Developing “City Roots”
Final printable board game and components are located at the end (or here).
Artist’s Statement
City Roots is a game that models gentrification as urban areas develop. Although gentrification is typically a negatively charged word, it is an incredibly complex process that often results in city benefits: reduced crime rates, increases in average income, and lots of other statistics that sound pretty nice. But when talking about statistics, it is all too easy to forget about the people.
City Roots moves players to understand and appreciate some of the complexity of urban development while building empathy for the real-world people who are hurt by gentrification — most notably, the lower-income residents who lose their homes and get pushed out of the developing city, who become part of the “displaced.”
Taking on the role of members of a city council, players must choose to enact or deny various policies. They witness the consequences of their choices as the board, visually representing the city, updates with each decision. Players must aim to increase economic prosperity of the city without displacing too many people, and in doing so, players see how lower-income people are pushed to the outskirts of the city or displaced entirely.
Concept Map
As a system game, we began our concept mapping by using causal loop diagrams (CLDs). After settling on gentrification as our topic, we searched for pre-existing CLDs about it. The following relationship graph shows some of main factors that contribute to city growth and gentrification, specifically for the city of San Francisco. (Source here.)
From this relationship graph and other research (below), we condensed the graph. We used the graph’s nodes directly to create objects in the game. Some of the main factors we felt would be affected by gentrification and could be represented in a game included high/low-income properties and residents, their relative salaries and cost of living, city revenue, and the presence of big corporations vs. small businesses. Their relationships are diagrammed below.
However, as discussed later, even this graph proved to be too complicated; having users keep track of all these elements proved complicated and often required a lot of calculations. After several rounds of further simplification, we decided to focus on 2 very specific “loops” that were the core of what we wanted to represent in our game. We focused on how the introduction of big corporations to a city increased factors like city revenue and the high-income population, but in turn pushed out local businesses and the low-income population. As such, we constructed our final model visualization:
Development of Formal Elements and Values
Our main goal was to make the player(s) aware that gentrification is a complicated system: it can cause both positive and negative effects in the city and is too nuanced to simply label as “good” or “bad”. Some other, more specific values we hoped they would understand from playing the game included:
- How easy it is to “forget” the personal impact when making big, sweeping decisions
- Gentrification displaces low-income people much more often than other groups
- Introducing big corporations helps the city’s economy grow
- Governments are incentivized to attract large businesses
- Gentrification often creates a less diverse community of high-income residents
- Outcomes are often beneficial to some groups but harmful to others
Background research
We consulted the following sources for our game as well as doing an interview with an economist.
We learned that gentrification is caused by limited land supply, as well as increased demand from outside factors. This causes the price to go up and no longer affordable by local residents. Preventing gentrification can be done by controlling the price, creating zoning laws, making sure that residence get higher income from the development, etc. This lead to our idea of basing our game of cost of living and zoning laws.
To decide how to integrate these findings into a game format, we started by brainstorming with the MDAO framework. We wanted to focus on group discussion to really explore the pros and cons of gentrification rather than painting it black and white. As such, objectives included understanding and weighing the trade-offs between different policy decisions as well as making the city a “better place” for each invested party. Some aesthetics included fellowship, challenge, and strategy to be able to agree (or agree to disagree) on various decisions yet keep with the interest of the city. From these, we came up with dynamics like morally complex decisions, group discussions, and being forced to compromise.
Although the idea was left out of the final game, we initially considered having each player represent a different invested party (i.e. low-income residents, businesses, city government, etc.) and each player would have certain sub-goals to aim for as well as survival conditions. Each player/party would vote on which policies to enact to make progress towards both their personal goals and the overall game objective.
After getting a better idea of what we wanted to work with through the MDAO framework, we decided to codify our ideas into the formal elements. The following lists our narrowed-down options of how we wanted to structure out game, leaving room for prototyping and playtesting to help make final decisions.
Initial Formal Elements (Brainstorming)
Players:
- Option 1: 1v1v1v1 — giving players a unique stake in the system might create more interesting conflict and discussion, but would also complicate the matter in terms of mechanics.
- Option 2: Players vs. system — players work together towards some goal, even if they might have sub-goals along the way
Objectives:
- Option 1: Prevent all low-income residents from being replaced
- Option 2: Bring in as many big corporations as possible to grow city revenue
- Option 3: Balance growing city revenue with displacing residents
- Note: Some of the objective options could be combined in different ways
Procedures:
- Vote on policies to enact each round that will affect the city state
- Update board state and statistics at the end of each round based on calculated rules (i.e. 10 displaced residents → 1 lower avg. salary of low-income residents, etc.)
Rules:
- Flip 3 policy cards, enact at least 2 by vote
- Many calculations explained on how to update board state
Resources:
- Low/High-income properties
- Low/High-income residents
- Big corporations
- Local businesses
- Tokens to keep track of city stats
Conflict:
- Adding big corporations displaces low-income residents
- Depending on the number of various tiles on the board, your city stats change
- Policies would create changes that could trade certain stats for others
Boundaries:
- 36x36 city board
Outcome:
- Option 1: No win/lose state, simply play 5 rounds and have players reflect on their city state and calculate a “score” based on their ending stats
- Option 2: Give them a goal to reach (within 5 rounds?) such as a certain level of city revenue
With clear options, we were able to prototype and playtest to finalize our game structure. As described in the next section, playtesting really helped us learn what options made sense in the context of our game, what options were too complicated to effectively implement, and what options created the most engaging game while also staying with our theme. The following is our finalized list of formal elements of City Roots.
Final Formal Elements
Players:
- Cooperative play of players vs. system
Objectives:
- Grow the city (city revenue) by bringing in big corporations and local businesses without displacing too many low-income residents (Construction)
Procedures:
- Enact policies and update the city-board to show the effects of that policy
Rules:
- See rules sheet. There are clear rules for how board pieces get updated/moved.
Resources:
- Low/High-income properties (units)
- Local businesses (units)
- Big corporations (units).
- All these can be affected by policy cards and help to determine the city revenue.
Conflict:
- Building big corporations increases city revenue but also displaces low-income residents, putting the winning and losing conditions in conflict.
Boundaries:
- The game boundary is the 36x36 board, representing the city.
- Players also create the magic circle by discussing and strategizing whether or not to play each policy card.
Outcome:
- Players win by reaching 20 city revenue, but often at the cost of displacing many low-income residents and squeezing out local businesses.
- Players lose if they displace a total of 7 low-income properties before winning.
- Both outcomes are meant to have the user reflect on how every decision comes with positive and negative effects such as helping the city economy or displacing low-income residents.
With our game’s formal elements decided, our next step was to establish the different objects in our system along with their relationships with each other. The following link includes a list of all objects in our game, their properties, behaviors, and relationships with other objects in the system. Writing this out helped us stay organized with how to design various aspects and loops of the system and keep the values at the core and the game balanced.
Testing and Iteration History
V0.0 (5.10.19) — Initial Prototype
- Paper prototype that debuted idea of board representing a city map and city properties.
- Question of what to include in game mechanics now that we’ve decided to create a board/card game.
V1.0 (5.14.19)
Initial Mechanics
- Removed Average Income and Average Cost of Living Scales
- Redesigned board mechanic: instead of replacing low-income properties and local businesses directly, they are moved one space each turn (depending on the policies Enacted) until they are pushed off the board
Snippet of “Time Passes” Calculation Guide — Players had to do 6 calculations each round
- Refined board and player pieces to include low-income housing, high-income housing, corporations, local businesses, low-income residents, and high-income residents.
- Introduced City Measurements (Income, Costs of Living, City Revenue)
- Created “Time Passes” calculation guide for players to calculate City Measurements and their decisions’ impact on city residents.
- Players must decide on 4 policies, one at a time, in each round before updating the board.
Playtesting (Nathan, Matt, Lucas — Game Design Students & TA)
Key Takeaways
- Calculating and counting pieces was difficult and disengaging
- “Cool concept, but too complex.” Consider sacrificing system complexity for simplicity and playability
- Keep players engaged the whole time. Players didn’t feel like they had an individual and compelling stake
Key Changes
- Streamline calculations
- Instead of draw 4 policies, and pick at least 2 per round, changed to draw 2 policies and pick at least 1 to speed up rounds
V2.0 (5.15.19–5.16.19)
Playtesting (Michael, Charles, Buddy — Stanford Undergraduates)
Playtesting (Bianca, Chris — Game Design Students)
Key Takeaways
- Calculations were still too complicated and math-heavy: They would be fun for a digital game with built-in calculation, but not an analog game
- “This hurts my brain.”
- “I’m a little too tired for this.”
- Game pieces (rubber bands and eraser caps represented residents) were difficult to move and manage
- Powerful policy cards like corporate taxes generated interesting discussion
- “Another business headquarters? F*ck yeah, baby!”
- Positive feedback to sensation of updating board and discussion decisions
- “It’s pretty realistic I gotta say.”
- “We felt like we were getting better.”
- Added a Win state (reach top of the City Revenue Scale) and a Lose state (all low-income residents are pushed off the board)
- Rewrote Policy Proposal Cards to reflect above changes: Policies now exclusively add or remove property cards on or off the board
- Rehauled calculations section to make it simpler. Now, players only calculate City Revenue based on number of local businesses and corporations
- Replaced “rounds” with “turns.” Instead of flipping 2 policies and picking 1, players discuss one policy proposal per turn
- Rewrote rules with iconography to explain mechanics
- Added “Displaced Section” for players to move displaced residents to
V3.0 (5.20.19)
Playtesting (Buddy, Mahnoor, Nour — Stanford Undergraduates)
Key Takeaways
- Rewritten rules with new iconography were extremely well-received
- Calculating revenue is simple and straightforward!
- Real emotional responses after major overhaul. Feeling responsible for the (unexpected) consequences
- “That’s how it actually happens…”
- “It makes you feel terrible about putting the people down.”
- “I don’t want to be mayor… ever.”
Key Changes
- Increased City Revenue scale to 20
- Changed lose state to make game last longer
