About
A small group with a long thesis.
Dissensus is an independent complexity-science research group in London. We study complex adaptive systems — and specifically what happens in them when the parts want different things — through formal, computational and empirical work.
What the group is for
The subject is complex adaptive systems: many interacting agents, no central controller, aggregate behaviour that none of the parts intends. Markets, institutions, minds and machine collectives all qualify, which is why the papers look scattered until you notice they ask one question in four places.
The question is what happens when the parts want different things. Acting together requires delegation, delegation produces friction, and that friction is measurable — in how well interests align, how much is at stake, and how badly intent degrades in transmission. The aim is not to remove it. Friction is the cost of existing among others who want other things; the aim is to measure it well enough to say something falsifiable.
The group is independent by construction: no institutional affiliation, no outside owner, no funder with editorial reach. Everything it produces is open — papers, code, datasets and tools, archived with DOIs so the work can be checked rather than taken on trust.
One programme, four fronts
The research is a single programme rather than a set of side interests. A paper on cryptocurrency volatility and a paper on machine ethics look unrelated until you notice they are both probing how a complex adaptive system behaves when its parts want different things. A separate public home for the programme is being reconsidered; until it exists, everything is published here.
The programme runs on four fronts, each a different place to catch the same phenomenon in the wild. Publications in all four are listed under Research; the ones still open to collaborators are on Projects.
- 01 Computational finance
- Market microstructure, volatility asymmetry, systemic risk, event-study methodology.
- 02 Political economy
- Legitimacy quantification, institutional friction, governance and consent mechanics.
- 03 Cognition & AI alignment
- Multi-agent coordination, machine ethics, training dynamics as development.
- 04 Formal methods
- The friction formalism itself — theorem-proved in Lean 4, tested in simulation.
Five rules, and what breaking them would look like
These are commitments rather than aspirations, which means each one is specific enough to fail. They are written down here so that if the group stops honouring one, the gap is visible to anyone who reads this page.
- 01
- Everything is free to read, run and citePapers, datasets and software go out with DOIs — CC-BY for text, MIT or Apache for code. Nothing the group produces sits behind a paywall or a request form. Breaking it looks like: an output described in public that you cannot actually get to.
- 02
- Every number traces back to code you can runReported results come from committed code with fixed seeds, and the datasets and pipelines are archived alongside them. Breaking it looks like: a figure whose provenance stops at “available on request”.
- 03
- We attack our own work before anyone else canEvery claim is stress-tested internally first, and collaborators are invited to break whatever survives. The goal is not to be right, it is to be less wrong sooner. Breaking it looks like: a confident headline with no stated failure mode.
- 04
- No funder, client or institution can change a resultNobody outside the group owns it, employs it or edits it. Commercial work and programme funding sit behind separate doors, and neither buys a conclusion. Breaking it looks like: a finding that happens to suit whoever paid for it.
- 05
- Work that fails gets published as having failedA null is reported as a null under the same inference standards a positive result would face — the accepted Digital Finance paper reports one about its own headline. Breaking it looks like: a project that quietly disappears instead of saying what happened.
Who does the work
Murad Farzulla
“We are more beholden to custom than reason; it is thus reason’s task to interrogate custom.”
I run Dissensus. My training is in finance — MSc Finance Analytics at King’s College London, after a First-Class BSc in Accounting & Finance at SOAS — and the research goes wherever the friction thesis leads: computational finance, political economy, AI alignment, formal methods. I develop the Axiom of Consent and Replicator-Optimization Mechanism frameworks the lab is organised around, and I hold the unfashionable view that a theory that cannot be computed, tested, or proved is not finished yet.
The full résumé lives at farzulla.org; the papers themselves are here, under Research.
Andrew Maksakov
Andrew keeps the lab’s empirical machinery running — distributed systems, GPU compute, and simulation pipelines — and is co-author on the ASRI systemic-risk index and the CBDC privacy-architecture work.
Felipe Pachano Azuaje, PhD
Felipe is an affiliated researcher contributing methodological perspectives across the programme — the standing outside view every small lab needs, without which a one-thesis lab starts agreeing with itself.
The two places money changes hands
Two separate doors, deliberately. A client owns a deliverable; a funder produces public goods. Neither buys a result, and neither buys editorial reach.
- 01 Commercial
- Organisations retain the group for applied analysisFriction analysis — mapping where competing interests generate structural conflict in a market, protocol or organisation, and decomposing it into alignment, stake and entropy. Also bespoke ASRI-style systemic-risk indices, Lean 4 formal verification with assumptions tracked in an explicit ledger, adversarial evaluation of multi-agent systems, and GARCH-family volatility modelling built on the open-source estimators we maintain. Engagements run as advisory, as scoped research with a written deliverable, or as custom tooling shipped with source and tests — a few at a time, so the research does not stall.
- 02 Funding
- Grants and philanthropy fund the programme itselfThe bottlenecks are concrete: compute for multi-agent and privacy experiments at real scale, research time to carry results through to publication, and the unglamorous distance between a working prototype and a replicable finding. What funding buys stays open — archived datasets, machine-checked Lean formalisations, and the index running daily at asri.dissensus.ai. Grants work at project or programme level with milestones agreed up front; academic partnership runs as joint proposals, co-authorship and shared infrastructure. Smaller ongoing support goes through Patreon.
Independent by construction
The legal shape of the lab
Dissensus is the research brand of Dissensus Ltd, a company registered in England and Wales (no. 17309927). The independence is the point: the company answers to no institution, no outside owner, and no funder. There is one director, and that is a legal structure rather than a reward — no collaborator or advisor is asked to take a company office. The rules the lab holds itself to are written down above, in public, where they can embarrass us if we break them.
We respond to substantive messages, and “substantive” is doing real work in that sentence. A first reply usually takes weeks rather than days. For applied work or funding, the section above is the fuller picture; to work on the research, start at Join.
Phone: 020 3807 1624 · Email: research@dissensus.ai