IN DUBIO PRO NATURA? A Philosophical Analysis of the Precautionary Principle in Environmental and Health Risk Governance

In this book, I apply a philosophical approach to study the precautionary principle in environmental (and health) risk decision-making. The principle says that unacceptable environmental and health risks should be anticipated, and they ought to be forestalled before the damage comes to fruition even if scientific understanding of the risks is inadequate. The study consists of introductory chapters, summary and seven original publications which aim at explicating the principle, critically analysing the debate on the principle, and constructing a basis for the well-founded use of the principle. Papers I-V present the main thesis of this research. In the two last papers, the discussion is widened to new directions. The starting question is how well the currently embraced precautionary principle stands up to critical philosophical scrutiny. The approach employed is analytical: mainly conceptual, argumentative and ethical. The study draws upon Anglo-American style philosophy on the one hand, and upon sources of law as well as concrete cases and decision-making practices at the European Union level and in its member countries on the other. The framework is environmental (and health) risk governance, including the related law and policy. The main thesis of this study is that the debate on the precautionary principle needs to be shifted from the question of whether the principle (or its weak or strong interpretation) is well-grounded in general to questions about the theoretical plausibility and ethical and socio-political justifiability of specific understandings of the principle. The real picture of the precautionary principle is more complex than that found (i.e. presumed) in much of the current academic, political and public debate surrounding it. While certain presumptions and interpretations of the principle are found to be sound, others are theoretically flawed or include serious practical problems. The analysis discloses conceptual and ethical presumptions and elementary understandings of the precautionary principle, critically assesses current practices invoked in the name of the precautionary principle and public participation, and seeks to build bridges between precaution, engagement and philosophical ethics. Hence, it is intended to provide a sound basis upon which subsequent academic scrutiny can build.

Sähköasemainvestointihankkeen määrittely

Tämän työn tavoitteena oli tehdä perusteltu esitys kahden suunnitellun sähköaseman toiminnan aloittamisen ajankohdasta sekä rakentamisen kestosta lupamenettelyineen. Työssä oli pyrkimys perustella asemien maantieteellinen sijainti, toteutustapa, rakenne sekä sähköasemien syöttöön tarvittavien 110 kV johtojen alustava rakenne ja reitti. Työssä on tarkasteltu uuden sähköaseman verkosto- ja kustannusvaikutuksia, sähköasemarakenteita ja niiden valintaa, sähköasemainvestointihankkeen vaiheita ja rakennuttamisprosessin läpivientiin tarvittavaa aikaa. Verkoston nykytilaa, käyttövarmuutta ja selviytymistä kuormituksen kasvusta on tarkasteltu sähköasemien toiminnan aloittamisen ajankohtien määrittämiseksi. Työn keskeisin painopiste oli sähköasemien rakentamisajankohdan määrittäminen. Sähköasemien toiminnan aloittamisen ajankohdan määrääväksi tekijäksi muodostuivat sähköasemien korvaustilanteet. Nykytilassa Valkealan haja-asutusalueen sähkönjakelua ei voida taata yksittäisen sähköaseman korvaustilanteessa, minkä takia sähköasemahankkeen valmistelu on aloitettava välittömästi. Kouvolan ydinkeskustan alueen maakaapeliverkon tehonsiirtokyky ja päämuuntajien reservitehokapasiteetti korvaustilanteissa riittävät vielä 5-10 vuotta riippuen suuresti alueen kuormituksen kasvusta.

Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

Generalizing sample tree information with semiparametric and parametric models

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Predicting the growth response to thinning for Scots pine stands using individual-tree growth models

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Individual-tree basal area growth models for scots pine, pubescent birch and Norway spruce on drained peatlands in Finland

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