nep-cmp New Economics Papers
on Computational Economics
Issue of 2023‒08‒14
twenty-two papers chosen by
Stan Miles, Thompson Rivers University


  1. Improved Financial Forecasting via Quantum Machine Learning By Sohum Thakkar; Skander Kazdaghli; Natansh Mathur; Iordanis Kerenidis; Andr\'e J. Ferreira-Martins; Samurai Brito
  2. Satellites Turn “Concrete”: Tracking Cement with Satellite Data and Neural Networks By Aspremont Alexandre; Ben Arous Simon; Bricongne Jean-Charles; Lietti Benjamin; Meunier Baptiste
  3. Neural networks can detect model-free static arbitrage strategies By Ariel Neufeld; Julian Sester
  4. Nowcasting World Trade with Machine Learning: a Three-Step Approach By Menzie D. Chinn; Baptiste Meunier; Sebastian Stumpner
  5. Leveraging Machine Learning for Multichain DeFi Fraud Detection By Georgios Palaiokrassas; Sandro Scherrers; Iason Ofeidis; Leandros Tassiulas
  6. Transaction Fraud Detection via Spatial-Temporal-Aware Graph Transformer By Yue Tian; Guanjun Liu
  7. Corrupted by Algorithms? How AI-Generated and Human-Written Advice Shape (Dis)Honesty By Leib, Margarita; Köbis, Nils; Rilke, Rainer Michael; Hagens, Marloes; Irlenbusch, Bernd
  8. How the Application of Machine Learning Systems Changes Business Processes: A multiple Case Study By Kunz, Pascal Christoph; Jussupow, Ekaterina; Spohrer, Kai; Heinzl, Armin
  9. Robust Hedging GANs By Yannick Limmer; Blanka Horvath
  10. A Scalable Reinforcement Learning-based System Using On-Chain Data for Cryptocurrency Portfolio Management By Zhenhan Huang; Fumihide Tanaka
  11. A Double Machine Learning Approach to Combining Experimental and Observational Data By Marco Morucci; Vittorio Orlandi; Harsh Parikh; Sudeepa Roy; Cynthia Rudin; Alexander Volfovsky
  12. An analysis of least squares regression and neural networks approximation for the pricing of swing options By Christian Yeo
  13. Competition in generative artificial intelligence foundation models By Christophe Carugati
  14. Subset-Row Inequalities and Unreachability in Path-based Formulations for Routing and Scheduling Problems By Stefan Faldum; Timo Gschwind; Stefan Irnich
  15. Combining Human Expertise with Artificial Intelligence: Experimental Evidence from Radiology By Nikhil Agarwal; Alex Moehring; Pranav Rajpurkar; Tobias Salz
  16. Uncovering the Semantics of Concepts Using GPT-4 and Other Recent Large Language Models By Gaël Le Mens; Balász Kovács; Michael T. Hannan; Guillem Pros
  17. Exact Solution of the Vehicle Routing Problem With Drones By Jeanette Schmidt; Christian Tilk; Stefan Irnich
  18. The role of Artificial Intelligence (AI) in agriculture and its impact on economy By Wójcik-Czerniawska, Agnieszka
  19. The role of Artificial Intelligence (AI) in agriculture and its impact on economy By Wójcik-Czerniawska, Agnieszka
  20. Forging AI Pathways: Portugal's Journey within the EU Digital Landscape By Gabriel Osório de Barros
  21. Artificial Intelligence and Inflation Forecasts By Miguel Faria-e-Castro; Fernando Leibovici
  22. Simulations in Models with Heterogeneous Agents, Incomplete Markets and Aggregate Uncertainty By Damián Pierri

  1. By: Sohum Thakkar (QC Ware Corp); Skander Kazdaghli (QC Ware Corp); Natansh Mathur (QC Ware Corp; IRIF - Universit\'e Paris Cit\'e and CNRS); Iordanis Kerenidis (QC Ware Corp; IRIF - Universit\'e Paris Cit\'e and CNRS); Andr\'e J. Ferreira-Martins (Ita\'u Unibanco); Samurai Brito (Ita\'u Unibanco)
    Abstract: Quantum algorithms have the potential to enhance machine learning across a variety of domains and applications. In this work, we show how quantum machine learning can be used to improve financial forecasting. First, we use classical and quantum Determinantal Point Processes to enhance Random Forest models for churn prediction, improving precision by almost 6%. Second, we design quantum neural network architectures with orthogonal and compound layers for credit risk assessment, which match classical performance with significantly fewer parameters. Our results demonstrate that leveraging quantum ideas can effectively enhance the performance of machine learning, both today as quantum-inspired classical ML solutions, and even more in the future, with the advent of better quantum hardware.
    Date: 2023–05
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2306.12965&r=cmp
  2. By: Aspremont Alexandre; Ben Arous Simon; Bricongne Jean-Charles; Lietti Benjamin; Meunier Baptiste
    Abstract: The Covid crisis has demonstrated the need for alternative data, in real-time and with global coverage. This paper exploits daily infrared images from satellites to track economic activity in advanced and emerging countries. We first develop a framework to read, clean and exploit satellite images. We construct an algorithm based on the laws of physics and machine learning to detect the heat produced by cement plants in activity. This allows to monitor in real-time if a cement plant is functioning. Using this information on more than 500 plants, we construct a satellite-based index tracking activity. Using this satellite index outperforms benchmark models and alternative indicators for nowcasting the activity in the cement industry and in the construction sector. Exploring the granularity of daily and plant-level data, using neural networks yields significantly more accurate predictions. Overall, combining satellite images and machine learning allows to track industrial activity accurately.
    Keywords: Data Science, Big Data, Satellite Data, Machine Learning, Nowcasting, Cement, Construction, Industry, Economic Activity, Neural Network
    JEL: C51 C81 E23 E37
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:bfr:banfra:916&r=cmp
  3. By: Ariel Neufeld; Julian Sester
    Abstract: In this paper we demonstrate both theoretically as well as numerically that neural networks can detect model-free static arbitrage opportunities whenever the market admits some. Due to the use of neural networks, our method can be applied to financial markets with a high number of traded securities and ensures almost immediate execution of the corresponding trading strategies. To demonstrate its tractability, effectiveness, and robustness we provide examples using real financial data. From a technical point of view, we prove that a single neural network can approximately solve a class of convex semi-infinite programs, which is the key result in order to derive our theoretical results that neural networks can detect model-free static arbitrage strategies whenever the financial market admits such opportunities.
    Date: 2023–06
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2306.16422&r=cmp
  4. By: Menzie D. Chinn; Baptiste Meunier; Sebastian Stumpner
    Abstract: We nowcast world trade using machine learning, distinguishing between tree-based methods (random forest, gradient boosting) and their regression-based counterparts (macroeconomic random forest, gradient linear boosting). While much less used in the literature, the latter are found to outperform not only the tree-based techniques, but also more “traditional” linear and non-linear techniques (OLS, Markov-switching, quantile regression). They do so significantly and consistently across different horizons and real-time datasets. To further improve performances when forecasting with machine learning, we propose a flexible three-step approach composed of (step 1) pre-selection, (step 2) factor extraction and (step 3) machine learning regression. We find that both pre-selection and factor extraction significantly improve the accuracy of machine-learning-based predictions. This three-step approach also outperforms workhorse benchmarks, such as a PCA-OLS model, an elastic net, or a dynamic factor model. Finally, on top of high accuracy, the approach is flexible and can be extended seamlessly beyond world trade.
    JEL: C53 C57 E37
    Date: 2023–06
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:31419&r=cmp
  5. By: Georgios Palaiokrassas; Sandro Scherrers; Iason Ofeidis; Leandros Tassiulas
    Abstract: Since the inception of permissionless blockchains with Bitcoin in 2008, it became apparent that their most well-suited use case is related to making the financial system and its advantages available to everyone seamlessly without depending on any trusted intermediaries. Smart contracts across chains provide an ecosystem of decentralized finance (DeFi), where users can interact with lending pools, Automated Market Maker (AMM) exchanges, stablecoins, derivatives, etc. with a cumulative locked value which had exceeded 160B USD. While DeFi comes with high rewards, it also carries plenty of risks. Many financial crimes have occurred over the years making the early detection of malicious activity an issue of high priority. The proposed framework introduces an effective method for extracting a set of features from different chains, including the largest one, Ethereum and it is evaluated over an extensive dataset we gathered with the transactions of the most widely used DeFi protocols (23 in total, including Aave, Compound, Curve, Lido, and Yearn) based on a novel dataset in collaboration with Covalent. Different Machine Learning methods were employed, such as XGBoost and a Neural Network for identifying fraud accounts detection interacting with DeFi and we demonstrate that the introduction of novel DeFi-related features, significantly improves the evaluation results, where Accuracy, Precision, Recall, F1-score and F2-score where utilized.
    Date: 2023–05
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2306.07972&r=cmp
  6. By: Yue Tian; Guanjun Liu
    Abstract: How to obtain informative representations of transactions and then perform the identification of fraudulent transactions is a crucial part of ensuring financial security. Recent studies apply Graph Neural Networks (GNNs) to the transaction fraud detection problem. Nevertheless, they encounter challenges in effectively learning spatial-temporal information due to structural limitations. Moreover, few prior GNN-based detectors have recognized the significance of incorporating global information, which encompasses similar behavioral patterns and offers valuable insights for discriminative representation learning. Therefore, we propose a novel heterogeneous graph neural network called Spatial-Temporal-Aware Graph Transformer (STA-GT) for transaction fraud detection problems. Specifically, we design a temporal encoding strategy to capture temporal dependencies and incorporate it into the graph neural network framework, enhancing spatial-temporal information modeling and improving expressive ability. Furthermore, we introduce a transformer module to learn local and global information. Pairwise node-node interactions overcome the limitation of the GNN structure and build up the interactions with the target node and long-distance ones. Experimental results on two financial datasets compared to general GNN models and GNN-based fraud detectors demonstrate that our proposed method STA-GT is effective on the transaction fraud detection task.
    Date: 2023–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2307.05121&r=cmp
  7. By: Leib, Margarita (Tilburg University); Köbis, Nils (Max Planck Institute for Human Development); Rilke, Rainer Michael (WHU Vallendar); Hagens, Marloes (Erasmus University Rotterdam); Irlenbusch, Bernd (University of Cologne)
    Abstract: Artificial Intelligence (AI) increasingly becomes an indispensable advisor. New ethical concerns arise if AI persuades people to behave dishonestly. In an experiment, we study how AI advice (generated by a Natural-Language-processing algorithm) affects (dis)honesty, compare it to equivalent human advice, and test whether transparency about advice source matters. We find that dishonesty-promoting advice increases dishonesty, whereas honesty-promoting advice does not increase honesty. This is the case for both AI and human advice. Algorithmic transparency, a commonly proposed policy to mitigate AI risks, does not affect behaviour. The findings mark the first steps towards managing AI advice responsibly.
    Keywords: Artificial Intelligence, machine behaviour, behavioural ethics, advice
    JEL: C91 D90 D91
    Date: 2023–07
    URL: http://d.repec.org/n?u=RePEc:iza:izadps:dp16293&r=cmp
  8. By: Kunz, Pascal Christoph; Jussupow, Ekaterina; Spohrer, Kai; Heinzl, Armin
    Date: 2022–05–11
    URL: http://d.repec.org/n?u=RePEc:dar:wpaper:138573&r=cmp
  9. By: Yannick Limmer; Blanka Horvath
    Abstract: The availability of deep hedging has opened new horizons for solving hedging problems under a large variety of realistic market conditions. At the same time, any model - be it a traditional stochastic model or a market generator - is at best an approximation of market reality, prone to model-misspecification and estimation errors. This raises the question, how to furnish a modelling setup with tools that can address the risk of discrepancy between anticipated distribution and market reality, in an automated way. Automated robustification is currently attracting increased attention in numerous investment problems, but it is a delicate task due to its imminent implications on risk management. Hence, it is beyond doubt that more activity can be anticipated on this topic to converge towards a consensus on best practices. This paper presents a natural extension of the original deep hedging framework to address uncertainty in the data generating process via an adversarial approach inspired by GANs to automate robustification in our hedging objective. This is achieved through an interplay of three modular components: (i) a (deep) hedging engine, (ii) a data-generating process (that is model agnostic permitting a large variety of classical models as well as machine learning-based market generators), and (iii) a notion of distance on model space to measure deviations between our market prognosis and reality. We do not restrict the ambiguity set to a region around a reference model, but instead penalize deviations from the anticipated distribution. Our suggested choice for each component is motivated by model agnosticism, allowing a seamless transition between settings. Since all individual components are already used in practice, we believe that our framework is easily adaptable to existing functional settings.
    Date: 2023–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2307.02310&r=cmp
  10. By: Zhenhan Huang; Fumihide Tanaka
    Abstract: On-chain data (metrics) of blockchain networks, akin to company fundamentals, provide crucial and comprehensive insights into the networks. Despite their informative nature, on-chain data have not been utilized in reinforcement learning (RL)-based systems for cryptocurrency (crypto) portfolio management (PM). An intriguing subject is the extent to which the utilization of on-chain data can enhance an RL-based system's return performance compared to baselines. Therefore, in this study, we propose CryptoRLPM, a novel RL-based system incorporating on-chain data for end-to-end crypto PM. CryptoRLPM consists of five units, spanning from information comprehension to trading order execution. In CryptoRLPM, the on-chain data are tested and specified for each crypto to solve the issue of ineffectiveness of metrics. Moreover, the scalable nature of CryptoRLPM allows changes in the portfolios' cryptos at any time. Backtesting results on three portfolios indicate that CryptoRLPM outperforms all the baselines in terms of accumulated rate of return (ARR), daily rate of return (DRR), and Sortino ratio (SR). Particularly, when compared to Bitcoin, CryptoRLPM enhances the ARR, DRR, and SR by at least 83.14%, 0.5603%, and 2.1767 respectively.
    Date: 2023–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2307.01599&r=cmp
  11. By: Marco Morucci; Vittorio Orlandi; Harsh Parikh; Sudeepa Roy; Cynthia Rudin; Alexander Volfovsky
    Abstract: Experimental and observational studies often lack validity due to untestable assumptions. We propose a double machine learning approach to combine experimental and observational studies, allowing practitioners to test for assumption violations and estimate treatment effects consistently. Our framework tests for violations of external validity and ignorability under milder assumptions. When only one assumption is violated, we provide semi-parametrically efficient treatment effect estimators. However, our no-free-lunch theorem highlights the necessity of accurately identifying the violated assumption for consistent treatment effect estimation. We demonstrate the applicability of our approach in three real-world case studies, highlighting its relevance for practical settings.
    Date: 2023–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2307.01449&r=cmp
  12. By: Christian Yeo
    Abstract: Least Squares regression was first introduced for the pricing of American-style options, but it has since been expanded to include swing options pricing. The swing options price may be viewed as a solution to a Backward Dynamic Programming Principle, which involves a conditional expectation known as the continuation value. The approximation of the continuation value using least squares regression involves two levels of approximation. First, the continuation value is replaced by an orthogonal projection over a subspace spanned by a finite set of $m$ squared-integrable functions (regression functions) yielding a first approximation $V^m$ of the swing value function. In this paper, we prove that, with well-chosen regression functions, $V^m$ converges to the swing actual price $V$ as $m \to + \infty$. A similar result is proved when the regression functions are replaced by neural networks. For both methods (least squares or neural networks), we analyze the second level of approximation involving practical computation of the swing price using Monte Carlo simulations and yielding an approximation $V^{m, N}$ (where $N$ denotes the Monte Carlo sample size). Especially, we prove that $V^{m, N} \to V^m$ as $N \to + \infty$ for both methods and using Hilbert basis in the least squares regression. Besides, a convergence rate of order $\mathcal{O}\big(\frac{1}{\sqrt{N}} \big)$ is proved in the least squares case. Several convergence results in this paper are based on the continuity of the swing value function with respect to cumulative consumption, which is also proved in the paper and has not been yet explored in the literature before for the best of our knowledge.
    Date: 2023–07
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2307.04510&r=cmp
  13. By: Christophe Carugati
    Abstract: This working paper examines how competition in foundation models (FMs) works.
    Date: 2023–07
    URL: http://d.repec.org/n?u=RePEc:bre:wpaper:node_9258&r=cmp
  14. By: Stefan Faldum (Johannes Gutenberg University Mainz); Timo Gschwind (RPTU Kaiserslautern-Landau); Stefan Irnich (Johannes Gutenberg University Mainz)
    Abstract: This work considers branch-price-and-cut algorithms for variants of the vehicle-routing problem in which subset-row inequalities (SRIs) are used to strengthen the linear relaxation. SRIs often help to substantially reduce the size of the branch-and-bound search tree. However, their use is computationally costly because they modify the structure of the respective column-generation subproblem which is a shortest-path problem with resource constraints (SPPRC). Each active SRI requires the addition of a resource to the labeling algorithm that is invoked for solving the SPPRC in every iteration. In the context of time-window constraints, the concept of unreachable customers has been used for preprocessing (time-window reduction, arc elimination, precedence identification) as well as for improving the dominance between labels in the elementary SPPRC and its relaxations. We show that the identification of unreachable customers can also help to improve the dominance due to a modified comparison of SRI-related resources. Computational experiments with a fully-fledged branch-price-and-cut algorithm for the (standard and electric) vehicle routing problem with time windows demonstrates the effectiveness of the approach: Overall computation times decrease, for some difficult instances they may even be cut in half, while the required modifications of a computer implementation for combining SRIs with unreachable customers is minor.
    Keywords: Routing, subset-row inequalities, labeling algorithm, unreachability, branch-price-and-cut
    Date: 2023–07–12
    URL: http://d.repec.org/n?u=RePEc:jgu:wpaper:2310&r=cmp
  15. By: Nikhil Agarwal; Alex Moehring; Pranav Rajpurkar; Tobias Salz
    Abstract: While Artificial Intelligence (AI) algorithms have achieved performance levels comparable to human experts on various predictive tasks, human experts can still access valuable contextual information not yet incorporated into AI predictions. Humans assisted by AI predictions could outperform both human-alone or AI-alone. We conduct an experiment with professional radiologists that varies the availability of AI assistance and contextual information to study the effectiveness of human-AI collaboration and to investigate how to optimize it. Our findings reveal that (i) providing AI predictions does not uniformly increase diagnostic quality, and (ii) providing contextual information does increase quality. Radiologists do not fully capitalize on the potential gains from AI assistance because of large deviations from the benchmark Bayesian model with correct belief updating. The observed errors in belief updating can be explained by radiologists’ partially underweighting the AI’s information relative to their own and not accounting for the correlation between their own information and AI predictions. In light of these biases, we design a collaborative system between radiologists and AI. Our results demonstrate that, unless the documented mistakes can be corrected, the optimal solution involves assigning cases either to humans or to AI, but rarely to a human assisted by AI.
    JEL: C50 C90 D47 D83
    Date: 2023–07
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:31422&r=cmp
  16. By: Gaël Le Mens; Balász Kovács; Michael T. Hannan; Guillem Pros
    Abstract: Recently, the world’s attention has been captivated by Large Language Models (LLMs) thanks to OpenAI’s Chat-GPT, which rapidly proliferated as an app powered by GPT-3 and now its successor, GPT-4. If these LLMs produce human-like text, the semantic spaces they construct likely align with those used by humans for interpreting and generating language. This suggests that social scientists could use these LLMs to construct measures of semantic similarity that match human judgment. In this article, we provide an empirical test of this intuition. We use GPT-4 to construct a new measure of typicality– the similarity of a text document to a concept or category. We evaluate its performance against other model-based typicality measures in terms of their correspondence with human typicality ratings. We conduct this comparative analysis in two domains: the typicality of books in literary genres (using an existing dataset of book descriptions) and the typicality of tweets authored by US Congress members in the Democratic and Republican parties (using a novel dataset). The GPT-4 Typicality measure not only meets or exceeds the current state-of-the-art but accomplishes this without any model training. This is a breakthrough because the previous state-of-the-art measure required fine-tuning a model (a BERT text classifier) on hundreds of thousands of text documents to achieve its performance. Our comparative analysis emphasizes the need for systematic empirical validation of measures based on LLMs: several measures based on other recent LLMs achieve at best a moderate correspondence with human judgments.
    Keywords: categories, concepts, deep learning, typicality, GPT, chatGPT, BERT, Similarity
    JEL: C18 C52
    Date: 2023–06
    URL: http://d.repec.org/n?u=RePEc:bge:wpaper:1394&r=cmp
  17. By: Jeanette Schmidt (Johannes Gutenberg University Mainz); Christian Tilk (University of Vienna); Stefan Irnich (Johannes Gutenberg University Mainz)
    Abstract: The vehicle routing problem with drones (VRP-D) is an extension of the capacitated vehicle routing problem, in which the fleet consists of trucks equipped with one drone each. A truck and its drone can either move together or separately. To operate alone, a truck can release its drone at the depot or at a customer location and likewise pick it up at a later location visited by the same truck. In this way, both trucks and drones deliver goods to customers working together as synchronized working units. A feasible route has to satisfy the capacity constraints of both the truck and the drone. The VRP-D consists of finding a minimum-cost set of feasible routes such that each customer is served exactly once by either a truck or a drone. We develop a branch-price-and-cut (BPC) algorithm to solve the VRP-D exactly for both standard objectives considered in the literature, i.e., the minimization of the total routing cost and the sum of the routes' durations. To solve the column-generation subproblems, we present a new forward and implicit bidirectional labeling algorithm defined over an artificial network. The new bidirectional labeling algorithm substantially accelerates the solution process compared its monodirectional counterpart. In several computational experiments, we analyze algorithmic components of the BPC algorithm, compare the cost and duration objectives, and highlight the impact of the drones' speed on the structure of VRP-D solutions. The final version of the BPC algorithm is able to solve VRP-D instances with up to 50 vertices to proven optimality within one hour of computation time.
    Keywords: routing, drone delivery, synchronization, branch-price-and-cut, bidirectional labeling
    Date: 2023–05–24
    URL: http://d.repec.org/n?u=RePEc:jgu:wpaper:2311&r=cmp
  18. By: Wójcik-Czerniawska, Agnieszka
    Abstract: In terms of the economy, agriculture plays a significant role. In agriculture, automation has become a major concern and a hot topic around the world. Food and employment demand are rising as a result of a rapidly expanding population. Using the new methods, billions of people were able to meet their dietary needs while also gaining employment opportunities. Farming has undergone an enormous change thanks to artificial intelligence. Crop yields have been protected by this technology from a variety of threats, including climate change, population growth, labour shortages, and concerns about global food security. Weeding, spraying, and irrigation are just a few of the many uses for artificial intelligence in agriculture that this paper examines in detail, with the help of sensors and other tools built into machine and drones. Water, pesticide, herbicide, and soil fertility use, as well as labour use, are all reduced thanks to these new technologies, which boost output while also improving product quality. Robots and drones are being used for weeding in agriculture, and this paper compiles the findings of numerous researchers to give readers an overview of the current state of automation in agriculture. Soil water sensing techniques and two automated weeding methods are discussed. It is discussed in this paper how drones can be used for spraying and crop monitoring, as well as the various methods they can employ.
    Keywords: Production Economics, Research and Development/Tech Change/Emerging Technologies
    Date: 2022–09–23
    URL: http://d.repec.org/n?u=RePEc:ags:haaepa:337138&r=cmp
  19. By: Wójcik-Czerniawska, Agnieszka
    Abstract: In terms of the economy, agriculture plays a significant role. In agriculture, automation has become a major concern and a hot topic around the world. Food and employment demand are rising as a result of a rapidly expanding population. Using the new methods, billions of people were able to meet their dietary needs while also gaining employment opportunities. Farming has undergone an enormous change thanks to artificial intelligence. Crop yields have been protected by this technology from a variety of threats, including climate change, population growth, labour shortages, and concerns about global food security. Weeding, spraying, and irrigation are just a few of the many uses for artificial intelligence in agriculture that this paper examines in detail, with the help of sensors and other tools built into machine and drones. Water, pesticide, herbicide, and soil fertility use, as well as labour use, are all reduced thanks to these new technologies, which boost output while also improving product quality. Robots and drones are being used for weeding in agriculture, and this paper compiles the findings of numerous researchers to give readers an overview of the current state of automation in agriculture. Soil water sensing techniques and two automated weeding methods are discussed. It is discussed in this paper how drones can be used for spraying and crop monitoring, as well as the various methods they can employ.
    Keywords: Production Economics, Research and Development/Tech Change/Emerging Technologies
    Date: 2022–09–23
    URL: http://d.repec.org/n?u=RePEc:ags:haaewp:337138&r=cmp
  20. By: Gabriel Osório de Barros (GEE - Gabinete de Estratégia e Estudos do Ministério da Economia e do Mar (Office for Strategy and Studies of the portuguese Ministry of Economy and Maritime Affairs))
    Abstract: This GEE paper provides a comprehensive assessment of the potential and challenges of Artificial Intelligence (AI), with a particular focus on Portugal in the context of the EU. Grounded in both global and EU contexts, the study identifies applications and transformative influence of AI across various sectors such as education, health, tourism, manufacturing, financial services or e-government. It also delves into the ethical, social and legal implications of widespread AI adoption, including data privacy concerns and the need for human oversight. The paper examines EU's current stance and policies on AI. Recognizing Portugal's particular opportunities, the study provides strategic recommendations for fostering AI education and training, promoting research and development, supporting AI startups and businesses, ensuring ethical use of AI and encouraging international collaboration. The implications of these strategies extend beyond technological advancement, touching upon broader societal, economic and philosophical issues. The future of AI is also approached, acknowledging both its potential and the inherent challenges of regulating this rapidly evolving field. While this paper provides an analysis of AI within Portugal's context, it is subject to certain limitations, where future research is needed. As the AI landscape continues to evolve, so will the opportunities and challenges it presents, requiring continuous study and proactive policymaking. The study concludes with a reflection on humanity's role in an increasingly automated world, underscoring the importance of balancing AI integration with the preservation of human values. As the architects of AI, mankind carries the responsibility to guide its path and its impact on our existence. We urge for the application of this power with prudence, foresight and empathy, to envision a future where humans and AI may not only coexist but prosper together. Finally, the future of AI is not merely a technological evolution but a chapter in humanity's journey, echoing our choices. It is a future we create, a narrative we pen and a legacy we leave.
    Keywords: Artificial Intelligence, Digital Economy
    JEL: K20 L86 O33 Q55
    Date: 2023–07
    URL: http://d.repec.org/n?u=RePEc:mde:wpaper:177&r=cmp
  21. By: Miguel Faria-e-Castro; Fernando Leibovici
    Abstract: We explore the ability of Large Language Models (LLMs) to produce conditional inflation forecasts during the 2019-2023 period. We use a leading LLM (Google AI's PaLM) to produce distributions of conditional forecasts at different horizons and compare these forecasts to those of a leading source, the Survey of Professional Forecasters (SPF). We find that LLM forecasts generate lower mean-squared errors overall in most years, and at almost all horizons. LLM forecasts exhibit slower reversion to the 2% inflation anchor. We argue that this method of generating forecasts is inexpensive and can be applied to other time series.
    Keywords: inflation forecasts; large language models; artificial intelligence
    JEL: E31 E37 C45 C53
    Date: 2023–07–14
    URL: http://d.repec.org/n?u=RePEc:fip:fedlwp:96478&r=cmp
  22. By: Damián Pierri (UBA/CONICET)
    Abstract: This paper present conditions to guarantee the convergence of simulations to a stochastic steady state, characterized by an invariant probability distribution, in an endowment economy with a finite number of heterogeneous agents, aggregate uncertainty and uncountable shocks. The results are robust to the presence of multiple discontinuous equilibria and do not require ad-hoc convexification techniques, like "sunspots". Thus, our results are numerically implementable. We work on a Markov environment with an enlarged state space, applied to an incomplete markets model, to characterize ergodic equilibria and differentiate them with respect to time-independent, and stationary ones. We show that, by imposing a mild restriction on the discontinuity set, every measurable time-independent selection can be used to approximate the stochastic steady state of the model. Considering the common practice of clustering agents according to, for instance, deciles of the wealth and assuming uncountable income shocks, the results in this paper can help to design calibration and estimation methods for heterogeneous agent models based on unconditional moments.
    Keywords: non-optimal economies, Markov equilibrium, heterogeneous agents, simulations
    JEL: C63 C68 D52 D58
    Date: 2023–07
    URL: http://d.repec.org/n?u=RePEc:aoz:wpaper:259&r=cmp

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