arXiv subjects

Andrea Falcón-Cortés

Publications and source records attributed to Andrea Falcón-Cortés.

5 recordsLinked to original sources

arXiv2022

A machine learning model to identify corruption in México's public procurement contracts

The costs and impacts of government corruption range from impairing a country's economic growth to affecting its citizens' well-being and safety. Public contracting between government dependencies and private sector instances, referred to as public procurement, is a fertile land of opportunity for corrupt practices, generating substantial monetary losses worldwide. Thus, identifying and deterring corrupt activities between the government and the private sector is paramount. However, due to several factors, corruption in public procurement is challenging to identify and track, leading to corrupt practices going unnoticed. This paper proposes a machine learning model based on an ensemble of random forest classifiers, which we call hyper-forest, to identify and predict corrupt contracts in México's public procurement data. This method's results correctly detect most of the corrupt and non-corrupt contracts evaluated in the dataset. Furthermore, we found that the most critical predictors considered in the model are those related to the relationship between buyers and suppliers rather than those related to features of individual contracts. Also, the method proposed here is general enough to be trained with data from other countries. Overall, our work presents a tool that can help in the decision-making process to identify, predict and analyze corruption in public procurement contracts.

cs.CY↗

arXiv2022

Practices of public procurement and the risk of corrupt behavior before and after the government transition in México

Corruption has a significant impact on economic growth, democracy, and inequality. It has severe consequences at the human level are incalculable. Public procurement, where public resources are used to purchase goods or services from the private sector, are particularly susceptible to corrupt practices. However, government turnover may bring significant changes in the way public contracting is done, and thus, in the levels and types of corruption involved in public procurement. In this respect, México lived a historical government transition in 2018, with the new government promising a crackdown on corruption. In this work, we analyze data from more than 1.5 million contracts corresponding from 2013 to 2020, to study to what extent this change of government affected the characteristics of public contracting, and we try to determine whether these changes affect how corruption takes place. To do this, we propose a statistical framework to compare the characteristics of the contracting practices within each administration, separating the contracts in different classes depending on whether or not they were made with companies that have now been identified as being involved in corrupt practices. We find that while the amount of resources spent with companies that turned out to be corrupt has decreased substantially, many of the patterns followed to contract these companies were maintained, and some of those in which changes did occur, are suggestive of a larger risk of corruption.

stat.AP↗

arXiv2020

Anderson-like localization transition of random walks with resetting

We study several lattice random walk models with stochastic resetting to previously visited sites which exhibit a phase transition between an anomalous diffusive regime and a localization regime where diffusion is suppressed. The localized phase settles above a critical resetting rate, or rate of memory use, and the probability density asymptotically adopts in this regime a non-equilibrium steady state similar to that of the well known problem of diffusion with resetting to the origin. The transition occurs because of the presence of a single impurity site where the resetting rate is lower than on other sites, and around which the walker spontaneously localizes. Near criticality, the localization length diverges with a critical exponent that falls in the same class as the self-consistent theory of Anderson localization of waves in random media. The critical dimensions are also the same in both problems. Our study provides analytically tractable examples of localization transitions in path-dependent, reinforced stochastic processes, which can be also useful for understanding spatial learning by living organisms.

cond-mat.stat-mech↗

arXiv2019

Collective learning from individual experiences and information transfer during group foraging

Living in groups brings benefits to many animals, such as a protection against predators and an improved capacity for sensing and making decisions while searching for resources in uncertain environments. A body of studies has shown how collective behaviors within animal groups on the move can be useful for pooling information about the current state of the environment. The effects of interactions on collective motion have been mostly studied in models of agents with no memory. Thus, whether coordinated behaviors can emerge from individuals with memory and different foraging experiences is still poorly understood. By means of an agent based model, we quantify how individual memory and information fluxes can contribute to improving the foraging success of a group in complex environments. In this context, we define collective learning as a coordinated change of behavior within a group resulting from individual experiences and information transfer. We show that an initially scattered population of foragers visiting dispersed resources can gradually achieve cohesion and become selectively localized in space around the most salient resource sites. Coordination is lost when memory or information transfer among individuals is suppressed. The present modelling framework provides predictions for empirical studies of collective learning and could also find applications in swarm robotics and motivate new search algorithms based on reinforcement.

q-bio.PE↗

arXiv2017

Localization transition induced by learning in random searches

We solve an adaptive search model where a random walker or Lévy flight stochastically resets to previously visited sites on a $d$-dimensional lattice containing one trapping site. Due to reinforcement, a phase transition occurs when the resetting rate crosses a threshold above which non-diffusive stationary states emerge, localized around the inhomogeneity. The threshold depends on the trapping strength and on the walker's return probability in the memoryless case. The transition belongs to the same class as the self-consistent theory of Anderson localization. These results show that similarly to many living organisms and unlike the well-studied Markovian walks, non-Markov movement processes can allow agents to learn about their environment and promise to bring adaptive solutions in search tasks.

cond-mat.stat-mech↗