We build forecasting models to predict second-hand car prices capable of predicting multiple car makes and models. Using a total of 141 car attributes we fit 15 machine learning models and rank their ability to accurately predict used automobile prices according to two performance criteria. In...

Information constraints rank high among barriers to agricultural technology adoption among small-scale farmers, particularly for complex bundles of complementary practices that may involve several components for successful implementation. Information communication technologies (ICTs) are...

Do agents believe to be agreeing more with others in the long-run? This paper designs an experiment to study how cognitive abilities affect actual and perceived disagreement in a standard sequential belief updating task with public signals. We document a persistent gap in the perception of...

Adaptive coding is defined as the adjustment of parameters of a cognitive process as a function of the task's statistical properties. Our aim is to demonstrate that adaptive coding maximizes performance in situations where decision-making follows a reinforcement learning process (RLM), and test...

We study learning by privately informed forward-looking agents in a simple repeated-action setting of social learning. Under a symmetric signal structure, forward-looking agents behave myopically for any degrees of patience. Myopic equilibrium is unique in the class of symmetric threshold...

A perennial task for the state is the creation and policing of categories. State-created categories have real world impacts on the public. The consequences of racial categorizations, for example, are well-documented. We examine a less studied consequence of state categorization, which are the...

We use a reinforcement model to compute the hedging policy for Credit Valuation Adjustment ( CVA ) problems. Reinforcement learning can be used to solve financial applications ofintertemporal choice. In finance, common problems of this kind include pricing and hedging ofcontingent claims,...

Activity, as well as expectations, often exhibit asymmetric dynamics by increasing gradually with occasional large downturns. Such dynamics emerge in a social learning model, which entails strong feedback between activity and information. In the model, activity reveals private information about...

We propose an actor-critic reinforcement learning (RL) algorithm for the optimal execution problem. We consider the celebrated Almgren-Chriss model in continuous time and formulate a relaxed stochastic control problem for execution under an entropy regularized mean-quadratic variation objective....

We consider two data-driven approaches to hedging, Reinforcement Learning and Deep Trajectory-based Stochastic Optimal Control, under a stepwise mean-variance objective. We compare their performance for a European call option in the presence of transaction costs under discrete trading schedules....