Priming the Governance System for Climate Change Adaptation: The Application of a Social Ecological Inventory (SEI) to Engage Actors in Niagara, Canada

Adaptive systems of governance are increasingly gaining attention in respect to complex and uncertain social-ecological systems. Adaptive co-management is one strategy to make adaptive governance operational and holds promise with respect to community climate change adaptation as it facilitates participation and learning across scales and fosters adaptive capacity and resilience. Developing tools which hasten the realization of such approaches are growing in importance. This paper describes explores the Social Ecological Inventory (SEI) as a tool to 'prime' a regional climate change adaptation network. The SEI tool draws upon the social-ecological systems approach in which social and ecological systems are considered linked. SEIs bridge the gap between conventional stakeholder analysis and biological inventories and take place through a six phase process. A case study describes the results of applying an SEI to prime an adaptive governance network for climate change adaptation in the Niagara Region of Canada. Lessons learned from the case study are discussed and highlight how the SEI catalyzed the adaptive co-management process in the case. Future avenues for SEIs in relation to climate change adaptation emerge from this exploratory work and offer opportunities to inform research and adaptation planning.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.

An Exploration of Professors’ Use of Twitter in Higher Education

Scant research has explored how professors in Canadian universities use Twitter as a teaching tool or to augment knowledge about their subject disciplines. This case study employed a mixed-method approach to examine how professors in an Ontario university use Twitter. Using a variation of the technology acceptance model, the survey (n = 17) found that professor participants—41.2% of whom use Twitter—perceive Twitter as somewhat useful as a teaching tool, not useful for finding and sharing information, and not useful for personal use. Participants’ gender and number of years teaching are not indicators of Twitter use. Furthermore, the level of support from peers and the university may be reasons why some do not use Twitter or have stopped using Twitter. Face-to-face interviews (n = 3) revealed that Twitter is not used in classrooms or lecture halls, but predominantly as a means of sharing information with students and colleagues. Another deterrent to using Twitter is not knowing who to follow. Findings indicate that some professors at this university embrace Twitter, but not necessarily as an in-class teaching tool. The challenge and the advantage of using Twitter is to discover and follow people who tweet material and to select relevant material to pass along to students and colleagues. Professor participants in the study found a use for the social network as a means to increase student engagement, create virtual information-exchange communities, and enrich their own learning.

Power, Contextual Intelligence and Leadership: Research Approaches for Understanding Participatory Community Climate Change Adaptation

Analysis of power in natural resources management is important as multiple stakeholders interact within complex, social-ecological systems. As a sub-set of these interactions, community climate change adaptation is increasingly using participatory processes to address issues of local concern. While some attention has been paid to power relations in this respect, e.g. evaluating international climate regimes or assessing vulnerability as part of integrated impact assessments, little attention has been paid to how a structured assessment of power could facilitate real adaptation and increase the potential for successful participatory processes. This paper surveys how the concept of power is currently being applied in natural resources management and links these ideas to agency and leadership for climate change adaptation. By exploring behavioural research on destructive leadership, a model is developed for informing participatory climate change adaptation. The working paper then concludes with a discussion of developing research questions in two specific areas - examining barriers to adaptation and mapping the evolution of specific participatory processes for climate change adaptation.

Dynamics in large scale networks

In this thesis we study the properties of two large dynamic networks, the competition network of advertisers on the Google and Bing search engines and the dynamic network of friend relationships among avatars in the massively multiplayer online game (MMOG) Planetside 2. We are particularly interested in removal patterns in these networks. Our main finding is that in both of these networks the nodes which are most commonly removed are minor near isolated nodes. We also investigate the process of merging of two large networks using data captured during the merger of servers of Planetside 2. We found that the original network structures do not really merge but rather they get gradually replaced by newcomers not associated with the original structures. In the final part of the thesis we investigate the concept of motifs in the Barabási-Albert random graph. We establish some bounds on the number of motifs in this graph.