Bayesian optimization (BO) is increasingly employed in critical applications such as materials design and drug discovery. An increasingly popular strategy in BO is to forgo the sole reliance on high-fidelity data and instead use an ensemble of information sources which provide inexpensive...

We present and analyze a theory of cooperative bargaining under asymmetric information, based on the equity principles of the conditional random dictatorship—choosing with equal probability one of the individuals to act as a dictator under the participation constraint of the other players. Our...

Accuracy and interpretability of a (non-life) insurance pricing model are essential qualities to ensure fair and transparent premiums for policy-holders, that reflect their risk. In recent years, the classification and regression trees (CARTs) and their ensembles have gained popularity in the...

We introduce the notion of a Conditional Hypothesis Testing System (CHTS) that specifies the concrete mode of non-Bayesian reactions for unexpected news in a conditional world. We show that Ortoleva's (2012) Hypothesis Testing Model (HTM) gives rise to a CHTS and thus provides an axiomatic...

Base rate neglect typically involves the underweighting of priors in a world with objective probability distributions (Kahneman and Tversky, 1973). In reality, however, individuals often face scenarios with ambiguous priors, in which they have no full confidence. We show, theoretically, that...

Bayesian decisions are observationally identical to decisions with judgment. Decisions with judgment test whether a judgmental decision is optimal and, in case of rejection, move to the closest boundary of the confidence interval, for a given confidence level. The resulting decisions condition...

We show that under standard assumptions the elements of the impact matrix of the structural vector autoregression (SVAR) are always at least set identified and bounded by the standard deviations of the corresponding reduced form errors. This result facilitates valid Bayesian inference without...

Pro-environmental waste disposal behavior plays a fundamental role in improving rural waste management and rural livability. Recent years have witnessed an increased social, political and academic interest in the influencing mechanism of pro-environmental waste disposal behavior. In particular,...

This paper investigates the ability of several generalized Bayesian vector autoregressions to cope with the extreme COVID-19 observations and discusses their impact on prior calibration for inference and forecasting purposes. It shows that the preferred model interprets the pandemic episode as a...