arXiv subjects

Marcos R. Fernandes

Publications and source records attributed to Marcos R. Fernandes.

3 recordsLinked to original sources

arXiv2024

Combining Combined Forecasts: a Network Approach

This paper studies how communication across experts prior to aggregation by a decision-maker affects the efficiency of forecast combination. When experts exchange information before reporting their forecasts, their signals become correlated through the communication network, altering aggregation efficiency even when forecasts are unbiased. The analysis introduces a statistic that characterizes how network structure shapes aggregation efficiency and shows that degree heterogeneity plays a central role. Among connected networks, regular networks attain the minimal level of aggregation distortion, while star networks generate the largest distortions within sparse connected structures. Random network benchmarks show that aggregation efficiency approaches the regular-network benchmark when expected degree either vanishes or becomes large as network size increases, whereas networks with constant expected degree generate intermediate distortions. These results provide a theoretical foundation for understanding how communication across experts affects forecast combination and establish a connection between the forecast combination literature and models of social learning in networks.

econ.TH↗

arXiv2023

Confirmation Bias in Social Networks

In this study, I present a theoretical social learning model to investigate how confirmation bias affects opinions when agents exchange information over a social network. Hence, besides exchanging opinions with friends, agents observe a public sequence of potentially ambiguous signals and interpret it according to a rule that includes confirmation bias. First, this study shows that regardless of level of ambiguity both for people or networked society, only two types of opinions can be formed, and both are biased. However, one opinion type is less biased than the other depending on the state of the world. The size of both biases depends on the ambiguity level and relative magnitude of the state and confirmation biases. Hence, long-run learning is not attained even when people impartially interpret ambiguity. Finally, analytically confirming the probability of emergence of the less-biased consensus when people are connected and have different priors is difficult. Hence, I used simulations to analyze its determinants and found three main results: i) some network topologies are more conducive to consensus efficiency, ii) some degree of partisanship enhances consensus efficiency even under confirmation bias and iii) open-mindedness (i.e. when partisans agree to exchange opinions with opposing partisans) might inhibit efficiency in some cases.

econ.TH↗

arXiv2022

GNSS/MEMS-INS Integration for Drone Navigation using EKF on Lie Groups

Building upon the theory of Kalman Filtering on Lie Groups, this paper describes an Extended Kalman Filter and Smoother for Loosely Coupled Integration of GNSS/INS tailored for post-processing applications. The approach employs a dynamic model on a matrix Lie Group that aggregates position, velocity, attitude, and the IMU biases as a single element of a Lie group. The development was motivated by a drone-borne Differential Interferometric SAR (DinSAR) application, which requires high-precision navigation information for short-flight missions using low-cost MEMS sensors. The filter and the Rauch-Tung-Striebel (RTS) smoother are both implemented and validated. The paper also presents a novel algorithm to initialize the heading value as an alternative to gyro-compassing or magnetometer-based alignments. The Mahalanobis Distance and the $χ^2$-test are employed during the filter update step to address the practical issue of outlier rejection for the GNSS measurements. The paper uses synthetic data to compare classic navigation schemes based on multiplicative quaternions and Euler angles. Finally, real data experiments demonstrate that the Kalman Filter based on Lie Groups performs better DinSAR processing than state-of-the-art commercial software.

cs.RO↗