We study a dynamic mean-variance portfolio optimization problem under the reinforcement learning framework, where an entropy regularizer is introduced to induce exploration. Due to the time-inconsistency involved in a mean-variance criterion, we aim to learn an equilibrium strategy. Under an...

This paper is the first to use the WeChat platform, one of the largest social networks, to conduct an online experiment of artificial investment games. We investigate how people's forecasts about the financial market and investment decisions are shaped by whether they can observe others'...

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...

We study the experimentation dynamics of a decision maker (DM) in a two-armed bandit setup (Bolton and Harris [1999]), where the agent holds ambiguous beliefs regarding the distribution of the return process of one arm and is certain about the other one. The DM entertains Multiplier preferences...

A key feature of online gaming, which serves as an important measure of consumer engagement with a game, is level progression, wherein players make play-or-quit decisions at each level of the game. Understanding users’ level progression behavior is therefore fundamental to game designers. In...