Serious Games Taxonomy: Game Theory Part 1
Welcome back! Let’s follow up my last post by explaining some of these game theory concepts. How do players interact with each other, and with the game world? What kind of information is available to them? I’ll be splitting up these concepts into several posts, to keep each post to a manageable length.
Cooperative vs. non-cooperative: In a cooperative game, players work together to achieve a goal, whereas non-cooperative games are more based on competition. Note that you can have games that combine the two. For example, when you have teams competing against each other, team members are cooperative within their teams, but non-cooperative against other teams.
A good way to use cooperative play for learning might be in a game that encourages team-work skills, or in an exploratory setting where students are working together to learn and uncover information about a topic, maybe even a “lab” type of game setting.
Zero-sum vs. non-zero-sum:In a zero-sum game, a player benefits from another’s loss. If you add up the total value of all players’ wins and losses, the sum should equal zero, making for a purely competitive game. The old card game, War, is a good example of a zero-sum game. If my card is stronger than my opponent’s, I take my opponent’s card, my gain is his loss. I can only win the game after I have taken all of my opponent’s cards, so the total that they’ve lost is equal to the total that I’ve gained. Contrarily, in a non-zero-sum game, players all suffer or gain together. If I’m playing a strategy game, perhaps a war game or simulation, I might devise a strategy that allows me to disable an enemy’s pieces, but at the same time, am I suffering a loss as well? It could be a loss of resources, loss of time, whatever, but the victory still comes at a cost. If I am allowed to negotiate with my opponent, we might come to some resolution that benefits us both. In realistic training situations, often you will have non-zero-sum settings.
Perfect information vs. imperfect information:This concept addresses how much each player knows about the game world. With perfect information, a player knows everything relevant about the game world, whereas with imperfect information, some important information remains unknown. Note that some participants may have perfect information, while others have imperfect information, and in the case of video games, participants might not all be human. For example, I could program a game in which a human player faces the game AI (artificial intelligence), and the AI knows all of the resources that I have as well as its own. On the other hand, while the AI has perfect information, I might be left with a very limited view of the AI’s resources, thus I have imperfect information.
In some video games, the developers implement a literal “fog of war;” enemy or un-traversed territory might be covered in fog or otherwise obscured, so that initially the player does not know what lies ahead. For example, a player might enter an enemy’s dungeon, and she does not yet have a map of the dungeon layout. As she visits different areas in the dungeon, those areas are automatically mapped, and now the player has a more complete idea of how to navigate the dungeon. Of course, this information obscurity mimics real life. In a war, for example, no combatant has a perfect set of information about the enemy situation. Throw in factors such as weather and other events outside of our immediate control, and our information is even more imperfect.
Those are all the concepts I’ll cover in this post. Take some time to read and digest them, and in the comments, please feel free to share your own examples of these concepts. What kinds of learning situations do you feel might favor some of these concepts over others?
Check back for my next post, where we’ll be going over more game theory concepts.
I’ll be honest, I’m kinda confused. But nice blog.
Thanks! Do you have any specific questions that perhaps I can clarify for you?