At the recent Realities 360 conference, I saw some confusion about the difference between a simulation and a game. And while I made some important distinctions in my book on the topic, I realize that it’s possible that it’s time to revisit them. So here I’m talking about some conceptual discriminations that I think are important.
As I’ve mentioned, simulations are models of the world. They capture certain relationships we believe to be true about the world. (For that matter, they can represent worlds that aren’t real, certainly the case in games.). They don’t (can’t) capture all the world, but a segment we feel it is important to model. We tend to validate these models by testing them to see if they behave like our real world. You can also think about simulations as being in a ‘state’ (set of values in variables), and move to others by rules. Frequently, we include some variability in these models, just as is reflected in the real world. Similarly, these simulations can model considerable complexity.
Such simulations are built out of sets of variables that represent the state of the world, and rules that represent the relationships present. There are several ways things change. Some variables can be changed by rules that act on the basis of time (while countdown timer = on, countdown = countdown -1). Variables can also interact (if countdown=0: if 1 g adamantium and 1 g dilithium, Temperature = Temperature +1000, adamantium = adamantium – 1g, dilithium = dilithium – 1g). Other changes are based upon learner actions (if learner flips the switch, countdown timer = on).
Note that you may already have a simulation. In business, there may already exist a model of particular processes, particularly if they’re proprietary systems.
From a learning point of view, simulations allow motivated and self-effective learners to explore the relationships they need to understand. However, we can’t always assume motivated and self-effective learners. So we need some additional work to turn a simulation into a learning experience.
One effective way to leverage simulations is to choose an initial state (or ‘space of states’, a start point with some variation), and a state (or set) that constitutes ‘win’. We also typically have states that also represent ‘fail’. We choose those states so that the learner can’t get to ‘win’ without understanding the necessary relationships. The learner can try and fail until they discover the necessary relationships. These start and goal states serve as scaffolding for the learning process. I call these simulations with start and stop states ‘scenarios’.
This is somewhat complicated by the existence of ‘branching scenarios’. There are initial and goal states and learner actions, but they are not represented by variable and rules. The relationships in branching scenarios are implicit in the links instead of explicit in the variables and rules. And they’re easier to build! Still, they don’t have the variability that typically is possible in a simulation. There’s an inflection point (qualitative, not quantitative) where the complexity of controlling the branches renders it more sensible to model the world as a simulation rather than track all the branches.
The problem here is that too often people will build a simulation and call it a game. I once reviewed a journal submission about a ‘game’ where the authors admitted that players thought it was boring. Sorry, then it’s not a game! The difference between a simulation and a game is a subjective experience of engagement on the part of the player.
So how do you get from a simulation to a game? It’s about tuning. It’s about adjusting the frequency of events, and their consequences, such that the challenge moves to fall into the zone between boring and frustrating. Now, for learning, you can’t change the fundamental relationships you’re modeling, but you can adjust items like how quickly events occur, and the importance of being correct. And it takes testing and refinement. Will Wright, a game designers’ game designer, once proposed that tuning is 9/10’s of the work! Now that’s for a commercial game, but it gives you and idea.
You can also use gamification, scores to add competition, but, please, only after you first expend the effort to make the game intrinsically interesting. Tap into why they should care about the experience, and bake that it.
Is it worth it to actually expend effort to make the experience engaging? I believe that the answer is yes. Perhaps not to the level of a game people will pay $60 to play, but some effort to manifest the innate meaningfulness is worth it. Games minimize the time to obtain competency because they optimize the challenge. You will have sticks as well as carrots, so you don’t need to put in $M budgets, but do tune until your learners have an engaging and effective experience.
So, does this help? What questions do you still have?