The Game: 5 players representing different member of ITP community spread rumors by word-of-mouth over 3 days

Goal: better understand ITP communication patterns by tracking the spread of viral information and visualizing social cliques


ITP List Serv Network Viz
ITP list serv visualization shows an imbalance in usage of ITP list serv (2nd year students use more frequently and mainly email other second year students) Also shows what students are “hubs” within the ITP community. Can the flow of information between the two classes of students be improved?

Zachary’s Karate Club Graph describes the friendships between the members of a US karate club in the 1970s.

5 Players Representing Different Social Cliques at ITP

Marc Abbey

Marc Abbey
1st Year Student
Rumor: ITP is raising money to expand floor.

David Rios

David Rios
Rumor: Foosball table needs to be packed up for thesis.


Vicci Ho
2nd Year Student
Rumor: Dan O’Sullivan will retire next year.


Amelia Winger-Bearskin
2nd Year Student
Rumor: Abhishek has been living on the floor for the past week.

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Shawn Van-Every
Rumor: Shawn will move his office to Brooklyn PolyTech.

The Winner: Amelia

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“Made sure rumor was about someone that was believable – was going to first do something else but not believable enough. Spread rumor on one day only – Friday. Kelly was worried upon hearing the rumor.  Told Leslie and a few other people, but not many people (7 people total) – but told in front of other people (while lined up in front of other people). Told people in public ways because overheard gossip is more fun to spread. Abhishek played along.”

2nd Place: Vicci

“Played actively for two days. Spread at TNO and told some people that I know would believe and tell other people. Many told friends. It was interesting.
Better strategy would be if you had more time to play the game. With 5 people spreading rumors all at once, people more likely to figure out something is happening. Stagger rumors.”

3rd Place: Marc

“It was very stressful. The “space” of ITP discussion is very relevant at the moment and people reacted with emotions, making it extremely difficult to lie to people.”

Tied for Last: Shawn

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“I only used word of mouth. Students believed me but not faculty. It was hard to keep a straight face. I secretly want to move my office to Brooklyn.”

Tied for Last: Rios

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“Got too busy to tell people. Only told people on Friday. Only told a few people. So I knew I had probably lost.”

2nd Years and 1st Years seem isolated from each other in word of mouth communication (Vicci’s graph only shows 2nd years, and Rios (resident) only told 2nd years)

For the game to be more successful and to learn more about spread of information, needs to happen for longer period of time with more players, especially people who represent different social cliques, and rumor needs to be believable enough.

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IMG_5847 - Version 2



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Rumors Game – Jiwon, Michelle, John and Haylee

To potentially improve communication between ITP students (especially between two classes) by better understanding social divisions/cliques and flow of information in the community by testing viral spread of information in ITP network via a Rumor Game.

Misinformation/rumors are a form of information that have a natural tendency to spread virally in social networks and could be used to create an interesting game to learn about the topology of the ITP community/network. Through a rumor game, could we learn about the social cliques of the ITP community, and how interconnected people are? And how this affects viral spread of information in the ITP network and conversely, other networks?

Last year, 2nd year students dominated the ITP list serv and first year students did not use as frequently but could often be found posting on Facebook. It is also harder to meet students in the other classes due to how the curriculum is setup. By understanding the current flow of information within the community, are there opportunities to improve the community and communication between various members of the community? Especially given the diverse makeup of the ITP community with many people from various countries and cultures. One of the strengths of the ITP community and program is the network of connections students make while attending the program.


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Team: Dan Melancon, David Tracy and Saki Hayashi

Strategy of the Commons is a game that explores the application of Game Theory to a dynamic system of exchange such as the CitiBike system in New York City.

Through gameplay we aim to address the following question: Can the implementation of system wide penalties and bonuses influence the behavior of self interested players? Furthermore, can we fine-tune these penalties and bonuses to achieve system balance?

The core gameplay works as such:
– Six stations distributed throughout the fourth floor of ITP.
– Each station starts with the exact same stock.
– Up to fifteen players must navigate the system, collecting points for redistributing stock
– Players intermittently receive missions that they must complete, or they will receive a penalty.
– If a station becomes completely empty, or completely filled, a system-wide penalty is activated
– the penalty steals one point from everyone in the system every five seconds and deposits these points at the unbalanced station as a bonus to whoever restores the system (by depositing stock at an empty station or taking stock from a full station)
– After ten minutes of play, the player with the highest point total wins.




The first play test with three stations and 15 players, touching RFID card to the reader.Screen Shot 2014-11-16 at 1.53.43 PM

The first play test, touching RFID card to the reader.


Prototyping RFID six readers


RFID card readers as stations the players touch
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The second play test with six stations and 15 people
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The web/ mobile interface from the mission control screen

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A player accepting points

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An experiment in the movements of shared bags of candy among students on the ITP floor. The intent is to analyze these movements and come to conclusions about the way in which students use and move about the fourth floor. We hypothesize that, like any system of shared finite resources, there are optimal areas to place them that will facilitate their movement and maximize sharing. Conversely, there are areas that are not conducive to movement of shared resources and will not help contribute to the system and to the spread of those resources. It is our goal to define these areas through running our experiment.

There are a few additional pieces to our experiment. Through the use of different candies, we can make assumptions about usertypes in our system and how users who prefer different candies utilize the floor differently. This, as well as through intentional re-placement of our candy bags in different areas, we can affect the system much like the rebalancing trucks in the Citibike system and visualize how those movements changed the behavior in the system.

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In our game, we observed the exchanges that occur in a barter economy of two groups of three players that are trying to fill their ITP sticker albums. The winner of the game would be the first player to fill the album. Both groups started the game apart, being able to trade only in their local markets. Later in the game, they were able to carry and exchange stickers with players from the other group in the global market. Each player acts at different stages of the game as a supplier, transporter, dealer and collector. The three boards (markets) not only serve as places to exchange stickers; but as places for the players to gather together, promoting social intelligence.

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For this week, we worked on the final implementation of the networked side of our game. Because our game consists of a array of networked station that players pick up and drop off stock, we had to create a server and database that constantly updated each stations stock and bonuses as well as each players point totals.

When a player arrives at a station, they swipe their RFID card and received an alert on their phone. If the player has stock, they are alerted that they can drop off their stock, or if the station is full , they are denied. If the station has only one available slot, the player is alerted that if they drop off they will put a system wide penalty into effect. If the player has no stock, they are alerted that can pick up from the station, or if it is empty they are denied. If the the station has only one stock, they alerted that they will put a system wide penalty into effect.
Because of these dynamics, each player needs a constant update of the status of the entire system. This includes every players scores as well each stations current stock and any bonues that are in effect. We were able to accomplish this using a node server connected to a mongo database. Each player accesses a client-side webpage that is continuously updated via websockets from the server.

The front-end of the client has a simple data viz showing each player points and station inventory. In the next couple days we will refine the front-end to more clearly let each player know whether they are holding stock or not. We will also experiment with further game dynamics to better create an exogenous network.

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Alejandro Puentes, Rodrigo Derteano, Sabrina Osmany

We have run a couple of tests so far. We are reviewing the rules and solving some issues with the website.

Overall Objective

The objective of the experiment is to observe a barter market self-organize, keeping track of the flow of stock (stickers) during the process.

Mechanism – The Sticker Exchange

The Sticker Exchange: 1) keeps track of all the transactions in the market, 2) collects data from the experiment, and 3) receives and disburses stickers from and to the players.


  • 1 Cashier
  • 1 Computer
  • 1 Script
  • 1 Database
  • Internet

Link to the Sticker Exchange

The Game:

The Sticker Exchange Market


  • The Sticker Exchange Market
  • 6 Players
  • 6 ITP sticker albums
  • 108 Stickers – There are 18 types of stickers in the game, and 6 stickers out of each type.


Each player will receive 18 stickers. Grouped into 3 groups of 6 stickers of the same type. In other words, at the beginning of the game, each player must hold all the available stock of three unique types of stickers in the market.

Player’s objective

Each player has to collect all the stickers needed to fill the ITP Sticker Album, and finish such a task before other players.


The Sticker Exchange chooses player number one. Turn rotation is clockwise.


  1. When it is your turn, raise your hand showing the stickers that you are willing to trade; you can either pass or trade as much as three stickers per turn.
  2. The other players can bet on your offer holding up the stickers that they are willing to trade—all bets must contain the exact number of stickers than your original offer.
  3. Choose one from all the bets.
  4. Both you and your fellow trader must hand in your traded stickers to the Sticker Exchange.
  5. After the Sticker Exchange processes the transaction, both traders will receive the traded stickers.

End of the game

The game ends when one players collects all the stickers needed to fill an album.

The stickers



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Aaron, T.K., and I are interested in behavior associated with reward – that is, the reasons why a person does certain things in light of a future payoff. To this end we have devised a game wherein the players compete for a future reward. The playing of the game, however, significantly interferes with their normal behavior, and it is our goal to investigate what makes such a game “worth it” to the dedicated player by implementing Time Wars, a winner-takes-all game to be played on the ITP floor.

In Time Wars, linear time is broken down into units of 10 minutes called intervals. Players can check in during each interval to receive a point for that interval (e.g. a check-in between 9:00am and 9:10am counts as one point). They do so by tapping their NYU student ID card to an RFID reader connected to an Arduino Yún which logs the check-in for that player. Players can only receive one point per interval (players cannot check in multiple times per interval to earn more points). Note that once an interval has passed, the point for that interval can never again be scored by any player, so there is no way to score points faster than the progression of time itself. This is why it is important to check in during as many intervals as possible.

One purposeful game design mechanic is that the gameplay is extremely disruptive, especially at ITP, where the state of being “in the zone” is often an albatross. This means that a dedicated player has to give up certain responsibilities in order to participate. Furthermore, the game cannot be played outside of ITP; its inherent physicality tethers it to the floor. We proved this concept two weeks ago when we created an online-only version of the game and tested it ourselves. T.K. checked in as late as 4:00am, which would near impossible to do in the physical game (not totally impossible, though).

Currently our system consists of a database (MongoDB), front-end (Node.js + Handlebars.js), microcontroller (Arduino Yún), and physical sensor (RFID reader). The database is ready, the front-end is nearly there, the microcontroller is reading and writing the database, and the RFID reader is nearly hooked up. We anticipate Friday we should have the components fully worked out, and will be able to officially launch the game at ITP, using the following days to analyze and fine-tune.

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David Tracy, Dan Melancon, Saki Hayashi

For this week’s assignment, our team built a realtime online reporting tool as a proof of concept. This is important for our final game because it provides the framework to keep all players updated on the state of the system.

In abstract, here’s how it works:


In practice, here’s how it works:

1. Users connect to server via phone browser.
2. A websocket connection (via socket.io) is established between the client (phone) and the server
3. The client broadcasts their geolocation, lat/long to the server. NOTE: In our final game, this will be a running point total instead of location, this is simply for Proof of Concept.
4. The server receives the coordinates and broadcasts them to any other connected clients.
5. When the client receives the location of a player it hasn’t seen before, it creates a new player entry and adds its coordinates to the DOM.
6. When the client receives updated coordinates from a player it has seen, it updates that player’s coordinates in the DOM.

The code respository can be found here:


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