Paal Fredrik Skjørten Kvarberg

I am a Doctoral Research Fellow in philosophy at the University of Oslo, and in the academic year of 2024-2025 I am a Recognised Student at the University of Oxford.I conduct research on political judgment and decision-making, focusing on evaluative frameworks for appraising policy decisions and institutional decision-making procedures.My PhD is part of the interdisciplinary Modeling Human Happiness project, which aims to develop models of well-being for appraising policy and measuring social progress. I am also a member of the Science and Democracy Research Group at the University of Oslo and a visiting student at the Uehiro Oxford Institute.In 2025, a group of researchers and I are running a Political Forecasting Tournament and I am involved in a project aiming to develop AI-based technology for learning and reasoning.Join the tournament here: prediksjonsturnering.no/Scroll down to learn more about me and my projects. My mail address is: paalfredrikskjorten(at)gmail.com

About me

My research interests revolve around political decision-making and the practical conditions for informed and rational public debate.During my bachelor's studies at the University of Bergen, I focused on questions in political theory. In my bachelor’s thesis, I wrote about John Rawls' political liberalism and the radical democratic theory of Jürgen Habermas. My master's thesis defends a theory of practical rationality and argues that there are rational answers to value questions.After completing my master’s thesis, I studied social science and computational linguistics at the University of Oslo. In 2020, I co-founded a startup to develop AI-based technology for deliberate practice and formative assessments, with support from the Norwegian Research Council. In 2022, I wrote an article outlining some relevant research. We are currently exploring the accuracy of AI-generated feedback on practice assignments in law and critical thinking (ex.phil). Additionally, we are interested in ways to improve reasoning and decision-making using formal ontologies and Bayesian networks. I recently wrote a paper about this as a submission to an essay competition.The main objective of my PhD research project is to identify the conditions for a valid and legitimate public evaluative framework for appraising policy and social progress. My hypothesis is that the best method for satisfying these conditions is to develop a national welfare index based on research into well-being and the values of citizens. A secondary objective of the project is to uncover the conceptual and causal structure of well-being. My hypothesis is that objective features of a person can explain what it means for that person to have positive experiences and values that are suitable for them. With Aksel Braanen Sterri and the Center for Long-Term Policy, I’ve written a policy note to the Norwegian government strategy for using quality of life in policy development.In 2024, a team of researchers and I received a grant to launch a political forecasting tournament that investigates the link between predictive accuracy and political decision-making. In this experimental research project, we test whether research-based methods for improving foresight can be easily learned and applied without extensive training and effort.I spend most of my time doing research or working on projects. However, I also enjoy trail running, skiing, and curating Spotify playlists. You may review some of my playlists under the 'Other' section or have a more thorough look at my projects in the 'Research' section.

Research

Here is a brief summary of ongoing and previous research. Scroll down to see abstracts and find links.Published Pieces
“Educational Technologies for Active Learning and Formative Assessment,” Norsk Filosofisk Tidsskrift, May 30, 2022
“Innspill til Nasjonal livskvalitetsstrategi” (Policy note to the Norwegian government) with Aksel Braanen Sterri and Langsikt, Regjeringen.no, Feb 29, 2024
“Two Directions for Research on Forecasting and Decision-Making,” EA Forum, Mar 11, 2023
“On Alienation: A Reconstructive Analysis of the Concept of Alienation,” Filosofisk Supplement, Jan 1, 2019Under Review or In Preparation
PhD Thesis: Towards a Public Model of Welfare
Chapter 1: “Public Models of Welfare: Validation and Legitimation of National Composite Welfare Indices for Appraisal of Policy and Social Progress”
Chapter 2: "Folk Intuitions About the Meaning of Well-being: Experimental Study into What People See as Components and Causes of Well-being" with Jinrui Liu
Chapter 3: “Affect, Judgment, and Psychological Integration: Analysis of the Relationship Between Core Aspects of Well-being”
Chapter 4: “Natural Normativity: Naturalistic Reconstruction of the Aristotelian Conception of Eudaimonia”
Chapter 5: “Objectivity and Individuality: Reference Class Pluralism in Objective Theories of Well-being”
Chapter 6: “Towards a Functional Theory of Health and Disorder: Theoretical Foundations for the Medical Model”UiO: Democracy Political Forecasting Tournament
“Practical Epistemic Methods: Experimental Forecasting Tournament Investigating the Costs and Benefits of Learning and Applying Methods for Good Political Judgment” with Jon Axel Rosén, Ole Hegle Sjøflot, Cathrine Holst, and Ole Røgeberg
“How to Improve Political Judgment? A Practical Consequentialist Perspective” with Cathrine Holst, Jon Axel Rosén, Ole Hegle Sjøflot, and Ole Røgeberg
“The Web of Belief: How Technology Can Automate Belief Formation Processes and Support Wise Decision-Making”

PhD Thesis: Towards a Public Model of Welfare

A growing body of research has shown that economic indicators such as GDP must be supplemented with quality-of-life data for valid appraisal of policy and social progress. Many countries are currently developing public evaluative frameworks to measure what matters directly and to integrate this knowledge into political decision-making procedures.The main objective of my PhD research project is to identify the conditions for a valid and legitimate public model of welfare that may inform the development of welfarist evaluative frameworks for political priority setting and to contribute to the development of such a model.My PhD project is part of Modeling Human Happiness (ModHap), an interdisciplinary research project that combines insights from philosophy and psychology to develop innovative scientific models of welfare for appraisal of policy and social progress. The aim is to inform academic research, public debate, and policymaking with robust frameworks for understanding and promoting well-being.

Public Models of Welfare: Validation and Legitimation of National Composite Welfare Indices for Appraisal of Policy and Social Progress

What knowledge is relevant for appraising policy and social progress, and how should public agencies communicate this knowledge to stakeholders and decision-makers in liberal democratic countries? This article aims to provide a rigorous answer to this question through a comparative assessment of four public evaluative frameworks: (i) the preference satisfaction model of welfare underlying the neo-classical approach to welfare economics; (ii) the capability approach, which underpins multidimensional frameworks for measuring both objective and subjective aspects of quality of life; (iii) the subjective well-being approach to the measurement of well-being-adjusted life-years (WELLBY); and (iv) the procedural value alignment approach, which focuses on developing public models of welfare. Each approach is systematically evaluated against widely endorsed principles of scientific validity, political legitimacy, and pragmatic feasibility. The article concludes with concrete recommendations for decision-makers, outlining how they should choose among these approaches based on their assessment of the relative importance of the principles.

Folk Intuitions About the Meaning of Well-being: Experimental Study into what People See as Components and Causes of Well-being

There is limited empirical research on what ordinary people see as intrinsic features of well-being, as opposed to causes or correlates. Using a vignette-based forced-choice experimental design, we systematically test folk-intuitions about the meaning of well-being. Specifically, we test whether the folk see 8 distinct well-being constructs as necessary to well-being, and whether various configurations of constructs are seen as jointly sufficient. The constructs include (i) hedonic emotion, (ii) eudaimonic emotion, (iii) satisfaction, (iv) health, (v) accomplishment, (vi) freedom, (vii) relationships, and (viii) morality.

Affect, Judgement and Psychological Integration: Analysis of the Relationship Between Core Aspects of Well-being

In this paper I present an extended argument in support of eudaimonic (functional) explanations of well-being. The argument seeks to demonstrate that rival theories, including theories that explain well-being in terms of evaluation (life satisfaction & informed desire satisfaction) and affect (phenomenal hedonism & attitudinal hedonism), either contradict our considered convictions regarding the meaning of well-being or are indeterminate. I argue that plausible versions of each theory can be made determinate if grounded in functional facts of the well-being subject. The conclusion of the argument is that functional integration in a sense inspired by the philosophy of Plato and Aristotle can accommodate the core attractions of both subjective and hedonic theories and that an eudaimonist nature-fulfillment theory of this sort best explains the nature and meaning of well-being.

Towards a Functional Theory of Health and Disorder: Theoretical Foundations for the Medical Model

According to the medical model of health and disorder, the human body is best conceived as a causal system of organ systems and traits whose function or dysfunction is sufficient to demarcate disorder from healthy human diversity. In this view, health and disorder are conceptual opposites grounded in a scientific understanding of the functional mechanisms of the human mind and body. Even though this general understanding of health and disorder is prevalent in both the medical professions and the lay public, researchers have not provided robust theoretical foundations for the view. Perhaps in part for this reason, the now-dominant understanding of health in clinical decision-making and priority setting is based on a subjective operationalisation of health metrics such as the QALY. Moreover, psychiatric diagnostic classifications like the DSM and ICD are based on a shallow theoretical understanding of mental disorders based on a clustering of harmful symptoms. In this paper, I offer a unifying theoretical perspective on the concepts of health and disorder that may constitute an alternative which is consistent with the medical model's naturalistic theoretical commitments. I present a view in which the grade of function in the traits and organs of the human body is sufficient to explain both health and disorder. The view is based on etiological theories of disorder, which define somatic and mental disorder as a failure of an internal mechanism to perform its function, where function is defined etiologically as the effect a mechanism was selected for in its recent evolutionary past.

Natural Normativity: Naturalistic Reconstruction of the Aristotelian Conception of Eudaimonia

In this paper, I develop and defend an naturalistic theory of well-being along the lines suggested by Aristotle. According to this idea, the conceptual structure of well-being track patterns of natural normativity that permeate the domain of life along the functional joints of nature. In one interpretation of the idea, it is intrinsically good for living beings to flourish in the sense of fulfilling and integrating their nature throughout life. Due to a number of concerns, contemporary proponents of the idea reject naturalistic interpretations of it. The chief concern is that a theory of well-being grounded in natural kind properties postulated by empirical sciences is likely to contradict our considered convictions regarding the meaning of well-being. In this article, I consider three influential forms of argument based on that concern. Through engagement with these arguments, I develop a scientific sort of Aristotelian naturalism that is compatible with a modern scientific worldview, and consistent with the premises to the most important objections to it. My conclusion is that the Aristotelian approach to ground well-being in nature has a lot of unexplored potential, and is a live alternative to subjective explanatory theories of well-being.

On Alienation: A Reconstructive Analysis of the Concept of Alienation

In the 1844 manuscripts, Marx relies on the concept of alienation in his critique of capitalism. In later writings, Marx came to disregard the idea of alienation because he thought it was too unclear to be the locus of a clear headed scientic analysis. In this paper I explain what Marx meant by alienation in the 1844 manuscripts, drawing on his vivid description of alienated labour. I then go on to give a reconstructive analysis of the concept of alienation, whereby I attempt to naturalize it. This is done by elucidating the semantics and logical structure of the concept of alienation, and thereafter by grounding it in causally ecacious social and natural kinds. If the analysis is correct, alienation is a general umbrella term denoting objective malfunction of some type of other in living beings, including biomedical malfunction in the sense of disease, and social malfunction in the sense of alienated labour. If the grounding is successful, alienation could conceivably be studied empirically, and enjoy scientic respectability.

UiO Political Forecasting Tournament

Forecasting tournaments have shown that epistemic methods combining psychological heuristics and statistical techniques can reliably enhance the forecasting accuracy of individuals and groups across various domains. However, little is known about the time and effort required to learn and implement these methods. This knowledge gap is significant, as organizations that rely heavily on foresight often cite the perceived costs of adopting such methods as a key barrier to their integration into workflows. Furthermore, the value of forecasting accuracy depends on the relevance of predictions to decision-making, yet the relationship between forecasting accuracy and practical decision-making remains poorly understood. Consequently, it is unclear when employing forecasting methods constitutes a cost-effective strategy for improving decision-making processes.The project’s core contributors include Paal Kvarberg (PI), Cathrine Holst, Jon Rosen, Ole Hegle Sjøflot, and Ole Røgeberg. It is part of the Science and Democracy research group, an interdisciplinary team investigating questions related to the institutional design of expert bodies and the role of experts in democratic governance.

Forecasting Tournament to Identify Costs and Benefits of Epistemic Methods to Forecasting and Decision-making

In a 9-month experimental forecasting tournament, we investigate the costs and benefits of learning and applying methods for good judgment in the context of political decision-making. Participants submit probability estimates for 108 carefully designed forecasting questions relevant to ongoing political debates in Norway. Using an innovative experimental design, we test an intervention that introduces epistemic methods such as calibration training, reference class forecasting, and analytical techniques for decomposing questions. Our analysis focuses on generating data to support cost-benefit evaluations of applying judgment-improvement methods in political decision-making contexts. To achieve this, we collect data on variables not rigorously studied in earlier experimental forecasting tournaments, including time investment, negative emotions like effort and anxiety, and levels of group agreement and comprehension. Forecasting accuracy is assessed using Brier score analysis. Additionally, we survey participants on their views regarding political issues to explore potential links between normative political beliefs and expectations about the future.

Practical Epistemic Methods: Cost-effective methods for improving judgement and decision-making

In this paper, I review findings from forecasting tournaments and other relevant studies, identifying a set of methods that can enhance the accuracy of individuals, teams, and organizations. I then address key limitations in our understanding of methods for good judgment, focusing on two primary obstacles to their widespread adoption in practical decision-making: costs and relevance.I discuss existing projects and initiatives aimed at overcoming these challenges and propose two promising directions for future research on forecasting and decision-making. The first involves conducting expected value assessments to evaluate the practical benefits of adopting forecasting methods. The second explores the development of quantitative models of relevance and reasoning, particularly through Bayesian networks.

The Web of Belief: How Technology Can Automate Belief Formation Processes and Support Wise Decision-Making

In this essay, I present a method for using technological innovations to improve rational belief formation and wise decision-making in an explainable manner. I assume a view of rationality in which beliefs are evaluated according to norms of intelligibility, accuracy and consistency. These norms can be quantified in terms of logical relations between beliefs. I argue that Bayesian networks are ideal tools for representing beliefs and their logical interconnections, facilitating belief evaluation and revision. Bayesian networks can be daunting for beginners, but new methods and technologies have the potential to make their application feasible for non-experts. AI technologies, in particular, have the potential to support or automate several steps in the construction and updating of Bayesian networks for reasoning in an explainable way. One of these steps consists of relating empirical evidence to theoretical and decision-relevant propositions. The result of using these methods and technologies would be an AI-powered inference engine we can query to see the rational support, empirical or otherwise, of key premises to arguments that bear on important practical decisions. With these technological innovations, decision support systems based on Bayesian networks may represent belief structures and improve our understanding, judgement, and decision-making.

Deliberate Practice with Rapid Formative Assessment

In 2020, Anders Evensen, Andreas Netteland and I initiated an innovation project with the support of the Norwegian Research Council, Innovation Norway, and Design and Architecture Norway. We have now developed a platform for making and doing practice assignments with automatic assessment and feedback in qualitative subjects like those of social science and the humanities.Research shows that practice and feedback is important to learning, so we think that there should be more practice and feedback in qualitative subjects. Multiple choice assignments are shallow and ineffective. The alternative, essay assignments, are labour-intensive to grade, so that is not a viable alternative for most teachers.With creative thinking and the application of recent innovations in the field of AI called natural language processing (NLP), Anders, Andreas and I found a way to automate assessment and feedback for practice assignments in text-based subjects. Using this technology, we facilitate for deliberate practice with rapid formative assessment in qualitative subjects, while collecting anonymised data for learning analytics and NLP research.

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