How They Work: RNG + Skill Check
This chapter shows the full life cycle of a round: seeding and sampling by the RNG, display of a resolvable state, the player’s skill input, and deterministic settlement. It also covers logging, fairness, latency, anti-tamper, and operator configuration.
Overview
PA Skill outcomes are a function of two inputs: a random sample and a player action. The system must keep these inputs separable, auditable, and stable across time.
Principle 1: Independence
Each round’s sample is independent of past rounds except where a title explicitly encodes state. No “hot/cold” biasing.
Principle 2: Determinism
Given the base draw and a specific player action, the settlement must be deterministic and reproducible.
Principle 3: Transparency
Players can see how their action changed the base outcome. Logs capture enough to prove it.
RNG Fundamentals
The RNG produces uniformly distributed samples over a defined state space. Implementation details vary, but the goals are uniformity, unpredictability, and testability.
Common Implementations
- Cryptographic PRNGs for unpredictability and resistance to prediction.
- PCG/Xoroshiro families for speed with proven statistical qualities.
- Hardware TRNG mixed into seeds to improve entropy.
Seeding & Entropy
Seeds initialize PRNG state. Good seeding prevents repetition and prediction.
Recommended Practices
- Combine TRNG bytes, monotonic clock, and device-unique IDs through a KDF.
- Reseed periodically and on critical events (boot, firmware update, maintenance door).
- Never expose raw seeds. Expose hash commitments for audit if desired.
Commit-Reveal (Optional)
System logs Note: Seed predictability or reuse across machines is a critical red flag.
From Sample to Display State
RNG output is transformed into a visible, interactive state. The mapping must be one-to-one or many-to-one but never biased.
| State Type | Sampling Method | Bias Pitfall | Mitigation |
|---|---|---|---|
| Symbol Grids | Index via cumulative distribution | Remainder bias in modulo | Use rejection sampling or 128-bit range |
| Hidden Tiles | Fisher–Yates shuffle | Poor shuffles | Uniform shuffle, test with dieharder |
| Number Draws | Reservoir sampling | Duplicates or order skew | Distinct sampling without replacement |
Skill Check Taxonomy
Recognition
Find the optimal move: swaps, merges, or target selection within a timer.
Precision
Tapping or dragging with tolerance. Hit-boxes and debounce prevent randomness.
Planning
Choose among options with different EV/variance trade-offs.
Round Lifecycle
- Stake: Player selects wager. Limits visible.
- Sample: RNG draws base state. Hash commitment optional.
- Render: State is displayed. Timer and controls appear.
- Resolve: Player action transforms state according to rules.
- Settle: Deterministic payout. Ticket/credit updated.
- Log: Seed hash, input, timestamps, state A→B, payout.
EV, Mastery, and Bounds
Skill raises expected value within a bounded range. Designs must prevent trivial infinite-EV loops.
EV Model
Let E₀ be EV without action, Δ be improvement from optimal action, and p be probability the player performs the optimal action. Then EV = E₀ + p·Δ.
Design Guards
- Single action per round or diminishing returns.
- Timer tuned to human ability, not superhuman macros.
- Payout tables calibrated to maintain house margin at mastery cap.
Latency, Input Windows, Accessibility
Latency Budget
- Touchscreen scan: <10 ms
- Game loop + render: <20 ms
- Input processing + settle: <10 ms
Target <50 ms end-to-end for perceived immediacy.
Accessibility
- Adjustable timers and speed.
- Clear focus outlines and large tap targets.
- Audio-visual cues for success/failure.
Audit Logs & Verification
| Field | Description | Purpose |
|---|---|---|
| Round ID | Monotonic unique identifier | Traceability |
| Seed Hash | H(seed) at sample time | Anti-manipulation proof |
| State A | Compact encoding of base draw | Reconstruction |
| Action | Player input and timestamp | Determinism check |
| State B | Resolved state after action | Outcome proof |
| Payout | Credits or ticket amount | Accounting |
Anti-Tamper & Integrity
- Secure boot and code signing on firmware.
- Encrypted storage of RNG state and logs.
- Intrusion sensors on doors with event logs.
- Remote attestation of hashes after updates.
- Watchdogs for clock drift and frame pacing anomalies.
Operator Configuration
Site Setup
- Stable power, UPS, and grounded outlets.
- Network hardening if remote reporting is enabled.
- Low-glare placement and clear signage.
Settings
- Wager tiers and limits per venue policy.
- Timer presets matched to audience skill.
- Maintenance windows with automatic integrity checks.
FAQ
Does the RNG change based on player wins?
No. Proper systems maintain independent, stationary distributions. Perceived streaks are variance.
How do I verify the skill is determinative?
Replay a round from logs. Apply different actions to State A and confirm settlement changes as specified.
Can latency make outcomes random?
Excess latency can blur skill. Keep the end-to-end input pipeline under ~50 ms and provide larger hit-boxes.
Is commit-reveal mandatory?
No, but it adds assurance. Hash commitments enable third-party audits without exposing seeds.
Resource Pack
/wp-content/uploads/paskill/. Keep filenames consistent with links above.