A multi-agent-based evolution model of innovation networks in dynamic environments

An innovation network can be considered as a complex adaptive system with evolution affected by dynamic environments. This paper establishes a multi-agent-based evolution model of innovation networks under dynamic settings through computational and logical modeling, and a multi-agent system paradigm. This evolution model is composed of several sub-models of agents' knowledge production by independent innovations in dynamic situations, knowledge learning by cooperative innovations covering agents' heterogeneities, decision-making for innovation selections, and knowledge update considering decay factors. On the basis of above-mentioned sub-models, an evolution rule for multi-agent based innovation network system is given. The proposed evolution model can be utilized to simulate and analyze different scenarios of innovation networks in various dynamic environments and support decision-making for innovation network optimization.

Reflections Upon the Experience of Longitudinal Research into Cultural Event Production in a Developing Destination

Longitudinal studies have the capacity to provide more nuanced explanations of tourism and event phenomena, taking account of complexity, change and context. This paper is a self-reflexive, methodological study of research practice. It investigates my experience of engaging with cultural event producers in an emerging destination over a seven-year period. Focussing on my research journey, it considers the social and relational dynamics associated with longitudinal research. Reciprocal relations and co-production of cultural events reveal nuanced information and expose fluid relationships and networks. Long-term engagement uncovers evolving practices and develops understanding of event processes embedded within their wider context.