Press Kit

Logo Assets

The Dissensus logo represents two interlocking forms in productive tension—dissensus as function, not dysfunction. Use the version appropriate for your background.

White Logo (Dark Backgrounds)

SVG PNG

Crimson Logo (Light Backgrounds)

SVG PNG

Brand Guidelines

Primary Color

Burgundy

#800020

Background

Near Black

#050505

Typography

Sans: System fonts (-apple-system, Inter)
Mono: SF Mono, Fira Code

Logo Usage

Minimum clear space: 50% of logo width.
Do not distort, rotate, or add effects.

Boilerplate Text

Use these descriptions when referencing Dissensus in publications or media.

Short Description (50 words)

Dissensus AI is a UK-based governance alignment research lab. Through its Adversarial Systems & Complexity Research Initiative (ASCRI), it investigates complex agent networks where equilibria are structurally impossible. We formalize friction dynamics across political economy, AI alignment, and financial microstructure, developing substrate-independent frameworks for measuring coordination overhead.

Long Description (150 words)

Dissensus AI is a UK-based governance alignment research lab. Its principal research programme, the Adversarial Systems & Complexity Research Initiative (ASCRI), investigates any sufficiently complex dynamic network of agents where equilibria are impossible to reach due to formalized friction constraints. These systems function through friction reduction rather than friction elimination, making them structurally adversarial yet persistently functional.

Founded in 2026, the lab develops formal frameworks including the Axiom of Consent (a pre-game-theoretic framework for delegation analysis) and the Replicator-Optimization Mechanism (computational unity across biological and abstract substrates). Research spans consent mechanics, machine morality, economic microstructure, and political legitimacy quantification. All non-proprietary outputs are published open-access under CC-BY 4.0 licensing.

One-Liner

"Dissensus formalizes the mechanics of friction in multi-agent systems."