Every game/economy diagram worth its salt contains a +Power ↔ +Action slide. It says something, but usually it’s not enough to understand what the hell is going on. Like traditional economic modeling, diagramming clarifies uncertainty by revealing a game’s internal structure or “wiring.” It forces a simplification of assumptions, and, like the effect of understanding the world from a Mercator projection map, the diagram itself shapes one’s understanding of reality.
The most valuable economy diagram anchors in player action, where currencies play a secondary role. All diagrams start with either spending time or money: the two raw ingredients that all output derives from in games. Each further step is an action-driven refinement of the last stage. Along the way, usually deriving from the final production step, is a “final” good. This is ultimately what entertainment produces, or the “bedrock”, akin to appealing to a Bartle-type.
Goods with Green + or Red – are currencies that maintain debits and credits, while player XP is orange (a store of value), since players can’t “spend” XP. A simple tally of red and green boxes along a particular currency type usually makes it obvious where there might be currency imbalances, or worse, contagion effects from one activity centre to the reward centre of another. The core loop almost “pops” out of the process, as final inputs may materialize into raw input enhancers.
AI, specifically ChatGPT’s Deep Research product, has been a boon for creating more rules-based output. Economy design has been slower in adopting AI, as the integration of Excel and Sheets is laughable, and the vibe game economy is still more meme than reality. I’ve gotten lackluster design feedback from ChatGPT, which makes sense given how little design philosophy is codified relative to other fields.
However, given the model described above, Deep Research can repeat distilling information into a standardized format. We can create an AI Economy visualization pipeline with a Figma plug-in, which is still very much in Alpha (and linked here). It works by having Deep Research create a JSON file, which converts a custom Figma plug-in into the economy model visualization.
Game economies are opaque, but I’ve become increasingly convinced that AI will play a larger role in mapping them out. For example, an automated agent could easily map the top 100 game economies along this ruleset.
