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A hands-on machine learning primer for social scientists : math, algorithms and code

Askitas, Nikos - 2024

This paper addresses the steep learning curve in Machine Learning faced by non-computer scientists, particularly social scientists, stemming from the absence of a primer on its fundamental principles. I adopt a pedagogical strategy inspired by the adage ”once you understand OLS, you can work...

Persistent link: https://www.econbiz.de/10015070152

Optimal price targeting

Smith, Adam N.; Seiler, Stephan; Aggarwal, Ishant - 2021

We examine the proﬁtability of personalized pricing policies that are derived using diﬀerent speciﬁcations of demand in a typical retail setting with consumer-level panel data. We generate pricing policies from a variety of models, including Bayesian hierarchical choice models, regularized...

Persistent link: https://www.econbiz.de/10012692296

Financial frictions and the wealth distribution

Fernández-Villaverde, Jesús; Hurtado, Samuel; Nuño, Galo - 2020

We postulate a nonlinear DSGE model with a financial sector and heterogeneous households. In our model, the interaction between the supply of bonds by the financial sector and the precautionary demand for bonds by households produces significant endogenous aggregate risk. This risk induces an...

Persistent link: https://www.econbiz.de/10012260513

Racial implications of police-algorithm interactions : evidence from rearrest predictions

Lee, Yong Suk - 2025

algorithm’s assessment. The experiment varied the visibility of the offender’s race (revealed to one group, hidden in another … algorithm’s assessment when the race of the profile is disclosed. However, these adjustments exhibit significant racial … algorithm’s assessments, while White officers did not. Our findings reveal the limited and nuanced effectiveness of algorithms …

Persistent link: https://www.econbiz.de/10015401979

Improving human deception detection using algorithmic feedback

Serra-Garcia, Marta; Genîzî, Ûrî - 2023 - This version: August 2023

algorithms do signiﬁcantly better. We provide participants algorithmic advice by ﬂagging videos for which an algorithm predicts a …

Persistent link: https://www.econbiz.de/10014304521

Collusion by algorithm: the role of unobserved actions

Martin, Simon; Rasch, Alexander - 2022

We analyze the effects of better algorithmic demand forecasting on collusive profits. We show that the comparative statics crucially depend on the whether actions are observable. Thus, the optimal antitrust policy needs to take into account the institutional settings of the industry in question....

Persistent link: https://www.econbiz.de/10013093034

Early detection of students at risk : predicting student dropouts using administrative student data and machine learning methods

Berens, Johannes; Schneider, Kerstin; Görtz, Simon; … - 2018

uses regression analysis, neural networks, decision trees, and the AdaBoost algorithm to identify student characteristics …

Persistent link: https://www.econbiz.de/10011906290

Won't get fooled again : a supervised machine learning approach for screening gasoline cartels

Silveira, Douglas; Vasconcelos, Silvinha Pinto; … - 2021

In this article, we combine machine learning techniques with statistical moments of the gasoline price distribution. By doing so, we aim to detect and predict cartels in the Brazilian retail market. In addition to the traditional variance screen, we evaluate how the standard deviation,...

Persistent link: https://www.econbiz.de/10012417720

Economic determinants of regional trade agreements revisited using machine learning

Blöthner, Simon; Larch, Mario - 2021

While traditional empirical models using determinants like size and trade costs are able to predict RTA formation reasonably well, we demonstrate that allowing for machine detected non-linear patterns helps to improve the predictive power of RTA formation substantially. We employ machine...

Persistent link: https://www.econbiz.de/10012602123

On the causes and consequences of deviations from rational behavior

Zēgners, Dainis; Sunde, Uwe; Strittmatter, Anthony - 2020

This paper presents novel evidence for the prevalence of deviations from rational behavior in human decision making – and for the corresponding causes and consequences. The analysis is based on move-by-move data from chess tournaments and an identification strategy that compares behavior of...

Persistent link: https://www.econbiz.de/10012226615