The part of modeling the ecosystem of candy companies and schools was a very interesting process. In the first design of our game, we let the two teams compete for a pool of “people.” The story behind that was that the candy company wanted to get as many people addicted to sugar as possible, and the school wanted to prevent that. The resource in the game was just money. So players could use the money to either buy an action card or buy a “people” card. Playtesters reported feeling weird about the concept of buying people, so we brainstormed a lot of other resources that the teams could compete for, such as influence, votes, lunch boxes, vending machine stocks, etc. Our teammate, Ecy, proposed that we could let the teams compete for votes from stakeholders who determine which side provides lunches for students. We found this idea model the reality well and doesn’t have the buying people component, which made the game feel weird. We also came up with the idea of separating the resources into money and support, with the school easier to get support and the company easier to get money. It felt great when our playtester who played on the school team, Gilbert, said, “We don’t get any money, and you don’t get any support, just the real world.”
This project taught me a lot about game balancing. In our game, the players need to manage when and how to spend their resources (i.e., money and support) to win. We experimented with several converters in our game. The players could use money/support to purchase cards, exchange them for the other resource, and gain votes from the school board. When we changed the price for cards or the resource exchange rates, players changed their strategies according to new prices/rates. In addition, the prices changed the game pacing significantly. An example was about the exchange rate, i.e., how many tokens of support/money can be exchanged for one token of money/support. With a card, there was a probability that they would get 2 of the desired resource. With an exchange, it was guaranteed that they get 1 of the desired resource. When the price of the card was 2, and the exchange rate was 3, players didn’t buy cards anymore but kept exchanging for a resource, avoiding risks. However, when we increased the exchange rate to 4, players purchased a lot more cards. It was fascinating to see how even small changes in the game balance led to big changes in players’ strategies.
In future game designs, I will calculate the balancing elements and playtest to note what element players are using the most and test with different rates. In the current game, getting weaker or stronger action cards or getting an advantage or disadvantage was mostly based on luck. I would hope to experiment with transitive game objects, with stronger cards more expensive, to introduce more strategies to the game.
