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How to Implement a Pure Reducer for Multiplayer Game Rules

A pure reducer makes multiplayer rules deterministic and replayable. Learn what belongs in the transition, how servers validate actions, and how to handle ordering, retries, timing, and hidden information.
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Implement game rules as a deterministic transition: give a function the current authoritative state, a typed action, and any rule-relevant context, then have it return the next state and a structured result. Keep networking, storage, rendering, clocks, and unseeded randomness outside that function. In competitive multiplayer, the server validates and applies accepted actions; clients may propose actions, predict outcomes, and render approved state, but they do not decide authoritative scores, damage, or ownership.

Define the reducer boundary

A reducer should express the game’s rules, not the application around them. A useful contract is:

reduce(state, action, explicitContext) -> transitionResult

This is a design pattern, not a universal API. The state represents the complete authoritative rule state; the action is a typed request that still requires validation; and the context carries inputs that affect rules but would otherwise be implicit, such as a time step or deterministic random source. The result can contain the next state and a structured outcome, such as accepted, rejected, or a set of derived events.

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EigenInteractive describes the State as “opaque to the engine”: the engine processes it without taking ownership of game-specific meaning. That separation lets the game define what a legal move means while surrounding infrastructure manages command handling and execution. EigenInteractive’s engine overview

Keep side effects at the edge

Do not let the reducer send network messages, write to a database, draw the interface, or read a process-wide clock or random generator. Those operations can make the same apparent input produce different results or make the rules difficult to test in isolation. Parse requests, authenticate callers, enforce permissions, and persist committed results outside the reducer.

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If a rule depends on time, pass the relevant elapsed time or simulation step explicitly. If it depends on randomness, pass a deterministic, seeded source or record the random outcomes as inputs. This makes the rule’s dependencies visible and gives tests and replays a way to reproduce them. Photon Fusion’s simulation-loop documentation

Return decisions as data

Have the transition report whether an action was accepted and, where useful, describe rule-derived events. The caller can then commit the state and perform external work, such as broadcasting an update or saving a checkpoint. Avoid having the reducer perform those effects itself: the same transition should be usable in a test, a replay, or a server process without changing its behavior.

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Make the server the authority

A pure reducer is easier to reason about, but purity does not make a client trustworthy. For competitive game outcomes, the server should validate requests and apply accepted transitions. Nakama’s authoritative model describes server-side validation and broadcasting of gameplay data; Epic’s UE4.27 documentation likewise describes the server as authoritative over game state, with connected clients holding approximations. Nakama: Authoritative Multiplayer · Epic Games: Networking Overview for UE4.27

Clients can send an intent such as “attempt to move” or “play this card.” They should not be allowed to submit an already-decided outcome such as “I dealt 20 damage” and have it accepted as fact. The server checks the request against the current state and rules, applies the transition, and distributes the resulting approved state. A shared implementation can run on client and server for prediction, but the server’s result remains authoritative.

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Handle ordering, versions, and retries

Replay and reliable multiplayer depend on knowing exactly which inputs were accepted and in what order. Preserve the initial state or seed, the ordered accepted actions, and every rule-affecting parameter. If a transition depends on an unrecorded clock read, global random state, or external value, an input log alone may not reproduce it.

For concurrent commands, define how a command relates to the state version it was based on. A practical server design serializes commits or uses another explicit concurrency policy so two commands derived from the same old version cannot both overwrite each other unnoticed. Reject stale commands or handle them under a documented policy; do not silently reorder them. One documented session design uses serialized commands, monotonically versioned transitions, and idempotency keys so a matching retry can return its earlier result. EigenInteractive: Sessions

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  • Attach a state version to commands if clients may act on a previously received snapshot.
  • Scope each idempotency key to its caller. A retry with the same key and same payload can receive the prior result; reuse with a different payload should be rejected.
  • Record accepted inputs in the order the authority applied them so a replay can follow the same transition sequence.
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Keep hidden information out of client state

Maintain the full rule state on the authority, then derive a separate observation for each player or seat. The reducer answers what is true in the game; observation logic answers what a particular player is allowed to know. Do not send secret cards, concealed objectives, or other hidden values to a client and rely on the interface to hide them. The engine documentation treats observations as projections and warns against sending hidden data to a player who should not see it. EigenInteractive: Observations

Choose a timing model that fits the game

Turn-based games

For a turn-based game, each accepted action can produce one serialized transition. This makes command ordering, version checks, and replay logs relatively direct: establish the current version, validate an action against it, apply the reducer, then commit the next version.

Real-time simulations

For continuous gameplay, keep the same separation but model the simulation as deterministic steps over explicit inputs and elapsed time. A client may simulate locally for responsiveness, then reconcile with server acknowledgements and authoritative state. Photon Fusion describes its network simulation loop for this kind of simulation; do not assume that a turn-based engine’s concurrency or timing model transfers to a sub-second physics game. Photon Fusion’s simulation-loop documentation · EigenInteractive’s engine overview

Trade-offs to decide deliberately

Decision Useful when Cost or limitation
Server-authoritative rules Competitive outcomes need a trusted decision point and centralized validation. Requires server infrastructure; client results cannot be treated as authoritative.
Client prediction with server reconciliation Immediate local feedback matters while final state must remain server-approved. Predicted outcomes may need correction when the server response arrives.
Serialized action transitions The game advances through discrete actions, as in many turn-based games. Not a substitute for a fixed-tick model in continuous real-time simulation.
Deterministic replay from ordered inputs You need reproducible debugging, replay, or recovery from recorded actions. Requires recording the seed or initial state and every rule-affecting input; hidden dependencies break reproduction.

These are architectural choices rather than universal performance guarantees. Server authority centralizes enforcement but has an operational cost. Running the same pure rules on both client and server can support prediction, but it does not transfer authority to the client.

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Test the boundary, not just individual rules

  • Given the same initial state, action sequence, and explicit context, verify that the reducer produces the same result.
  • Test invalid and stale actions as well as accepted ones, including attempts that would alter another player’s hidden or unauthorized state.
  • Replay a recorded sequence from its initial state or seed and compare the reconstructed state with the committed result.
  • Test retry behavior at the command-handling layer: an identical retry should not apply a transition twice, while a reused request identity with a different payload should fail.
  • Keep observation tests separate: confirm each player receives only the information allowed for that seat.

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