A computational method to track the evolution of business models in the Digital Economy

Companies within the Digital Economy are evolving their business models as they take advantage of the opportunities afforded by emerging digital technologies. There is a need to develop methods that will allow researchers and policy makers to understand the existence of, and relationships between, the different business models within the Digital Economy and track their evolution. Such methods could also help quantify the size and growth of the Digital Economy. This paper presents a computational method, which utilizes machine learning and web scraping, to identify new business models, and a taxonomy of organisations, through the analysis of a firm’s webpage. The work seeks to provide an autonomous tool that provides regular output tracking trends in the number of firms in a market, their business model and changes in activity from product to service over time. This information would provide valuable and actionable insight for researchers, firms and markets

Digital Ecosystems: Ecosystem-Oriented Architectures

We view Digital Ecosystems to be the digital counterparts of biological ecosystems. Here, we are concerned with the creation of these Digital Ecosystems, exploiting the self-organising properties of biological ecosystems to evolve high-level software applications. Therefore, we created the Digital Ecosystem, a novel optimisation technique inspired by biological ecosystems, where the optimisation works at two levels: a first optimisation, migration of agents which are distributed in a decentralised peer-to-peer network, operating continuously in time; this process feeds a second optimisation based on evolutionary computing that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant constraints. The Digital Ecosystem was then measured experimentally through simulations, with measures originating from theoretical ecology, evaluating its likeness to biological ecosystems. This included its responsiveness to requests for applications from the user base, as a measure of the ecological succession (ecosystem maturity). Overall, we have advanced the understanding of Digital Ecosystems, creating Ecosystem-Oriented Architectures where the word ecosystem is more than just a metaphor.Comment: 39 pages, 26 figures, journa

A Connectionist Theory of Phenomenal Experience

When cognitive scientists apply computational theory to the problem of phenomenal consciousness, as many of them have been doing recently, there are two fundamentally distinct approaches available. Either consciousness is to be explained in terms of the nature of the representational vehicles the brain deploys; or it is to be explained in terms of the computational processes defined over these vehicles. We call versions of these two approaches vehicle and process theories of consciousness, respectively. However, while there may be space for vehicle theories of consciousness in cognitive science, they are relatively rare. This is because of the influence exerted, on the one hand, by a large body of research which purports to show that the explicit representation of information in the brain and conscious experience are dissociable, and on the other, by the classical computational theory of mind – the theory that takes human cognition to be a species of symbol manipulation. But two recent developments in cognitive science combine to suggest that a reappraisal of this situation is in order. First, a number of theorists have recently been highly critical of the experimental methodologies employed in the dissociation studies – so critical, in fact, it’s no longer reasonable to assume that the dissociability of conscious experience and explicit representation has been adequately demonstrated. Second, classicism, as a theory of human cognition, is no longer as dominant in cognitive science as it once was. It now has a lively competitor in the form of connectionism; and connectionism, unlike classicism, does have the computational resources to support a robust vehicle theory of consciousness. In this paper we develop and defend this connectionist vehicle theory of consciousness. It takes the form of the following simple empirical hypothesis: phenomenal experience consists in the explicit representation of information in neurally realized PDP networks. This hypothesis leads us to re-assess some common wisdom about consciousness, but, we will argue, in fruitful and ultimately plausible ways

A Taxonomy of Workflow Management Systems for Grid Computing

With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. We also survey several representative Grid workflow systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure

Modeling economic systems as locally-constructive sequential games

Real-world economies are open-ended dynamic systems consisting of heterogeneous interacting participants. Human participants are decision-makers who strategically take into account the past actions and potential future actions of other participants. All participants are forced to be locally constructive, meaning their actions at any given time must be based on their local states; and participant actions at any given time affect future local states. Taken together, these essential properties imply real-world economies are locally-constructive sequential games. This paper discusses a modeling approach, Agent-based Computational Economics, that permits researchers to study economic systems from this point of view. ACE modeling principles and objectives are first concisely presented and explained. The remainder of the paper then highlights challenging issues and edgier explorations that ACE researchers are currently pursuing

Using Cyber-enabled Transaction Data to Study Productivity and Innovation in Organizations

This paper draws on recent research in a wide variety of disciplines to identify the key elements necessary to build an empirical infrastructure that will advance research on one of the key building blocks of science and innovation policy: organizations. We argue that cyber-tools and new data will permit researchers to examine the innovation process |both successes and failures| and explore business performance and business dynamics at the level of the appropriate economic entity. We develop a roadmap that outlines how the new data can be developed, from harvesting the web to direct observation from deep within companies. The paper identifies a set of research questions and an approach whose pursuit could be used to develop a national research data infrastructure for the study of innovation and organizational performance. One key element of the approach is to identify and study innovation processes within organizations by collecting data on inputs and outcomes of innovation projects (or initiatives) within organizations. Another is the collection of representative data by business function/processes across firms, a proven statistical and economic approach (Sturgeon et al. 2006, Brown 2008, Lewin et al 2008). Finally, we argue that the work to develop new data from deep within firms should involve the participation of computer and information scientists. Opportunities for quasi experimental approaches to data collection, and noninvasive techniques to harvest data from within firms (i.e., auto-populating of researcher databases) need to be explored. More generally, the bringing together of scientists to consider business microdata privacy/access and data collection from organizations is itself significant, with potential for creating opportunities in a broad range of applications.

Empowering citizens' cognition and decision making in smart sustainable cities

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Advances in Internet technologies have made it possible to gather, store, and process large quantities of data, often in real time. When considering smart and sustainable cities, this big data generates useful information and insights to citizens, service providers, and policy makers. Transforming this data into knowledge allows for empowering citizens' cognition as well as supporting decision-making routines. However, several operational and computing issues need to be taken into account: 1) efficient data description and visualization, 2) forecasting citizens behavior, and 3) supporting decision making with intelligent algorithms. This paper identifies several challenges associated with the use of data analytics in smart sustainable cities and proposes the use of hybrid simulation-optimization and machine learning algorithms as an effective approach to empower citizens' cognition and decision making in such ecosystemsPeer ReviewedPostprint (author's final draft

Design Tools

This book aims at encompassing the panorama of design tools being developed, tested and adopted by researchers and professors at the Department of Design of Politecnico di Milano. The tools are organized in a taxonomy that reflects the path of choice of a possible user in need for the right tool for a task to be performed. The taxonomy is based on a formalization of the design process proposed by the authors, which characterizes the Design System at Politecnico di Milano. The book essentially offers two main contributions: an original taxonomy that guides towards the organization of design tools and their usage with different actors; a representative collection of design tools developed within the Department of Design of Politecnico di Milano with specific instructions on how to use them. Design Tools is addressed both to practitioners and academics in the field of design that are interested in getting to know more about the discourse around design tools in general and in particular how this discourse takes a shape within Politecnico di Milano and resolves in usable and shareable tools

ARCHETYPES OF DIGITAL BUSINESS MODELS IN LOGISTICS START-UPS

Our work develops an archetypical representation of current digital business models of Start-Ups in the logistics sector. In order to achieve our goal, we analyze the business models of 125 Start-Ups. We draw our sample from the Start-Up database AngelList and focus on platform-driven businesses. We chose Start-Ups as they often are at the forefront of innovation and thus have a high likelihood of operating digital business models. Following well-established methodological guidelines, we construct a taxonomy of digital business models in multiple iterations. We employ different algorithms for cluster analysis to find and generate clusters based on commonalities between the business models across the dimensions and characteristics of the taxonomy. Ultimately, we use the dominant features of the emerging patterns within the clusters to derive archetypes