4 research outputs found
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Sustainability Hacking: conceptual development and empirical exploration
Systemic humanitarian, environmental, and socio-political problems are impeding current and future generations from meeting their very basic needs. The speed and scope of mainstream responses to the world’s most pressing problems are limited by agency failures and by the ‘rules of the game’.
In this context, this research contributes to theory and practice by formulating and exploring the concept of Sustainability Hacking, a particularly advantageous change driver in situations where information is limited, resources are scarce, stakes are high, and decision-making is urgent.
This research was conducted through 3 sequential stages. First, the researcher has systematically reviewed the literature on sociotechnical system change for sustainability. This review exposed and discussed 15 theoretical foundations that shape what changes are perceived as desirable and attainable, as well as how to navigate between all the coexisting pathways to drive positive change. By examining these foundations, it became possible to pinpoint opportunities for future contributions.
Among them was the idea of investigating the meaning, characteristics and potential implications of Hacking as a change driver of sociotechnical systems. These were revealed in the 2nd research stage, after interviewing self-declared Hackers and cybersecurity experts to understand how they used the term and how they pursued their desired systemic changes. This stage provided the definition, as well as 9 dominant characteristics of System Hacking.
The term refers to exploring unconventional solutions to a problem within sociotechnical systems. ‘Unconventional’ here means deviating from embedded institutions, i.e. the rules of the game in a society. Institutions represent sources of stability, coherence, and continuity of systems, while simultaneously shaping public expectations of what changes are viable and the heuristics of how they should be pursued. Differently from conventional approaches, system Hackers are not aiming at changing rules, neither are they passively complying with them. Instead, they work around the ‘rules of the game’ to accomplish ‘good-enough’ results promptly.
The 3rd research stage consisted of investigating and working with Sustainability Hacks, i.e. System Hacks addressing pressing sustainability problems. This was performed through a combination of Action Research and Case Studies. Benefitting from a diverse database of 19 cases, the researcher conducted a cross-case analysis, which provided comprehensive observations on the 15 main similarities and 10 differences that constitute the key analytical variables of Sustainability Hacking. Furthermore, the analysis derived 5 Archetypes that can be used as frames of reference to provide guidance for practitioners evaluating possibilities of addressing pressing sustainability problems, as well as to support future academic contributions in this nascent field of research.Gates Cambridg
Bioinspired metaheuristic algorithms for global optimization
This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions
Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter
In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF