HOW DO COMMUNICATION STRUCTURES SHAPE THE PROCESS OF KNOWLEDGE TRANSFER? - AN AGENT-BASED MODEL

Knowledge diffusion is a complex process. Knowledge is intangible and therefore is not easy to capitalize within an organization, or share between a set of individuals. The aim of this paper is to study the impact of two different structures of communication on both processes of knowledge transfer and individual learning, in the context of a community of practice. We will specifically compare two types of communication structures (through face-to-face interactions and through a forum) by using agent-based models. Results show that each structure has a different impact on individual learning and knowledge transfer. Though, communication through face-to-face interactions seems to make individuals learn slower than on a web forum. Conclusions are widely discussed.knowledge; communication structure; communities of practice; agent-based models

Knowledge transfer dynamics : how to model knowledge in the first place?

In this paper, we study both processes of direct and indirect knowledge transfer, from a modelling perspective, using agent-based models. In fact, there are several ways to model knowledge. We choose to study three different representations, and try to determine which one allows to better capture the dynamics of knowledge diffusion within a social network. Results show that when knowledge is modelled as a binary vector, and not cumulated, this enables us to observe some heterogeneity in agents' learning and interactions, in both types of knowledge transfer

HOW DO COMMUNICATION STRUCTURES SHAPE THE PROCESS OF KNOWLEDGE TRANSFER? - AN AGENT-BASED MODEL

Knowledge diffusion is a complex process. Knowledge is intangible and therefore is not easy to capitalize within an organization, or share between a set of individuals. The aim of this paper is to study the impact of two different structures of communication on both processes of knowledge transfer and individual learning, in the context of a community of practice. We will specifically compare two types of communication structures (through face-to-face interactions and through a forum) by using agent-based models. Results show that each structure has a different impact on individual learning and knowledge transfer. Though, communication through face-to-face interactions seems to make individuals learn slower than on a web forum. Conclusions are widely discussed

Learning in a community of practice : Complete vs. incomplete information

This paper is a contribution to the modelling of interactions within social networks. We use an agent-based model to explore the learning process, in a particular form of social network: a community of practice. It is based on an empirical study, made in a research centre in France. We shed light on the importance of two parameters in learning in communities : the availability of the community members, and the information they have about their neighbourhood. The first parameter represents their willingness and engagement in the development of their community, whereas the second one gives us an idea on agents' ability to build a correct shared representation of the competencies of the other agents. We lead two sets of simulations, simulations with complete information and simulations with incomplete information

Advances in Computational Social Science and Social Simulation

Aquesta conferència és la celebració conjunta de la "10th Artificial Economics Conference AE", la "10th Conference of the European Social Simulation Association ESSA" i la "1st Simulating the Past to Understand Human History SPUHH".Conferència organitzada pel Laboratory for Socio­-Historical Dynamics Simulation (LSDS-­UAB) de la Universitat Autònoma de Barcelona.Readers will find results of recent research on computational social science and social simulation economics, management, sociology,and history written by leading experts in the field. SOCIAL SIMULATION (former ESSA) conferences constitute annual events which serve as an international platform for the exchange of ideas and discussion of cutting edge research in the field of social simulations, both from the theoretical as well as applied perspective, and the 2014 edition benefits from the cross-fertilization of three different research communities into one single event. The volume consists of 122 articles, corresponding to most of the contributions to the conferences, in three different formats: short abstracts (presentation of work-in-progress research), posters (presentation of models and results), and full papers (presentation of social simulation research including results and discussion). The compilation is completed with indexing lists to help finding articles by title, author and thematic content. We are convinced that this book will serve interested readers as a useful compendium which presents in a nutshell the most recent advances at the frontiers of computational social sciences and social simulation researc

Knowledge transfer dynamics : how to model knowledge in the first place?

In this paper, we study both processes of direct and indirect knowledge transfer, from a modelling perspective, using agent-based models. In fact, there are several ways to model knowledge. We choose to study three different representations, and try to determine which one allows to better capture the dynamics of knowledge diffusion within a social network. Results show that when knowledge is modelled as a binary vector, and not cumulated, this enables us to observe some heterogeneity in agents' learning and interactions, in both types of knowledge transfer

PRIOR KNOWLEDGE VERSUS CONSTRUCTED KNOWLEDGE: WHAT IMPACT ON LEARNING?

The aim of this paper is to model the process of learning within a social network and compare the levels of learning in two different situations: one where individuals know others' competencies as given data and interact on this basis; and one where individuals know nothing about others' competencies but rather build this knowledge over time, according to their past interactions. For this purpose, we build an agent-based model, and model these two scenarios of simulations. Results are partly studied using network analysis, and they show that in the second type of simulations agents are able to identify the most competent agents in the network and increase their competencies. Results also show that learning is easier when there is no prior knowledge of others' competencies. Otherwise, agents deal with a congestion effect that slows down the learning process.Learning, knowledge, network, agent-based model

Learning in a community of practice : Complete vs. incomplete information

This paper is a contribution to the modelling of interactions within social networks. We use an agent-based model to explore the learning process, in a particular form of social network: a community of practice. It is based on an empirical study, made in a research centre in France. We shed light on the importance of two parameters in learning in communities : the availability of the community members, and the information they have about their neighbourhood. The first parameter represents their willingness and engagement in the development of their community, whereas the second one gives us an idea on agents' ability to build a correct shared representation of the competencies of the other agents. We lead two sets of simulations, simulations with complete information and simulations with incomplete information.learning, interaction, competencies, community of practice, agent-based model

Structuring knowledge transfer from experts to newcomers

ACL-4International audiencePurpose – The purpose of this paper is to focus on the process of knowledge transfer within social networks composed of a pool of experts, and newcomers whose aim is primarily to acquire new knowledge, such as communities of practice. The authors wish to understand which communication system and which information about others' knowledge should be provided to get to a better diffusion of knowledge. Design/methodology/approach – Agent‐based models and social network analysis are used and many simulations are run, in which communication mode and information about others' knowledge are varied. Findings – Results emphasize the part played by newcomers in the process of direct knowledge transfer. They constitute additional sources of knowledge and act as intermediaries. Results also show that in a process of indirect transfer of knowledge, they have only little influence on the process of individual learning. These results enable the authors to formulate some recommendations to facilitate knowledge transfer within a knowledge intensive community. Non‐hierarchical structures of communication should be preferred and the participation of newcomers in the activities of the community fully encouraged. Originality/value – This paper combines agent‐based modelling and social networks analysis to investigate the field of knowledge transfer and enables the identification of the key elements in the process of knowledge diffusion within a community of practice. It thus provides some solution to eventual congestion problems in the access to the knowledge held within the community