Imagining Knowledge, a Formal Account of Design

Design, as in designing artifacts like cars or computer programs, is one of those aspects of rational agency hardly even mentioned in traditional logical theory. As an engineering discipline, design obviously involves reasoning but seems to depend much more on a mix of factual knowledge, experimenting and imagination. We will present a formal framework for the dynamic interplay between knowledge and imagination inspired by C-K theory Hatchuel and Weil (2003a) and discuss the possible directions for further development of a 'logic of design'

Data Science as a New Frontier for Design

The purpose of this paper is to contribute to the challenge of transferring know-how, theories and methods from design research to the design processes in information science and technologies. More specifically, we shall consider a domain, namely data-science, that is becoming rapidly a globally invested research and development axis with strong imperatives for innovation given the data deluge we are currently facing. We argue that, in order to rise to the data-related challenges that the society is facing, data-science initiatives should ensure a renewal of traditional research methodologies that are still largely based on trial-error processes depending on the talent and insights of a single (or a restricted group of) researchers. It is our claim that design theories and methods can provide, at least to some extent, the much-needed framework. We will use a worldwide data-science challenge organized to study a technical problem in physics, namely the detection of Higgs boson, as a use case to demonstrate some of the ways in which design theory and methods can help in analyzing and shaping the innovation dynamics in such projects.Comment: International Conference on Engineering Design, Jul 2015, Milan, Ital

The Generation of Common Purpose in Innovation Partnerships : a Design Perspective

The official version of the article is available here : http://www.emeraldinsight.com/journals.htm?articleid=17042798&ini=aobInternational audiencePurpose - Scholars and practitioners have both emphasized the importance of collaboration in innovation context. They have also largely acknowledged that the definition of common purpose is a major driver of successful collaboration, but surprisingly, researchers have put little effort into investigating the process whereby the partners define the common purpose. This research aims to explore the Generation of Common Purpose (GCP) in innovation partnerships. Design/methodology/approach - An action-research approach combined with modeling has been followed. Our research is based on an in-depth qualitative case study of a cross-industry exploratory partnership through which four partners, from very different arenas, aim to collectively define innovation projects based on micro-nanotechnologies. Based on a design reasoning framework, the mechanisms of GCP mechanism are depicted. Findings - Regarding GCP, two main interdependent facets are identified: (1) the determination of existing intersections between the parties' concept and knowledge spaces ('Matching'); (2) an introspective learning process that allows the parties to transforms those spaces ('Building'). Practical implications - The better understanding of the GCP and the specific notion of "C-K profiles", which is an original way to characterize each partner involved in a partnership, should improve the capabilities of organizations to efficiently define collaborative innovation projects. Originality/value - This article explores one of the cornerstones of successful collaboration in innovation: the process whereby several parties define the common purpose of their partnership

Brainstorming versus creative design reasoning: A theory-driven experimental investigation of novelty, feasibility and value of ideas

International audienceIn industrial settings, brainstorming is seen as an effective technique for creativity in innovation processes. However, bulk of research on brainstorming is based on an oversimplified view of the creativity process. Participants are seen as idea generators and the process aims at maximizing the quantity of ideas produced, and the evaluation occurs post-process based on some originality and feasibility criteria. Design theories can help enrich this simplistic process model. The present study reports an experimental investigation of creativity process within the context of real-life design ideation task. Results lead to the rejection of the classical 'quantity breeds quality' hypothesis. Rather, we observe that successful groups are the ones who produce a few original propositions that hold great value for users while looking for ways to make those propositions feasible

Uncovering the specificities of CAD tools for industrial design with design theory – style models for generic singularity

International audienceAccording to some casual observers, computer-aided design (CAD) tools are very similar. These tools are used to design new artifacts in a digital environment; hence, they share typical software components, such as a computing engine and human-machine interface. However, CAD software is dedicated to specific professionals—such as engineers, three-dimensional (3D) artists, and industrial designers (IDs)—who claim that, despite their apparent similarities, CAD tools are so different that they are not substitutable. Moreover, CAD tools do not fully meet the needs of IDs. This paper aims at better characterizing CAD tools by taking into account their underlying design logic, which involves relying on recent advances in design theory. We show that engineering CAD tools are actually modeling tools that design a generic variety of products; 3D artist CAD tools not only design but immediately produce single digital artefacts; and ID CAD tools are neither a mix nor an hybridization of engineering CAD and 3D artist CAD tools but have their own logic, namely to create new conceptual models for a large variety of products, that is, the creation of a unique original style that leads to a generic singularity. Such tools are useful for many creative designers beyond IDs

Out-of-class novelty generation: an experimental foundation *

International audienceConstructive machine learning aims at finding one or more instances of a domain which will exhibit some desired properties. Such a process bears a strong similarity with a design process where the ultimate objective is the generation of previously unknown and novel objects by using knowledge about known objects. The aim of the present work is to bring ideas from design theory to machine learning and elaborate an experimental procedure allowing the study of design through machine learning approaches. To this end, we propose an actionable definition of creativity as the generation of out-of-distribution novelty. We assess several metrics designed for evaluating the quality of generative models on this new task. Through extensive experiments on various types of generative models, we find architectures and hyperparameter combinations which lead to out-of-distribution novelty. Such generators can then be used to search a semantically richer and broader space than standard generative models would allow

Multiple forms of applications and impacts of a design theory -ten years of industrial applications of C-K theory

International audienceC-K theory has been developed by Armand Hatchuel and Benoit Weil and then by other researchers since 1990s. In this paper we show that its very abstract nature and its high degree of universality actually supported a large variety of industrial applications. We distinguish three types of applications: 1) C-K theory provides a new language, that supports new analysis and descriptive capacity and new teachable individual models of thoughts; 2) C-K theory provides a very general framework to better characterize the validity domain and the performance conditions of existing methods, leading to potential improvement of these methods ; 3) C-K theory is the conceptual model at the root of new design methods that are today largely used in the industry

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Transformation digitale, par l'intelligence artificielle et la valorisation des données

Billet de blogLa massification des données ouvre la voie pour un nouveau domaine de compétitivité qui, à la fois, menace les entreprises et offre un potentiel d'innovation important. Cependant, les entreprises dont le cœur du métier n'est pas les NTICS doivent entamer des programmes de transformations profondes pour valoriser leurs données afin d'obtenir un avantage concurrentiel. Depuis de nombreuses années, et à travers un réseau de partenaires industriels, le Centre de Gestion Scientifique mène des travaux qui cherchent, d'une part, à clarifier les clefs de réussite d'une transformation par les données et l'intelligence artificielle (obstacles récurrents, facteurs d'inertie, risques...), et d'autre part, à fournir les méthodologies de transformation par l'IA et des démarches pour intégrer et industrialiser le processus de développement des modèles prédictifs. Le papier restitue la vague des big data dans son contexte historique et fait le lien avec la transformation digitale. Ensuite, je chercherai à éclairer les raisons des difficultés rencontrées par de nombreuses entreprises. Puis, j’avancerai des hypothèses explicatives qui offrent aussi l’intérêt de pointer vers les solutions possibles

CROWD-BASED DATA-DRIVEN HYPOTHESIS GENERATION FROM DATA AND THE ORGANISATION OF PARTICIPATIVE SCIENTIFIC PROCESS

International audienceIn scientific process, hypothesis generation is one the most important steps where creativity is needed most. As the science becomes more open and data-driven, it becomes interesting to analyse whether a crowdsourcing approach might be beneficial in this step. First, we characterize the process as a design process. Then, based on a real-life case study, we analyse and highlight difficulties and challenges for crowd-based hypothesis generation. Last, we give a generic process model for organizing in similar challenges in other data-based scientific hypothesis generation contexts