It began on a whiteboard in Zurich in 2019, four years before any large AI language model went public. Six columns, eleven jurisdictions, four ink colours. The claim was structural. A decision-shaped output is governed by multiplicative logic, not additive logic. Three laws specify the form. Five substrates have so far been tested. The other three programs apply the structure. Foundational is where the structure itself lives.
Foundational is the program where the three laws are developed independently of the substrates on which they operate. The three substrate programs each apply the laws to a specific empirical surface. Foundational is what each of them depends on.
Most decision theories rest on a quiet assumption. They assume the factors driving a choice combine additively, that you can sum motivation, opportunity, capacity, and context, weigh the result, and predict the outcome. The Mehrhoff Research Program rests on a different claim, one that took seventeen years of operational observation to formalise and three lines of marker ink on a whiteboard to draw. The factors driving a decision combine multiplicatively. If any one of them approaches zero, the decision collapses, regardless of how strong the other three are.
The Foundational program develops these claims abstractly and establishes the methodological apparatus the rest of the program rests on. The three substrate programs each test the structure in their own empirical surface. Each finding either confirms the structural prediction, refines it under domain-specific conditions, or surfaces a counter-example the Foundational program then has to address. The traffic runs both ways. It is the only way to keep a structural claim honest.
What makes the structure distinctive is the substrate-invariance claim. The three laws are not psychological generalisations about human cognition. They are structural specifications of how decision-shaped systems behave, wherever the underlying physical-law constraints operate. That includes human agents and human institutions. It also includes AI agents, hybrid workflows where humans and machines alternate, and the quantum measurement processes at the boundary of physical law itself.
Five substrates that share no operational similarity, governed by the same structural form. The biological human substrate where the laws were first observed. The biological neural substrate one layer below the conscious agent. The artificial substrate where the additive consensus in machine learning meets its multiplicative correction. The hybrid substrate where human and machine alternate inside the same decision sequence. The physical substrate where the structural form recurs in the physics that produces every other substrate.
The substrate one layer below the conscious agent. Neural decision-formation processes, cognitive mechanisms, and the biological pathways through which a choice forms before it reaches the level of intention. The same multiplicative structure operates here, with the parameter values determined by underlying neurobiology and the structural form preserved.
The substrate of autonomous artificial agents under the same four-force structure that governs human decisions. The additive consensus in machine learning is structurally wrong on exactly the same grounds the additive consensus in behavioural science is wrong, and the cost of getting it wrong compounds through training, evaluation, and deployment.
The substrate of human judgement and machine suggestion alternating inside the same decision sequence. The cascade links the two side-by-side, with the same four-force logic operating on both sides of the alternation. This is where most consequential decisions of the next decade will actually be made.
The substrate of physical and mathematical systems where cascade structure is native to the underlying physics. The deepest available test of substrate invariance, with quantum measurement as the most fundamental empirical reach (the boundary where the multiplicative pattern observed in human, neural, artificial, and hybrid substrates recurs in the physics that produces every other substrate).
The substrate of human actors and human institutional decision-making. The laws were first observed here, working inside operational financial-services contexts, before any of them had names. Decades of behavioural data across financial markets, electoral systems, public health, education, healthcare, and pharmacology now provide the deepest available test surface.
Decision outcomes are governed by structural forces, not by stable attributes. The same multiplicative logic recurs at three scales and across domains.
Action under complexity is determined by the dynamic interaction of four forces. The interaction itself is the mechanism. Additive models systematically under-perform because they cannot represent it.
How individual decisions aggregate into institutional outcomes through three sequential phases. Ignition. Polarisation. Institutionalisation.
A criterion for when a structural pattern recurs across scales and domains. The pattern transfers only where structural identity holds.
Patrick Mehrhoff is a German entrepreneur, independent researcher, AI engineer, and author. He is the author of the three original theories that constitute the Mehrhoff Research Program. He built the work the studio carries.
Most practitioners spend a career inside one domain. He worked as a practitioner at six of the frontiers that redefined how society organises information, money, and value over the last two decades. The consumer internet, Swiss fintech, European monetary policy, Southeast Asian financial publishing, institutional crypto assets, and the first wave of applied AI.
The same structural observation appeared at every frontier. By 2019, on a whiteboard at crypto infrastructure provider Wyden in Zurich, the pattern had become visible to itself. It did not yet have a name. Four years before any AI language model became publicly available, the complete logic of the Three Laws of Decision-Making was already on the whiteboard.
The studio was built from Bangkok, without institutional affiliation and without external funding. The Rubens workshop model defines how it operates. The founder sets the intellectual direction at every decisive step. The tools and collaborators execute within it.
The first program states the architecture and demonstrates the laws that govern it. The second tests those laws against the empirical record of human decision-making. Together they establish the studio's canonical core and its first empirical substantiation. Further programs are in active preparation.
The laws tested against the empirical record from every sector, industry, and discipline where humans make decisions. From financial risk to clinical judgement to educational selection to electoral behaviour, the same multiplicative architecture governs the outcome. Every test in the program substantiates the structural finding the foundational program advanced.
Tested against nine foundational scholars, three of them Nobel laureates, and across fifteen structural dimensions. Every cell is independently challengeable.
Every dimension independently challengeable.
Most frequently cited as predecessors.
Among the nine scholars tested.
Confirmed across all nine scholars.
The three laws describe the same multiplicative structure that governs decisions across scales. What each audience recognises in them is different. Each doorway below opens into the same set of laws, framed for the way you are most likely to find your own work inside them.
The literature in your field has been reporting tail-failure for decades. Outliers, noise, model misspecification. The three laws name a different culprit. The structural form of the model itself. Read the laws and find the place where the assumption you never questioned has been doing the work all along.
Every senior decision-maker has watched a model work on average and break where the decision mattered. The three laws name what is happening. The breakage is not an edge case the model failed to anticipate. It is the multiplicative architecture finding its zero. Read the laws and the pattern beneath every model break of your career becomes legible at once.
Each major institutional cascade of the past fifteen years was investigated as if it were unique. The reports document different operational failures in different sectors. The three laws read across the reports and recognise one structural pattern. Read the laws and the recurrence stops looking like coincidence.
Every philosophical tradition that has tried to describe the world has eventually arrived at the same conclusion. The three laws turn the conclusion into a working structural explanation. Decisions form. They cascade. Their patterns transfer across scales. Read the laws the way our studio reads them.
Most foundational research is finished before a student arrives. The three laws sit early enough in their life that the field around them is still to being created. A graduate student who reads them now enters a body of work inside its formative window. The reading path is curated for entry from any quantitative or social-science background.
Universities train within additive frameworks. Journals review within them. Regulators write rules under them. Funders evaluate against them. The three laws name the structural assumption beneath the entire foundational layer. It has been invisible because no curriculum, no review, no framework has ever required it to be examined. Read the corpus and the link your institution has been operating without becomes legible.
The first sketch of the Three Universal Laws. Drawn four years before any AI language model became publicly available.
Sketched at an office in Zurich. The board maps the direction of the markets, the forces driving them, the audiences they affected, the problems those audiences encountered, and the responses the market was developing. The structural logic that became the First, Second, and Third Laws is already on the board, in the language of practice.
Mehrhoff Research operates outside academia and outside industry. An independent science and research studio with no external funding, no board, and no university partnership. Founded 2019 in Zurich, domiciled in Tallinn, run from Bangkok.
The work is published open access. Every paper passes adversarial peer review before deposit. Every claim names the empirical conditions under which it would be falsified. Every citation traces to a primary source. The standard is not the institution. The standard is the work.
Deposited on Zenodo, permanently archived.
Data points analysed across all empirical validations.
Foundational and human decision-making.
No paywall, no journal embargo.
A horizon decades away. The daily practice of moving toward it. Seven principles the studio holds itself to, with or without anyone looking. Five codes the studio answers to when no external authority requires them. Each statement enforced internally. Each verifiable from outside.
Vision and mission, stated directly. What the studio is working toward, and what it does today.
Seven standards held against every paper, every conversation, every relationship the studio enters. Decision-making, rigour, independence, transparency, originality, curiosity, and courage.
Self-regulated science. Published standards, open methods, full results regardless of what they show.
Press, academic peers, conference organisers, policy bodies, industry practitioners, and readers reach the studio here.
Six formats, from public keynote to closed doctoral seminar. The full range is welcome; the substance of the questions decides the invitation.
Four positions, from visiting researcher to fellowship, with two scheduled per calendar year. A residency produces sharper work than a single talk because the conversation extends.
Press kit with bios, headshots, and topic briefs is ready to download. Lectures, learning materials, and the architecture itself are licensable.