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on Big Data |
| By: | Olivier Gergaud; Victor Ginsburgh |
| Abstract: | The paper discusses briefly the methods that are used to evaluate ex ante and/or ex post cultural events. We suggest that Google Trends, which is now quite common can also easily be used in this case. |
| Keywords: | Festivals, European Capital of Culture, Subsidies, Tourism |
| Date: | 2019–10 |
| URL: | http://d.repec.org/n?u=RePEc:eca:wpaper:2013/294517&r=all |
| By: | Nicholas Economides (Professor of Economics, NYU Stern School of Business, New York, New York 10012); Ioannis Lianos (Professor of Global Competition Law and Public Policy, Faculty of Laws, University College London, and Hellenic Competition Commission) |
| Abstract: | The recent controversy on the intersection of competition law with the protection of privacy, following the emergence of big data and social media is a major challenge for competition authorities worldwide. Recent technological progress in data analytics may greatly facilitate the prediction of personality traits and attributes from even a few digital records of human behaviour. There are different perspectives globally as to the level of personal data protection and the role competition law may play in this context, hence the discussion of integrating such concerns in competition law enforcement may be premature for some jurisdictions. However, a market failure approach may provide common intellectual foundations for the assessment of harms associated to the exploitation of personal data, even when the specific legal system does not formally recognize a fundamental right to privacy. The paper presents a model of market failure based on a requirement provision in the acquisition of personal information from users of other products/services. We establish the economic harm from the market failure and the requirement using the traditional competition law toolbox and focusing more on situations in which the restriction on privacy may be analysed as a form of exploitation. Eliminating the requirement and the market failure by creating a functioning market for the sale of personal information is imperative. This emphasis on exploitation does not mean that restrictions on privacy may not result from exclusionary practices. However, we analyse these in a separate study. Besides the traditional analysis of the requirement and market failure, we note that there are typically informational asymmetries between the data controller and the data subject. The latter may not be aware that his data was harvested, in the first place, or that the data will be processed by the data controller for a different purpose, or shared and sold to third parties. The exploitation of personal data may also result from economic coercion, on the basis of resource-dependence or lock-in of the user, the latter having no other choice, in order to enjoy the consumption of a specific service provided by the data controller or its ecosystem, than to consent to the harvesting and use of his data. A behavioural approach would also emphasise the possible internalities (demand-side market failures) coming out of the bounded rationality, or the fact that people do not internalise all consequences of their actions and face limits in their cognitive capacities. The paper also addresses the way competition law could engage with exploitative conduct leading to privacy harm, both for ex ante and ex post enforcement. With regard to ex ante enforcement, the paper explores how privacy concerns may be integrated in merger control as part of the definition of product quality, the harm in question being merely exploitative (the possibility the data aggregation provides to the merged entity to exploit (personal) data in ways that harm directly consumers), rather than exclusionary (harming consumers by enabling the merged entity to marginalise a rival with better privacy policies), which is examined in a separate paper. With regard to ex post enforcement, the paper explores different theories of harm that may give rise to competition law concerns and suggest specific tests for their assessment. In particular, we analyse old and new exploitative theories of harm relating to excessive data extraction, personalised pricing, unfair commercial practices and trading conditions, exploitative requirement contracts, behavioural manipulation. We are in favour of collective action to restore the conditions of a well-functioning data market and the report makes a number of policy recommendations. |
| Keywords: | personal information; Internet search; Google; Facebook; digital; privacy; restrictions of competition; exploitation; market failure; hold up; merger; abuse of a dominant position; unfair commercial practices; excessive data extraction; self-determination; behavioural manipulation; remedies; portability; opt out. |
| JEL: | K21 L1 L12 L4 L41 L5 L86 L88 |
| Date: | 2019–10 |
| URL: | http://d.repec.org/n?u=RePEc:net:wpaper:1915&r=all |
| By: | Philippe Goulet Coulombe; Maxime Leroux; Dalibor Stevanovic; Stéphane Surprenant |
| Abstract: | We move beyond Is Machine Learning Useful for Macroeconomic Forecasting? by adding the how. The current forecasting literature has focused on matching specific variables and horizons with a particularly successful algorithm. To the contrary, we study a wide range of horizons and variables and learn about the usefulness of the underlying features driving ML gains over standard macroeconometric methods. We distinguish 4 so-called features (nonlinearities, regularization, cross-validation and alternative loss function) and study their behavior in both the data-rich and data-poor environments. To do so, we carefully design a series of experiments that easily allow to identify the “treatment” effects of interest. We conclude that (i) more data and nonlinearities are true game-changers for macroeconomic prediction, (ii) the standard factor model remains the best regularization, (iii) cross-validations are not all made equal (but K-fold is as good as BIC) and (iv) one should stick with the standard L2 loss. The forecasting gains of nonlinear techniques are associated with high macroeconomic uncertainty, financial stress and housing bubble bursts. This suggests that Machine Learning is useful for macroeconomic forecasting by mostly capturing important nonlinearities that arise in the context of uncertainty and financial frictions. |
| Keywords: | Machine Learning,Big Data,Forecasting, |
| JEL: | C53 C55 E37 |
| Date: | 2019–10–17 |
| URL: | http://d.repec.org/n?u=RePEc:cir:cirwor:2019s-22&r=all |
| By: | Acemoglu, Daron (MIT); Restrepo, Pascual (Boston University) |
| Abstract: | Artificial Intelligence is set to influence every aspect of our lives, not least the way production is organized. AI, as a technology platform, can automate tasks previously performed by labor or create new tasks and activities in which humans can be productively employed. Recent technological change has been biased towards automation, with insufficient focus on creating new tasks where labor can be productively employed. The consequences of this choice have been stagnating labor demand, declining labor share in national income, rising inequality and lower productivity growth. The current tendency is to develop AI in the direction of further automation, but this might mean missing out on the promise of the "right" kind of AI with better economic and social outcomes. |
| Keywords: | automation, artificial intelligence, jobs, inequality, innovation, labor demand, productivity, tasks, technology, wages |
| JEL: | J23 J24 |
| Date: | 2019–10 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp12704&r=all |
| By: | Timothy Shields (Economic Science Institute, Chapman University; Argyros School of Business and Economics, Chapman University); Eric Schniter (Economic Science Institute, Chapman University; Argyros School of Business and Economics, Chapman University); Daniel Sznycer (Department of Psychology, University of Montreal) |
| Abstract: | Trust-based interactions with robots are increasingly common in the marketplace, workplace, on the road, and in the home. However, a looming concern is that people may not trust robots as they do humans. While trust in fellow humans has been studied extensively, little is known about how people extend trust to robots. Here we compare trust-based investments and emotions from across three nearly identical economic games: human-human trust games, human-robot trust games, and human-robot trust games where the robot decision impacts another human. Robots in our experiment mimic humans: they are programmed to make reciprocity decisions based on previously observed behaviors by humans in analogous situations. We find that people invest similarly in humans and robots. By contrast, the social emotions elicited by the interactions (but not non-social emotions) differed across human and robot trust games, and did so lawfully. Emotional reactions depended on how one’s trust game decision interacted with the partnered agent’s decision, and whether another person was affected economically and emotionally. |
| Keywords: | Trust; Robots; Artificial Intellgience; Emotion; Experiment |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:chu:wpaper:18-22&r=all |
| By: | Yaya, OlaOluwa S; Ogbonna, Ephraim A; Furuoka, Fumitaka; Gil-Alana, Luis A. |
| Abstract: | This paper proposes a nonlinear unit root test based on the artificial neural network-augmented Dickey-Fuller (ANN-ADF) test for testing hysteresis in unemployment. In this new unit root test, the linear, quadratic and cubic components of the neural network process are used to capture the nonlinearity in the time-series data. Fractional integration methods, based on linear and nonlinear trends are also used in the paper. By considering five European countries such as France, Italy, Netherland, Sweden, and the United Kingdom, the empirical findings indicate that there is still hysteresis in these countries. Among batteries of unit root tests applied, both the ARNN-ADF and fractional integration tests fail to reject the hypothesis of unemployment hysteresis in all the countries. |
| Keywords: | Unit root process; Nonlinearity; Neuron network: Time-series; Hysteresis; Unemployment; Europe; Labour market. |
| JEL: | C22 |
| Date: | 2019–10–19 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:96621&r=all |
| By: | Vondra, Marek; Touš, Michal; Teng, Sin Yong |
| Abstract: | Biogas production is one of the most promising pathways toward fully utilizing green energy within a circular economy. The anaerobic digestion process is the industry standard technology for biogas production due to its lowered energy consumption and its reliance on microbiology. Even in such an environmental-friendly process, liquid digestate is still produced from the remains of digested bio-feedstock and will require treatment. With unsuitable treatment procedure for liquid digestate, the mass of bio-feedstock can potentially escape the circular supply chain within the economy. This paper recommends the implementation of evaporator systems to provide a sustainable liquid digestate treating mechanism within the economy. Studied evaporator systems are represented by vacuum evaporation in combination with ammonia scrubber, stripping and reverse osmosis. Nevertheless, complex multi-dimensional decisions should be made by stakeholders before implementing such systems. Our work utilizes a novel techno-economics model to study the techno-economics robustness in implementing recent state-of-art vacuum evaporation systems with exploitation of waste heat from combined heat and power (CHP) units in biogas plants (BGP). To take into the account the stochasticity of the real world and robustness of the analysis, we used the Monte-Carlo simulation technique to generate more than 20,000 of different possibilities for the implementation of the evaporation system. Favourable decision pathways are then selected using a novel methodology which utilizes the artificial neural network and a hyper-optimized decision tree classifier. Two pathways that give the highest probability of providing a fast payback period are identified. Descriptive statistics are also used to analyse the distributions of decision parameters that lead to success in implementing the evaporator system. The results highlighted that integration of evaporation system are favourable when transport costs and incentives for CHP units are large and while feed-in tariffs for electricity production and specific investment costs are low. The result of this work is expected to pave the way for BGP stakeholders and decision makers in implementing liquid digestate treating technologies within the currently existing infrastructure. |
| Keywords: | Anaerobic Digestion; Machine Learning; Vacuum Evaporation; Liquid Digestate; Biogas Plant; Energy Consumption; Nutrient Recovery; Circular economy; Ammonium sulphate solution |
| JEL: | C0 C1 C6 C8 E0 E2 E3 E6 L1 L6 L9 |
| Date: | 2019–09–20 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:95770&r=all |