EU Competition Policy: Algorithmic Collusion in the Digital Single Market

E-commerce promises a digital environment with ‘more perfect’ market characteristics. Although consumers may benefit from digital efficiencies, firms’ exploitation of such benefits may require new policy to regulate in line with the European Commission’s Digital Single Market Strategy. Price-setting algorithms are central to this dichotomy, as faster and more transparent pricing strategies could conceivably maintain algorithmic price-fixing cartels – which Article 101 of the Treaty on the Functioning of the European Union may prove inadequate in tackling. This paper looks to remedy a perceived failure in the literature to appreciate the legal and economic analysis necessary to inform an alternative policy. It will assess the anti-competitive impact of pricing algorithms by contrasting the online and offline economic environments against which policy is set. It will evaluate the effectiveness of current policy in tackling explicit and tacit algorithmic collusion, accounting for its impact upon reasonable business practices, consumer welfare, liability and enforcement, and legal concepts which can be difficult to apply to the digital market. As long-term consumer welfare could be sacrificed by enforcing short-term remedies, it is advised that policy returns to its ordoliberal roots: prioritising the maintenance of healthy competition over current welfare-first economics which lack sufficient clarity to regulate algorithms

The participation dividend of taxation: how citizens in Congo engage more with the state when it tries to tax them

This article provides evidence from a fragile state that citizens demand more of a voice in the government when it tries to tax them. I examine a field experiment randomizing property tax collection across 356 neighborhoods of a large Congolese city. The tax campaign was the first time most citizens had been registered by the state or asked to pay formal taxes. It raised property tax compliance from 0.1% in control to 11.6% in treatment. It also increased political participation by about 5 percentage points (31%): citizens in taxed neighborhoods were more likely to attend town hall meetings hosted by the government or submit evaluations of its performance. To participate in these ways, the average citizen incurred costs equal to their daily household income, and treated citizens spent 43% more than control. Treated citizens also positively updated about the provincial government, perceiving more revenue, less leakage, and a greater responsibility to provide public goods. The results suggest that broadening the tax base has a “participation dividend,” a key idea in historical accounts of the emergence of inclusive governance in early modern Europe and a common justification for donor support of tax programs in weak states

Essays on Structural Econometric Modeling and Machine Learning

This dissertation is composed of three independent chapters relating the theory and empirical methodology in economics to machine learning and important topics in information age . The first chapter raises an important problem in structural estimation and provide a solution to it by incorporating a culture in machine learning. The second chapter investigates a problem of statistical discrimination in big data era. The third chapter studies the implication of information uncertainty in the security software market. Structural estimation is a widely used methodology in empirical economics, and a large class of structural econometric models are estimated through the generalized method of moments (GMM). Traditionally, a model to be estimated is chosen by researchers based on their intuition on the model, and the structural estimation itself does not directly test it from the data. In other words, not sufficient amount of attention is paid to devise a principled method to verify such an intuition. In the first chapter, we propose a model selection for GMM by using cross-validation, which is widely used in machine learning and statistics communities. We prove the consistency of the cross-validation. The empirical property of the proposed model selection is compared with existing model selection methods by Monte Carlo simulations of a linear instrumental variable regression and oligopoly pricing model. In addition, we propose the way to apply our method to Mathematical Programming of Equilibrium Constraint (MPEC) approach. Finally, we perform our method to online-retail sales data to compare dynamic model to static model. In the second chapter, we study a fair machine learning algorithm that avoids a statistical discrimination when making a decision. Algorithmic decision making process now affects many aspects of our lives. Standard tools for machine learning, such as classification and regression, are subject to the bias in data, and thus direct application of such off-the-shelf tools could lead to a specific group being statistically discriminated. Removing sensitive variables such as race or gender from data does not solve this problem because a disparate impact can arise when non-sensitive variables and sensitive variables are correlated. This problem arises severely nowadays as bigger data is utilized, it is of particular importance to invent an algorithmic solution. Inspired by the two-stage least squares method that is widely used in the field of economics, we propose a two-stage algorithm that removes bias in the training data. The proposed algorithm is conceptually simple. Unlike most of existing fair algorithms that are designed for classification tasks, the proposed method is able to (i) deal with regression tasks, (ii) combine explanatory variables to remove reverse discrimination, and (iii) deal with numerical sensitive variables. The performance and fairness of the proposed algorithm are evaluated in simulations with synthetic and real-world datasets. The third chapter examines the issue of information uncertainty in the context of information security. Many users lack the ability to correctly estimate the true quality of the security software they purchase, as evidenced by some anecdotes and even some academic research. Yet, most of the analytical research assumes otherwise. Hence, we were motivated to incorporate this “false sense of security” behavior into a game-theoretic model and study the implications on welfare parameters. Our model features two segments of consumers, well-and ill-informed, and the monopolistic software vendor. Well-informed consumers observe the true quality of the security software, while the ill-informed ones overestimate. While the proportion of both segments are known to the software vendor, consumers are uncertain about the segment they belong to. We find that, in fact, the level of the uncertainty is not necessarily harmful to society. Furthermore, there exist some extreme circumstances where society and consumers could be better off if the security software did not exist. Interestingly, we also find that the case where consumers know the information structure and weight their expectation accordingly does not always lead to optimal social welfare. These results contrast with the conventional wisdom and are crucially important in developing appropriate policies in this context

Financial Fraud Detection and Data Mining of Imbalanced Databases using State Space Machine Learning

Risky decisions made by humans exhibit characteristics common to each decision. The related systems experience repeated abuse by risky humans and their actions collude to form a systemic behavioural set. Financial fraud is an example of such risky behaviour. Fraud detection models have drawn attention since the financial crisis of 2008 because of their frequency, size and technological advances leading to financial market manipulation. Statistical methods dominate industrial fraud detection systems at banks, insurance companies and financial marketplaces. Most efforts thus far have focused on anomaly detection problems and simple rules in the academic literature and industrial setting. There are unsolved issues in modeling the behaviour of risky agents in real-world financial markets using machine learning. This research studies the challenges posed by fraud detection, including the problem of imbalanced class distributions, and investigates the use of Reinforcement Learning (RL) to model risky human behaviour. Models have been developed to transform the relevant financial data into a state-space system. Reinforcement Learning agents uncover the decision-making processes by risky humans and derive an optimal path of behaviour at the end of the learning process. States are weighted by risk and then classified as positive (risky) or negative (not-risky). The positive samples are composed of features that represent the hidden information underlying the risky behaviour. Reinforcement Learning is implemented as unsupervised and supervised models. The unsupervised learning agent searches for risky behaviour without any previous knowledge of the data; it is not “trained” on data with true class labels. Instead, the RL learner relates samples through experience. The supervised learner is trained on a proportion (e.g. 90%) of the data with class labels. It derives a policy of optimal actions to be taken at each state during the training stage. One policy is selected from several learning agents and then the model is exposed to the other proportion (e.g. 10%) of data for classification. RL is hybridized with a Hidden Markov Model (HMM) in the supervised learning model to impose a probabilistic framework around the risky agent’s behaviour. We first study an insider trading example to demonstrate how learning algorithms can mimic risky agents. The classification power of the model is further demonstrated by applying it to a real-world based database for debit card transaction fraud. We then apply the models to two problems found in Statistics Canada databases: heart disease detection and female labour force participation. All models are evaluated using appropriate measures for imbalanced class problems: “sensitivity” and “false positive”. Sensitivity measures the number of correctly classified positive samples (e.g. fraud) as a proportion of all positive samples in the data. False positive counts the number of negative samples classified positive as a proportion of all negative samples in the data. The intent is to maximize sensitivity and minimize the false positive rate. All models show high sensitivity rates while exhibiting low false positive rates. These two metrics are ideal for industrial implementation because of high levels of identification at a low cost. Fraud detection rate is the focus with detection rates of 75-85% proving that RL is a superior method for data mining of imbalanced databases. By solving the problem of hidden information, this research can facilitate the detection of risky human behaviour and prevent it from happening

Spam Reviews Detection in the Time of COVID-19 Pandemic: Background, Definitions, Methods and Literature Analysis

This work has been partially funded by projects PID2020-113462RB-I00 (ANIMALICOS), granted by Ministerio Espanol de Economia y Competitividad; projects P18-RT-4830 and A-TIC-608-UGR20 granted by Junta de Andalucia, and project B-TIC-402-UGR18 (FEDER and Junta de Andalucia).During the recent COVID-19 pandemic, people were forced to stay at home to protect their own and others’ lives. As a result, remote technology is being considered more in all aspects of life. One important example of this is online reviews, where the number of reviews increased promptly in the last two years according to Statista and Rize reports. People started to depend more on these reviews as a result of the mandatory physical distance employed in all countries. With no one speaking to about products and services feedback. Reading and posting online reviews becomes an important part of discussion and decision-making, especially for individuals and organizations. However, the growth of online reviews usage also provoked an increase in spam reviews. Spam reviews can be identified as fraud, malicious and fake reviews written for the purpose of profit or publicity. A number of spam detection methods have been proposed to solve this problem. As part of this study, we outline the concepts and detection methods of spam reviews, along with their implications in the environment of online reviews. The study addresses all the spam reviews detection studies for the years 2020 and 2021. In other words, we analyze and examine all works presented during the COVID-19 situation. Then, highlight the differences between the works before and after the pandemic in terms of reviews behavior and research findings. Furthermore, nine different detection approaches have been classified in order to investigate their specific advantages, limitations, and ways to improve their performance. Additionally, a literature analysis, discussion, and future directions were also presented.Spanish Government PID2020-113462RB-I00Junta de Andalucia P18-RT-4830 A-TIC-608-UGR20 B-TIC-402-UGR18European Commission B-TIC-402-UGR1