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Economy Diagramming Using Figma & AI

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.

7 Things Squad Busters Needs to Experiment with NOW

The game needs to go crazier. If game development aligns with an explore-exploit model, Squad Busters must continue exploring. That means covering as much exploration space as possible. Eric Seufert makes similar claims in UA creative testing, and those lessons apply similarly to games. The changes from 2.0 were big, but they need to be bigger.

  1. Deck Loadout Everywhere

Due to short mobile session times, there is insufficient time to build depth through in-round progression. Marvel Snap is a goddamn gift from the CCG gods in this light, and remains the top tier benchmark. Players must be able to theory craft, which rarely happens in real-time. In auto chess, players have specific deployment phases, which is also true for CCGs, MOBAs, and Vampire Survivors. Players also enter with a known strategy.

Squad League gives players this choice, but it’s limited to one game mode and buried in the UX. The experience should focus on building lineups and encouraging a specific playstyle.

  1. Alliances

Building on autochess mechanics, my blop of units creates unclear strategy readability for me and my opponent. Alliance abilities are granted only when a certain number of units share an alliance trait, improving  strategy readability.

  1. 70% of squad abilities are conditional passives

The abilities are boring. Heroes and Squaddies need more if/then-type abilities. Players want a match runbook, and maximizing triggers based on specific real-time gameplay patterns adds an enormous layer of depth.

  1. Squads survive encounters stronger, not weaker.

In Squad Busters, players face off against multiple enemy teams rather than a single enemy. Even after a successful encounter, other players attack as they lick their wounds. This typical play pattern in Apex Legends mirrors packs of wolves (three-man squads) roaming for weakened prey.

This difference is that Apex’s victory over prey means power increases through a leveling system or the dead enemy’s loot. Squad Busters features only a single chest key for defeated enemies and a mad dash for gems that attracts MORE enemy attention.

  1. Drop Gems or Chests

In Squad Busters, some modes determine ranking by the number of gems players collect from PvE enemies and killed player units. The game also utilizes coins, which can be used to purchase chests that unlock powerful units. It’s too much to manage and too hard to cut one or the other from the herd. Maybe gems automatically level up the Squad (no more chests), or Last Man Standing becomes the default mode.

  1. Single health bar and improved animation loops

Readability remains a pressing issue, with numerous health bars to monitor. A single bar that’s the sum of all my units ‘ health and my own gives me only two points of focus during combat: my health bar and the enemies. Blinking red units, the death sound effect, or other indicators can still provide more subtle feedback on particular unit status.

Squad Busters 2.0 & SpaceX’s Raptor 3 Engine

Squad Busters’ original thesis has been falsified. What once seemed like KPI paradise: hypercasual-esque core, aggregating Supercell’s beloved IPs à la Super Smash Bros., and infinite consumable-based monetization with zero marginal cost, is now unrecognizable. I was brought in, and called it the game with Supercell’s biggest ceiling! Yet, just a year later, the hypercasual core has vanished, giving way to multiple player-activated abilities and completely removing consumables. Squad Buster 2.0 attempts to simplify nagging launch criticisms, including combat readability, character UX, progression speed, agency out-of-round with unit selection, and agency in-round with abilities. It gets halfway there, but in solving some issues, surfaces others.

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Overwatch Uses This One Content Productivity Hack

Overwatch’s new Stadium mode officially marks the launch* of Overwatch 2, building on the previous release’s perk system. Inside Stadium, there’s one content productivity hack, known previously only to the savviest Steam roguelike indie developers: conditional passives.

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Law & Economic Order, A Game Economist Investigation

Pokémon’s patent of spherical objects throwing of cartoon creatures threatens Palword’s lifeblood, while Tim Sweeney has lifted, at least a percentage point, in total gaming GDP with its injunction success.

  • How does Apple’s rent-seeking rate change in the face of this ruling?
  • Should Apple lower its rate to 15%, like it did in subscriptions?
    • Remember, it faced competition primarily from “webstores” too.

We premier a new segment: SOLVE that for EQUILIBRIUM. We discuss the marginal monetization effects and debate the benefits of personalization opportunities (hint: there are none) with webstores. @Chris is intrigued by Joost’s piece on rising game costs, while AI’s effects on the industry are measured in the Solow model. @Phil insists rising game costs mean rising revenue and stable margins, while Eric has his own doubts.

Eric’s on IP Laws, Joost’s On Gaming Costs

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