One of the most fascinating examples of social innovation I've been tracking recently was the We Counter Hate platform, by Seattle-based agency POSSIBLE (now part of Wunderman Thompson Seattle) that sought to reduce hate speech on Twitter by turning retweets of these hateful messages into donations for a good cause. Here's how it worked: Using machine learning, it first identified hateful speech on the platform. A human moderator then selected the most offensive and most dangerous tweets and attached an undeletable reply, which informed recipients that if they retweet the message, a donation will be committed to an anti-hate group. In a beautiful twist this non-profit was Life After Hate, a group that helps members of extremist groups leave and transition to mainstream life. Unfortunately (and ironically) on the very day I reached out to the team, Twitter decided to allow users to hide replies in their feeds in an effort to empower people faced with bullying and harassment, eliminating the reply function which was the main mechanism that gave #WeCounterHate its power and led to it being able to remove more than 20M potential hate speech impressions.

This paper considers a multi-armed bandit game where the number of arms is much larger than the maximum budget and is effectively infinite. We characterize necessary and sufficient conditions on the total budget for an algorithm to return an {\epsilon}-good arm with probability at least 1 - {\delta}. In such situations, the sample complexity depends on {\epsilon}, {\delta} and the so-called reservoir distribution {\nu} from which the means of the arms are drawn iid. While a substantial literature has developed around analyzing specific cases of {\nu} such as the beta distribution, our analysis makes no assumption about the form of {\nu}. Our algorithm is based on successive halving with the surprising exception that arms start to be discarded after just a single pull, requiring an analysis that goes beyond concentration alone. The provable correctness of this algorithm also provides an explanation for the empirical observation that the most aggressive bracket of the Hyperband algorithm of Li et al. (2017) for hyperparameter tuning is almost always best.

When aggregating preferences of agents via voting, two desirable goals are to incentivize agents to participate in the voting process and then identify outcomes that are Pareto efficient. We consider participation as formalized by Brandl, Brandt, and Hofbauer (2015) based on the stochastic dominance (SD) relation. We formulate a new rule called RMEC (Rank Maximal Equal Contribution) that is polynomial-time computable, ex post efficient and satisfies the strongest notion of participation. It also satisfies many other desirable fairness properties. The rule suggests a general approach to achieving very strong participation, ex post efficiency and fairness.

In March of 2016, a twenty-seven-year-old Syrian refugee named Rakan Ghebar began discussing his mental health with a counsellor. Ghebar, who has lived in Beirut since 2014, lost a number of family members to the civil war in Syria and struggles with persistent nervous anxiety. Before he fled his native country, he studied English literature at Damascus University; now, in Lebanon, he works as the vice-principal at a school for displaced Syrian children, many of whom suffer from the same difficulties as he does. When Ghebar asked the counsellor for advice, he was told to try to focus intently on the present. By devoting all of his energy to whatever he was doing, the counsellor said, no matter how trivial, he could learn to direct his attention away from his fears and worries.

Fahad Aziz is the Co-founder of Caremerge, an "award-winning technology company revolutionizing communication and coordination of care for seniors." As an industry leader in the tech space, Aziz has his finger on the pulse of disruptive healthcare tech. He says a few technologies are poised to seriously upend'business as usual' in healthcare in the near future, and they are not necessarily what you'd expect. In 2012, Vinod Khosla predicted that in time, "technology will replace 80% of what doctors do." He is spot on, according to Aziz.

Efficiency--no agent can be made better off without making another one worse off--and strategyproofness--no agent can obtain a more preferred outcome by misrepresenting his preferences--are two cornerstones of economics and ubiquitous in important areas such as voting, auctions, or matching markets. Within the context of random assignment, Bogomolnaia and Moulin have shown that two particular notions of efficiency and strategyproofness based on stochastic dominance are incompatible. However, there are various other possibilities of lifting preferences over alternatives to preferences over lotteries apart from stochastic dominance. In this paper, we give an overview of common preference extensions, propose two new ones, and show that the above-mentioned incompatibility can be extended to various other notions of strategyproofness and efficiency in randomized social choice.

The probabilistic serial rule is one of the most well-established and desirable rules for the random assignment problem. We present the egalitarian simultaneous reservation social decision scheme – an extension of probabilistic serial to the more general setting of randomized social choice. We consider various desirable fairness, efficiency, and strategic properties of social decision schemes and show that egalitarian simultaneous reservation compares favorably against existing rules. Finally, we define a more general class of social decision schemes called simultaneous reservation, that contains egalitarian simultaneous reservation as well as the serial dictatorship rules. We show that outcomes of simultaneous reservation characterize efficiency with respect to a natural refinement of stochastic dominance.

We are developing an ontology of microsocial concepts for use in an instructional system for teaching cross-cultural communication. We report here on that part of the ontology relating to interpersonal relationships. We first explicate the key concepts of commitment, shared plans, and good will. Then in terms of these we present a formal account of the host-guest relationship.