Governance for an independent research studio is more consequential than governance for a typical organisation. The studio is making knowledge claims that have to hold up against the standard institutional checks (peer review, replication, conflict-of-interest disclosure, funding transparency) without the institutional structures that would normally enforce those checks. The five governance items below name the mechanisms the studio uses to enforce those checks on itself.
Independent science has no external regulatory body. No professional self-regulatory organisation. No supervisory authority. No registration with any standards committee. The studio chose this position. The studio also chose to write down a Code of Professional Conduct anyway, and to publish it. The five governance commitments below are the Code.
The studio's principles describe what guides the work. The governance items describe the mechanisms that hold the studio to its principles. A principle is a statement of value. A governance item is an operational commitment that can be audited from the public surface of the work. Without governance, principles are decoration.
The five items below cover the structural checks that any research-producing entity has to pass to be taken seriously: independence from funding capture, openness of scientific output, disclosure of methods including AI use, ethical conduct in research design, and disclosure of commercial interests. The list is deliberately short. Each item names a commitment that the studio has already made, not a commitment the studio aspires to.
Where the principle and the governance item share a word (most notably independence), the redundancy is intentional. The principle states the value. The governance item states the operational mechanism. The double mention reinforces that the principle is not a slogan but is operationally enforced.
Each governance item below is operationally specific. Each names a mechanism the studio uses, not a value the studio claims. Each is held against the public surface of the studio's work so any reader can verify whether the standard matches the practice.
All foundational research is published openly. Papers are deposited on Zenodo with permanent DOIs, accessible to any reader without paywall, subscription, or institutional access requirement. Methods and empirical material are documented to allow reproduction by any researcher with the same data. The methodology that produces the open research remains proprietary and separately licensable, with licensing revenue intended to fund the continued production of the open work.
AI tools are used in the writing, formalisation, and analytical phases of research, and this use is disclosed in every journal in accordance with current academic norms. The intellectual contribution of the work is human. AI is a tool, not a thinker. The 2019 Zurich whiteboard establishes that the program's logic predates publicly available AI by approximately four years, which is itself part of the disclosure regime.
The studio conducts no human-subjects research and uses no proprietary or restricted-access data. All empirical work uses publicly available datasets, and the provenance of every dataset is documented in the paper that uses it. All claims are bounded by the evidence available, and the studio resists the temptation to overclaim where empirical support is incomplete.
Where commercial applications of the research exist or come into existence, those applications are disclosed in any context where the disclosure is material. The research itself is conducted independently of any commercial outcome. Findings are published whether or not they support the commercial application.
Mehrhoff Research operates under Mehrhoff Digital OU, an Estonian company held entirely by its founder. The studio receives no external funding from grant councils, universities, foundations, or commercial backers. Research questions are set by the studio's own intellectual agenda, with no third-party stakeholders shaping topic selection, methodology, or publication decisions. The independence is operational, structural, and audit-trail-verifiable from the studio's filings.
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.
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.
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 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 three laws at the micro, macro, and meta scales, derived and stated formally. Plus the empirical evidence that the multiplicative architecture holds across biological, artificial, and hybrid substrates. Every paper in every other program traces its structural claims back to this canonical core.
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.
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.
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.