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SDT

Rule Conductor

Surface Labels

Rule baseline Fairness / judgment Edge-case radar Small blast radius Competitive meta stability
SDT character

Rule Conductor

A game should be a fair test of skill inside the magic circle created by clear rules, where even a loss has a clear, understandable reason.

Core Traits

SDT believes that fun only lasts on top of explainable rules. No matter how flashy an idea is, if judgments wobble, interaction priorities are unclear, or exceptions start popping everywhere, players end up reacting to luck and bugs instead of skill.
So SDT anchors the system not in a designer's intuition but in a team-agreed spec, and sets a baseline that carries through implementation, QA, and live ops. In short, SDT is most energized by making play fair and predictable by carefully organizing rules / numbers / exceptions.

For SDT, control and constraints are not about suppressing fun; they help draw a clearer magic circle where play begins to carry meaning. When rules define what is allowed, players can invest in choices, and that investment returns as readable outcomes.

In practice, SDT asks first: "What does this decision charge to fairness, predictability, and risk? " They aren't against better feel or stronger presentation, but they watch for the moment a change breaks rule consistency or invites exploitation and balance collapse.
Teams with SDT tend to patch often without losing trust in the core loop, and in competitive contexts they have a higher chance of shipping "explainable updates".

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Core Values

North Star

A game should be a fair test of skill inside the magic circle created by clear rules, where even a loss has a clear, understandable reason.

Situational Behavior

  • When requirements are vague, you first align on what players must be able to predict and which exceptions are allowed, then you decide how far to push feel/presentation.
  • In technical trade-offs, you prioritize determinism, hit/judgment consistency, and sync stability because small inconsistencies become fairness problems.
  • Under schedule pressure, you prefer safe scope cuts that keep the baseline stable rather than shipping a feature that expands edge cases.
  • When feedback conflicts (design intent vs player complaints vs engineering constraints), you re-center the discussion on reproducible cases and agreed rules.

Operational Style

  • You treat balance as testable conditions, not vibes: describe when/where a rule should apply and what response must occur.
  • You iterate quickly but keep patches small; you prefer many observable, low-blast-radius changes over a single big swing.
  • In live ops, you assume exploits/meta collapse are debt that will come due unless guardrails and monitoring exist early.
  • You set clear release gates for fairness-critical changes: regression checks and evidence from logs/replays before shipping.
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Strengths

  • Create 'alignable baselines' so the team doesn't fight on gut feelings. You document judgments, exceptions, priorities, and readability definitions so implementation and QA run on the same rails.
  • Lower long-term live-ops cost. By finding edge cases and exploit paths early, you reduce incidents and emergency patches.
  • Shines in: Polishing, competitive launches, live service stabilization, and safety before/after large updates (reworks/seasons).

Trade-offs

  • Can look slow early on. If validation/cleanup gets heavy while you're still searching for 'what is fun', the pace of experimental learning can drop.
  • Over-prioritizing consistency can push the emotional arc and feel improvements down the list. You may end up choosing what is correct but less fun.

Team Chemistry

  • When working with Rule Conductor (SDT), share the target emotion/presentation first, then define judgment rules and exception bounds that won't break while keeping that goal. That's the fastest path.
  • With Feel Artist (CET), managing reproducible cases before/after changes (hit checks, i-frames, cancels, input buffer) together reduces conflict and makes updates safer.
  • With Data Pilot (SET), align that metrics do not replace fairness/readability. Data Pilot (SET) finds phenomena via metrics, and Rule Conductor (SDT) locks fixes with a baseline; that division works well.

Representative Games

TEKKEN 8

TEKKEN 8

VALORANT

VALORANT

Baba Is You

Baba Is You

Into the Breach

Into the Breach

Tom Clancy's Rainbow Six Siege

Tom Clancy's Rainbow Six Siege

League of Legends

League of Legends

References

Work Link
Hunicke, R., LeBlanc, M., & Zubek, R. (2004). MDA: A Formal Approach to Game Design and Game Research. https://aaai.org/papers/ws04-04-001-mda-a-formal-approach-to-game-design-and-game-research/
Juul, J. (2002). The Open and the Closed: Games of Emergence and Games of Progression. https://www.jesperjuul.net/text/openandtheclosed.html
Juul (DiGRA DOI record) https://dl.digra.org/index.php/dl/article/view/214
Pacini, R., & Epstein, S. (1999). The relation of rational and experiential information processing styles to personality, basic beliefs, and the ratio-bias phenomenon. https://doi.org/10.1037/0022-3514.76.6.972
Denisova, A., et al. (2024). Towards Democratisation of Games User Research. https://doi.org/10.1145/3677108
Isbister, K., & Hodent, C. (Eds.). (2018). Game Usability: Advice from the Experts for Advancing UX Strategy and Practice in Videogames. https://global.oup.com/academic/product/game-usability-9780198794844