We set up an agent-based macromodel focusing on consumption-saving without the assumption of utility maximization, but preserving certain "rational" aspects of human choice based on the idea of ecological rationality Todd et al. (2012). In this framework we address the classical problem of the...

We consider a simple dynamic model of environmental taxation that exhibits time inconsistency. There are two categories of firms, Believers, who take the tax announcements made by the Regulator to face value, and Non-Believers, who perfectly anticipate the Regulator's decisions, albeit at a...

We consider a simple dynamic model of environmental taxation that exhibits time inconsistency. There are two categories of firms, Believers, who take the tax announcements made by the Regulator to face value, and Non-Believers, who perfectly anticipate the Regulator's decisions, albeit at a...

I characterize the fixed points of continuous and (neighbor-)constricting functions f:ℝ^{v}→ℝ^{v} and show that each recursively defined sequence x^{k 1}=f(x^{k}), k=0,1,2,... converges to a fixed point of f. The results are applied to generalize the existing results on convergence of...

We analyze optimal monetary policy when a central bank has to learn about an unknown coefficient that determines the effect of surprise inflation on aggregate demand. We derive the optimal policy under active learning and compare it to two limiting cases---certainty equivalence policy and...

Tuning one's shower in some hotels may turn into a challenging coordination game with imperfect information. The temperature sensitivity increases with the number of agents, making the problem possibly unlearnable. Because there is in practice a finite number of possible tap positions, identical...

Probabilistic Graphical Models (PGMs) offer a robust yet intuitive framework to deal with uncertainty and complexity and have been effectively applied to diverse problems across multiple domains. While the majority of work has focused on cross-sectional data, there has been a recent increase of...

We propose to solve large scale Markowitz mean-variance (MV) portfolio allocation problem using reinforcement learning (RL). By adopting the recently developed continuous-time exploratory control framework, we formulate the exploratory MV problem in high dimensions. We further show the...

We approach the continuous-time mean-variance (MV) portfolio selection with reinforcement learning (RL). The problem is to achieve the best tradeoff between exploration and exploitation, and is formulated as an entropy-regularized, relaxed stochastic control problem. We prove that the optimal...