Information asymmetry in games enables players with the information advantage to manipulate others' beliefs by strategically revealing information to other players. This work considers a double-sided information asymmetry in a Bayesian Stackelberg game, where the leader's realized action, sampled from the mixed strategy commitment, is hidden from the follower. In contrast, the follower holds private information about his payoff. Given asymmetric information on both sides, an important question arises: \emph{Does the leader's information advantage outweigh the follower's?} We answer this question affirmatively in this work, where we demonstrate that by adequately designing a signaling device that reveals partial information regarding the leader's realized action to the follower, the leader can achieve a higher expected utility than that without signaling. Moreover, unlike previous works on the Bayesian Stackelberg game where mathematical programming tools are utilized, we interpret the leader's commitment as a probability measure over the belief space. Such a probabilistic language greatly simplifies the analysis and allows an indirect signaling scheme, leading to a geometric characterization of the equilibrium under the proposed game model.

We propose deviation-based learning, a new approach to training recommender systems. In the beginning, the recommender and rational users have different pieces of knowledge, and the recommender needs to learn the users' knowledge to make better recommendations. The recommender learns users' knowledge by observing whether each user followed or deviated from her recommendations. We show that learning frequently stalls if the recommender always recommends a choice: users tend to follow the recommendation blindly, and their choices do not reflect their knowledge. Social welfare and the learning rate are improved drastically if the recommender abstains from recommending a choice when she predicts that multiple arms will produce a similar payoff.

Jim Simons is the greatest moneymaker in modern financial history, and no other investor – Warren Buffett, Peter Lynch, Ray Dalio, Steve Cohen, or George Soros – can touch his record. His firm has earned profits of more than $100 billion, and between 1994 and 2004, its signature fund, The Medallion Fund, averaged 70 per cent annual return. Medallion's returns don't seem to correlate with known factors and the only thing most people get to know is that the strategy is "statistical arbitrage". People are confounded by the fact that the proliferation of other quantitative hedge funds in recent years hasn't caused Medallion's performance to deteriorate. Last year, there was a very readable book about Jim Simons: On the man who solved the markets – How Jim Simons Launched the Quant Revolution, Penguin 2019.

Researchers at the University of Geneva have recently developed a new defense mechanism that works by bridging machine learning with cryptography. The new system, outlined in a paper pre-published on arXiv, is based on Kerckhoffs' second cryptographic principle, which states that both defense and classification algorithms are known, but the key is not. In recent decades, machine learning algorithms, particularly deep neural networks (DNNs), have achieved remarkable results in performing a vast array of tasks. Nonetheless, these algorithms are exposed to substantial security threats, particularly adversarial attacks, limiting their implementation on trust-sensitive tasks. "Despite the remarkable progress achieved by deep networks, they are known to be vulnerable to adversarial attacks," Olga Taran, one of the researchers who carried out the study, told TechXplore.