AI and machine learning methods are increasingly interacting with and seeking information from people, robots, and other learning agents. Consequently, the learning dynamics of these agents creates fundamentally new challenges for existing methods. Motivated by the application of learning to offer personalized deals to users, we highlight these challenges by studying a variant of the framework of "online learning using expert advice with bandit feedback." In our setting, we consider each expert as a learning agent, seeking to more accurately reflect real-world applications. The bandit feedback leads to additional challenges in this setting: at time t, only the expert i t that has been selected by the central algorithm (forecaster) receives feedback from the environment and gets to learn at this time. A natural question to ask is whether it is possible to be competitive with the best expert j* had it seen all the feedback, i.e., competitive with the policy of always selecting expert j* . We prove the following hardness result — without any coordination between the forecaster and the experts, it is impossible to design a forecaster achieving no-regret guarantees. We then consider a practical assumption allowing the forecaster to guide the learning process of the experts by blocking some of the feedback observed by them from the environment, i.e., restricting the selected expert i t to learn at time t for some time steps. With this additional coordination power, we design our forecaster LIL that achieves no-regret guarantees, and we provide regret bounds dependent on the learning dynamics of the best expert j* .

We study a problem of optimal information gathering from multiple data providers that need to be incentivized to provide accurate information. This problem arises in many real world applications that rely on crowdsourced data sets, but where the process of obtaining data is costly. A notable example of such a scenario is crowd sensing. To this end, we formulate the problem of optimal information gathering as maximization of a submodular function under a budget constraint, where the budget represents the total expected payment to data providers. Contrary to the existing approaches, we base our payments on incentives for accuracy and truthfulness, in particular, peer prediction methods that score each of the selected data providers against its best peer, while ensuring that the minimum expected payment is above a given threshold. We first show that the problem at hand is hard to approximate within a constant factor that is not dependent on the properties of the payment function. However, for given topological and analytical properties of the instance, we construct two greedy algorithms, respectively called PPCGreedy and PPCGreedyIter, and establish theoretical bounds on their performance w.r.t. the optimal solution. Finally, we evaluate our methods using a realistic crowd sensing testbed.

The way in which social conventions emerge in communities has been of interest to social scientists for decades. Here we report on the emergence of a particular social convention on Twitter—the way to indicate a tweet is being reposted and to attribute the content to its source. Initially, different variations were invented and spread through the Twitter network. The inventors and early adopters were well-connected, active, core members of the Twitter community. The diffusion networks of these conventions were dense and highly clustered, so no single user was critical to the adoption of the conventions. Despite being invented at different times and having different adoption rates, only two variations came to be widely adopted. In this paper we describe this process in detail, highlighting insights and raising questions about how social conventions emerge.