## About the Course

Every day, individuals take decisions that influence their own lives, as well as those of others. Individual decisions including whether to drive a car or take public transportation, to live a healthy life, affect not only the individual taking the decision, but also lives of others. Decisions on whether to take the car, or use public transportation influences the total carbon footprint and pollution levels in the city, and if most individuals lead healthy lives, medical costs and insurance fall for everyone. Individual decisions are affected to a first order, by access to information, resources, incentives, and knowledge of social peers' decisions.

Assume that we have a social network $G=(V,E)$, where, $|V|=N+1$, which includes $S$, a privileged system node. Two directed edges $e_{i,j}$ , $e_{j,i}$ exist between any pair of connected nodes $i,j$, indicating the direction of flow of information between the pair. An edge $e_{S, j}$ exists between the system $S$ and every individual $j$. Each directed edge $e_{i,j}$ has an associated probability $p_{i,j}$ that node $j$ observes information from node $i$. The system may influence the probability $p_{i,j}$ by increasing the cost for node $j$ of accessing information about node $i$, for example by pushing information about $i$ down the information list that $j$ accesses.

Individuals and systems take decisions to maximize their utilities. The system designs the mechanism; changing the mechanism alters payoffs of individual players. Individuals as well as the system have resource constraints that influence their decisions. Individuals may have limited time or money; and the system may have limited resources. For example, a transportation network may need to manage traffic congestion when some roads are under repair while minimizing total travel time.

The goal is to understand how aspects including resource constraints (e.g. time, money, physical resources), information, mechanisms, network structure and network size influence decisions made by individuals. Game theory assumes rational actors-we seek to understand what happens when we relax the rationality assumption. The big question to which we seek an answer: Can we guide networks with resource bounded actors to a state that maximizes social welfare?

We will read papers from Computer Science and Behavioral Economics, brainstorm open problems and work on homework that illuminates central ideas.

After taking this class, students should be able to critique research papers, formulate an original research agenda and develop algorithms and systems to address their research question.

### Textbooks

The following texts are recommended but not required, for reference.

There are many research papers that will help understand the course content. Please check the references for this course for more information