ERI is something I’ve been working on for a long time. It’s a personal set of tools I use for all sorts of things and has been incredibly valuable to me as a designer. I was originally going to outline it all in one massive post, but I think given the scope of the framework it’s smarter to do things bit by bit. Also diagrams, because they are the shit.

So what is ERI? It stands for Experimentation-Repetition-Imitation and is a way of deconstructing interactions between players and things they are playing with. It’s an attempt to stand alongside tools like the MDA framework, Koster’s theory of fun and Jesse Schell’s lense system. These are all tools for visualising the complex relationships that result in the experience of play and the entities we call games. ERI doesn’t specifically relate to games, but it was inspired by my investigation of them and is incredibly useful for analysing them

In my studies of game design frameworks, I’ve always found they focus heavily on the object of design, be it software, ruleset or so forth. Game design is primarily shaped by rules gleaned from experience- maintain a high level of feedback, clearly communicate objectives, actively engage the player’s attention etc. However, while there is ususally an explanation for why these rules should be followed, it’s often fairly abrupt. Depth is added by the design community endlessly picking apart individual games and pondering their unique properties. I feel this is something of a legacy of the old days of videogame design, when the task was more to do with software and hardware management than understanding the interplay between a complex interactive object and the people interacting with it. You can bet your ass that the guys who made pong didn’t spend weeks pondering how to optimise player experience. They were too busy figuring out how to make it look like pixels were bouncing off other pixels. Modern game theory is excellent at high-level categorization and analysis, but not very good at reconciling or constructing games at a low level. My history as a gamer of all trades and a researcher into pre-ludology game and play theory has inclined me towards other angles of approach.

ERI is an attempt to provide a new perspective. It serves primarily as a troubleshooting tool, one capable of breaking down interactions into their components. Every action we take is caused by something, and those causes are what the ERI framework spins around. If you can understand what causes someone to seek out powerful weapons in a game, or throw up their hands in frustration, you can control or engineer those situations. These may seem simple tasks, but how about figuring out why a particular part of your game is particularly prone to people griefing, or why you’re getting wildly conflicting feedback on some scene or mechanic?

Like most analysis tools, I’ve found that, having worked for ages developing and figuring out ERI, I can use the same cognitive toolset assertively. Rather than waiting for trouble to show up, I can run prototypes or concepts past a checklist using the ERI framework before I even begin to work. Consequently, I’m able to identify problems before they occur and even, joy of joys, identify and improve on things that are bad, but so nuanced that players wouldn’t be able to pick them out as a specific problem so they would slip right on by playtesting and iteration.

Despite all this, the framework is young. It’s at the stage where it needs to get out into the real world and get beaten into a more robust, practical form. So don’t be afraid to dig in, modify it, improve it and adapt it. I’d like this to become a useful tool for anyone interested in games and play.

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I’m not going to go into detail here, since I’ll be posting big crunchy posts every week for a few months on the framework and examples of its use, but I’ll try and give a super quick overview.

The framework breaks down three powerful behavioural motivators, based on established biological and psychological tendencies: we explore and experiment, we repeat patterns of thought and action constantly and we subconsciously copy people who we think are cool. Each of these tendencies has a set of properties that make them somewhat harder to predict than you might otherwise think. For example, when you repeat something a lot, you get better at it. However, you only get better at it in certain specific ways. In some activities, getting really, really good at doing a component can actually make you worse at doing the overall activity.

By focusing on the causes and the subtleties of these behaviours, ERI allows you to take extremely complex interactions and pick them apart quickly and efficiently, isolating the most probable source of an issue. It also trains your mind to avoid making some false assumptions that seem pretty natural until you look really closely at how the gears spin.

Over the coming few weeks I’m going to look in-depth at each of the three behavioural mechanics in turn. I’ll also try and do some design analysis or maybe even an analytical lets play showing how I use the framework to examine designs. If you bookmark this post, I’ll be updating it with links to the new articles as I go.