Mobile transitions : exploring synergies for urban sustainability research

Urban sustainability approaches focusing on a wide range of topics such as infrastructure and mobility, green construction and neighbourhood planning, or urban nature and green amenities have attracted scholarly interest for over three decades. Recent debates on the role of cities in climate change mitigation have triggered new attempts to conceptually and methodologically grasp the cross-sectorial and cross-level interplay of enrolled actors. Within these debates, urban and economic geographers have increasingly adopted co-evolutionary approaches such as the social studies of technology (SST or ‘transition studies’). Their plea for more spatial sensitivity of the transition approach has led to promising proposals to adapt geographic perspectives to case studies on urban sustainability. This paper advocates engagement with recent work in urban studies, specifically policy mobility, to explore conceptual and methodological synergies. It emphasises four strengths of an integrated approach: (1) a broadened understanding of innovations that emphasises not only processes of knowledge generation but also of knowledge transfer through (2) processes of learning, adaptation and mutation, (3) a relational understanding of the origin and dissemination of innovations focused on the complex nature of cities and (4) the importance of individual actors as agents of change and analytical scale that highlights social processes of innovation. The notion of urban assemblages further allows the operationalisation of both the relational embeddedness of local policies as well as their cross-sectoral actor constellations

Adaptation to criticality through organizational invariance in embodied agents

Many biological and cognitive systems do not operate deep within one or other regime of activity. Instead, they are poised at critical points located at phase transitions in their parameter space. The pervasiveness of criticality suggests that there may be general principles inducing this behaviour, yet there is no well-founded theory for understanding how criticality is generated at a wide span of levels and contexts. In order to explore how criticality might emerge from general adaptive mechanisms, we propose a simple learning rule that maintains an internal organizational structure from a specific family of systems at criticality. We implement the mechanism in artificial embodied agents controlled by a neural network maintaining a correlation structure randomly sampled from an Ising model at critical temperature. Agents are evaluated in two classical reinforcement learning scenarios: the Mountain Car and the Acrobot double pendulum. In both cases the neural controller appears to reach a point of criticality, which coincides with a transition point between two regimes of the agent's behaviour. These results suggest that adaptation to criticality could be used as a general adaptive mechanism in some circumstances, providing an alternative explanation for the pervasive presence of criticality in biological and cognitive systems.Comment: arXiv admin note: substantial text overlap with arXiv:1704.0525

Sustainability experiments in the agri-food system : uncovering the factors of new governance and collaboration success

In recent years, research, society and industry recognize the need to transform the agri-food system towards sustainability. Within this process, sustainability experiments play a crucial role in transforming the structure, culture and practices. In literature, much attention is given to new business models, even if the transformation of conventional firms toward sustainability may offer opportunities to accelerate the transformation. Further acceleration could be achieved through collaboration of multiple actors across the agri-food system, but this calls for a systems approach. Therefore, we developed and applied a new sustainability experiment systems approach (SESA) consisting of an analytical framework that allows a reflective evaluation and cross-case analysis of multi-actor governance networks based on business and learning evaluation criteria. We performed a cross-case analysis of four agri-food sustainability experiments in Flanders to test and validate SESA. Hereby, the key factors of the success of collaboration and its performance were identified at the beginning of a sustainability experiment. Some of the key factors identified were risk sharing and the drivers to participate. We are convinced that these results may be used as an analytical tool for researchers, a tool to support and design new initiatives for policymakers, and a reflective tool for participating actors

On the right track? : evaluation as a tool to guide spatial transitions

Spatial developments are becoming more and more non-linear, dynamic and complex with a wide range of possible actors. The awareness of uncertainty in spatial planning is growing and therefore, projects need to integrate a high level of flexibility. But at the same time, a growing demand for taking more informed and well-argued decisions is noticeable. Predictions out of the ‘best estimated model’ are no longer credible and no longer accepted, because they are too fragile and uncertain. How can we keep these long-lasting, multi-actor projects in permanent transition on the right track? This article presents an evaluation methodology that goes beyond the traditional, rational evaluation attitudes with a low level of flexibility being too linear to match the current spatial developments. There is a need for more interrelated, alert and flexible means of evaluation, co-evolving with the processes and current dynamics in spatial planning. Therefore, different evaluation approaches are introduced, depending on the specific interdependencies of the object of evaluation and its context. Subsequently, the theoretical framework is translated towards a more practical level. A case study conducted in Flanders illustrates the current spatial developments and a possible evaluation approach, incorporated from the beginning of the process, to guide this kind of projects

Partitioning Relational Matrices of Similarities or Dissimilarities using the Value of Information

In this paper, we provide an approach to clustering relational matrices whose entries correspond to either similarities or dissimilarities between objects. Our approach is based on the value of information, a parameterized, information-theoretic criterion that measures the change in costs associated with changes in information. Optimizing the value of information yields a deterministic annealing style of clustering with many benefits. For instance, investigators avoid needing to a priori specify the number of clusters, as the partitions naturally undergo phase changes, during the annealing process, whereby the number of clusters changes in a data-driven fashion. The global-best partition can also often be identified.Comment: Submitted to the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP

Nonequilibrium Phase Transitions in Directed Small-World Networks

Many social, biological, and economic systems can be approached by complex networks of interacting units. The behaviour of several models on small-world networks has recently been studied. These models are expected to capture the essential features of the complex processes taking place on real networks like disease spreading, formation of public opinion, distribution of wealth, etc. In many of these systems relations are directed, in the sense that links only act in one direction (outwards or inwards). We investigate the effect of directed links on the behaviour of a simple spin-like model evolving on a small-world network. We show that directed networks may lead to a highly nontrivial phase diagram including first and second-order phase transitions out of equilibrium.Comment: 4 pages, RevTeX format, 4 postscript figs, uses eps

StreamLearner: Distributed Incremental Machine Learning on Event Streams: Grand Challenge

Today, massive amounts of streaming data from smart devices need to be analyzed automatically to realize the Internet of Things. The Complex Event Processing (CEP) paradigm promises low-latency pattern detection on event streams. However, CEP systems need to be extended with Machine Learning (ML) capabilities such as online training and inference in order to be able to detect fuzzy patterns (e.g., outliers) and to improve pattern recognition accuracy during runtime using incremental model training. In this paper, we propose a distributed CEP system denoted as StreamLearner for ML-enabled complex event detection. The proposed programming model and data-parallel system architecture enable a wide range of real-world applications and allow for dynamically scaling up and out system resources for low-latency, high-throughput event processing. We show that the DEBS Grand Challenge 2017 case study (i.e., anomaly detection in smart factories) integrates seamlessly into the StreamLearner API. Our experiments verify scalability and high event throughput of StreamLearner.Comment: Christian Mayer, Ruben Mayer, and Majd Abdo. 2017. StreamLearner: Distributed Incremental Machine Learning on Event Streams: Grand Challenge. In Proceedings of the 11th ACM International Conference on Distributed and Event-based Systems (DEBS '17), 298-30