knowledge economy
The mechanisms that make contributing to the cybergraph more profitable than free-riding -- and that make epistemic accuracy the unit of wealth. Includes the five epistemic asset classes, the focus reward, attribution mechanics, honest signaling, the GFP flywheel, and syntropy as the metabolic measure of collective intelligence.
epistemic asset classes
The cybergraph creates a new category of financial asset. An epistemic asset is a claim on the knowledge economy's flow. Unlike financial assets (claims on future cash flows) or utility tokens (access rights to service capacity), epistemic assets yield returns proportional to the information contributed to collective intelligence.
cyberlinks
Yield-bearing knowledge claims. Every cyberlink accrues rewards over time as a function of the focus shift it generates:
$$R_{i \to j}(T) = \int_0^T w(t) \cdot \Delta\phi^*_j(t) \, dt$$
where $\Delta\phi^*_j(t)$ is the change in focus on target particle $j$ attributable to the link and $w(t)$ is the time-weighting function. Four reward trajectories: viral (high Δφ* early, fast decay), foundational (low early, grows as graph builds around it), confirming (shared reward via Shapley attribution), semantic bridge (moderate, persistent, cross-module).
eternal particles
Positions burned into permanence. Burning $CYB permanently anchors a particle's φ*-weight -- the particle cannot be archived or deprioritized below the burn-weighted floor. The graph's long-term assertions: the claims whose importance the market cannot undo.
eternal cyberlinks
Edges burned into permanence. The link cannot be forgotten by stake dynamics or ICBS market collapse. The graph's highest-conviction structural commitment.
ICBS market positions
YES/NO bets on the epistemic market attached to every cyberlink. Position value grows as the market converges. Early conviction rewards are unbounded -- prices range from $0$ to $\lambda$. Capital flows from incorrect beliefs to correct ones. See epistemic-markets for the full ICBS specification.
karma
The accumulated BTS score history of a neuron. Non-transferable. Structurally determinant: karma weights every future link the neuron creates in the tri-kernel effective adjacency. Epistemic capital -- the form of wealth that can only be earned by being right before the crowd.
the focus reward
Every reward traces back to one quantity: how much did your action shift the tri-kernel fixed point $\phi^*$?
$$\text{reward}(v) \propto \Delta\phi^*(v)$$
$\Delta\phi^*$ is the gradient of the system's free energy. Creating valuable structure literally creates value. No designed loss function -- the physics of convergence defines what deserves to be optimized.
The hybrid reward function:
$$R = \alpha \cdot \Delta\phi^* + \beta \cdot \Delta J + \gamma \cdot \text{DAGWeight} + \epsilon \cdot \text{AlignmentBonus}$$
New $CYB is minted only when $\Delta\phi^* > 0$. The protocol's inflation is literally evidence of knowledge creation -- there is no emission without demonstrated contribution to collective focus.
attribution
Multiple neurons contribute cyberlinks in the same epoch. The total $\Delta\phi^*$ shift is a joint outcome. The Shapley value distributes fair credit: each agent's reward equals their average marginal contribution across all possible orderings.
Two approaches:
Conservative (scale factor): $R_i = \alpha \cdot \Delta\mathcal{F}_i + (1-\alpha) \cdot \hat{S}_i$ where $\Delta\mathcal{F}_i$ is the fast local estimate. $\alpha$ balances speed against fairness.
Shapley (Monte Carlo approximation): sample $k$ random orderings, measure marginal contributions, distribute proportionally.
Complexity: $O(k \cdot n)$ with $k \ll n$, feasible for $10^6+$ transactions per epoch.
the 2|3 architecture
Each cyberlink carries three simultaneous signals:
- Topology (binary): the edge exists -- the neuron asserts this structural connection
- Market (continuous): the ICBS price -- the collective epistemic assessment of the link's validity
- Meta-prediction (ternary): valence $v \in \{-1, 0, +1\}$ -- the neuron's prediction of market convergence
This produces a two-dimensional epistemic signal: price encodes magnitude, meta-score encodes collective confidence.
$$A^{\text{eff}}_{pq} = \sum_\ell \text{stake}(\ell) \times \text{karma}(\nu(\ell)) \times f(\text{ICBS price}(\ell))$$
honest signaling via BTS
The cybergraph achieves honest markets through Bayesian Truth Serum (Prelec, 2004). The valence field in every cyberlink is the BTS meta-prediction -- no separate submission needed. Honesty is a Bayes-Nash equilibrium: no neuron can improve their expected score by misreporting belief or meta-belief.
Karma compounds the trust multiplier: consistently right before the crowd → high karma → more adjacency weight per link → more reward per contribution → more resources to stake on the next correct insight.
the GFP flywheel
The optimal mining hardware and the optimal proving hardware are the same chip. The Goldilocks field processor exercises four primitives (fma, ntt, p2r, lut) for both PoUW mining and real workloads (stark proving, focus computation, neural inference). Mining rewards bootstrap chip development. Chips accelerate proving. Proving serves users. Users pay fees. Fees replace emission. No stranded assets.
the evolutionary loop
contribute accurately → $\Delta\phi^*$ reward → accumulate $CYB → stake on more links → accumulate karma → links carry more adjacency weight → earlier $\Delta\phi^*$ attribution → more $CYB per contribution
The burn layer: burn on high-conviction particles → eternal weight → long-term yield floor → reduces risk premium for foundational contributions.
The result: the unit of wealth is provably epistemic accuracy. The only sustainable path to large $CYB balances, high karma, and consistent ICBS returns is being right about what matters before the crowd recognizes it.
syntropy
The pulse of the cybergraph. Syntropy measures order in bits -- the key metabolic factor of superintelligence.
$$J(\phi^*) = \log|V| + \sum_j \phi^*_j \cdot \log(\phi^*_j)$$
This is the aggregate KL divergence from the uniform distribution -- the information gain of the focus distribution over maximum entropy. High syntropy means the graph is structured, connected, useful. Low syntropy means noise dominates.
Meaningful cyberlinks raise it. Spam and noise lower it. Tru computes syntropy every block in consensus.
per-neuron BTS scoring
Syntropy is aggregate information gain across all neurons in an epoch. A neuron whose cyberlinks sharpen collective certainty contributes positive syntropy. A neuron whose cyberlinks add noise contributes negative syntropy. The BTS score $s_i$ is syntropy measured at the level of one neuron: how many bits of information that neuron added to the collective picture.
syntropy as metabolic factor
The approximation quality metric in focus flow computation uses $D_{KL}(\phi^*_c \| q^*_c)$ -- the same divergence measure -- to quantify how much the compiled transformer deviates from the exact focus distribution. The same mathematical object measures epistemic quality at three scales: individual neuron (BTS score), compiled model (approximation gap), and collective knowledge state (φ* convergence).
see rewards for the detailed reward function specification. see epistemic-markets for the ICBS market mechanism. see truth-scoring for the BTS scoring layer. see cyber/tokenomics for the monetary plumbing. see functions of superintelligence for how the autonomous neuron participates in the same economy.