Queried by: developerssubnet ownersanalytics
The Big Picture
AdjustmentAlpha controls how aggressively costs respond to demand. Higher alpha = faster adjustments, more volatile costs. Lower alpha = slower, smoother adjustments. It's the smoothing factor in the exponential adjustment formula.
Why This Matters
How volatile are registration costs? AdjustmentAlpha determines this. High alpha means costs spike quickly with demand; low alpha means gradual changes.
Example Scenario
Query AdjustmentAlpha(netuid=1) returns the adjustment sensitivity parameter. Combined with demand data, you can model expected cost changes.
Common Questions
- What's a typical value?
- Varies by subnet design. Higher values for subnets wanting responsive pricing, lower for subnets wanting stable costs.
Use Cases
- Understand how quickly costs adjust
- Model registration cost dynamics
- Research difficulty adjustment algorithms
- Design subnet economic parameters
- Debug unexpected cost changes
Purpose & Usage
Purpose
Control adjustment sensitivity - how aggressively costs respond to demand changes.
Common Query Patterns
- Query by netuid
- Research adjustment dynamics
- Model cost predictions
Query Keys
| # | Name | Type | Description |
|---|---|---|---|
| 1 | key1 | u16 | key1 (u16) |
Stored Value
Relationships
Modified By
Related Events
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 AdjustmentAlpha storage
const key1 = 0;
const result = await api.query
[stringCamelCase("SubtensorModule")]
[stringCamelCase("AdjustmentAlpha")](
key1
);
console.log("AdjustmentAlpha:", result.toHuman());Runtime Info
View Source- Pallet
- SubtensorModule
- Storage Kind
- Map
- First Version
- v126
- Current Version
- v393