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.Additionally, I am the project leader of the UiO: Democracy Political Forecasting Tournament and a project aiming to develop AI-based technology for learning and reasoning.Scroll down to learn more about me and my projects. Click on the links in the header for more details. My mail address is: paalfredrik(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: Public Models of Well-being: Towards Welfarist Evaluative Frameworks Grounded in the Values of Citizens
Chapter 1: “Public Models of Welfare: Legitimation and Validation of National Composite Welfare Indices”
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: “Scientific Eudaimonism: Naturalistic Reconstruction of the Aristotelian Conception of Well-being”
Chapter 5: “Objectivity and Individuality: Reference Class Pluralism in Objective Theories of Well-being”
Chapter 6: “Towards a Functional Theory of Health and Disorder”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: Welfarist Evaluative Frameworks Grounded in the Values of Citizens

Driven by compelling reasons to seek alternatives to GDP and preference satisfaction models of welfare for policy appraisal, many countries have begun measuring quality of life using a wide range of indicators that track both subjective and objective factors. A pressing question now facing these countries is how, if at all, this data can be effectively used for evaluative purposes to inform policymaking. My PhD project is part of Modeling Human Happiness, 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: Assessment of Public Evaluative Frameworks for Appraisal of Policy and Social Progress

This paper argues that a consistent and transparent assessment of policy and social progress requires the aggregation of data into a stable composite index capable of facilitating interpersonal comparisons of benefits. Such aggregation methods, I contend, should be guided by public models of well-being that clearly distinguish between the causes, correlations, and constituents of welfare. These models must ensure that the constituents of welfare resonate with citizens’ values, while the causes of well-being are grounded in robust scientific evidence. However, both citizens and researchers often disagree about the meaning of well-being, giving rise to a significant objection: any policy appraisal based on a specific model of well-being risks illegitimacy by marginalizing citizens with alternative conceptions of well-being and the good life. In response, I defend a particular version of the value-alignment approach to the development of public models of welfare, which aims to address this and related objections. In doing so, I propose a framework for evaluating the theoretical validity of public welfare models and the political legitimacy of their use in constructing clear, unidimensional evaluative metrics for political decision-making. I conclude that while such metrics can play a meaningful role in policy appraisal, their use should be limited to contexts where the values embedded in public evaluative frameworks are appropriately aligned with those of the citizenry.

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: Modelling the Normative Significance of Functional Traits

This essay presents and defends a functional theory of health in which the degree to which some person is healthy is determined on the basis of medical facts regarding the expected causal effects of her present health condition on natural functions in her body over time. This is modeled as a causal graph representing levels of function in the interrelated traits, faculties and organ systems of the human body and mind. Parameters of the model grounds a quantitative account of health informed by clinical research. This theory of health can inform naturalistic theories of medical disorder, as well as the development of health metrics for prioritisation.

Scientific Eudaimonism: Naturalistic Reconstruction of the Aristotelian Conception of Well-being

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.

UiO: Democracy 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, working individually and in teams, 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.All participants collaborate in teams; however, teams in the experimental condition receive structured guidelines for discussion and collaboration. Their individual judgments are pooled and aggregated into a team prediction using a statistical algorithm. 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 previously studied in 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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