Predictions about the future have been made since the earliest days of humankind, but today, we are living in a brave new world of prediction. Today’s predictions are produced by machine learning algorithms that analyze massive quantities of personal data. Increasingly, important decisions...

We present a methodology for analyzing the impact of algorithmic recommendations on product market competition, addressing concerns that have been raised in both academic and policy circles regarding their potential anti-competitive effects. Our analysis demonstrates that recommender systems...

Sensor arrays play a key role in the detection of soil gas. Nevertheless, suboptimal sensor combinations may culminate in a myriad of complications, such as overlapping selectivities, superfluous features, and deterioration of performance. To mitigate those challenges, we innovatively developed...

This paper considers the scenario where the assortment provided by the seller can influence customers' evaluation of item utility. A possible consequence is that certain items in an assortment become "star items" to customers, and customers over-evaluate their utilities. We call such a...

In today’s world, most of the data is generated through computer applications. These applications can be used to predict and analyze the future. To achieve this phenomenon, we train the machines to read data and accordingly predict the future which is called as Machine Learning. Machine...

Algorithms, especially those based on artificial intelligence, play an increasingly important role in our economy. They are used by market participants to make pricing, output, quality, and inventory decisions; to predict market entry, expansion, and exit; and to predict regulatory moves. In a...

In financial markets, the success of an investor depends on the quality of information and the speed of his decision. Therefore, an accurate prediction allows investors to make informed decisions about investments and reduce risks. Until today, various researches have been carried out in order...

We provide a general framework to consider the effects of algorithmic demand and supply prediction in matching platforms, such as Task Rabbit for labor markets or Amazon for goods markets, where a significant proportion of matches are repeat transactions. We show that algorithmic repeat match...

This paper presents an algorithm for simulating multiple equilibria in otherwise-linearmodels with perfect foresight and occasionally-binding constraints. We extend theguess-verify approach of Guerrieri and Iacoviello (2015) to detect and simulate multipleperfect foresight equilibria, and also...

We review the modelling and pricing of American options both in classical mathematical finance and in reinforcement learning (RL). Some of the main difficulties when pricing an American put option with reinforcement learning is to make sure that the continuation value one step before maturity is...