This paper estimates time preference parameters using commonly-applied methodologies, with the aim of investigating the link between these measures and actual economic behaviour. An experiment was conducted in the city of Thies, in Senegal, using the unique reference numbers of banknotes as a...

This paper estimates time preference parameters using commonly-applied methodologies, with the aim of investigating the link between these measures and actual economic behaviour. An experiment was conducted in the city of Thies, in Senegal, using the unique reference numbers of banknotes as a...

Companies are increasingly adopting Artificial Intelligence (AI) today. Recently however debates started over the risk of human cognitive biases being replicated (and scaled) by AI. Research on biases in AI predicting consumer choice is incipient and focuses on observable biases. We provide a...

This research explores whether there are systematic cross-national differences in choice-inferred risk preferences between Americans and Chinese. Study 1 found(a) that the Chinese were signi®cantly more risk seeking than the Americans, yet(b) that both nationals predicted exactly the opposite...

I generalize the long-run risks (LRR) model of Bansal and Yaron (2004) by incorporating recursive smooth ambiguity aversion preferences from Klibanoff et al. (2005, 2009) and time-varying ambiguity. Relative to the Bansal-Yaron model, the generalized LRR model is as tractable but more flexible...

This work reports an online experiment with a general-population sample examining the performance of budget-choice tasks for elicitation of risk attitudes. First, I compare the investment task of Gneezy and Potters (1997) with the standard choice- list method of Holt and Laury (2002), and...

A growing number of empirical studies have aimed to identify dynamic inconsistency by combining diverse sets of elicitation designs and tested the extent to which this important component of individual heterogeneity predicts behavior; however, relatively little consensus on which designs are the...

This paper explores the quality of Scope 3 emission data in terms of divergence and composition and the performance of machine learning models in predicting Scope 3 emissions. We do so using the Scope 3 emission datasets of three of the largest data providers (Refinitiv Eikon, and ISS). We find...