This paper explores the question whether boundedly rational agents learn to behave optimally when asked to voluntarily contribute to a public good. The decision process of individuals is described by an Evolutionary Algorithm. We analyze the learning process of purely and impurely altruistic...

Dieser Beitrag gibt einen Überblick über Ansatzpunkte und Möglichkeiten zur Integration von Lernprozessen in volkswirtschaftliche Fragestellungen. Es werden alternative Methoden vorgestellt, individuelle aber auch gesellschaftliche Lernenvorgänge in ökonomischen Modellen zu erfassen.

We propose a simple approach to bridge between portfolio theory and machine learning. The outcome is an out-of-sample machine learning efficient frontier based on two assets, high risk and low risk. By rotating between the two assets, we show that the proposed frontier dominates the...

A treatment of policy design for learnability in worlds where agents have potentially misspecified their learning models has yet to surface. This paper provides such a treatment. We begin with the notion that because the profession has yet to settle on a consensus model of the economy, it is...

We construct realistic equity option market simulators based on generative adversarial networks (GANs). We consider recurrent and temporal convolutional architectures, and assess the impact of state compression. Option market simulators are highly relevant because they allow us to extend the...

The model presented here is an estimated medium-scale model for the United States (US) economy developed to forecast and analyse policy issues for the US. The model is specified to track the deviation of the medium-run developments from the balanced-growth-path via an estimated CES production...

Data driven companies effectively use regression machine learning methods for making predictions in many sectors. Cloud-based Azure Machine Learning Studio (MLS) has a potential of expediting machine learning experiments by offering a convenient and powerful integrated development environment....

We present a numerically efficient approach for machine-learning a risk-neutral measure for paths of simulated spot and option prices up to a finite horizon under convex transaction costs and convex trading constraints. This approach can then be used to implement a stochastic implied volatility...

In recent years, the learnability of rational expectations equilibria (REE) and determinacy of economic structures have rightfully joined the usual performance criteria among the sought-after goals of policy design. Some contributions to the literature, including Bullard and Mitra (2001) and...

We study equilibrium selection in a common-interest voting model with three alternatives. In the model, symmetric Bayesian Nash Equilibria (BNE) of varying efficiency are known to exist. Employing evolutionary adaptive learning simulations, we find interesting new equilibria. In simulations, we...