Gamification represents an effective way to incentivize user behavior across a number of computing applications. However, despite the fact that physical activity is essential for a healthy lifestyle, surprisingly little is known about how gamification and in particular competitions shape human physical activity. Here we study how competitions affect physical activity. We focus on walking challenges in a mobile activity tracking application where multiple users compete over who takes the most steps over a predefined number of days. We synthesize our findings in a series of game and app design implications. In particular, we analyze nearly 2,500 physical activity competitions over a period of one year capturing more than 800,000 person days of activity tracking. We observe that during walking competitions, the average user increases physical activity by 23%. Furthermore, there are large increases in activity for both men and women across all ages, and weight status, and even for users that were previously fairly inactive. We also find that the composition of participants greatly affects the dynamics of the game. In particular, if highly unequal participants get matched to each other, then competition suffers and the overall effect on the physical activity drops significantly. Furthermore, competitions with an equal mix of both men and women are more effective in increasing the level of activities. We leverage these insights to develop a statistical model to predict whether or not a competition will be particularly engaging with significant accuracy. Our models can serve as a guideline to help design more engaging competitions that lead to most beneficial behavioral changes.

The advents of online retailing and advertising have created new opportunities for online platforms to incorporate algorithmic techniques to improve shoppers’ experience and drive user engagement, which, in return, can help with the long-term growth of these platforms, and also to help with having socially aware operations that consider fairness across different demographic groups. Motivated by the product-ranking problem in online shopping, this paper introduces and studies a new class of combinatorial optimization problems over the space of permutations, which is referred to as “sequential submodular maximization.” Using this class of problems, it provides algorithmic solutions for maximizing users’ engagement and also for balancing the users’ engagement across different demographic groups of shoppers to obtain fairness. In particular, they propose an optimal (1 − 1/e)-approximation algorithms for maximizing users’ engagement and a bicriteria ((1 − 1/e)2, (1 − 1/e)2) for maximizing users’ engagement subject to group fairness constraints.

We generalize the serial dictatorship (SD) and probabilistic serial (PS) mechanism for assigning indivisible objects (seats in a school) to agents (students) to accommodate distributional constraints. Such constraints are motivated by equity considerations. Our generalization of SD maintains several of its desirable properties, including strategyproofness, Pareto optimality, and computational tractability while satisfying the distributional constraints with a small error.Our generalization of the PS mechanism finds an ordinally efficient and envy-free assignment while satisfying the distributional constraint with a small error. We show, however, that no ordinally efficient and envy-free mechanism is also weakly strategyproof. Both of our algorithms assign at least the same number of students as the optimum fractional assignment. s 1 is used to resolve this partial assignment. After the (i 4 , s 1 )-updates the constraints are:In the next assignment step, we have f (t 5 , s 1 ) = 0.5 and i 5 is partially assigned to s 1 . In the resolution step, s 2 is used to resolve this assignment. After the (i 5 , s 2 ) updates, the constraints are:Next, we have f (t 1 , s 1 ) = 0, and f (t 1 , s 2 ) = 0 and therefore i 6 is assigned to the outside option.Finally, when i 7 is about to get assigned, we have f (t 2 , s 1 ) = 0.5 and therefore i 7 is partially assigned to s 1 . In the following resolution step, we use the outside option, i.e. φ to resolve this partial assignment. After the (i 7 , φ)-updates, the constraints are:2 ≤ x t 1 ,

We study an optimization problem capturing a core operational question for online retailing platforms.Given models for the users' preferences over products as well as the number of items they are willing to observe before clicking on one or abandoning the search, what is the best way to rank the relevant products in response to a search query?In order to capture both popularity and diversity effects, we model the probability that a user clicks on an element from a subset of products as a monotone submodular function of this set. We also assume that the patience level of the users, or the number of items they are willing to observe before clicking on one or abandoning the search, is a given random variable. Under those assumptions, the objective functions capturing user engagement or platform revenue can be written as a new family of submodular optimization problems over a sequence of elements.We call this family of natural optimization problems sequential submodular optimization. By building on and extending the literature on submodular maximization subject to matroid constraints, we derive a 1 − 1/e optimal approximation algorithm for maximizing user engagement and a bi-criteria approximation algorithm for maximizing revenue subject to a lower bound on user engagement.

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