A variational-autoencoder approach to solve the hidden profile task in hybrid human-machine teams

Algorithmic agents, popularly known as bots, have been accused of spreading misinformation online and supporting fringe views. Collectives are vulnerable to hidden-profile environments, where task-relevant information is unevenly distributed across individuals. To do well in this task, information aggregation must equally weigh minority and majority views against simple but inefficient majority-based decisions. In an experimental design, human volunteers working in teams of 10 were asked to solve a hidden-profile prediction task. We trained a variational auto-encoder (VAE) to learn people's hidden information distribution by observing how people's judgements correlated over time. A bot was designed to sample responses from the VAE latent embedding to selectively support opinions proportionally to their under-representation in the team. We show that the presence of a single bot (representing 10\% of team members) can significantly increase the polarization between minority and majority opinions by making minority opinions less prone to social influence. Although the effects on hybrid team performance were small, the bot presence significantly influenced opinion dynamics and individual accuracy. These findings show that self-supervised machine learning techniques can be used to design algorithms that can sway opinion dynamics and group outcomes.

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A variational-autoencoder approach to solve the hidden profile task in hybrid human-machine teams

10.31234/osf.io/s67zf ◽

2021 ◽

Author(s):

Niccolo Pescetelli ◽

Patrik Reichert

Keyword(s):

Team Performance ◽

Opinion Dynamics ◽

Relevant Information ◽

Simple Majority ◽

Information Distribution ◽

Team Members ◽

Online Social Media ◽

Variational Autoencoder ◽

Hidden Profile ◽

Hybrid Teams

Online, social media bots have been accused to spread misinformation and support extreme or minority-held opinions. However, bots in hybrid human-machine teams can also be designed to improve team performance. In this paper, we study the effect of a single minority-supporting bot in hybrid teams in a carefully controlled experiment. People working in teams of 10 were asked to solve a hidden-profile prediction task, where task-relevant information was scattered unequally across team members. To do well in this task, pieces of information shared by the minority and the majority of players should be integrated. Simple majority-based decisions are not enough to perform well as information held by minority players is also valuable. We used a variational auto-encoder to train a bot to learn people's information distribution by observing how people's judgements correlated over time. After training, a bot was designed to increase team performance by selectively supporting opinions proportionally to their under-representation in the team. We show that the presence of a single bot (representing 10\% of team members) can significantly increase the polarization between minority and majority opinions by making minority opinions less prone to social influence. Although the effects on hybrid team performance were negligible, the bot presence significantly influenced team opinion dynamics. These findings show that unsupervised learning can be used to program bots that can improve team performance.

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Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2

Current Medicinal Chemistry ◽

10.2174/2213275912666191102162959 ◽

2020 ◽

Vol 28 (2) ◽

pp. 253-265 ◽

Cited By ~ 3

Author(s):

Gabriela Bitencourt-Ferreira ◽

Amauri Duarte da Silva ◽

Walter Filgueira de Azevedo

Keyword(s):

Machine Learning ◽

Binding Affinity ◽

Predictive Performance ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Scoring Functions ◽

Cyclin Dependent Kinase ◽

Learning Models ◽

Learning Techniques ◽

Machine Learning Models

Background: The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors for this enzyme. CDK2 is a protein target for the development of drugs intended to modulate cellcycle progression and control. Such drugs have potential anticancer activities. Objective: Our goal here is to review recent applications of machine learning methods to predict ligand- binding affinity for protein targets. To assess the predictive performance of classical scoring functions and targeted scoring functions, we focused our analysis on CDK2 structures. Methods: We have experimental structural data for hundreds of binary complexes of CDK2 with different ligands, many of them with inhibition constant information. We investigate here computational methods to calculate the binding affinity of CDK2 through classical scoring functions and machine- learning models. Results: Analysis of the predictive performance of classical scoring functions available in docking programs such as Molegro Virtual Docker, AutoDock4, and Autodock Vina indicated that these methods failed to predict binding affinity with significant correlation with experimental data. Targeted scoring functions developed through supervised machine learning techniques showed a significant correlation with experimental data. Conclusion: Here, we described the application of supervised machine learning techniques to generate a scoring function to predict binding affinity. Machine learning models showed superior predictive performance when compared with classical scoring functions. Analysis of the computational models obtained through machine learning could capture essential structural features responsible for binding affinity against CDK2.

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Local mortality estimates during the COVID-19 pandemic in Italy

Journal of Population Economics ◽

10.1007/s00148-021-00857-y ◽

2021 ◽

Author(s):

Augusto Cerqua ◽

Roberta Di Stefano ◽

Marco Letta ◽

Sara Miccoli

Keyword(s):

Machine Learning ◽

Excess Mortality ◽

Control Method ◽

Local Level ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Mortality Data ◽

Official Method ◽

Learning Techniques ◽

Mortality Estimates

AbstractEstimates of the real death toll of the COVID-19 pandemic have proven to be problematic in many countries, Italy being no exception. Mortality estimates at the local level are even more uncertain as they require stringent conditions, such as granularity and accuracy of the data at hand, which are rarely met. The “official” approach adopted by public institutions to estimate the “excess mortality” during the pandemic draws on a comparison between observed all-cause mortality data for 2020 and averages of mortality figures in the past years for the same period. In this paper, we apply the recently developed machine learning control method to build a more realistic counterfactual scenario of mortality in the absence of COVID-19. We demonstrate that supervised machine learning techniques outperform the official method by substantially improving the prediction accuracy of the local mortality in “ordinary” years, especially in small- and medium-sized municipalities. We then apply the best-performing algorithms to derive estimates of local excess mortality for the period between February and September 2020. Such estimates allow us to provide insights about the demographic evolution of the first wave of the pandemic throughout the country. To help improve diagnostic and monitoring efforts, our dataset is freely available to the research community.

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