From biased lending algorithms to chatbots that spew violent hate speech, AI systems already pose many risks to society. While policymakers have a responsibility to tackle pressing issues of algorithmic fairness, privacy, and accountability, they also have a responsibility to consider broader,...

Why do organizations conduct job interviews? The traditional view of interviewing holds that interviews are conducted, despite their steep costs, to predict a candidate’s future performance and fit. This view faces a twofold threat: the behavioral and algorithmic threats. Specifically, an...

In today’s competitive environment, projects must be implemented at a lower cost and in less time, and resources must be used optimally. Therefore, project management and scheduling using an efficient tool is a necessity. Resource constraint project scheduling (RCPSP) is one of the most widely...

Cyber security and resilience are major challenges in our modern economies; this is why they are top priorities on the agenda of governments, security and defense forces, management of companies and organizations. Hence, the need of a deep understanding of cyber risks to improve resilience. We...

The paper gives a novel path integral formula inspired by large deviation theory and Malliavin calculus. The proposed finite-dimensional approximation of integrals on path space will be a new higher-order weak approximation of multidimensional stochastic differential equations where the dominant...

Sampled distributions are used to price, hedge and produce scenarios for derivative products which is essential for risk management. This approach accumulates many types of discretisation errors. These can be linked to data quality input, to calibration algorithms, discretisation in time,...

This paper examines the short-run weak-form efficiency of equities, using a proprietary data set permitting analysis of orderbook characteristics to measure short-term price predictability. The results show that high levels of algorithmic trader activity in a stock lowers the level of short-run...

The estimation of the PIN model and its extensions has posed significant challenges due to various computational problems. To address these issues, we propose a novel estimation method called the Expectation-Conditional Maximization (ECM) algorithm, which can serve as an alternative to existing...

We propose a Reinforcement Learning (RL) algorithm for generating a trading strategy in a realistic setting, that includes transaction costs and factors driving the asset dynamics. We benchmark our algorithm against the analytical optimal solution, available when factors are linear and...