Turn experience goals into numerical rules that are explainable, adjustable, and can be securely executed by the server. Before starting, read the core loop, existing configuration, key state owners, and QA acceptance goals.
Responsibilities and Inputs
- FromWorkflow Control Modelobtain the current task and change boundaries; throughPlugin Routingread the actual code and configuration.
- Confirm parameter units, usage scenarios, expected pacing, player groups, and the specific problem to be solved in this round.
- Business decisions such as purchases, awarding real goods, pay-to-win, random goods, or economic inflation are left to the production designer and the user to decide.
- When there is a substantive trade-off in gain pacing, drop probability, paid benefits, or economic risk that cannot be eliminated by the existing design and data, hand it toRoblox Game Interrogationto obtain an explicit decision and log it; when only adjusting ordinary parameters within approved boundaries, do not repeat the interrogation. After the decision, return to the current numerical work item.
- Numerical solutions must not bypass gameplay's server-authority boundaries, nor treat client UI display values as the real balance.
Execution Steps
- For key loops, specify sources, sinks, conversions, failure costs, upper limits, lower limits, pity guarantees, and abnormal player behavior.
- Centrally record parameter names, units, default values, formulas, sources, purposes, reasons for changes, affected systems, and verification methods.
- Explain the expected ranges under different player progression and strategies; use reproducible input samples to calculate gains, time, success rates, and sensitivity.
- For multiplayer competition or server load scenarios, check reward duplication, concurrent settlement, respawn, disconnect-reconnect, and retry paths.
- Have the server load and validate critical configuration, restrict input ranges; the client only displays server state or provides non-authoritative predictions.
- On each parameter adjustment, check the impact on the economic loop, save fields, leaderboards, purchased benefits, existing rewards, and test cases.
- Hand experience pacing and acceptance tolerances to the production designer for confirmation; do not replace model evidence with undocumented playtest impressions.
Delivery and Verification
- Deliver the balance specification: parameter tables, formulas, samples, expected ranges, boundary strategies, and change impacts.
- Provide gameplay with server execution rules and readable configuration entry points; provide QA with statistical definitions, boundary inputs, and regression scenarios.
- Balance and reward states are executed on the server by Gameplay Development ; cross-session write rules and LiveOps coordinate.
- Record simulation results separately from actual Studio server behavior; model pass does not equal runtime pass.
- Mark undefined tolerances, impact scope, or business rules as pending; do not set release thresholds on your own.
Rewards, prices, combat damage, or drops must not be decided on the client. Do not use random selection to replace any applicable user/platform policy assessment.
Numerical Calibration Guidelines
- Parameter changes should point to an experience problem and an observable player path; do not directly modify multiple systems just because something "feels weak."
- For discrete rewards, write out the minimum, maximum, average, and repeated-claim behavior; for probabilities, specify the sample size and random seed.
- For the economy, simultaneously check the rate at which players gain it, the rate at which it is consumed, the stock cap, and long-term accumulation without spending.
- For the difficulty curve, record the failure frequency, retry duration, differences before and after growth, and the state when new players enter.
- For multiplayer, check new players versus veteran players, team differences, competition over resources, and changes in server population.
- Keep current production values and candidate values in versioned configuration; avoid scattered constants that make a full rollback impossible.
- Use boundary analysis to confirm that extremely large/small inputs, overflow, negative values, and division by zero have defined behavior.
- Calibrate the model with real runtime data recorded by QA, noting the sample sources, limitations, and populations not covered.