Living Labs as a navigation system for innovative business models in the music industry

Media industries and other rapidly evolving, complex, uncertain markets have a hard time to survive if they do not optimize or radically change their business models. This paper analyses the potential of involving all relevant stakeholders of the value network in the development of a business model by means of a panel based multi-method Living Lab approach. Using an in-depth case study analysis, a critical analysis of both the potential value and the weaknesses of such an approach are being assessed. Although some difficulties exist, opening this innovation process and involving external actors in a structural way has the potential to increase the value creation and sustainability of the business model. This paper also stresses the importance of multidisciplinary research on multi-stakeholder involvement in business model innovation

Introduction

This book brings together for the first time the collected wisdom of international leaders in the theory and practice in the emerging field of cultural heritage crowdsourcing. It features eight accessible case studies of groundbreaking projects from leading cultural heritage and academic institutions, and four thought-­‐provoking essays that reflect on the wider implications of this engagement for participants and on the institutions themselves

Principles and Concepts of Agent-Based Modelling for Developing Geospatial Simulations

The aim of this paper is to outline fundamental concepts and principles of the Agent-Based Modelling (ABM) paradigm, with particular reference to the development of geospatial simulations. The paper begins with a brief definition of modelling, followed by a classification of model types, and a comment regarding a shift (in certain circumstances) towards modelling systems at the individual-level. In particular, automata approaches (e.g. Cellular Automata, CA, and ABM) have been particularly popular, with ABM moving to the fore. A definition of agents and agent-based models is given; identifying their advantages and disadvantages, especially in relation to geospatial modelling. The potential use of agent-based models is discussed, and how-to instructions for developing an agent-based model are provided. Types of simulation / modelling systems available for ABM are defined, supplemented with criteria to consider before choosing a particular system for a modelling endeavour. Information pertaining to a selection of simulation / modelling systems (Swarm, MASON, Repast, StarLogo, NetLogo, OBEUS, AgentSheets and AnyLogic) is provided, categorised by their licensing policy (open source, shareware / freeware and proprietary systems). The evaluation (i.e. verification, calibration, validation and analysis) of agent-based models and their output is examined, and noteworthy applications are discussed.Geographical Information Systems (GIS) are a particularly useful medium for representing model input and output of a geospatial nature. However, GIS are not well suited to dynamic modelling (e.g. ABM). In particular, problems of representing time and change within GIS are highlighted. Consequently, this paper explores the opportunity of linking (through coupling or integration / embedding) a GIS with a simulation / modelling system purposely built, and therefore better suited to supporting the requirements of ABM. This paper concludes with a synthesis of the discussion that has proceeded. The aim of this paper is to outline fundamental concepts and principles of the Agent-Based Modelling (ABM) paradigm, with particular reference to the development of geospatial simulations. The paper begins with a brief definition of modelling, followed by a classification of model types, and a comment regarding a shift (in certain circumstances) towards modelling systems at the individual-level. In particular, automata approaches (e.g. Cellular Automata, CA, and ABM) have been particularly popular, with ABM moving to the fore. A definition of agents and agent-based models is given; identifying their advantages and disadvantages, especially in relation to geospatial modelling. The potential use of agent-based models is discussed, and how-to instructions for developing an agent-based model are provided. Types of simulation / modelling systems available for ABM are defined, supplemented with criteria to consider before choosing a particular system for a modelling endeavour. Information pertaining to a selection of simulation / modelling systems (Swarm, MASON, Repast, StarLogo, NetLogo, OBEUS, AgentSheets and AnyLogic) is provided, categorised by their licensing policy (open source, shareware / freeware and proprietary systems). The evaluation (i.e. verification, calibration, validation and analysis) of agent-based models and their output is examined, and noteworthy applications are discussed.Geographical Information Systems (GIS) are a particularly useful medium for representing model input and output of a geospatial nature. However, GIS are not well suited to dynamic modelling (e.g. ABM). In particular, problems of representing time and change within GIS are highlighted. Consequently, this paper explores the opportunity of linking (through coupling or integration / embedding) a GIS with a simulation / modelling system purposely built, and therefore better suited to supporting the requirements of ABM. This paper concludes with a synthesis of the discussion that has proceeded

Overview on agent-based social modelling and the use of formal languages

Transdisciplinary Models and Applications investigates a variety of programming languages used in validating and verifying models in order to assist in their eventual implementation. This book will explore different methods of evaluating and formalizing simulation models, enabling computer and industrial engineers, mathematicians, and students working with computer simulations to thoroughly understand the progression from simulation to product, improving the overall effectiveness of modeling systems.Postprint (author's final draft

Fake News Detection in Social Networks via Crowd Signals

Our work considers leveraging crowd signals for detecting fake news and is motivated by tools recently introduced by Facebook that enable users to flag fake news. By aggregating users' flags, our goal is to select a small subset of news every day, send them to an expert (e.g., via a third-party fact-checking organization), and stop the spread of news identified as fake by an expert. The main objective of our work is to minimize the spread of misinformation by stopping the propagation of fake news in the network. It is especially challenging to achieve this objective as it requires detecting fake news with high-confidence as quickly as possible. We show that in order to leverage users' flags efficiently, it is crucial to learn about users' flagging accuracy. We develop a novel algorithm, DETECTIVE, that performs Bayesian inference for detecting fake news and jointly learns about users' flagging accuracy over time. Our algorithm employs posterior sampling to actively trade off exploitation (selecting news that maximize the objective value at a given epoch) and exploration (selecting news that maximize the value of information towards learning about users' flagging accuracy). We demonstrate the effectiveness of our approach via extensive experiments and show the power of leveraging community signals for fake news detection

A Prototype to Support Business Model Innovation through Crowdsourcing and Artificial Intelligence

The development of new and innovative business models is a central challenge for many companies, particularly for small and medium-sized companies. Information systems could support these companies by actively guiding them through a business model development process. However, the existing business model development tools only provide passive support for their users (e.g., digital whiteboards). Therefore, we set out to develop a prototype that actively supports its users by generating business model ideas. Informed by an existing design theory, we built a prototype relying on hybrid intelligence (i.e., the combination of human and artificial knowledge). The prototype iteratively generates new business model ideas by recombining existing knowledge, posts the ideas to a crowdsourcing platform for evaluation, and learns from the crowds’ evaluation. This demonstration paper presents the prototype, the challenges we faced during its implementation, and directions for future research on machine-supported business model development

An ontology roadmap for crowdsourcing innovation intermediaries

Ontologies have proliferated in the last years, essentially justified by the need of achieving a consensus in the multiple representations of reality inside computers, and therefore the accomplishment of interoperability between machines and systems. Ontologies provide an explicit conceptualization that describes the semantics of the data. Crowdsourcing innovation intermediaries are organizations that mediate the communication and relationship between companies that aspire to solve some problem or to take advantage of any business opportunity with a crowd that is prone to give ideas based on their knowledge, experience and wisdom, taking advantage of web 2.0 tools. Various ontologies have emerged, but at the best of our knowledge, there isn’t any ontology that represents the entire process of intermediation of crowdsourcing innovation. In this paper we present an ontology roadmap for developing crowdsourcing innovation ontology of the intermediation process. Over the years, several authors have proposed some distinct methodologies, by different proposals of combining practices, activities, languages, according to the project they were involved in. We start making a literature review on ontology building, and analyse and compare ontologies that propose the development from scratch with the ones that propose reusing other ontologies. We also review enterprise and innovation ontologies known in literature. Finally, are presented the criteria for selecting the methodology and the roadmap for building crowdsourcing innovation intermediary ontology.(undefined