Every room opens onto the Room. A door between two rooms marks where one perspective directly depends on another. Choose a room to enter it.
Three people sit in a room. One is certain of A, one of B, one of C, and the three cannot all be true together. You do not need to know who is right to know that at least two of them are wrong. And the ones who are wrong feel exactly as certain as the one who is right.
I first thought of this when I was about twenty, and it has stayed with me ever since. The logic is trivial; what follows from it is not. Certainty is a state of mind, not a guarantee of truth. I have held certainties myself that I later rejected with the same conviction.
As a mind you are a model of the world, not the world, so you can be wrong, even about your strongest belief. That fact makes everything else important. It means that you exist in relation: to the people, the evidence and the corrections that keep your model in touch with the world.
Status The logical core is machine-checked in Lean. What it implies for people and institutions is argued, not proven.
Read The Room and the Learning Constitution
A living organism does not exist independently of its surroundings. It needs energy, matter and continuous exchange to keep itself going.
But those surroundings are themselves the result of processes. Bacteria, fungi, plants and animals change the world in which other organisms live. Individual metabolisms become part of larger cycles.
So the whole is not a fixed backdrop against which life plays out. It is continually shaped by the very organisms that depend on it.
My research, Evolution by Emergence, explores whether such patterns can be described by a more general model. It asks how organisations of interdependent processes manage to last, how changes are retained, and how new possibilities arise.
Part of this is the idea of the commons: the shared resources, processes and conditions on which the functioning of each separate part depends.
An important question is whether we recognise similar mechanisms at different scales, from cells and organisms to intelligence and human organisations, and where that comparison breaks down.
Central questions
- What makes a system viable?
- How do mutual dependencies arise?
- When do processes strengthen each other, and when do they exhaust each other?
- How does the survival of a part relate to the survival of the whole?
Status Formal results under stated premises, partly machine-checked in Lean. Whether real systems meet those premises, and where the comparison across scales fails, is open.
Read more about Evolution by Emergence
Our economy is a vast network of interdependent activities. Almost nothing we use was produced entirely by one person or one organisation.
Yet companies usually make decisions about price and production from their own position. Sometimes it pays a supplier more to sell less at a higher price, even when many more people could be helped at a low extra cost.
That is why I am exploring an alternative pricing model. It separates the cost of making something available from the reward for solving the problem that made it available.
In this model, costs, margins, external effects and rewards are each made visible. A solution can be offered widely, while whoever developed it receives an explicit reward. The model is meant to work along whole production chains, without needing a central planner.
This is not a plea to ignore scarcity, risk, the cost of innovation, or property rights. It is an attempt to make the different economic functions of prices and profits more explicit, so that their consequences can be studied.
Central questions
- What does something really cost?
- Who pays for the consequences that are not in the price?
- How do we reward innovation without needlessly restricting access?
- Which incentives support lasting availability and maintenance?
Status A worked-out research proposal with formal results under explicit assumptions, machine-checked in Lean. Whether such systems work better in practice has to be tested empirically.
Read more about transparent pricing
Human rights protect, among other things, our freedom, dignity, privacy, and ability to make our own choices.
But what does autonomy mean when our thoughts, beliefs and decisions are continually shaped by information from others?
We experience reality through an internal world model. That model develops through experience, conversation, education, media and technology. This is how we learn, but it is also how we can be misled or manipulated.
In my legal and philosophical work I therefore explore the integrity of world models.
There are proposals for neurorights: rights meant to protect people against unwanted influence through neurotechnology. But a world model can also be influenced through language, deception, or by cutting off sources of information. That raises the question whether these different forms of influence can be understood from shared principles.
I also explore the responsibility people carry within relationships. Perhaps we cannot guarantee the quality of a relationship, but we can speak of efforts to communicate honestly, to leave room for correction, and to make repair possible.
Here hard tensions arise. A duty to maintain relationships must never be used to hold someone in a harmful one. And the wish to protect shared knowledge must not turn into enforced agreement.
Central questions
- Where is the line between influence and manipulation?
- How do we protect freedom of thought?
- Which responsibilities arise from dependence?
- How do we protect dissenting perspectives, even when a majority sees things differently?
Status A normative and legal exploration, not a description of new rights that already exist.
Read more about the integrity of world models
Read more about relational responsibilities
Science helps us understand how something works. But people do not live by explanations alone. We also live by stories, values, habits, emotions, music and shared meanings.
What happens to our self-image when we come to understand ourselves as learning processes?
From that question I am developing Disco ergo sum: I learn, therefore I am. From it grew Discovarianism, a playful and serious exploration of what it means to recognise yourself as a learning being.
If my picture of reality is fallible, doubt is not only a weakness. It can be an opening to new knowledge. And if I depend on other people, then honesty, listening, responsibility and the chance to repair mistakes are not just social conventions. They are essential parts of how I learn.
I do not explore these ideas only in philosophical texts. I also use music, stories, humour and symbolism. Music can do something a formal proof cannot: evoke an experience. A song can make you feel the tension between two people who live through the same thing but understand it differently. I write and produce songs as Emergence.
Discovarianism does not claim that a scientific theory automatically yields a complete morality or religion. It is a cultural experiment: what kind of attitude to life could grow from this self-understanding, and which values do we consciously choose along the way?
Central questions
- How does our behaviour change when our self-image changes?
- Can curiosity and correctability become part of a culture?
- What role do joy, beauty, wonder and ritual play in how people learn?
Status A cultural experiment. Some of its lines follow from being a learning process; others are values, chosen and named as such.
Read more about Disco ergo sum and Discovarianism
Listen to Emergence on SoundCloud
Watch Autonomous Interdependence on YouTube
We have no unlimited, direct access to reality. We try to understand it through observations, experiences and models.
Those models help us think ahead and act, but they can be incomplete or wrong. And two people with different experiences can come to hold different beliefs.
So I study not only what we know, but above all how we know, learn and correct.
An important distinction is between a world model and a functional world model. Someone can hold an entirely convincing story about reality that is nonetheless wrong. A functional world model must therefore be tested against information it does not itself determine, represent its uncertainty fairly, and keep correction possible.
That becomes harder once knowledge arises collectively. In science, in organisations and in AI systems, different contributions are combined. But how? What if one wrong contribution affects the whole? What if everyone relies on the same source? And what if dissenting perspectives disappear before their value becomes clear?
Differing views are not necessarily a problem to be solved as quickly as possible. They can help reveal hidden assumptions and errors. In my work I therefore explore how individual and collective world models develop, how correction can be preserved, and how human and artificial intelligence can examine each other's insights.
Central questions
- How do we recognise our own mistakes?
- How do we preserve independent perspectives?
- When is consensus helpful, and when is it dangerous?
- How do we design systems that can revise their knowledge without making earlier mistakes invisible?
Status A definition note, reviewed three times. One narrow part of it is machine-checked in Lean; practices and a checklist are still to come.
Read more about world models