Consent Mechanics
The formal theory of consent-holding in multi-agent systems. When can delegation be legitimate? What are the structural conditions for valid consent? How do we measure consent deficits?
dis·sen·sus /dɪˈsɛnsəs/ — from Latin dissentire: to disagree. In political philosophy (Rancière, Mouffe), the productive friction that emerges when consensus is impossible or undesirable. Not dysfunction—function.
Axiomatic Consent Mechanics and Tripartite Friction Topology in Substrate-Agnostic Replication-Optimizing Multi-Agent Networks: Toward a Phenomenologically-Contained Metatheoretic Foundation for Pre-Strategic Delegation Dynamics Across Scale-Invariant Coordination Substrates with Applications to Decentralized Systems, Machine Morality, and Political Economy
Or simply: coordination has overhead.
→ Read the Research ManifestoGame theory presupposes participation. It does not account for how agents enter strategic interaction, why payoff structures take the forms they do, or what occurs when participation is refused. It does not interrogate who defines the rules, whose interests they serve, or whether the game itself is legitimate.
We study the layer underneath. Before strategy, there is delegation. Before delegation, there is consent. Before consent, there is stake—someone's optimization being transferred to another's control. Regulatory announcements shift sentiment, yet decentralised networks remain structurally unresponsive—the protocol operates outside institutional jurisdiction.
We formalize this friction. Not to eliminate it—friction is the cost of existence in an adversarial environment—but to measure it. To make visible the invisible taxes that coordination imposes. To quantify the legitimacy deficits that traditional political theory only gestures toward.
The Axiom of Consent is a pre-game-theoretic framework for analyzing multi-agent systems. The central claim:
In any system containing two or more optimizing agents, action requires delegation, delegation produces friction, and friction has measurable stakes.
Quantifying legitimacy in adversarial environments. Introduces stakes-weighted consent alignment as a formal measure of political legitimacy. The framework integrates social choice theory, mechanism design, and empirical political economy.
Meta-TheoryA unified computational framework for self-replicating, optimization-executing systems across physical and abstract substrates. Formal invariants shared by biological replicators, optimization algorithms, and mathematical structures.
A formal research programme investigating sufficiently complex dynamic networks of agents where equilibria are impossible to reach due to formalised friction constraints. These systems function merely through a reduction of friction—drawing from game theory—and are thus adversarial against themselves, yet persist through dissensus. systems.ac →
ActiveThe formal theory of consent-holding in multi-agent systems. When can delegation be legitimate? What are the structural conditions for valid consent? How do we measure consent deficits?
Substrate-independent criteria for moral status. If an AI system meets functional criteria for consent-giving, what follows? The genre mimicry hypothesis: AI safety as statistical reproduction of professional norms.
Derivatives as simultaneously remedy and poison. The hedging paradox: when risk management becomes wealth transfer infrastructure. Market microstructure as friction measurement.
Empirical validation of friction dynamics. Why do infrastructure failures move markets more than regulatory announcements? The enforcement capacity hypothesis.
Dissensus operates as a distributed research initiative investigating the mechanics of friction—the coordination costs that multi-agent systems impose, and the structural asymmetries that emerge from unexamined delegation.
We seek collaborators willing to rigorously test these frameworks—to identify edge cases, expose failure modes, and refine the underlying formalisations.
Enquiries and collaboration proposals are welcome.
Submit Collaboration Proposal →Private K3s compute cluster for high-throughput social simulation and agent-based modeling. Privacy-first architecture with dedicated research infrastructure.
resurrexi.io →Local LLM inference (Qwen3 80B), dual AMD GPU configuration, 262k context window. Offline-capable research workflows.
All non-proprietary simulation code, datasets, and methodologies published under CC-BY-4.0. Reproducibility is not optional.
github.com/studiofarzulla →Infrequent updates on new papers, framework developments, and research commentary.