We are seeking applicants for a graphic design and data visualization research contract. Key tasks include helping develop the visual identity of an analog board game that demonstrates how conspiracy theory spreads online. The game seeks to explain the influence of algorithms and AI in the visibility and spread of conspiracy online. So far, the game is currently being prototyped, but help is needed in testing and designing the art for the game board and its assets.
We are looking for an applicant who:
- Can create high-quality visuals for the game board and game assets
- Understands fundamentals of data visualization and visual analytics
- Has some knowledge on AI/algorithms, disinformation, or conspiracy
- Experience working with games is an asset
Some of the tasks include:
- Designing a game board
- Designing card assets
- Playtesting game iterations (in person if permitting and/or online)
- Playtest related materials
- Offer creative insights and avenues for visualization challenges
- Help in the creation of blog posts or written descriptions of the method and work being done.
The position will last from October 1, 2021 to March 31, 2022 at 10 hours per week (at Concordia TRAC Union rates for a research assistantship).
If you are interested, please provide the following to scottbdejong[at]gmail.com by no later than September 27, 2021:
- Small portfolio of existing work
Written by Scott DeJong and Fenwick McKelvey
“Anyone who has ever struggled with poverty knows how extremely expensive it is to be poor.” – James Baldwin
Clara Nelson’s monthly costs exceed their income and, due to the large amount of junk food their daughter is consuming, their healthcare costs too are becoming excessive. Public transit is inaccessible so Clara spends what little income they have on their car. The public education system is poorly funded forcing Clara to spend money on private education.
Clara’s struggles are just one data point that is shown to you as leader of Canada. Because in Democracy 4 you are in charge of both Clara’s future and the entire country. You can assign new policies, implement taxes, increase federal spending on public transit (or anything else), or even completely undermine the democratic process of your nation. Well, that is until you become so unpopular you are voted out, assassinated, or forced to resign.
Let’s say you want to help Clara. For instance, you implement a very high junk food tax, you increase spending on healthcare and state schools, and improve national public transit. All of this is intended to help lower Clara’s monthly costs. Initially this will be successful. You can give yourself a pat on the back as you watch Clara’s life improve. However, every action you take has consequences.
Decision making in Democracy 4 takes place in a dynamic, interrelated web where the impacts of your actions might not be known for some time. So shortly after Clara’s life begins to improve, you see a news story recounting the teary-eyed cry for help from the owner of a family-run candy store that is going to have to close its doors due to your tax. Shortly after, an enraged car salesman discusses how his business can barely stay afloat. You might have gotten Clara’s vote, but you lost others in the process. So was it worth it? Did you maximize your power for partisan support and personal objectives effectively? Should you even have cared about Clara and the working poor, or would the middle class have been a better target group for your policies? How could you have better optimized your engagement with different demographics in regard to their class positions?
I begin with Clara’s struggles and my own reflections on how to solve them as a way to introduce how Democracy 4 represents class. Through reflections on two class-focused playthroughs, I will discuss how the desire for political simulation impacts and is reflected in the game’s presentation of class precarity and mobility.
What is Democracy 4?
Democracy 4, a political simulation game developed by Positech Games, is focused on having players manage their political utopia (or dystopia) as leader of a Western state. The fourth edition of the series, the game primarily uses data management as the mechanic for running a nation. In fact, the above snapshot is an elaboration of this data which players get glimpses of through the handful of news stories that pepper their screen each political quarter (the game’s presentation of time). As Figure 1 shows, unlike other simulation games the interface resembles that of an interactive spreadsheet rather than nation management.
Players are positioned as a recently elected leader of a Western nation and are told that their goal is to run the country and get re-elected. This means that players are motivated to take actions that appease their cabinet ministers, party funders and key demographics, which leads to interesting decision making processes when it comes to a player’s moral desires as a leader.
The game’s developer, Cliff Harris describes the core design goal of the game as a neural network, where every action is interconnected. When players hover over an icon, like “Obesity” (Figure 2), they will see orange and green arrows flowing into or away from it. These represent what is positively impacting the policy/issue (green arrows flowing in), negatively impacting it (orange arrows flowing in), what it positively impacts (green arrows flowing out) and what it negatively impacts (orange arrows flowing out). On my first playthrough I spent quite a long time following arrows across the network to see how things were interconnected. In the end I felt that I had more of a headache than a clear indication of how an act would impact another policy area. Sometimes it was apparent, and other times it was challenging to connect the dots.
Since the only way to lose the game is to get voted out, players need to properly navigate this network of relations to secure votes. On the left hand side of the screen the entire population is divided into different demographics. An indicator shows how popular you, as the country’s leader, are with each demographic. This allows players to determine where their governmental decisions are having an impact, who they might want to curate policies towards, and who they might choose to ignore. Most of these demographics are tied to a class position (poor, middle class or upper class), and if you hover over them, you will see the “flows” of policies which increase or decrease your popularity with them.
In order to succeed in the game, you need to optimize your play around the people. Every action players make should be meant to impact something exactly how they see fit. If players want to build a utopia for the lower class, run a highly religious state, or increase power for the wealthy – it all comes down to their policy decisions.
Class position is a central aspect of the demographics and decision making made by players in Democracy 4. At the start, the game procedurally generates 200 citizens which statistically represent the approximate population of your nation. Each citizen has a set income and monthly amount of spending as well as specific voting demographics they fall into. All of this is translated as data which players can view and trace as they introduce and alter policies.
Citizen class position and economic precarity is predetermined: they either belong to the poor, middle or rich class. Each character starts the game with a “coded disposable income,” which are their monthly costs, and an original income, which is how much they make. However, policy decisions can impact factors such as disposable income which suggests that class mobility is possible but also deeply relational to the game’s simulation of politics (expanded on below). Players can track how their policies are influencing people’s livelihood by putting them in more debt or providing them with more wealth. Providing more funding to initiatives, implementing new policies, or altering taxes all directly impact the lives of these simulated citizens. If choosing to play with a focus on the economic class position of their citizens, players need to decide between their desire to alleviate class struggle with the difficulties of staying in power. Class markers can dictate likelihood to vote, and class position can alter support and popularity of player decisions. While class is meant to be a factor of consideration, I wondered how it changes the game when the player makes class mobility their core objective. I tried to do a playthrough where I focused on helping characters that started in the poorest class position to explore how this tension plays out.
What happened to Clara?
I ran two different playtests exploring how class mobility, the poverty line, and people’s quality of life changed based on my actions. For the first playtest I chose the individual with the lowest disposable income, Clara Nelson (Figure 3). According to the game they are a self-employed capitalist (one of the game’s predetermined voter demographics) who had a -27,000 (CAD) spending deficit. This meant that each month their life expenses were higher than their income. I put my efforts into reducing what they had to spend money on in the hopes of improving their life. I altered how much Clara spent on housing, the percent of their income they spent on taxes, the costs of sending their child to private or public schools, and what support Clara got towards school lunches and food stamps for their family. Every policy decision was determined based on its impact on Clara’s life.
Clara’s life got better. They slowly started to climb out of monthly overspending and life was beginning to look positive. But once my second term began I was impacted by the repercussions of this intense focus. My overspending on social programs created a national financial collapse. This unraveled all of my work as big business left the country and national debt actually made Clara worse off than when I started. I clearly was not going to get re-elected and my goal resulted in disaster.
The View From Below
For the second playtest, I focused on individuals with the lowest original income in my nation. I tried to help Adele Côté, an environmentalist who the game classified as “100% poor”. Like with Clara, I experimented to see how my actions affected their class status in the game.
In just half a term, I had doubled Adele’s disposable income, but only by accepting a lower median income nationally (Figure 4 and 5). However, learning from my mistakes with Clara, I attempted to balance my social policies with increased taxes for the rich to offset their costs and was more balanced in how I scaled policy measures. I learned that I had to optimize my play by discerning which decisions would offer Adele the most aid without having a large amount of blowback on the national economy.
Through Adele’s playtest I realized that the game would not alter their original income, only their disposable income (how much they had extra each month). In fact, I actually improved their disposable income into a range that would be considered middle class, but this made no difference to the game as they remained within the 100% poor bracket (Figure 4 above). This meant that class position was hard coded for Adele. While one could change the economic precarity of citizens through play, it became impossible to separate them from their class coded position. No matter what the government did, their class position was set in stone.
From my gameplay observations, class position is more than just the pregenerated characters’ economic precarity. Unfortunately, this is not easily apparent in the game system. The simulation focuses on larger national issues, so a focus this intense on class highlights only some of the life choices that these citizens had made. In some cases these made little sense. Both Clara and Adele spent a significant portion of their income on housing. Clara doesn’t seem to have the disposable income to support this, and according to Adele’s other spending they are also benefiting from rent controls. This shows a disconnect in how the game presents Adele’s housing situation. They are simultaneously playing for housing and rent, which suggests home ownership and that they are renting an apartment which is quite an expensive and unrealistic undertaking for someone “100% poor”. Additionally, both of them were sending their children to private schools, which did not change even when I abolished private schools in Clara’s runthrough. Perhaps that was a bug, or the game insinuated that my nation had shadow schools. However, it suggests that even with optimal play the game has prescribed behaviours and actions that the player cannot overcome.
It is important to mention that my own personal play makes a difference in these games. In no way am I claiming to have played these scenarios as effectively as possible. In fact, that might actually be impossible as the ‘neural network’ of Democracy 4 is not entirely visible to players, making it challenging to gauge the impact of each individual action until it might be too late.
Class is presented as individual categories and labels but becomes interlinked and dependent on government actions. Every decision I made positively helped someone move up in life, while negatively impacting others. Class in Democracy 4 is not an individual situation, it is a dynamic network of relations, some of which the player cannot even understand. The poor can become middle class, the middle class wealthy, and the wealthy richer – or alternate mobilities can occur. Perhaps you tax the wealthy and provide more funding to the poor, directly altering the size of the middle class. Class mobility feels controllable but you can never actually change the label that the game provides to them from their starting demographic. Every decision you make results in repercussions on how people exist within your nation, and their propensity to support you. In this manner, Democracy 4’s ‘neural network’ places class position alongside economic policies interconnected to simulated existence.
In doing so, it removes individual agency and places the onus of class mobility on the role of those in power. In an interview, developer Cliff Harris said “we are treating class as being purely economic”. Clara and Adele were defined by their economic position; their digitally manifested existence was purely based on how my policies impacted their spending of money. Unlike other games, class aspirations for mobility are not witnessed. The only indication is abstracted based on a popularity value for you as a leader. Class becomes less important than voter demographics where those who the game classifies as “poor” are only a concern if they fall into another voting demographic that supports your party’s values. This means that player decisions quickly enter a tension between moral and economic objectives for the nation. Class position becomes a cog in the political machine, and you as the player get to decide how much or little you care about it.
We seek candidates for 1-2 positions focused on information design and visualization. Positions consist of designing reports and web interactives for a large dataset of images. Working as part of a national team, positions will enhance the group’s visual presence and its ability to communicate research findings through infographics and other data visualizations.
The role will consist primarily of designing creative solutions for how to visualize the given dataset collected from Canadian social media using Adobe tools (or free software equivalents) or data visualization libraries. Fluency in meme culture, templates and digital politics are considered an asset.
Contracts will start immediately and average 100 hours or more depending on budget, experience and availability.
– Fluent in HTML5/CSS and JS
– Knowledge of UX/UI creative thinking
– Ability to create web-based data visualizations
– Experience in graphic design
– Proficiency in Photoshop, Illustrator or equivalent design tools.
– D3.js, react.js, jQuery
– Batch image processing
Submit a resume and cover letter giving examples of past work to: fenwick.mckelvey[at]concordia.ca
INRS’ Nenic Lab in collaboration with Concordia’s Algorithmic Media Observatory seek a post-doctoral associate for a position of 2 years (potentially extendable to 3 years pending funding) to work with respective directors, Professors J. Roberge and F. McKelvey on their collaborative projects. Funded by Canada’ Social Sciences and Humanities Research Council, the first project Media Governance after AI involves 1) media archeology, 2) controversy mapping, 3) policy analysis and 4) speculative design related to the adoption of AI systems in media regulation. The second project Shaping AI is a global partnership funded by SSHRC and its international partners. Led by C. Katzenbach at HIIG Berlin, the project is part of a consortium comprising scholars from the University of Warwick and the Media Lab, Paris as well as Canada. Shaping AI deals with the framing of AI by media, policy-leaders and computer scientists in Canada, France, Germany and the United Kingdom.
We are looking for a critical scholar who sees their work at the intersection of multiple social sciences. Especially, we seek candidates who have an interest and experience in researching historical and contemporary entanglements between technology, media and policy-making. Fields of specialization might include but are not limited to: Science & Technology Studies, Communication and Media Studies, Histories of Science, Sociology, or Anthropology. An interest in digital methods is an asset.
A strong research profile, organizational and networking skills, experience in collaborative and interdisciplinary research, and publishing projects. You will be primarily responsible for your own research as well as the organization of group work (curation, book editing projects, etc.) with our international team. As the research team is based in Montreal and spans over a Francophone and an Anglophone institution, the capacity to operate in both official languages of Canada would be considered an asset.
Salary is CA$41,931/year (including 13% benefits) plus generous support for research and travel. Importantly, said salary is calculated on a 4 workdays/week basis (or 80% of a full-time position).
Start date: November 16th, 2020 or to be determined with the successful candidate. Review of applications will start as early as October 30th, 2020 and will continue until the position is filled.
Contact and Application Information:
Please send a CV, a cover letter detailing current/future research directions and work experience (maximum two pages), as well as a writing sample to both fenwick.mckelvey(at)concordia.ca and jonathan.roberge(at)ucs.inrs.ca
All qualified candidates are encouraged to apply; however, priority will be given to Canadians and permanent residents.