Simplicial models of social aggregation I

This paper presents the foundational ideas for a new way of modeling social aggregation. Traditional approaches have been using network theory, and the theory of random networks. Under that paradigm, every social agent is represented by a node, and every social interaction is represented by a segment connecting two nodes. Early work in family interactions, as well as more recent work in the study of terrorist organizations, shows that network modeling may be insufficient to describe the complexity of human social structures. Specifically, network theory does not seem to have enough flexibility to represent higher order aggregations, where several agents interact as a group, rather than as a collection of pairs. The model we present here uses a well established mathematical theory, the theory of simplicial complexes, to address this complex issue prevalent in interpersonal and intergroup communication. The theory enables us to provide a richer graphical representation of social interactions, and to determine quantitative mechanisms to describe the robustness of a social structure. We also propose a methodology to create random simplicial complexes, with the purpose of providing a new method to simulate computationally the creation and disgregation of social structures. Finally, we propose several measures which could be taken and observed in order to describe and study an actual social aggregation occurring in interpersonal and intergroup contexts.Comment: 31 page

A Relational Hyperlink Analysis of an Online Social Movement

In this paper we propose relational hyperlink analysis (RHA) as a distinct approach for empirical social science research into hyperlink networks on the World Wide Web. We demonstrate this approach, which employs the ideas and techniques of social network analysis (in particular, exponential random graph modeling), in a study of the hyperlinking behaviors of Australian asylum advocacy groups. We show that compared with the commonly-used hyperlink counts regression approach, relational hyperlink analysis can lead to fundamentally different conclusions about the social processes underpinning hyperlinking behavior. In particular, in trying to understand why social ties are formed, counts regressions may over-estimate the role of actor attributes in the formation of hyperlinks when endogenous, purely structural network effects are not taken into account. Our analysis involves an innovative joint use of two software programs: VOSON, for the automated retrieval and processing of considerable quantities of hyperlink data, and LPNet, for the statistical modeling of social network data. Together, VOSON and LPNet enable new and unique research into social networks in the online world, and our paper highlights the importance of complementary research tools for social science research into the web

What Drives Volunteers to Accept a Digital Platform That Supports NGO Projects?

Technology has become the driving force for both economic and social change. However, the recruitment of volunteers into the projects of non-profit-making organizations (NGO) does not usually make much use of information and communication technology (ICT). Organizations in this sector should incorporate and use digital platforms in order to attract the most well-prepared and motivated young volunteers. The main aim of this paper is to use an extended Technology Acceptance Model (TAM) to analyze the acceptance of a technological platform that provides a point of contact for non-profit-making organizations and potential volunteers. The TAM is used to find the impact that this new recruitment tool for volunteers can have on an ever-evolving industry. The TAM has been extended with the image and reputation and visual identity variables in order to measure the influence of these non-profit-making organizations on the establishment and implementation of a social network recruitment platform. The data analyzed are from a sample of potential volunteers from non-profit-making organizations in Spain. A structural equation approach using partial least squares was used to evaluate the acceptance model. The results provide an important contribution to the literature about communication in digital environments by non-profit-making organizations as well as strategies to improve their digital reputation

An Agent-based approach to modelling integrated product teams undertaking a design activity.

The interactions between individual designers, within integrated product teams, and the nature of design tasks, all have a significant impact upon how well a design task can be performed, and hence the quality of the resultant product and the time in which it can be delivered. In this paper we describe an ongoing research project which aims to model integrated product teams through the use of multi-agent systems. We first describe the background and rationale for our work, and then present our initial computational model and results from the simulation of an integrated product team. The paper concludes with a discussion of how the model will evolve to improve the accuracy of the simulation

Robust modeling of human contact networks across different scales and proximity-sensing techniques

The problem of mapping human close-range proximity networks has been tackled using a variety of technical approaches. Wearable electronic devices, in particular, have proven to be particularly successful in a variety of settings relevant for research in social science, complex networks and infectious diseases dynamics. Each device and technology used for proximity sensing (e.g., RFIDs, Bluetooth, low-power radio or infrared communication, etc.) comes with specific biases on the close-range relations it records. Hence it is important to assess which statistical features of the empirical proximity networks are robust across different measurement techniques, and which modeling frameworks generalize well across empirical data. Here we compare time-resolved proximity networks recorded in different experimental settings and show that some important statistical features are robust across all settings considered. The observed universality calls for a simplified modeling approach. We show that one such simple model is indeed able to reproduce the main statistical distributions characterizing the empirical temporal networks