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.

Teaching by leading and managing learning environments

The aim of this chapter is to promote an understanding of how different environments or settings within which students are asked or required to learn - such as large groups, small groups and laboratory and practice settings – have an impact on how they approach their learning and hence on the design and delivery of teaching. It provides an overview of underpinning principles and concepts before exploring their application in practice. The focus is on face-to-face teaching and learning.

The effects of trade show environments on visitors

Tourism trade shows that are open to the public as well as to buyers and sellers are an emerging channel for the promotion of products to potential tourists. However, few studies have explored the influence of environmental stimuli on non-business visitors’ emotions. Moreover, the moderating effect of visitors’ expectations remains under-studied in the context of trade show management. To address this issue, this study reports on research derived from 611 respondents at a Taiwanese tourism trade show through a modified Mehrabian-Russell model. Structural equation modeling of the data shows that positive emotions positively influence behavioral intentions, but negative emotions do not negatively influence behavioral intentions. Among the three stimuli (i.e., information rate, service staff quality, and atmospherics), only information rate and service staff quality positively affect positive emotions and negatively affect negative emotions. The results show that visitors with high and low trade show visit expectations react differently to environmental stimuli at trade shows.