# An Equation for Casual Games? The Appointment Mechanic Formula

Wouldn’t it be great if there was a formula for creating a successful casual game?

As game designers, we have watched the evolution of casual games. Many of  the early successes leaned heavily on appointment mechanics. The topic of behavioral conditioning comes to mind.

A quick review: what is an appointment mechanic? A few examples:

• A player get a currency bonus the first time they log on to the game within a given period, say once a day.
• A customer buys a drink at a bar between 5:00-7:00 pm on weekdays, and gets that drink half-off (appointment mechanics don’t have to be only virtual).

These mechanics are designed to both hook a user and keep them coming back.

Operant conditioning is a term coined by the granddaddy of behavioral psychology B.F. Skinner. As any good Psych 101 student knows, Skinner demonstrated that rats (and by extension, humans) learn as a result of reliable consequences. Push green lever; get a treat. Push red lever; get a shock. Rats learn to push the green lever; humans learn to log-in once a day to get their daily spin bonus. Or so goes the theory anyway.

Following Skinner, psychologists have actually been able to develop a reliable formula for predicting how and when non-human subjects will learn to respond in the desired way. The Matching Law reflects the findings that in pigeons, at least, “relative rates of responding matched relative rates of reinforcement” (Edwards, et al. 313). In other words, give the rat X number of treats, and Y number of shocks and the rat will have learned to only push the green lever in Z number of days.  Essentially what the Matching Law says is that there is a size and a frequency of reinforcement and that you can balance those two things to achieve the optimal conditioning.

Cool. Let’s do it. Let’s hook all of our players with this simple formula. Fabulous.

Here’s the problem. In a game, we do not know what the size of the reward is. With rats (or pigeons) in a box, you can be very precise about the nature and size of the reward. One food pellet? Two? Go crazy, make it three? You can easily determine the units of reward. In a game, the rewards are far less concrete. Of course, designers can give players fixed amounts of currency. But is that why we’re playing games? What is the reward of being able to show your friend an achievement you’ve gotten in a game? What is the reward for doing something that you could not previously do in a game? These kinds of rewards exist in the mind of the player; they are bigger or smaller depending on the player’s mood and depending on the player.

“…it is important to recognize that the generalized matching equation, like all other versions of the matching law, allows us to make quantitative predictions about how an organism will allocate its time or behavior in a given circumstance only if we (a) have sufficient historical data to solve the equation and (b) know that current conditions are equivalent to those under which the equation was solved” (Edwards, et al. 313).

Can we imagine a world in which it would be possible to properly calibrate these rewards? Yes, of course we can. If one had a large enough amount of cross-game player data, one could observe which types of rewards are more effective for what particular player. One could begin developing profiles of players and perhaps one could find patterns that would allow you to predict from a little bit of data what reward profile is likely to be optimal for a given player. Once you’ve determined that for one game or one set of games, you could, if the player becomes identified in other games, move it from game to game. If you had other sorts of sensors, you could monitor chat.

In the future, maybe we’ll be able to monitor body temperature and heart rate. We’ll measure and record every instance of where and when a player plays, and how they were feeling when they played. This gives us history and environment, making it more likely we could determine what rewards are likely to be optimal for the player at any given time…. this is probably not happening next year. Thank god.

Image courtesy of funnycutepics.com

For now, there is no formula for game success any more then there is a formula for writing a best selling novel or making a hit movie. Just as always, there is no replacement for experience, good planning, trial and error, genius, and a sprinkling of luck.

Stop looking for a formula and get on with learning your craft.

Reference

Edwards, Timothy L., T. Mary Foster , Alan Poling and Marc Weeden Source:The Psychological Record. 61.2 (Spring 2011):p313.

## One thought on “An Equation for Casual Games? The Appointment Mechanic Formula”

1. Good post. Just don’t take my metrics this morning, still in bathrobe.