Our work on the relevance ranking / search part started from a novel observation, initiated in a JAIR paper, showing that the Probability Ranking Principle (PRP), central to current search systems fails in adversarial/strategic environments. In order to tackle the above we introduced the study of both an agent perspective, dealing with how content owners promote their content, and a mediator perspective, dealing with how we can modify search systems in order to lead to high social welfare when document authors are strategic. This led to quite a few results, and in 2022 to a SIGIR perspective paper that sum up this revolutionary angle in the main informational retrieval forum. In addition, as part of this work we established the first controlled experiments setup for the study of strategic information retrieval.
Our work on on-line recommendations introduced two novel paradigms: A game -theoretic approach to recommendation ecosystems, and individually rational / incentive compatible on-line explore and exploit. The first part, initiated in a Neurips 2018 paper, showed that we must take publishers’ game-theoretic incentives into account, if we aim at stable, fair, welfare maximizing outcomes. This work had several follow-ups in and had remarkable success influencing industry. The complementary part of our work on on-line recommendations deals with the fact that in modern explore and exploit systems, exploration is done by the participants themselves, and therefore should be aligned with their incentives. One example of a fundamental contribution is an ICML paper joint with an ERC visitor.
Our work on recommendation systems also revealed a remarkable synergy we completely did not envision to start with -- we initiated as part of our project the study of language-based games, focusing on on-line recommendation/persuasion, leading to JAIR and TACL papers, pioneering a novel bridge between game theory and natural language processing.
The work on segmentation/clustering focused on both the use of these methods in strategic predictions, as well as on work connecting them to social aspects. In a Neurips paper we have shown how predictors can successfully take into account competing parties’ predictions, later extending it in an EC paper to work on how a platform can offer useful predictors to competitors. We established a connection between privacy, incentives, and segmentation in an MOR paper. In this paper we show that we can obtain truthful privately preserving segmentation when we have a system consisting of many users. In an AAAI paper, we show a deep connection between game theoretic modelling to fair clustering. More specifically we show that without actual economic treatment of welfare fair classification leads to inferior results, and how to correct for these.
The work on network analysis/effects started as independent track, and led to two breakthroughs that emerged due to the emergence of insightful connections to other parts of the project. One of the major observations of our work in the context of search/relevance ranking is the fact that authors tend to mimic highly ranked authors. This led us to work on influencing social herding in data science. Another social aspect we realized is crucial is sybil attacks in economic mechanisms, originating from the fact the on-line social environment allows for creating multiple copies of participants. We have shown when and how one can tolerate such attacks in the context of regression and segmentation in an EC paper.
