Structural changes in economics during the last fifty years

This essay portrays the major currents in recent economic thinking against the orthodoxy and dogmatism of neoclassical economics. It places behavioral economics, experimental economics, evolutionary economics, ecological economics, new institutional economics, agent-based computational economics and post-autistic economics vis-à-vis the classical and the neoclassical economics. It concludes that we may expect a synthesis of all these strands of economic thinking in the near future that will replace neoclassical economics from the citadel of mainstream. Teaching of these strands of new economics has already begun in many universities, although in an un-integrated manner. However, until the neoclassical microeconomics and macroeconomics are replaced by their alternatives and necessary as well as convincing tools of economic analysis are developed, neoclassicism would not give way to modern economics.Behavioral; experimental; evolutionary; ecological; new institutional; agent-based computational; post-autistic; classical; neoclassical, economics; bounded rationality; heterodox; individualism; pluralism

Towards simulating the emergence of environmentally responsible behavior among natural resource users : an integration of complex systems theory, machine learning and geographic information science

La gouvernance pour le développement durable comporte de nombreux défis. L'un de ces défis consiste à mieux comprendre les systèmes socio-écologiques gouvernés. Dans de tels systèmes, l'apprentissage par essais et erreurs implique le risque de conséquences inattendues, irréversibles et néfastes. De plus, en raison de la complexité des systèmes socio-écologiques, les leçons tirées d'expériences à petite échelle ne peuvent pas toujours être applicables à des problèmes à grande échelle. Un autre aspect difficile des problèmes de développement durable est que ces problèmes sont souvent multidisciplinaires et composés de composants qui sont chacun étudiés individuellement dans une discipline différente, mais il existe peu d'informations sur leur comportement ensemble. Un troisième défi de la gouvernance pour le développement durable est qu'il est souvent nécessaire d'impliquer les parties prenantes dans des actions de gestion et des mesures d'intervention coûteuses pour les individus qui y participent. De plus, dans de nombreuses situations de ce type, les incitations financières et l'application des réglementations se soldent par un échec et ne constituent donc pas des options de gouvernance. Dans cette thèse, les défis ci-dessus sont abordés dans un exemple de contrôle des perturbations forestières avec une approche intégrée. Pour éviter le problème des effets indésirables irréversibles et pour permettre des expériences répétées, une approche de simulation est utilisée. Pour relever le défi de la multidisciplinarité des problèmes des systèmes socio-écologiques, deux modèles sont développés indépendamment - portant sur les aspects sociaux et écologiques du système de l'étude - et ils sont ensuite couplés de telle sorte que la sortie de chaque modèle est utilisée comme entrée pour l'autre modèle. Pour résoudre le problème de l'engagement des parties prenantes, un plan est proposé pour la promotion d'un comportement respectueux de l'environnement. Ce plan est basé sur l'offre de reconnaissance à ceux qui adoptent volontairement le comportement responsable. Le modèle écologique de cette étude, qui simule la propagation d'une perturbation forestière, est construit à l'aide de l’apprentissage automatique supervisé. Le modèle social de cette étude, qui simule l'émergence d'une nouvelle norme de comportement, est construit à l'aide de l'apprentissage par renforcement. Les deux modèles sont testés et validés avant couplage. Le modèle couplé est ensuite utilisé comme un laboratoire virtuel, où plusieurs expériences sont réalisées dans un cadre hypothétique et selon différents scénarios. Chacune de ces expériences est une simulation. A travers ces simulations, cette étude montre qu'avec un algorithme de prise de décision approprié et avec suffisamment de temps pour l'interaction entre une entité gouvernante et la société, il est possible de créer une motivation pour un comportement responsable dans la société. En d'autres termes, il est possible d'encourager la participation volontaire des acteurs à l'action pour le développement durable, sans que l'entité gouvernante ait besoin d'utiliser des incitations financières ou d'imposer son autorité. Ces résultats peuvent être applicables à d'autres contextes où un comportement responsable des individus ou des entreprises est recherché afin d'atténuer l'impact d'une perturbation, de protéger une ressource écologique, ou de faciliter une transition sectorielle vers la durabilité.Governance for sustainable development involves many challenges. One of those challenges is to gain insight about the social-ecological systems being governned. In such systems, learning by trial and error involve the risk of unexpected, irreversible and adverse consequences. Moreover, due to complexity of social-ecological systems, lessons learned from small scale experiments may not be applicable in large-scale problems. Another challenging aspect of problems of sustainable development is that these problems are often multidisciplinary and comprised of components that are each studied individually in a different discipline, but little information exists about their behavior together as a whole. A third challenge in governance for sustainable development is that often it is necessary to involve stakeholders in management actions and intervention measures that are costly for individuals who participate in them. Moreover, in many of these situations financial incentives or enforcement of regulations result in failure, and are thus not options for governance. In this thesis, the above challenges are addressed in an example case of forest disturbance control with an integrated approach. To avoid the problem of irreversible adverse effects and to allow repeated experiments, a simulation approach is used. To tackle the challenge of multidisciplinarity of problems of social-ecological systems, two models are independently developed – pertaining to social and ecological aspects of the system of the study – and they are subsequently coupled in such a way that the output of each model served as an input for the other. To address the problem of engagement of stakeholders, a scheme is proposed for promotion of environmentally responsible behavior. This scheme is based on offering recognition to those who voluntarily perform the responsible behavior. The ecological model of this study, which simulates the spread of a forest disturbance, is built using Supervised Machine Learning. The social model of this study, which simulates the emergence of a new norm of behavior, is built using Reinforcement Learning. Both models are tested and validated before coupling. The coupled model is then used as a virtual laboratory, where several experiments are performed in a hypothetical setting and under various scenarios. Each such experiment is a simulation. Through these simulations, this study shows that with an appropriate decision-making algorithm and with sufficient time for interaction between a governing entity and the society, it is possible to create motivation for responsible behavior in the society. In other words, it is possible to encourage voluntary participation of stakeholders in action for sustainable development, without the need for the governing entity to use financial incentives or impose its authority. These results may be applicable to other contexts where responsible behavior by individuals or enterprises is sought in order to mitigate the impact of a disturbance, protect an ecological resource, or facilitate a sectoral transition towards sustainability

A Review of Platforms for the Development of Agent Systems

Agent-based computing is an active field of research with the goal of building autonomous software of hardware entities. This task is often facilitated by the use of dedicated, specialized frameworks. For almost thirty years, many such agent platforms have been developed. Meanwhile, some of them have been abandoned, others continue their development and new platforms are released. This paper presents a up-to-date review of the existing agent platforms and also a historical perspective of this domain. It aims to serve as a reference point for people interested in developing agent systems. This work details the main characteristics of the included agent platforms, together with links to specific projects where they have been used. It distinguishes between the active platforms and those no longer under development or with unclear status. It also classifies the agent platforms as general purpose ones, free or commercial, and specialized ones, which can be used for particular types of applications.Comment: 40 pages, 2 figures, 9 tables, 83 reference

Soil and water bioengineering: practice and research needs for reconciling natural hazard control and ecological restoration

Soil and water bioengineering is a technology that encourages scientists and practitioners to combine their knowledge and skills in the management of ecosystems with a common goal to maximize benefits to both man and the natural environment. It involves techniques that use plants as living building materials, for: (i) natural hazard control (e.g., soil erosion, torrential floods and landslides) and (ii) ecological restoration or nature-based re-introduction of species on degraded lands, river embankments, and disturbed environments. For a bioengineering project to be successful, engineers are required to highlight all the potential benefits and ecosystem services by documenting the technical, ecological, economic and social values. The novel approaches used by bioengineers raise questions for researchers and necessitate innovation from practitioners to design bioengineering concepts and techniques. Our objective in this paper, therefore, is to highlight the practice and research needs in soil and water bioengineering for reconciling natural hazard control and ecological restoration. Firstly, we review the definition and development of bioengineering technology, while stressing issues concerning the design, implementation, and monitoring of bioengineering actions. Secondly, we highlight the need to reconcile natural hazard control and ecological restoration by posing novel practice and research questions

A review of machine learning applications in wildfire science and management

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table

Spatio-Temporal Deforestation Measurement Using Automatic Clustering

Deforestation is one of the crucial issues in Indonesia. In 2012, deforestation rate in Indonesia reached 0.84 million hectares, exceeding Brazil. According to the 2009 Guinness World Records, Indonesia's deforestation rate was 1.8 million hectares per year between 2000 and 2005. An interesting view is the fact that Indonesia government denied the deforestation rate in those years and said that the rate was only 1.08 million hectares per year in 2000 and 2005. The different problem is on the technique how to deal with the deforestation rate. In this paper, we proposed a new approach for automatically identifying the deforestation area and measuring the deforestation rate. This approach involves differential image processing for detecting Spatio-temporal nature changes of deforestation. It consists series of important features extracted from multiband satellite images which are considered as the dataset of the research. These data are proceeded through the following stages: (1) Automatic clustering for multiband satellite images, (2) Reinforcement Programming to optimize K-Means clustering, (3) Automatic interpretation for deforestation areas, and (4) Deforestation measurement adjusting with elevation of the satellite. For experimental study, we applied our proposed approach to analyze and measure the deforestation in Mendawai, South Borneo. We utilized Landsat 7 to obtain the multiband images for that area from the year 2001 to 2013. Our proposed approach is able to identify the deforestation area and measure the rate. The experiment with our proposed approach made a temporal measurement for the area and showed the increasing deforestation size of the area 1.80 hectares during those years

Safety of autonomous vehicles: A survey on Model-based vs. AI-based approaches

The growing advancements in Autonomous Vehicles (AVs) have emphasized the critical need to prioritize the absolute safety of AV maneuvers, especially in dynamic and unpredictable environments or situations. This objective becomes even more challenging due to the uniqueness of every traffic situation/condition. To cope with all these very constrained and complex configurations, AVs must have appropriate control architectures with reliable and real-time Risk Assessment and Management Strategies (RAMS). These targeted RAMS must lead to reduce drastically the navigation risks. However, the lack of safety guarantees proves, which is one of the key challenges to be addressed, limit drastically the ambition to introduce more broadly AVs on our roads and restrict the use of AVs to very limited use cases. Therefore, the focus and the ambition of this paper is to survey research on autonomous vehicles while focusing on the important topic of safety guarantee of AVs. For this purpose, it is proposed to review research on relevant methods and concepts defining an overall control architecture for AVs, with an emphasis on the safety assessment and decision-making systems composing these architectures. Moreover, it is intended through this reviewing process to highlight researches that use either model-based methods or AI-based approaches. This is performed while emphasizing the strengths and weaknesses of each methodology and investigating the research that proposes a comprehensive multi-modal design that combines model-based and AI approaches. This paper ends with discussions on the methods used to guarantee the safety of AVs namely: safety verification techniques and the standardization/generalization of safety frameworks