nep-cmp New Economics Papers
on Computational Economics
Issue of 2021‒05‒24
28 papers chosen by
Stan Miles
Thompson Rivers University

  1. BBE: Simulating the Microstructural Dynamics of an In-Play Betting Exchange via Agent-Based Modelling By Dave Cliff
  2. Deep Learning Classification: Modeling Discrete Labor Choice By Maliar, Lilia; Maliar, Serguei
  3. ALIENs and Continuous Time Economies By Goutham Gopalakrishna
  4. From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses By Sean Cao; Wei Jiang; Junbo L. Wang; Baozhong Yang
  5. Exchange Rate Prediction with Machine Learning and a Smart Carry Trade Portfolio By Filippou, Ilias; Rapach, David; Taylor, Mark P; Zhou, Guofu
  6. Urban economics in a historical perspective: Recovering data with machine learning By Combes, Pierre-Philippe; Gobillon, Laurent; Zylberberg, Yanos
  7. Identifying residential consumption patterns using data-mining techniques: A large-scale study of smart meter data in Chengdu, China By Kang, J.; Reiner, D.
  8. Prime locations By Ahlfeldt, Gabriel; Albers, Thilo; Behrens, Kristian
  9. Quand l’intelligence artificielle théorisera les organisations By Philippe Baumard
  10. A Fully Quantization-based Scheme for FBSDEs By Giorgia Callegaro; Alessandro Gnoatto; Martino Grasselli
  11. Machine Learning on residential electricity consumption: Which households are more responsive to weather? By Kang, J.; Reiner, D.
  12. An efficient Monte Carlo method for utility-based pricing By Laurence Carassus; Massinissa Ferhoune
  13. Platform Design When Sellers Use Pricing Algorithms By Johnson, Justin; Rhodes, Andrew; Wildenbeest, Matthijs
  14. Deep Graph Convolutional Reinforcement Learning for Financial Portfolio Management -- DeepPocket By Farzan Soleymani; Eric Paquet
  15. The Race of Man and Machine: Implications of Technology When Abilities and Demand Constraints Matter By Gries, Thomas; Naudé, Wim
  16. Application of Three Different Machine Learning Methods on Strategy Creation for Profitable Trades on Cryptocurrency Markets By Mohsen Asgari; Hossein Khasteh
  17. Reassessing the Resource Curse using Causal Machine Learning By Hodler, Roland; Lechner, Michael; Raschky, Paul A.
  18. Daily Tracker of Global Economic Activity. A Close-Up of the Covid-19 Pandemic By Diaz, Elena Maria; Pérez-Quirós, Gabriel
  19. On sensitivity of Genetic Matching to the choice of balance measure By Adeola Oyenubi
  20. Robo-Advising: Enhancing Investment with Inverse Optimization and Deep Reinforcement Learning By Haoran Wang; Shi Yu
  21. Bad machines corrupt good morals By Köbis, Nils; Bonnefon, Jean-François; Rahwan, Iyad
  22. Autonomous algorithmic collusion: Economic research and policy implications By Assad, Stephanie; Calvano, Emilio; Calzolari, Giacomo; Clark, Robert; Denicolò, Vincenzo; Ershov, Daniel; Johnson, Justin; Pastorello, Sergio; Rhodes, Andrew; XU, Lei; Wildenbeest, Matthijs
  23. Firm-level Risk Exposures and Stock Returns in the Wake of COVID-19 By Davis, Steven J; Hansen, Stephen; Seminario-Amez, Cristhian
  24. Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice By Babii, Andrii; Chen, Xi; Ghysels, Eric; Kumar, Rohit
  25. Behavioural Economics, What Have we Missed? Exploring “Classical” Behavioural Economics Roots in AI, Cognitive Psychology, and Complexity Theory By Steve J. Bickley; Benno Torgler
  26. Flexible Work Arrangements in Low Wage Jobs: Evidence from Job Vacancy Data By Adams-Prassl, Abigail; Balgova, Maria; Qian, Matthias
  27. Applications of artificial intelligence in supply chain management: Identification of main research fields and greatest industry interests By Lechtenberg, Sandra; Hellingrath, Bernd
  28. Green Energy Pricing for Digital Europe By Crampes, Claude; Lefouili, Yassine

  1. By: Dave Cliff
    Abstract: I describe the rationale for, and design of, an agent-based simulation model of a contemporary online sports-betting exchange: such exchanges, closely related to the exchange mechanisms at the heart of major financial markets, have revolutionized the gambling industry in the past 20 years, but gathering sufficiently large quantities of rich and temporally high-resolution data from real exchanges - i.e., the sort of data that is needed in large quantities for Deep Learning - is often very expensive, and sometimes simply impossible; this creates a need for a plausibly realistic synthetic data generator, which is what this simulation now provides. The simulator, named the "Bristol Betting Exchange" (BBE), is intended as a common platform, a data-source and experimental test-bed, for researchers studying the application of AI and machine learning (ML) techniques to issues arising in betting exchanges; and, as far as I have been able to determine, BBE is the first of its kind: a free open-source agent-based simulation model consisting not only of a sports-betting exchange, but also a minimal simulation model of racetrack sporting events (e.g., horse-races or car-races) about which bets may be made, and a population of simulated bettors who each form their own private evaluation of odds and place bets on the exchange before and - crucially - during the race itself (i.e., so-called "in-play" betting) and whose betting opinions change second-by-second as each race event unfolds. BBE is offered as a proof-of-concept system that enables the generation of large high-resolution data-sets for automated discovery or improvement of profitable strategies for betting on sporting events via the application of AI/ML and advanced data analytics techniques. This paper offers an extensive survey of relevant literature and explains the motivation and design of BBE, and presents brief illustrative results.
    Date: 2021–05
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2105.08310&r=
  2. By: Maliar, Lilia; Maliar, Serguei
    Abstract: We introduce a deep learning classification (DLC) method for analyzing equilibrium in discrete-continuous choice dynamic models. As an illustration, we apply the DLC method to solve a version of Krusell and Smith's (1998) heterogeneous-agent model with incomplete markets, borrowing constraint and indivisible labor choice. The novel feature of our analysis is that we construct discontinuous decision functions that tell us when the agent switches from one employment state to another, conditional on the economy's state. We use deep learning not only to characterize the discrete indivisible choice but also to perform model reduction and to deal with multicollinearity. Our TensorFlow-based implementation of DLC is tractable in models with thousands of state variables.
    Keywords: classification; deep learning; discrete choice; Indivisible labor; intensive and extensive margins; logistic regression; neural network
    Date: 2020–10
    URL: http://d.repec.org/n?u=RePEc:cpr:ceprdp:15346&r=
  3. By: Goutham Gopalakrishna (Swiss Finance Institute (EPFL); Ecole Polytechnique Fédérale de Lausanne)
    Abstract: I develop a new computational framework called Actively Learned and Informed Equilibrium Nets (ALIENs) to solve continuous time economic models with endogenous state variables and highly non-linear policy functions. I employ neural networks that are trained to solve supervised learning problems that respect the laws governed by the economic system in the form of general parabolic partial differential equations. The economic information is encoded as regularizers that disciplines the deep neural network in the learning process. The sub-domain of the high dimensional state space that carries the most economic information is learned actively in an iterative loop, enforcing the random training points to be sampled from areas that matter the most to ensure convergence. I utilize a state-of-the art distributed framework to train the network that speeds up computation time significantly. The method is applied to successfully solve a model of macro-finance that is notoriously difficult to handle using traditional finite difference schemes.
    Date: 2021–05
    URL: http://d.repec.org/n?u=RePEc:chf:rpseri:rp2134&r=
  4. By: Sean Cao; Wei Jiang; Junbo L. Wang; Baozhong Yang
    Abstract: An AI analyst we build to digest corporate financial information, qualitative disclosure and macroeconomic indicators is able to beat the majority of human analysts in stock price forecasts and generate excess returns compared to following human analyst. In the contest of “man vs machine,” the relative advantage of the AI Analyst is stronger when the firm is complex, and when information is high-dimensional, transparent and voluminous. Human analysts remain competitive when critical information requires institutional knowledge (such as the nature of intangible assets). The edge of the AI over human analysts declines over time when analysts gain access to alternative data and to in-house AI resources. Combining AI’s computational power and the human art of understanding soft information produces the highest potential in generating accurate forecasts. Our paper portraits a future of “machine plus human” (instead of human displacement) in high-skill professions.
    JEL: G11 G12 G14 G31 M41
    Date: 2021–05
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:28800&r=
  5. By: Filippou, Ilias; Rapach, David; Taylor, Mark P; Zhou, Guofu
    Abstract: We establish the out-of-sample predictability of monthly exchange rate changes via machine learning techniques based on 70 predictors capturing country characteristics, global variables, and their interactions. To guard against overfi tting, we use the elastic net to estimate a high-dimensional panel predictive regression and find that the resulting forecast consistently outperforms the naive no-change benchmark, which has proven difficult to beat in the literature. The forecast also markedly improves the performance of a carry trade portfolio, especially during and after the global financial crisis. When we allow for more complex deep learning models, nonlinearities do not appear substantial in the data.
    Keywords: carry trade; deep neural network; Elastic Net; exchange rate predictability
    JEL: C45 F31 F37 G11 G12 G15
    Date: 2020–09
    URL: http://d.repec.org/n?u=RePEc:cpr:ceprdp:15305&r=
  6. By: Combes, Pierre-Philippe; Gobillon, Laurent; Zylberberg, Yanos
    Abstract: A recent literature has used a historical perspective to better understand fundamental questions of urban economics. However, a wide range of historical documents of exceptional quality remain underutilised: their use has been hampered by their original format or by the massive amount of information to be recovered. In this paper, we describe how and when the flexibility and predictive power of machine learning can help researchers exploit the potential of these historical documents. We first discuss how important questions of urban economics rely on the analysis of historical data sources and the challenges associated with transcription and harmonisation of such data. We then explain how machine learning approaches may address some of these challenges and we discuss possible applications.
    Keywords: History; Machine Learning; Urban Economics
    JEL: C45 C81 N90 R11 R12 R14
    Date: 2020–09
    URL: http://d.repec.org/n?u=RePEc:cpr:ceprdp:15308&r=
  7. By: Kang, J.; Reiner, D.
    Abstract: The fine-grained electricity consumption data created by advanced metering technologies offers an opportunity to understand residential demand from new angles. Although there exists a large body of research on demand response in short- and long-term forecasting, a comprehensive analysis to identify household consumption behaviour in different scenarios has not been conducted. The study’s novelty lies in its use of unsupervised machine learning tools to explore residential customers’ demand patterns and response without the assistance of traditional survey tools. We investigate behavioural response in three different contexts: 1) seasonal (using weekly consumption profiles); 2) holidays/festivals; and 3) extreme weather situations. The analysis is based on the smart metering data of 2,000 households in Chengdu, China over three years from 2014 to 2016. Workday/weekend profiles indicate that there are two distinct groups of households that appear to be white-collar or relatively affluent families. Demand patterns at the major festivals in China, especially the Spring Festival, reveal various types of lifestyle and households. In terms of extreme weather response, the most striking finding was that in summer, at night-time, over 72% of households doubled (or more) their electricity usage, while consumption changes in winter do not seem to be significant. Our research offers more detailed insight into Chinese residential consumption and provides a practical framework to understand households’ behaviour patterns in different settings.
    Keywords: Residential electricity, household consumption behaviour, China, machine learning
    JEL: C55 D12 R22 Q41
    Date: 2021–05–12
    URL: http://d.repec.org/n?u=RePEc:cam:camdae:2143&r=
  8. By: Ahlfeldt, Gabriel; Albers, Thilo; Behrens, Kristian
    Abstract: We harness big data to detect prime locations---large clusters of know-ledge-based tradable services---in 125 global cities and track changes in the within-city geography of prime service jobs over a century. Historically smaller cities that did not develop early public transit networks are less concentrated today and have prime locations farther away from their historic cores. We rationalize these findings in an agent-based model that features extreme agglomeration, multiple equilibria, and path dependence. Both city size and public transit networks anchor city structure. Exploiting major disasters and using a novel instrument---subway potential---we provide causal evidence for these mechanisms and disentangle size- from transport network effects.
    Keywords: agent-based model; internal city structure; multiple equilibria and path dependence; Prime services; transport networks
    JEL: R38 R52 R58
    Date: 2020–11
    URL: http://d.repec.org/n?u=RePEc:cpr:ceprdp:15470&r=
  9. By: Philippe Baumard (ESD R3C - Équipe Sécurité & Défense - Renseignement, Criminologie, Crises, Cybermenaces - CNAM - Conservatoire National des Arts et Métiers [CNAM])
    Abstract: This article explores the feasibility of machines inventing and theorizing organizations. Most machine learning models are automated statistical processes that barely achieve a formal induction. In that sense, most current learning models do not generate new theories, but, instead, recognize a pre-existing order of symbols, signs or data. Most human theories are embodied and incarnated: they spawn from an organic connection to the world, which theorists can hardly escape. This article is organized in three parts. First, we study the history of artificial intelligence, from its foundation in the 19th century to its recent evolution, to understand what an artificial intelligence would be able to do in terms of theorization... Which leads us, in a second step, to question the act of scientific production in order to identify what can be considered a human act, and what can be the subject of modelling and autonomous learning led by a machine. The objective here is to assess the feasibility of substituting man with machine to produce research. In a third and final part, we propose four modes of theoretical exploration that are already the work of machines, or that could see, in the future, a complete substitution of man by machine. We conclude this article by sharing several questions about the future of research in organizational theory, and its utility, human or machine, for organizations and society.
    Abstract: Cet article 1 explore la possibilité qu'une intelligence machine puisse théoriser des organisations ; et qu'elle le fasse mieux qu'une intelligence humaine dans un proche futur. La plupart des modèles d'apprentissage des machines sont des processus statistiques automatisés qui sont à peine capables d'une induction formelle et ne génèrent pas de nouvelles théories, mais reconnaissent plutôt un ordre préexistant. Les théories humaines sont incarnées ; elles naissent d'un lien organique avec le monde, auquel les théoriciens ne peuvent échapper. Cet article envisage de surmonter cet obstacle pour accueillir une révolution théorique apportée par l'IA.
    Keywords: AI,artificial intelligence,organization theory,sociology of knowledge,cognitive theory
    Date: 2019–11
    URL: http://d.repec.org/n?u=RePEc:hal:journl:hal-03218196&r=
  10. By: Giorgia Callegaro; Alessandro Gnoatto; Martino Grasselli
    Abstract: We propose a quantization-based numerical scheme for a family of decoupled FBSDEs. We simplify the scheme for the control in Pag\`es and Sagna (2018) so that our approach is fully based on recursive marginal quantization and does not involve any Monte Carlo simulation for the computation of conditional expectations. We analyse in detail the numerical error of our scheme and we show through some examples the performance of the whole procedure, which proves to be very effective in view of financial applications.
    Date: 2021–05
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2105.09276&r=
  11. By: Kang, J.; Reiner, D.
    Abstract: The introduction of smart meters has created opportunities for both utilities and policymakers to understand residential electricity consumption in greater depth. Machine learning techniques have distinct advantages over traditional approaches in dealing with extremely large volumes of high-resolution usage data. We introduce a novel clustering method to detect household behaviour using different types of weather data as proxies. Based on this approach, we combine Irish smart meter and weather data to identify and characterize clear differences in the daily patterns between workdays and weekends in both summer and winter and investigate how households respond to changing weather patterns. We also examine the relationships between response groups and household demographic features using different statistical tests. We find the magnitude of the effect of occupancy-related variables in the clustering of weather sensitivity to be larger than incomerelated factors. This proposed new approach could be the basis of a classification model to identify households that are more responsive to different types of weather. Tariff design could benefit from such a model and enable specific schemes to be developed that would target weather-sensitive households and result in improved load management.
    Keywords: Weather sensitivity, smart metering data, unsupervised learning, clusters, residential electricity, consumption patterns, Ireland
    JEL: C55 D12 R22 Q41
    Date: 2021–05–12
    URL: http://d.repec.org/n?u=RePEc:cam:camdae:2142&r=
  12. By: Laurence Carassus; Massinissa Ferhoune
    Abstract: We propose an efficient numerical method, based on the Lambert function, for the computation and study of the reservation price as well as the value function in the case of illiquidity. Our theoretical results are illustrated by numerical simulations.
    Date: 2021–05
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2105.08804&r=
  13. By: Johnson, Justin; Rhodes, Andrew; Wildenbeest, Matthijs
    Abstract: Using both economic theory and Artificial Intelligence (AI) pricing algorithms, we investigate the ability of a platform to design its marketplace to promote competition, improve consumer surplus, and even raise its own profits. We allow sellers to use Q-learning algorithms (a common reinforcement-learning technique from the computer-science literature) to devise pricing strategies in a setting with repeated interactions, and consider the effect of platform rules that reward firms that cut prices with additional exposure to consumers. Overall, the evidence from our experiments suggests that platform design decisions can meaningfully benefit consumers even when algorithmic collusion might otherwise emerge but that achieving these gains may require more than the simplest steering policies when algorithms value the future highly. We also find that policies that raise consumer surplus can raise the profits of the platform, depending on the platform's revenue model. Finally, we document several learning challenges faced by the algorithms.
    Keywords: Algorithms; artificial intelligence; Collusion; platform design
    JEL: K21 L00
    Date: 2020–11
    URL: http://d.repec.org/n?u=RePEc:cpr:ceprdp:15504&r=
  14. By: Farzan Soleymani; Eric Paquet
    Abstract: Portfolio management aims at maximizing the return on investment while minimizing risk by continuously reallocating the assets forming the portfolio. These assets are not independent but correlated during a short time period. A graph convolutional reinforcement learning framework called DeepPocket is proposed whose objective is to exploit the time-varying interrelations between financial instruments. These interrelations are represented by a graph whose nodes correspond to the financial instruments while the edges correspond to a pair-wise correlation function in between assets. DeepPocket consists of a restricted, stacked autoencoder for feature extraction, a convolutional network to collect underlying local information shared among financial instruments, and an actor-critic reinforcement learning agent. The actor-critic structure contains two convolutional networks in which the actor learns and enforces an investment policy which is, in turn, evaluated by the critic in order to determine the best course of action by constantly reallocating the various portfolio assets to optimize the expected return on investment. The agent is initially trained offline with online stochastic batching on historical data. As new data become available, it is trained online with a passive concept drift approach to handle unexpected changes in their distributions. DeepPocket is evaluated against five real-life datasets over three distinct investment periods, including during the Covid-19 crisis, and clearly outperformed market indexes.
    Date: 2021–05
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2105.08664&r=
  15. By: Gries, Thomas (University of Paderborn); Naudé, Wim (University College Cork)
    Abstract: In "The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment," Acemoglu and Restrepo (2018b) combine the task-based model of the labor market with an endogenous growth model to model the economic consequences of artificial intelligence (AI). This paper provides an alternative endogenous growth model that addresses two shortcomings of their model. First, we replace the assumption of a representative household with the premise of two groups of households with different preferences. This allows our model to be demand constrained and able to model the consequences of higher income inequality due to AI. Second, we model AI as providing abilities, arguing that "abilities" better characterises the nature of the services that AI provide, rather than tasks or skills. The dynamics of the model regarding the impact of AI on jobs, inequality, wages, labor productivity and long-run GDP growth are explored.
    Keywords: technology, artificial intelligence, productivity, labor demand, income distribution, growth theory
    JEL: O47 O33 J24 E21 E25
    Date: 2021–04
    URL: http://d.repec.org/n?u=RePEc:iza:izadps:dp14341&r=
  16. By: Mohsen Asgari; Hossein Khasteh
    Abstract: AI and data driven solutions have been applied to different fields with outperforming and promising results. In this research work we apply k-Nearest Neighbours, eXtreme Gradient Boosting and Random Forest classifiers to direction detection problem of three cryptocurrency markets. Our input data includes price data and technical indicators. We use these classifiers to design a strategy to trade in those markets. Our test results on unseen data shows a great potential for this approach in helping investors with an expert system to exploit the market and gain profit. Our highest gain for an unseen 66 day span is 860 dollars per 1800 dollars investment. We also discuss limitations of these approaches and their potential impact to Efficient Market Hypothesis.
    Date: 2021–05
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2105.06827&r=
  17. By: Hodler, Roland; Lechner, Michael; Raschky, Paul A.
    Abstract: We reassess the effects of natural resources on economic development and conflict, applying a causal forest estimator and data from 3,800 Sub-Saharan African districts. We find that, on average, mining activities and higher world market prices of locally mined minerals both increase economic development and conflict. Consistent with the previous literature, mining activities have more positive effects on economic development and weaker effects on conflict in places with low ethnic diversity and high institutional quality. In contrast, the effects of changes in mineral prices vary little in ethnic diversity and institutional quality, but are non-linear and largest at relatively high prices.
    Keywords: Africa; Causal machine learning; conflict; economic development; mining; resource curse
    JEL: C21 O13 O55 Q34 R12
    Date: 2020–09
    URL: http://d.repec.org/n?u=RePEc:cpr:ceprdp:15272&r=
  18. By: Diaz, Elena Maria; Pérez-Quirós, Gabriel
    Abstract: This paper develops a novel indicator of global economic activity, the GEA Tracker, which is based on commodity prices selected recursively through a genetic algorithm. The GEA Tracker allows for daily real-time knowledge of international business conditions using a minimum amount of information. We find that the GEA Tracker outperforms its competitors in forecasting stock returns, especially in emerging markets, and in predicting standard indicators of international business conditions. We show that an investor would have inexorably profited from using the forecasts provided by the GEA Tracker to weight his/her portfolio. Finally, the GEA Tracker allows us to present the daily evolution of global economic activity during the COVID-19 pandemic.
    Keywords: commodity prices; factor models; Genetic Algorithm; Global Economic Activity
    JEL: F44 G17 Q02
    Date: 2020–11
    URL: http://d.repec.org/n?u=RePEc:cpr:ceprdp:15451&r=
  19. By: Adeola Oyenubi
    Abstract: This paper considers the sensitivity of Genetic Matching (GenMatch) to the choice of balance measure. It explores the performance of a newly introduced distributional balance measure that is similar to the KS test but is more evenly sensitive to imbalance across the support. This measure is introduced by Goldman & Kaplan (2008) (i.e. the GK measure). This is important because the rationale behind distributional balance measures is their ability to provide a broader description of balance. I also consider the performance of multivariate balance measures i.e. distance covariance and correlation. This is motivated by the fact that ideally, balance for causal inference refers to balance in joint density and individual balance in a set of univariate distributions does not necessarily imply balance in the joint distribution.Simulation results show that GK dominates the KS test in terms of Bias and Mean Square Error (MSE); and the distance correlation measure dominates all other measure in terms of Bias and MSE. These results have two important implication for the choice of balance measure (i) Even sensitivity across the support is important and not all distributional measures has this property (ii) Multivariate balance measures can improve the performance of matching estimators.
    Keywords: Genetic matching, balance measures, causal inference, Machine learning
    JEL: I38 H53 C21 D13
    Date: 2020–11
    URL: http://d.repec.org/n?u=RePEc:rza:wpaper:840&r=
  20. By: Haoran Wang; Shi Yu
    Abstract: Machine Learning (ML) has been embraced as a powerful tool by the financial industry, with notable applications spreading in various domains including investment management. In this work, we propose a full-cycle data-driven investment robo-advising framework, consisting of two ML agents. The first agent, an inverse portfolio optimization agent, infers an investor's risk preference and expected return directly from historical allocation data using online inverse optimization. The second agent, a deep reinforcement learning (RL) agent, aggregates the inferred sequence of expected returns to formulate a new multi-period mean-variance portfolio optimization problem that can be solved using deep RL approaches. The proposed investment pipeline is applied on real market data from April 1, 2016 to February 1, 2021 and has shown to consistently outperform the S&P 500 benchmark portfolio that represents the aggregate market optimal allocation. The outperformance may be attributed to the the multi-period planning (versus single-period planning) and the data-driven RL approach (versus classical estimation approach).
    Date: 2021–05
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2105.09264&r=
  21. By: Köbis, Nils; Bonnefon, Jean-François; Rahwan, Iyad
    Abstract: Machines powered by Artificial Intelligence (AI) are now influencing the behavior of humans in ways that are both like and unlike the ways humans influence each other. In light of recent research showing that other humans can exert a strong corrupting influence on people’s ethical behavior, worry emerges about the corrupting power of AI agents. To estimate the empirical validity of these fears, we review the available evidence from behavioral science, human-computer interaction, and AI research. We propose that the main social roles through which both humans and machines can influence ethical behavior are (a) role model, (b) advisor, (c) partner, and (d) delegate. When AI agents become influencers (role models or advisors), their corrupting power may not exceed (yet) the corrupting power of humans. However, AI agents acting as enablers of unethical behavior (partners or delegates) have many characteristics that may let people reap unethical benefits while feeling good about themselves, indicating good reasons for worry. Based on these insights, we outline a research agenda that aims at providing more behavioral insights for better AI oversight.
    Keywords: machine behavior; behavioral ethics; corruption; artificial intelligence
    Date: 2021–05
    URL: http://d.repec.org/n?u=RePEc:tse:wpaper:125602&r=
  22. By: Assad, Stephanie; Calvano, Emilio; Calzolari, Giacomo; Clark, Robert; Denicolò, Vincenzo; Ershov, Daniel; Johnson, Justin; Pastorello, Sergio; Rhodes, Andrew; XU, Lei; Wildenbeest, Matthijs
    Abstract: Markets are being populated with new generations of pricing algorithms, powered with Artificial Intelligence, that have the ability to autonomously learn to operate. This ability can be both a source of efficiency and cause of concern for the risk that algorithms autonomously and tacitly learn to collude. In this paper we explore recent developments in the economic literature and discuss implications for policy.
    Keywords: Algorithmic Pricing; Antitrust; Competition Policy; Artificial Intelligence; Collusion; Platforms.
    JEL: D42 D82 L42
    Date: 2021–03
    URL: http://d.repec.org/n?u=RePEc:tse:wpaper:125584&r=
  23. By: Davis, Steven J; Hansen, Stephen; Seminario-Amez, Cristhian
    Abstract: Firm-level stock returns differ enormously in reaction to COVID-19 news. We characterize these reactions using the Risk Factors discussions in pre-pandemic 10-K filings and two text-analytic approaches: expert-curated dictionaries and supervised machine learning (ML). Bad COVID-19 news lowers returns for firms with high exposures to travel, traditional retail, aircraft production and energy supply -- directly and via downstream demand linkages -- and raises them for firms with high exposures to healthcare policy, e-commerce, web services, drug trials and materials that feed into supply chains for semiconductors, cloud computing and telecommunications. Monetary and fiscal policy responses to the pandemic strongly impact firm-level returns as well, but differently than pandemic news. Despite methodological differences, dictionary and ML approaches yield remarkably congruent return predictions. Importantly though, ML operates on a vastly larger feature space, yielding richer characterizations of risk exposures and outperforming the dictionary approach in goodness-of-fit. By integrating elements of both approaches, we uncover new risk factors and sharpen our explanations for firm-level returns. To illustrate the broader utility of our methods, we also apply them to explain firm-level returns in reaction to the March 2020 Super Tuesday election results.
    Date: 2020–09
    URL: http://d.repec.org/n?u=RePEc:cpr:ceprdp:15314&r=
  24. By: Babii, Andrii; Chen, Xi; Ghysels, Eric; Kumar, Rohit
    Abstract: The importance of asymmetries in prediction problems arising in economics has been recognized for a long time. In this paper, we focus on binary choice problems in a data-rich environment with general loss functions. In contrast to the asymmetric regression problems, the binary choice with general loss functions and high-dimensional datasets is challenging and not well understood. Econometricians have studied binary choice problems for a long time, but the literature does not offer computationally attractive solutions in data-rich environments. In contrast, the machine learning literature has many computationally attractive algorithms that form the basis for much of the automated procedures that are implemented in practice, but it is focused on symmetric loss functions that are independent of individual characteristics. One of the main contributions of our paper is to show that the theoretically valid predictions of binary outcomes with arbitrary loss functions can be achieved via a very simple reweighting of the logistic regression, or other state-of-the-art machine learning techniques, such as boosting or (deep) neural networks. We apply our analysis to racial justice in pretrial detention.
    Date: 2020–10
    URL: http://d.repec.org/n?u=RePEc:cpr:ceprdp:15418&r=
  25. By: Steve J. Bickley; Benno Torgler
    Abstract: In this chapter, we ask (conceptually and methodologically) what exactly is behavioural economics and what are its roots? And further, what may we have missed along the way? We argue that revisiting “classical” behavioural economics concepts and methods will benefit the wider behavioural economics program by questioning its yardstick approach to ‘Olympian’ rationality and optimisation and in doing so, exploring the ‘how’ and ‘why’ of economic behaviours (micro, meso, and macro) in greater detail and clarity. We also do the same for fields which share similar ontological and epistemological roots with “classical” behavioural economics. In particular, cognitive psychology, complexity theory, and artificial intelligence. By engaging in debate and investing thought into multiple layers of the ontology-epistemology- methodology, we look to engage in ‘deeper’ (and potentially more profound) scientific discussions. We also explore the utility and implications of mixed methods in behavioural economics research, policy, and practice.
    Keywords: Behavioural Economics; Cognitive Psychology; Complexity Theory; Artificial Intelligence
    Date: 2021–05
    URL: http://d.repec.org/n?u=RePEc:cra:wpaper:2021-21&r=
  26. By: Adams-Prassl, Abigail; Balgova, Maria; Qian, Matthias
    Abstract: In this paper, we analyze firm demand for flexible jobs by exploiting the language used to describe work arrangements in job vacancies. We take a supervised machine learning approach to classify the work arrangements described in more than 46 million UK job vacancies. We highlight the existence of very different types of flexibility amongst low and high wage vacancies. Job flexibility at low wages is more likely to be offered alongside a wage-contract that exposes workers to earnings risk, while flexibility at higher wages and in more skilled occupations is more likely to be offered alongside a fixed salary that shields workers from earnings variation. We show that firm demand for flexible work arrangements is partly driven by a desire to reduce labor costs; we find that a large and unexpected change to the minimum wage led to a 7 percentage point increase in the proportion of flexible and non-salaried vacancies at low wages
    Keywords: job vacancies; Labour Demand; labour market flexibility; minimum wage
    JEL: C45 C81 J21 J23 J32 J33
    Date: 2020–09
    URL: http://d.repec.org/n?u=RePEc:cpr:ceprdp:15263&r=
  27. By: Lechtenberg, Sandra; Hellingrath, Bernd
    Abstract: Advances in the area of computing power, data storage capabilities, etc., are changing the way business is done, particularly regarding how businesses use and apply artificial intelligence. To better understand how artificial intelligence is used in supply chain management, this paper identifies and compares the main research fields investigating this topic as well as the primary industry interests in it. For this, we performed a structured literature review that shows which methods of artificial intelligence are applied to which problems of supply chain management in the scientific literature. Then, we present industry-driven applications to provide an overview of fields that are most relevant to industry. Based on these results, indications for future research are derived.
    Keywords: artificial intelligence,supply chain management,logistics,applications,industry-driven
    Date: 2021
    URL: http://d.repec.org/n?u=RePEc:zbw:ercisw:37&r=
  28. By: Crampes, Claude; Lefouili, Yassine
    Abstract: This paper investigates the trade-offs associated with the digitalization of the energy sector. Arguing that digitalization has both bright and dark sides, we study the extent to which it can help make energy systems efficient and sustainable. We first discuss how digitalization affects the responsiveness of demand, and explore its implications for spot pricing, load shedding, and priority service. In particular, we highlight the conditions under which digital technologies that allow demand to be more responsive to supply are likely to be used. We then turn to the way digitalization can contribute to the decarbonization of the energy sector, and discuss the promises and limitations of artificial intelligence in this area. Finally, we contend that policymakers should pay special attention to the privacy concerns raised by the digitalization of the energy sector and the cyberattacks that it enables.
    Keywords: Electricity; dynamic pricing; digitalisation; artificial Intelligence
    Date: 2021–05
    URL: http://d.repec.org/n?u=RePEc:tse:wpaper:125578&r=

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