, we developed design patterns for an ontology to derive a homogeneous access structure from the heterogeneous document landscape. Like in [19], we describe an Advanced Ontology-based Information Retrieval System (AIRS) that includes this ontology to generate retrieval strategies and to find document relationships. In a case study we demonstrate, how the concept of the AIRS can be used to combine the knowledge of different document systems to improve various workshop processes.

Jan Werrmann

Backmatter

Titel: Advances in Collective Intelligence 2011
herausgegeben von: Jörn Altmann
Ulrike Baumöl
Bernd J. Krämer
Verlag: Springer Berlin Heidelberg
Electronic ISBN: 978-3-642-25321-8
Print ISBN: 978-3-642-25320-1
DOI: https://doi.org/10.1007/978-3-642-25321-8