Betthäuser et al. (2023) examine the effects of the COVID-19 pandemic on the learning progress of school-aged children. They collect 291 estimates from 42 studies. Their meta-analysis-corrected estimate implies a substantial decline in students' learning (Cohen's d = −0.14, 95% confidence...

This paper provides an overview of behaviors and learning of heterogeneous economic agents -whether individuals or firms- within complex evolving economies. It aims to explore the microfoundation of agency and learning, two fundamental aspects that constitutively contribute to the emergence of...

Using a novel and unique panel dataset of individual-level professional forecasts at short, medium, and very-long horizons, we provide new stylized facts about survey forecasts. We present direct evidence that forecasters use multivariate models in an environment with imperfect information about...

Much research has examined the drivers of firms' R&D investments. However, many questions remain with respect to the role of R&D as a learning target and as a means of achieving legitimacy, particularly in the context of imitative R&D strategy. We develop a theory that integrates different...

We develop a deep reinforcement learning (RL) framework for an optimal market-making (MM) trading problem, specifically focusing on price processes with semi-Markov and Hawkes Jump-Diffusion dynamics. We begin by discussing the basics of RL and the deep RL framework used; we deployed the...

In a misspecified social learning setting, agents are condescending if they perceive their peers as having private information that is of lower quality than it is in reality. Applying this to a standard sequential model, we show that outcomes improve when agents are mildly condescending. In...

We introduce a collective experimentation problem where a continuum of agents choose the timing of irreversible actions under uncertainty and where public feedback from the actions arrives gradually over time. The leading application is the adoption of new technologies. The socially optimal...

We study the standard reputation model with a long-run (LR) player facing a sequence of short-run (SR) opponents, with one difference: the SR players are uncertain about the monitoring structure, while the LR player knows it. We construct examples where the standard reputation result breaks...

Strategic learning self-efficacy reflects the confidence of managers in their ability to learn from the outcomes of past strategic decisions and apply that knowledge to new decisions. This research examines the relationship between strategic learning self-efficacy and a firm's industry-adjusted...

We study a duel game in which each player has incomplete knowledge of the game parameters. We present a simple, heuristically motivated and easily implemented algorithm by which, in the course of repeated plays, each player estimates the missing parameters and consequently learns his optimal...

Intensive care units (ICUs) operate with fixed capacities and face uncertainty such as demand variability, leading to demand-driven, early discharges to free up beds. These discharges can increase readmission rates, negatively impacting patient outcomes and aggravating ICU bottleneck congestion....

We introduce a collective experimentation problem where a continuum of agents choose the timing of irreversible actions under uncertainty and where public feedback from the actions arrives gradually over time. The leading application is the adoption of new technologies. The socially optimal...

Belief‐updating biases hinder the correction of inaccurate beliefs and lead to suboptimal decisions. We complement Rabin and Schrag's (1999) portable extension of the Bayesian model by including conservatism in addition to confirmatory bias. Additionally, we show how to identify these two...

People often form mental models based on incomplete information, revising them as new relevant data becomes available. In this paper, we experimentally investigate how individuals update their models when data on predictive variables are gradually revealed. We find that people's models tend to...

Employee turnover poses a significant challenge to organizations, leading to increased costs and disruptions in workforce stability. In this study, we propose a novel hybrid model for employee turnover prediction that integrates Genetic Algorithms (GA) for feature selection with LightGBM for...

Identifying sensitive periods in which the returns to investments into skills are especially high is challenging, but crucial for an effective and efficient timing of parental or public investments aimed at fostering children's skills. We can detect sensitive periods with a novel design by...

I study the efficient design of a queue to dynamically allocate a scarce resource to long‐lived agents. Agents can be served multiple times, and their valuations fluctuate over time with some persistence. Each agent privately learns whether his prevailing valuation is high or low only when...