If you’re here, reading a game designer blog discussing analysis paralysis, then I’m pretty sure you probably have some idea of what I mean by the words “Analysis Paralysis” (hereafter referred to as AP). If you don’t, don’t fret! Jump back to Part 1 and read the “What is AP?” section for a primer on the subject.
This is the fifth article in a series of 7, each article in this series will go into depth on one potential cause of AP, examples of situations where it can be problematic, and present some solutions that can improve the situation.
- The Black Box
- The Paradox of Choice
- The Prisoner’s Dilemma
- The Maze to Victory
- Relationship Status: It's Complicated - this article
- Sophie’s Choice
- Bigger vs Better
If you have any suggestions, examples, questions, or want to point out something I’ve missed, please do so in the comments. This is not a scientifically rigorous examination of AP, and I would be happy to include any contributions you have!
Relationship Status: It’s Complicated
The problem of too much complexity.
Asking the same rules questions every game.
Needing to review rules or documents consistently for experienced players.
Talking through options to determine resulting effects of a single choice.
Discussing interpretations of ambiguous rules.
Expressions of surprise due to cascading impact of decisions made.
Frustration with unexpected or negative results that were not foreseen.
Cause of Problem:
Despite the title, there is nothing complicated about this AP problem. This is the problem where the circumstances around the application of a rule, or the consequences of making a choice are complicated enough to defy analysis or prediction.
Problem 1: Circumstantial Application
This first problem comes about when a rule exists that applies to obscure situations, conditions that are difficult to obtain, or rely on subtle differentiations from opportunities that are much more commonly encountered. The reason that this is an AP problem is that obscure, complicated conditions are often difficult to remember and implement correctly from memory. They often require additional time and effort to re-read, analyze, and interpret that may often result in no action due to being inapplicable.
This can often be compounded by subtle shadings in meaning that reduce the interpretation clarity of a rule that can lead to players arguing the applicability of the rule, or by designing a game that includes powerful effects that are balanced by having limited applicability leading to players spending a great deal of analysis effort determining how to bring those circumstances into play.
Most recently I have experienced this particular issue playing a standard version of Pandemic and the role of Researcher. Normally a player may spend an action to give or take a city cards to or from another player if both players are currently located in the city being transferred. The Researcher “may give any 1 of your City cards when you Share Knowledge. It need not match your city. A player who Shares Knowledge with you on their turn can take any 1 of your City cards.” The ambiguity of this statement combined with “Share Knowledge” meaning to give OR take a card and requiring that players also recall the specific conditions of the Share Knowledge action lead to a complicated situation that (in my experience) has required clarification at least once every time the Researcher has been played.
Multiple BGG and Reddit threads requesting clarification also lend support that this is problematic. Although there are updates that clarify this card, the AP inducing problem comes from the specific conditions attached to using this card – that it only applies to city cards flowing out from the Researcher TO other players (whether they are given or taken) but that cards flowing in to the Researcher must still obey normal game rules.
Problem 2: Chaos Theory
The BFF of the Paradox of Choice problem is the Chaos Theory of game decisions. Instead of trying to decide between many multitudes of options that are all different – this is attempting to make a single choice while playing in a hall of mirrors. Regardless of how small the decision or change is, it gets reflected to you a thousand times over from every imaginable direction.
The Chaos Theory is that the choice being considered produces an impact that is so far-reaching or difficult to determine, that it cannot reasonably be evaluated. It’s the idea that the flap of a butterfly’s wings in Brazil can set off a tornado in Texas. When the outcome is so complex to evaluate, it becomes effectively random – but within a deterministic system like a board game the possibility exists that it can be predicted if you think about it long enough.
Have you ever watched the TV show The Price is Right and seen a contestant standing at the top of the Plinko board unable to decide where to drop the disc? Maybe they shift back and forth between several different drop points at the urging of the crowd? Although it is theoretically possible to predict the path of the Plinko disc down the board and determine where it will land if you have enough information, in reality doing that is so complex, there are so many variable at play, that the path it takes is essentially random. You could drop the disk in the same spot a hundred times and never have it follow the same path twice in a row.
This complexity of outcomes, the potential for seemingly random influences, and the illusion of control and predictability combine to form a dangerous situation for AP.
The universal solution to complexity is simplification. The ironic part of solving the problem however is that making simplification work can by complicated.
One of the most challenging parts of clarifying an obscure corner case or subtle shade of meaning, is explaining it. Expressing something clearly, concisely, and accurately so that a first-time reader can understand, remember, and apply it is not easy. If your players are having problems deciphering your rules, the solution may be one of finding another way to express it. A good editor and plenty of first-time playtesters are going to be your most valuable ally to solving this kind of problem.
Subtle distinctions in rules can easily get lost or misinterpreted. If you can’t clarify them in such a way that they are interpreted correctly the first time – perhaps the problem can be better solved by making the rule more broadly applicable. A simpler, more distinct, and more often used rule can be a good quick shortcut to improving how players interact with your game and reduce the amount of time spent reading and figuring it out.
However, making something more generic can also make it less interesting, and may negatively unbalance a game. The best games are a blend of simplicity and depth, simple choices that produce a wide variety of interesting and complex situations – and achieving that balance is a difficult thing to do.
The holy grail of solving complexity is making it simpler. How? We can worry about the details later. Complexity can be seen in two ways, either you are writing too much text to cover all the effects which is a great indicator that something needs to be simplified, or you are running into ‘cascade’ moments. Decisions that trigger a landslide of consequences in a chain. Chaining can often be the result of cycles occurring in a game, where a choice has an effect that can also result in a reversal and reapplication of the original effect. Do a quick search on MTG Infinite Combos for some light reading.
Solving these problems usually can be done by breaking the chain. This can be done by eliminating any reset effect – either by adding a rule to create the exception or removing the reset entirely. Or by adding a timer that limits the scale of the impact. Adding statefulness to an activated ability is one such method to break the chain. When something is activated, it remains in that activated state and cannot be used again until the end of a turn is one way to do that. Another is applying a counter that only allows something to be activated x times before it is removed from play – or even a condition that any attempt to activate it a second time in a turn causes it to be discarded immediately.
Another approach to simplification is to subdivide complicated rules or effects. If an action or decision does A, B, and C – perhaps A, B, and C can be split apart into three separate decisions that can be performed independently and in any order. Breaking apart complicated choices into smaller, simpler, more discrete effects is a quick way to reduce this form of AP.
If you have any comments, suggestions, or examples that you would like to share about this week’s topic, please tell us about them in the comments.
Next week we will be looking at Sophie's Choice.