About me

I have a masters degree in philosophy from the University of Bergen and have studied social science and computational linguistics at the University of Oslo. In 2020 I co-founded Disputas, a startup working to develop software for learning and critical thinking.My research interests revolve around the practical preconditions for informed and rational public debate. This has expanded my core research interests to several topics in the cognitive sciences, including informatics, AI, psychology, informal logic and linguistics.In my PhD project I analyse the concept well-being in the context of priority setting in politics and health care. I am also the project leader of an empirical research project which investigates the link between judgemental forecasting and political decision making. I believe that good judgement is a skill that can be learned with deliberate practice guided by formative assessments, and have done research into the effects and preconditions of practice and feedback in qualitative subjects.I spend most of my time doing research or working on other projects. However, I also enjoy trail running, skiing and curating spotify playlists. See the 'other' page for more on this.

Public Models of Well-being

My PhD is a part of the interdisciplinary research project Modeling Human Happiness at the University of Oslo. This project integrates insights from philosophy and psychology to develop models of well-being that can inform national quality of life measurement and political priority setting. The group consists of several psychologists and philosophers.The aim of my PhD project is to support the effort to apply data on well-being in policy evaluation and development, so that we can learn which policies are the most cost-effective ways to make people better off.

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Experimental Forecasting Tournament

I am the project leader of an empirical research project into judgemental forecasting and decision making. In this project, Cathrine Holst, Ole Hegle, Jon Rosén and I run a prediction tournament to measure the costs and benefits of a light epistemic intervention introducing evidence based methods for good judgement in a context similar to that of expert advice committees who make public policy recommendations to state agencies. The project is incubated with the Science and Democracy Research Group, and is supported by UiO: Democracy.

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Technology for Deliberate Practice and Formative Assessment

With teachers, computer scientists and cognitive science researchers I have collaborated on several projects aiming to design teaching resources and facilitate for engaging and effective learning.With a group of designers and software engineers, I am building and testing software that uses AI to provide automatic feedback on practice assignments in qualitative subjects. We are currently exploring the accuracy of AI-based instant formative assessments on practice assignments and student satisfaction with AI-generated feedback.With Aurora Tjelflaat, the Norwegian Humanist Association, and Gyldendal Publishing, I am engaged in a project investigating exploring the effects of design-driven methods for creating teaching materials on the learning and motivation of students. This project is called Deep Dives.

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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.

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Eudaimonism and its Rivals: An Extended Argument for Nature Fulfillment Theories 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.

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Towards a Functional Theory of Health: 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.

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

We believe that the unclear costs and benefits of using epistemic methods are key barriers to their adoption. To alleviate this knowledge gap, we want to run an empirical study to investigate the following hypotheses: Epistemic interventions informed by research can significantly improve the in-group comprehension of expert advice committees (H1), transparency of their recommendations (H2), and the accuracy of the predictions on which those recommendations are based (H3), without incurring significant time-costs (H4).To test these hypotheses we are going to run a novel pre-post experimental tournament design which we call Tandem Tournament. In this design, we recruit participants to take part in a forecasting tournament where the prize is 15,000 NOK to the team who can give the most accurate predictions on 50 forecasting questions given over 5 months. Participants can register to join the tournament as a group, or alone, in which case they will be assigned to groups.However, and here is the innovation, all groups are further divided into two groups, a control and an experimental condition, where the winner of both is eligible to win the prize. This ensures that we can control for confounders while still motivating participants. We select questions that are likely to be interesting to participants and make a small lottery with a prize pool of 10,000 NOK that is eligible for participants who complete all tasks.

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Two Directions for Research on Forecasting and Decision-making

In this paper I review findings from forecasting tournaments and some other relevant studies. In light of this research, I identify a set of methods that can be used to improve the accuracy of individuals, teams, or organisations. I then note some limitations of our knowledge of methods for good judgement and identify two obstacles to the wide adoption of these methods to practical decision-making. The two obstacles have to do with costs and relevance.I also review projects and initiatives to overcome the obstacles, and note two directions for research on forecasting and decision-making that seem particularly promising to me. They have to do with expected value assessments and quantitative models of relevance and reasoning in the form of bayesian networks.

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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.

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Using AI to Enable Active Learning

Computer-assisted deliberate practice with rapid formative assessment on practice assignments informed by learning analytics

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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Music Playlists

I take great pride in my spotify playlists. Here are some of them.The Jungle
Space Odyssey
Strong Feelings, Groovy Rhythms
Into the Forest