Uncertainty: The Special Sauce for Learning Games and Gamification

The most boring experience is a certain one. Most of us watch movies and read books because we want to know what happens at the end. Some stories tell us the end up front but there’s still uncertainty: we want to know how the characters get from here to there.

And games are no different. Uncertainty in games creates tension. We don’t know whether we’ll win, what will happen next, or whether we can replicate our great form last time out. However, we don’t want too much uncertainty: at a certain point we lose the ability to predict what might happen and then the uncertainty becomes more like pure randomness.

Imagine being able to consciously manipulate uncertainty so that the players in your learning game or playful learning experience are just the right amount of uncertain: just the right kind of uncertain. You would have them in the palm of your hand, pushing forward into the experience to resolve the uncertainty.

In his book, Uncertainty in Games, Greg Costikyan outlines different types of uncertainty. If you’re creating a learning game or playful experience, you can use his taxonomy of types of uncertainty to consciously mould the uncertainty in your experience and motivate your players. I’ve summarised the types here, with suggestions on how to use each.

Review this list of types of uncertainty and ask: is this type present in my game or experience? Is it working well? Could I include it, or should I remove it? Can I tweak things to make it work better and create excitement and motivation?

Performative uncertainty comes from players not knowing whether or not they’ll be able to achieve the task, even if they know in theory how they should do it. Dexterity is the classic course of this kind of uncertainty and athletic sports are full of it. Handled right, this can be a very visceral kind of fun and can result in a lot of laughs.

But there’s not much uncertainty if you know you’ll almost always miss the dart board. A learning game that relies on players being dextrous throughout may become frustrating to somebody who lacks the right kind of dexterity. And some games that require this may exclude some people with a disability.

Consider whether this can be only an element of the game: maybe in a team game, the dexterity side of things can be one person’s role, with other roles available that need more mental skills like planning or problem-solving.

Solver’s uncertainty happens mainly in puzzles or puzzle games. The task is clear, a solution exists but can I find it? It doesn’t have to be a puzzle, however: finding the most optimal ‘move’ in a turn-based game creates this kind of uncertainty.

Creating puzzles is an art in itself, so take care in building puzzles into your game or playful experience. However allowing space for finding the optimal ‘move’ can be a case of playing with the goal, obstacles and resources presented to players at each point. Consider trade-offs like:

• High-risk and high return versus safe but low return
• Slow versus risky
• Depth versus breadth
• Long-term versus short-term
• Contributing to goal A versus contributing to goals B & C

What will other players do? What move will they make? This is the core of player unpredictability.

This can be thrilling, especially if the stakes have been raised and players care about doing well. You can heighten the tension by giving lots of viable options and making sure the options have consequences for the state of the game. Worker placement games are an ideal example: if you have a choice of spots but the other player may take any if you don’t take this turn, you have to spend time thinking about what they’re likely to do.

Pure randomness has very limited appeal: how long would you play a game where the winner was determined by the flip of a coin? However, controlled randomness as part of a larger setting can lead to a lot of tension. In another article, I explored different kinds of randomness for use in games in great depth.

Randomness can lead to great moments if the result can be built into the narrative no matter what it is. A ‘failure’ that is then woven into the story of the game for laughs or as a tragedy can be extremely fulfilling.

As Poker demonstrates, trying to work out the odds to inform your choices can be very engaging for players. And if you have zero randomness in tactical games, the better player will almost always win and there’s a temptation, as in chess, to sit and work out possibilities many moves ahead, which can slow things down.

This brings us nicely to analytic complexity: uncertainty is introduced by the idea that it’s not realistic to work out the perfect move, so you can only approximate and hope for the best. This is the key form of uncertainty in Chess.

In many learning games this level of complexity may not be what you want, so take care with this kind of uncertainty. However, for a complex business simulation, you might want to introduce this.

If you don’t know which cards your opponent holds, then your best move is uncertain. Hidden information can be brought into games in many ways, although cards face-down or held by an opponent are one of the most common. In the famous game Cluedo (Clue in the US), cards contain crucial hidden information about whodunit, where and how.

Although it has flaws overall as a game, Cluedo is a great case study in some good ways to handle hidden information, because:

• The hidden information is drawn from a limited, known pool (i.e. the hidden room, weapon and person can only be one of the available options, listed on the board)
• Players can peek at some hidden information, reducing the uncertainty and informing their choices and actions

Think about what information could be set (at least for a time) but hidden in your game. What range could it come from? How would it affect player decisions? How could they find out something about the hidden info? For instance, maybe players could ‘pay’ to see a hidden card: how much would that be worth?

We want to know what happens next in a story. If we’re invested in the story, we anticipate finding out. This is the genesis of the whole idea of a ‘cliffhanger’.

But it works best when the story is strong and when the players care about the characters and the outcomes. So invest time and storytelling techniques in building these things if you want to use this kind of tension. Consider getting the players to co-create the story so that they’re really invested in it.

And think about when to let the tension draw out, like the cliffhanger and break between episodes of a drama or box set.

Fruit machines work on the idea that we don’t know when we’re going to get a reward. This is a well-studied motivator, all the way back to seminal experiments with rats pulling levers and dogs salivating at bells.

This is one of the simplest to build into your games. Make it so that x effort doesn’t always lead to x result, or at least not straight away. Maybe success buys the players a ticket but their number still needs to come up. Combining reward with randomness can achieve this and the resulting motivation can surprisingly be much more powerful than rewards always being guaranteed.

In his book, Greg Costikyan also talks about:

• Development anticipation (uncertainty about how a game will develop or grow over time, for example with expansions and new content)
• Uncertainty of perception (uncertainty about exactly what the player is seeing in each new moment, maybe because the game is so high-speed)
• Meta uncertainty (uncertainty about what the game actually is and where its boundaries lie)

These are interesting but quite difficult to use in learning games.

The most important thing is to get a sense of where the uncertainty in your game or gameful experience comes from and consciously adapt it until it’s working for you in terms of getting and keeping players engaged and focused on the right things.