Non-Basic Needs: Making Space for Incommensurability in the Structure of Well-Being

The concept of need is commonly overlooked by philosophers and social scientists. Often considered exclusively instrumental and/or demarcating minimal attainments, needs are commonly allowed only a minor role in accounts of well-being and related moral and political theories. While this may be true of some conceptions of needs, this thesis defends the critical importance of a different kind of need. These ‘personal needs’ fulfil all necessary conditions for genuine needs, but instead mark out ultimate ends that are far from basic. Moreover, rather than representing preconditions for the lives of human beings in general, personal needs are specific to individuals. Yet also unlike subjective preferences and aims, personal needs are the requirements of things a person is objectively committed to and cannot give up. // Personal needs directly relate to a person’s private evaluation of their own life. Yet they also have wide relevance to other contexts of evaluation within and without philosophy. They play a structural role in a new framework for conceptualising well-being and its role in ethics and policy. In particular, personal needs introduce incommensurability into the fundamental structure of persons’ interests. Located in the same context of individual choice as utility theory, they represent a direct, fundamental challenge to formally monistic teleological conceptions of well-being prevailing in much of social science, policy, and philosophy. Among various potential connections, this framework promises to (a) make sense of some people’s claims that they cannot be compensated for certain losses, (b) help motivate the incommensurability claimed to exist between dimensions in multidimensional well-being measurement (including those drawing on the capabilities approach), and (c) inform approaches to interpersonal distribution that oppose aggregation. This thesis also touches on issues concerning the concept of well-being, the objectivity or subjectivity of well-being, axiology, and coherentist practical reason

Collective and Individual Rationality: Some Episodes in the History of Economic Thought

This thesis argues for the fundamental importance of the opposition between holistic and reductionistic world-views in economics. Both reductionism and holism may nevertheless underpin laissez-faire policy prescriptions. Scrutiny of the nature of the articulation between micro and macro levels in the writings of economists suggests that invisible hand theories play a key role in reconciling reductionist policy prescriptions with a holistic world. An examination of the prisoners' dilemma in game theory and Arrow's impossibility theorem in social choice theory sets the scene. The prisoners' dilemma epitomises the collective irrationality coordination problems lead to. The source of the dilemma is identified as the combination of interdependence in content and independence in form of the decision making process. Arrovian impossibility has been perceived as challenging traditional views of the relationship between micro and macro levels in economics. Conservative arguments against the possibility in principle of a social welfare function are criticised here as depending on an illicit dualism. The thesis then reviews the standpoints of Smith, Hayek and Keynes. For Smith, the social desirability of individual self-seeking activity is ensured by the 'invisible hand' of a god who has moulded us so to behave, that the quantity of happiness in the world is always maximised. Hayek seeks to re-establish the invisible hand in a secular age, replacing the agency of a deity with an evolutionary mechanism. Hayek's evolutionary theory, criticised here as being based on the exploded notion of group selection, cannot underpin the desirability of spontaneous outcomes. I conclude by arguing that Keynes shares the holistic approach of Smith and Hayek, but without their reliance on invisible hand mechanisms. If spontaneous processes cannot be relied upon to generate desirable social outcomes then we have to take responsibility for achieving this ourselves by establishing the appropriate institutional framework to eliminate macroeconomic prisoners' dilemmas

Democratizing machine learning

Modelle des maschinellen Lernens sind zunehmend in der Gesellschaft verankert, oft in Form von automatisierten Entscheidungsprozessen. Ein wesentlicher Grund dafür ist die verbesserte Zugänglichkeit von Daten, aber auch von Toolkits für maschinelles Lernen, die den Zugang zu Methoden des maschinellen Lernens für Nicht-Experten ermöglichen. Diese Arbeit umfasst mehrere Beiträge zur Demokratisierung des Zugangs zum maschinellem Lernen, mit dem Ziel, einem breiterem Publikum Zugang zu diesen Technologien zu er- möglichen. Die Beiträge in diesem Manuskript stammen aus mehreren Bereichen innerhalb dieses weiten Gebiets. Ein großer Teil ist dem Bereich des automatisierten maschinellen Lernens (AutoML) und der Hyperparameter-Optimierung gewidmet, mit dem Ziel, die oft mühsame Aufgabe, ein optimales Vorhersagemodell für einen gegebenen Datensatz zu finden, zu vereinfachen. Dieser Prozess besteht meist darin ein für vom Benutzer vorgegebene Leistungsmetrik(en) optimales Modell zu finden. Oft kann dieser Prozess durch Lernen aus vorhergehenden Experimenten verbessert oder beschleunigt werden. In dieser Arbeit werden drei solcher Methoden vorgestellt, die entweder darauf abzielen, eine feste Menge möglicher Hyperparameterkonfigurationen zu erhalten, die wahrscheinlich gute Lösungen für jeden neuen Datensatz enthalten, oder Eigenschaften der Datensätze zu nutzen, um neue Konfigurationen vorzuschlagen. Darüber hinaus wird eine Sammlung solcher erforderlichen Metadaten zu den Experimenten vorgestellt, und es wird gezeigt, wie solche Metadaten für die Entwicklung und als Testumgebung für neue Hyperparameter- Optimierungsmethoden verwendet werden können. Die weite Verbreitung von ML-Modellen in vielen Bereichen der Gesellschaft erfordert gleichzeitig eine genauere Untersuchung der Art und Weise, wie aus Modellen abgeleitete automatisierte Entscheidungen die Gesellschaft formen, und ob sie möglicherweise Individuen oder einzelne Bevölkerungsgruppen benachteiligen. In dieser Arbeit wird daher ein AutoML-Tool vorgestellt, das es ermöglicht, solche Überlegungen in die Suche nach einem optimalen Modell miteinzubeziehen. Diese Forderung nach Fairness wirft gleichzeitig die Frage auf, ob die Fairness eines Modells zuverlässig geschätzt werden kann, was in einem weiteren Beitrag in dieser Arbeit untersucht wird. Da der Zugang zu Methoden des maschinellen Lernens auch stark vom Zugang zu Software und Toolboxen abhängt, sind mehrere Beiträge in Form von Software Teil dieser Arbeit. Das R-Paket mlr3pipelines ermöglicht die Einbettung von Modellen in sogenan- nte Machine Learning Pipelines, die Vor- und Nachverarbeitungsschritte enthalten, die im maschinellen Lernen und AutoML häufig benötigt werden. Das mlr3fairness R-Paket hingegen ermöglicht es dem Benutzer, Modelle auf potentielle Benachteiligung hin zu über- prüfen und diese durch verschiedene Techniken zu reduzieren. Eine dieser Techniken, multi-calibration wurde darüberhinaus als seperate Software veröffentlicht.Machine learning artifacts are increasingly embedded in society, often in the form of automated decision-making processes. One major reason for this, along with methodological improvements, is the increasing accessibility of data but also machine learning toolkits that enable access to machine learning methodology for non-experts. The core focus of this thesis is exactly this – democratizing access to machine learning in order to enable a wider audience to benefit from its potential. Contributions in this manuscript stem from several different areas within this broader area. A major section is dedicated to the field of automated machine learning (AutoML) with the goal to abstract away the tedious task of obtaining an optimal predictive model for a given dataset. This process mostly consists of finding said optimal model, often through hyperparameter optimization, while the user in turn only selects the appropriate performance metric(s) and validates the resulting models. This process can be improved or sped up by learning from previous experiments. Three such methods one with the goal to obtain a fixed set of possible hyperparameter configurations that likely contain good solutions for any new dataset and two using dataset characteristics to propose new configurations are presented in this thesis. It furthermore presents a collection of required experiment metadata and how such meta-data can be used for the development and as a test bed for new hyperparameter optimization methods. The pervasion of models derived from ML in many aspects of society simultaneously calls for increased scrutiny with respect to how such models shape society and the eventual biases they exhibit. Therefore, this thesis presents an AutoML tool that allows incorporating fairness considerations into the search for an optimal model. This requirement for fairness simultaneously poses the question of whether we can reliably estimate a model’s fairness, which is studied in a further contribution in this thesis. Since access to machine learning methods also heavily depends on access to software and toolboxes, several contributions in the form of software are part of this thesis. The mlr3pipelines R package allows for embedding models in so-called machine learning pipelines that include pre- and postprocessing steps often required in machine learning and AutoML. The mlr3fairness R package on the other hand enables users to audit models for potential biases as well as reduce those biases through different debiasing techniques. One such technique, multi-calibration is published as a separate software package, mcboost

Debating deliberative democracy: how deliberation changes the way people reason

The concepts of deliberation and deliberative democracy have attracted much attention in political theory over the past twenty years. At first seen as both highly idealised and unreflective of reality, they have now shed this accusation of impracticality, as practitioners and policy makers alike have attempted to institute deliberative principles on a national and international scale. Running alongside this has been the desire to both understand political deliberation and its effects more fully, and to then apply this new information back to deliberative democratic theory. This thesis sits in the latter tradition, presenting an empirical investigation of political deliberation and then discussing how it relates back to deliberative models of democracy. Where it departs from all of the contemporary experimental work, however, is the methodology and conceptual model it is founded upon. Embracing the decision and game theoretic approaches, I develop a three-fold framework to study the effects of deliberation on individual decision-making. After outlining two levels of ‘preference’ and ‘issue’, I focus on the third, which I term agency. I then compare a particular case of agency revision, which moves people from individualistic to team reasoning, before developing and putting into action an experimental test of the phenomenon. Finally, I then combine these results with the most recent drive in deliberative democracy towards a systemic approach, and derive an alternative, more positive argument for this recasting

Inequalities' Impacts: State of the Art Review

By way of introduction This report provides the fi rm foundation for anchoring the research that will be performed by the GINI project. It subsequently considers the fi elds covered by each of the main work packages: ● inequalities of income, wealth and education, ● social impacts, ● political and cultural impacts, and ● policy effects on and of inequality. Though extensive this review does not pretend to be exhaustive. The review may be “light” in some respects and can be expanded when the analysis evolves. In each of the four fi elds a signifi cant number of discussion papers will be produced, in total well over 100. These will add to the state of the art while also covering new round and generating results that will be incorporated in the Analysis Reports to be prepared for the work packages. In that sense, the current review provides the starting point. At the same time, the existing body of knowledge is broader or deeper depending on the particular fi eld and its tradition of research. The very motivation of GINI’s focused study of the impacts of inequalities is that a systematic study is lacking and relatively little is known about those impacts. This also holds for the complex collection of, the effects that inequality can have on policy making and the contributions that policies can make to mitigating inequalities but also to enhancing them. By contrast, analyses of inequality itself are many, not least because there is a wide array of inequalities; inequalities have become more easily studied comparatively and much of that analysis has a signifi cant descriptive fl avour that includes an extensive discussion of measurement issues. @GINI hopes to go beyond that and cover the impacts of inequalities at the same time

Multi-Criteria Decision Analysis (MCDA) for Agricultural Sustainability Assessment

Multi Attribute Utility Theory (MAUT), Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) and Elimination methods of Multi-Criteria Decision analysis (MCDA) are tested to assess and compare the sustainability of different agricultural systems. Indicators and composite indicators are derived from data gathered using the agricultural sustainability categories of Productivity, Stability, Efficiency, Durability, Compatibility and Equity (PSEDCE). Agricultural systems around the world face challenges from current agricultural practices, over-exploitation of natural resources, population growth and climate change. As a result, understanding agricultural sustainability has become a global issue. Assessment is a first step in benchmarking and tracking agricultural sustainability and can support related policy and programmes. This thesis applied the PSEDCE categories to understand more about the complexities inherent to agricultural sustainability assessment. Agricultural sustainability assessment (ASA) requires a wide variety of ecological, economic and social information with various methods. In the first part of this thesis, a systematic analysis of the scientific soundness and user-friendliness of eight ASA approaches revealed that MCDA based ASA is the preferred holistic method. MCDA can take into account both qualitative and quantitative indicators of all dimensions of sustainability and analyze them to draw a comprehensive picture. As a multifaceted, complex issue, agricultural sustainability assessment is well-suited to MCDA, which is able to handle large data sets including stakeholders’ perspectives. Given that it is a relatively new analysis procedure in the study of agriculture, only a few researchers have applied this technique to measure sustainability. Considering these findings, three MCDA methods, MAUT, PROMETHEE and Elimination, were tested to measure the relative sustainability of five agricultural systems in coastal Bangladesh. To investigate the performance of MAUT, PROMETHEE, and Elimination, a total of 50 indicators from agricultural sustainability categories of PSEDCE were tested. From these 50 indicators, 15 composite indicators were developed through proportionate normalization and hybrid aggregation rules of arithmetic mean and geometric mean. The 15 composite indicators were used in MAUT and PROMETHEE analysis, and the 50 indicators were used in Elimination analysis. The analyses show that MAUT is able to aggregate diverse information and stakeholders’ perspectives to generate a robust score that enables a comparison of sustainability across the different agricultural systems. PROMETHEE is a non-compensatory approach that can also accommodate a variety of information and provide thresholds for ranking relative agricultural sustainability for each of the five agricultural systems. Elimination ranks the sustainability of agricultural systems through a set of straightforward decision rules expressed in the form of “if … then …” conditions. Elimination appears to be quick and less complex, whereas MAUT and PROMETHEE are regarded as fairly complicated and require software to find potential solutions. Overall, the study shows that MAUT, PROMETHEE and Elimination can handle multidimensional data and can be applied for relative assessment of sustainability of agricultural systems. However, selection of the appropriate criteria, stakeholders’ perspectives and the purpose of the assessment are very important and must be considered carefully for inclusion in MCDA methods for agricultural sustainability assessment. The results of the case studies also demonstrate that these approaches have the potential to become a useful framework for agricultural sustainability assessment and related policy development and decision-making