If you’re making a game and your playtesters keep saying “the RNG feels unfair,” it may feel like they are complaining about it being too hard, but actually, your difficulty balance may be fine. The issue is often information design: what the player can know about, when they can discover this information, and how important that information is to the eventual outcome. Great hidden-information games do not feel random. They feel tense but still learnable, because even a loss points back to a decision the player understands.
Seeing Imperfect Information in Real Hands
Imperfect information means players make decisions without seeing the full state. Hidden hands, face-down cards, unrevealed draws, secret objectives, and simultaneous picks all qualify. Luck still matters, but timing is more important: think about when the player is required to commit to a decision, what they learn when they do, and how the overall picture shifts as information is unveiled.
Rather than keeping this abstract, let’s look at a concrete example using a platform with plenty of card games on offer. To start, open Lucky Rebel and choose a poker-based table game. As you play, try to understand each hand as five different moments: (1) initial state, what is visible before anyone acts; (2) commitment, what you must choose while still guessing; (3) reveal, what information appears next and what stays hidden; (4) resolution, what is now locked in; (5) reset, what carries forward into the next hand.
For each moment, jot down an inference a skilled player could make, a trap they might anticipate, and one question that remains open. You are not trying to study poker here; you are tracking how a system hands the player just enough information to form an impression, then asks them to act, then pays that action off with a reveal that either confirms or corrects the model. To deepen the comparison, repeat the same audit with a different poker-style game on Lucky Rebel, paying attention to where the decision window tightens or expands. Those moments are what you need to recreate if you are building a game, whether it’s a digital card battler, tactics game, or social deduction piece.
Once you have grasped that sequence, attach the correct term to what you just observed. You can learn more about this in Games with Imperfect Information. Don’t worry too much about the algorithms here; just focus on the core idea: when two states are indistinguishable to the player, you are effectively designing one decision moment, not two different moments. Recognizing this will make you a better designer.
The Decision Window Is the Real Unit of Design
Players do not experience “mechanics.” They experience moments where they must commit with partial knowledge. A decision window is that moment, and it is where hidden information either creates tension or creates fog.
Freeze the game at any choice and ask:
- What does the player know right now?
- What are they committing to when they act?
- What new information arrives immediately after?
If those answers are clear, players form hypotheses, test them, and improve. If they are blurry, players invent explanations, and may start to blame the RNG because they don’t know what’s happening.
Limit the Possibility Space Without Killing Mystery
Hidden information is only interesting when the player’s guess space has shape. A face-down card becomes compelling when the player can narrow it to a few plausible options, then make a choice that expresses that read. When every secret could be anything, you have not created a compelling game; you have created a coin flip, and that doesn’t feel appealing to a lot of players.
Modern design is about adding possibility within limits. Deckbuilding rules constrain what can exist. Revealed history tells you what is already gone. Public resources and costs signal what kinds of plays are even possible.
Treat every hidden element as a question the player is allowed to answer over time. If your system gives no path to narrowing, players stop reading and start hoping. That’s not what you want as a designer.
Make Randomness Legible Through Feedback
Players misread randomness when outcomes arrive without an interpretable context. Streaks feel meaningful. Rare events feel personal. A few bad draws can overwrite a hundred neutral ones. You cannot delete those reactions, but you can design feedback that helps players learn what distributions look like within a session.
Expose memory. In physical games, the discard pile often does this. In digital games, any persistent trace that shows what has left the deck, what has been revealed, or what is still unknown can reduce noise.
Most importantly, pay off commitment with information. If a player makes a read and commits, the reveal should teach them something concrete, even if they lose. That learning loop is what turns imperfect information from frustration into depth.
Imperfect information needs to be carefully balanced and relevant to the game: the player will not know everything, but what they do know will matter. Pay attention to creating clear decision windows, bounded possibilities, and meaningful reveals, and your card game will feel alive, instead of arbitrary.
