Current Value
50Relevant for: validatorsstakersanalyticssubnet owners
The Big Picture
The scaling law determines how stake translates to influence and rewards. A linear relationship (power=1) means 2x stake = 2x rewards. Sub-linear (power<1) benefits smaller stakers; super-linear (power>1) benefits whales. This constant sets the initial exponent, shaping the network's economic equality vs concentration trade-off.
Why This Matters
This affects your staking strategy. Sub-linear scaling means diminishing returns at high stake, encouraging distribution. Super-linear scaling rewards concentration.
Example
With ScalingLawPower of 0.5 (sqrt), a validator with 4x stake gets 2x influence (sqrt(4)=2). Small stakers get proportionally more influence. With power of 2, that 4x stake gives 16x influence, heavily favoring whales.
Common Questions
- What's the typical value?
- Bittensor typically uses linear or slightly sub-linear scaling to balance fairness with rewarding significant stake commitment.
- Can this change?
- Yes, via developers or hyperparameter updates. Changes significantly impact validator economics and staker incentives.
Use Cases
- Reward calculations
- Incentive formulas
Code Examples
import { ApiPromise, WsProvider } from "@polkadot/api";
import { stringCamelCase } from "@polkadot/util";
const provider = new WsProvider("wss://entrypoint-finney.opentensor.ai:443");
const api = await ApiPromise.create({ provider });
// Query InitialScalingLawPower constant
const value = api.consts[stringCamelCase("SubtensorModule")][stringCamelCase("InitialScalingLawPower")];
console.log("InitialScalingLawPower:", value.toHuman());Type Information
- Type
- u16
- Byte Size
- 2 bytes
- Encoding
- fixed
- Raw Hex
- 0x3200
Runtime Info
- Pallet
- SubtensorModule
- First Version
- v101
- Latest Version
- v101
- Current Runtime
- v393