You can better design games when you can keep two clear and separate pictures in your head at once: what your game incentivizes the player to do, and what you want the player to do. Often designers only have a grasp on what they want the player to do so their playtesting consists of doing those things and making sure they work. Regardless of the clarity of the designer’s vision, the player does not have access to it and will follow the game’s incentive structure. Even relatively small misalignments between intent and design can cause the end result to look markedly different if not broken. For instance, an RTS with a slight imbalances in unit design will see players neglect most of the unit types available to them in favor of building the one imbalanced unit or its counter.
Sid Meier’s famous quote that “games are a series of interesting decisions” rests at the core of how many designers understand their craft. But we seldom examine what makes decisions interesting. Intuition can take us pretty far, since we all have plenty of experience with tough choices we’ve had to make throughout our lives, in games and out. But the structure of these choices, and how to design such choices in a controlled environment like a game, is seldom discussed in much detail. Three freely available pieces of media that have tried to tackle this problem in games design are Jon Schafer on what makes decisions interesting., Ian Schreiber’s tips on decision-making, and Sid Meier’s GDC Talk on Interesting Decisions (Leigh Alexander’s summary). I find all of these to be vague and unsatisfying. This article is my attempt to build up a theoretical groundwork for interesting decisions in strategy game design.
When you play a game, you manipulate game objects using various actions, all of which are defined in the game’s rules. In this post, we’ll look at game objects and how to represent their state.
In my article introducing near and far randomness, I talked in vague terms of the distance of randomness from the player’s actions, and how randomness forces variation in the course of the game’s events, sometimes at the cost of agency. Now, to further elaborate on the thought process of near and far randomness, I will elaborate on what makes certain cases of near randomness onerous, and through that develop ways to maintain the closeness of randomness to player action, but at the same time tame the damage done to agency.
Players and designers spend an incredible amount of time and effort grappling with randomness’ role in strategy games. In this article I’ll develop a model for understanding the impact of randomness on agency, which will hopefully clarify the use of randomness as a design tool.
Agency is the player’s sensation that they participate meaningfully in the game.
In my article on analogy, I hinted that designers design abstract mechanics and then apply analogy to make the system most accessible to players. That is not a representation of how most people design video games today, it was merely a set-up for talking about analogy without diving too deeply into the properties of the abstract systems that underlie games.
Analogy is how the designer bridges the gap between the player and the abstract mechanics of the game. Analogy makes relatable and relevant what would otherwise be a litany of abstractions and seemingly arbitrary relationships.
My method of understanding and designing strategy games starts off with a triad of critical concepts which I call the AVA paradigm–a somewhat memorable acronym combining the first letters of Agency, Variety, and Analogy. The interaction of these three concepts lies at the core of strategy game design. They each represent certain values which one must balance when designing any strategy game, and their substance is what attracts players and keeps them playing.