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Статті в журналах з теми "Price competition; learning; experiment"

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Byrne, David P., and Nicolas de Roos. "Learning to Coordinate: A Study in Retail Gasoline." American Economic Review 109, no. 2 (February 1, 2019): 591–619. http://dx.doi.org/10.1257/aer.20170116.

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Анотація:
This paper studies equilibrium selection in the retail gasoline industry. We exploit a unique dataset that contains the universe of station-level prices for an urban market for 15 years, and that encompasses a coordinated equilibrium transition mid-sample. We uncover a gradual, three-year equilibrium transition, whereby dominant firms use price leadership and price experiments to create focal points that coordinate market prices, soften price competition, and enhance retail margins. Our results inform the theory of collusion, with particular relevance to the initiation of collusion and equilibrium selection. We also highlight new insights into merger policy and collusion detection strategies. (JEL G34, L12, L13, L71, L81, Q35)
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Monica Capra, C., Jacob K. Goeree, Rosario Gomez, and Charles A. Holt. "Learning and Noisy Equilibrium Behavior in an Experimental Study of Imperfect Price Competition*." International Economic Review 43, no. 3 (August 2002): 613–36. http://dx.doi.org/10.1111/1468-2354.t01-1-00029.

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Horiguchi, Yuji, Yukino Baba, Hisashi Kashima, Masahito Suzuki, Hiroki Kayahara, and Jun Maeno. "Predicting Fuel Consumption and Flight Delays for Low-Cost Airlines." Proceedings of the AAAI Conference on Artificial Intelligence 31, no. 2 (February 11, 2017): 4686–93. http://dx.doi.org/10.1609/aaai.v31i2.19095.

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Low-cost airlines (LCAs) represent a new category of airlines that provides low-fare flights. The rise and growth of LCAs has intensified the price competition among airlines, and LCAs require continuous efforts to reduce their operating costs to lower flight prices; however, LCA passengers still demand high-quality services. A common measure of airline service quality is on-time departure performance. Because LCAs apply efficient aircraft utilization and the time between flights is likely to be small, additional effort is required to avoid flight delays and improve their service quality. In this paper, we apply state-of-the-art predictive modeling approaches to real airline datasets and investigate the feasibility of machine learning methods for cost reduction and service quality improvement in LCAs. We address two prediction problems: fuel consumption prediction and flight delay prediction. We train predictive models using flight and passenger information, and our experiment results show that our regression model predicts the amount of fuel consumption more accurately than flight dispatchers, and our binary classifier achieves an area under the ROC curve (AUC) of 0.75 for predicting a delay of a specific flight route.
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Ghosh, Susobhan, Easwar Subramanian, Sanjay P. Bhat, Sujit Gujar, and Praveen Paruchuri. "VidyutVanika: A Reinforcement Learning Based Broker Agent for a Power Trading Competition." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 914–21. http://dx.doi.org/10.1609/aaai.v33i01.3301914.

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A smart grid is an efficient and sustainable energy system that integrates diverse generation entities, distributed storage capacity, and smart appliances and buildings. A smart grid brings new kinds of participants in the energy market served by it, whose effect on the grid can only be determined through high fidelity simulations. Power TAC offers one such simulation platform using real-world weather data and complex state-of-the-art customer models. In Power TAC, autonomous energy brokers compete to make profits across tariff, wholesale and balancing markets while maintaining the stability of the grid. In this paper, we design an autonomous broker VidyutVanika, the runner-up in the 2018 Power TAC competition. VidyutVanika relies on reinforcement learning (RL) in the tariff market and dynamic programming in the wholesale market to solve modified versions of known Markov Decision Process (MDP) formulations in the respective markets. The novelty lies in defining the reward functions for MDPs, solving these MDPs, and the application of these solutions to real actions in the market. Unlike previous participating agents, VidyutVanika uses a neural network to predict the energy consumption of various customers using weather data. We use several heuristic ideas to bridge the gap between the restricted action spaces of the MDPs and the much more extensive action space available to VidyutVanika. These heuristics allow VidyutVanika to convert near-optimal fixed tariffs to time-of-use tariffs aimed at mitigating transmission capacity fees, spread out its orders across several auctions in the wholesale market to procure energy at a lower price, more accurately estimate parameters required for implementing the MDP solution in the wholesale market, and account for wholesale procurement costs while optimizing tariffs. We use Power TAC 2018 tournament data and controlled experiments to analyze the performance of VidyutVanika, and illustrate the efficacy of the above strategies.
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Stone, P., R. E. Schapire, M. L. Littman, J. A. Csirik, and D. McAllester. "Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions." Journal of Artificial Intelligence Research 19 (September 1, 2003): 209–42. http://dx.doi.org/10.1613/jair.1200.

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Анотація:
Auctions are becoming an increasingly popular method for transacting business, especially over the Internet. This article presents a general approach to building autonomous bidding agents to bid in multiple simultaneous auctions for interacting goods. A core component of our approach learns a model of the empirical price dynamics based on past data and uses the model to analytically calculate, to the greatest extent possible, optimal bids. We introduce a new and general boosting-based algorithm for conditional density estimation problems of this kind, i.e., supervised learning problems in which the goal is to estimate the entire conditional distribution of the real-valued label. This approach is fully implemented as ATTac-2001, a top-scoring agent in the second Trading Agent Competition (TAC-01). We present experiments demonstrating the effectiveness of our boosting-based price predictor relative to several reasonable alternatives.
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Liu, Jinfei, Jian Lou, Junxu Liu, Li Xiong, Jian Pei, and Jimeng Sun. "Dealer." Proceedings of the VLDB Endowment 14, no. 6 (February 2021): 957–69. http://dx.doi.org/10.14778/3447689.3447700.

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Анотація:
Data-driven machine learning has become ubiquitous. A marketplace for machine learning models connects data owners and model buyers, and can dramatically facilitate data-driven machine learning applications. In this paper, we take a formal data marketplace perspective and propose the first en<u> D </u>-to-end mod <u>e</u> l m <u>a</u> rketp <u>l</u> ace with diff <u>e</u> rential p <u>r</u> ivacy ( Dealer ) towards answering the following questions: How to formulate data owners' compensation functions and model buyers' price functions? How can the broker determine prices for a set of models to maximize the revenue with arbitrage-free guarantee, and train a set of models with maximum Shapley coverage given a manufacturing budget to remain competitive ? For the former, we propose compensation function for each data owner based on Shapley value and privacy sensitivity, and price function for each model buyer based on Shapley coverage sensitivity and noise sensitivity. Both privacy sensitivity and noise sensitivity are measured by the level of differential privacy. For the latter, we formulate two optimization problems for model pricing and model training, and propose efficient dynamic programming algorithms. Experiment results on the real chess dataset and synthetic datasets justify the design of Dealer and verify the efficiency and effectiveness of the proposed algorithms.
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Hansen, Karsten T., Kanishka Misra, and Mallesh M. Pai. "Frontiers: Algorithmic Collusion: Supra-competitive Prices via Independent Algorithms." Marketing Science 40, no. 1 (January 2021): 1–12. http://dx.doi.org/10.1287/mksc.2020.1276.

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Анотація:
We show that the long-run prices from independent machine learning algorithms depend on the informational value of price experiments. If low, the long-run prices are consistent with the static Nash equilibrium; however, if high, the long-run prices are supra-competitive.
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Orzen, Henrik, and Martin Sefton. "An experiment on spatial price competition." International Journal of Industrial Organization 26, no. 3 (May 2008): 716–29. http://dx.doi.org/10.1016/j.ijindorg.2007.05.007.

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Elzeki, Omar M., Mohamed Abd Elfattah, Hanaa Salem, Aboul Ella Hassanien, and Mahmoud Shams. "A novel perceptual two layer image fusion using deep learning for imbalanced COVID-19 dataset." PeerJ Computer Science 7 (February 10, 2021): e364. http://dx.doi.org/10.7717/peerj-cs.364.

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Background and Purpose COVID-19 is a new strain of viruses that causes life stoppage worldwide. At this time, the new coronavirus COVID-19 is spreading rapidly across the world and poses a threat to people’s health. Experimental medical tests and analysis have shown that the infection of lungs occurs in almost all COVID-19 patients. Although Computed Tomography of the chest is a useful imaging method for diagnosing diseases related to the lung, chest X-ray (CXR) is more widely available, mainly due to its lower price and results. Deep learning (DL), one of the significant popular artificial intelligence techniques, is an effective way to help doctors analyze how a large number of CXR images is crucial to performance. Materials and Methods In this article, we propose a novel perceptual two-layer image fusion using DL to obtain more informative CXR images for a COVID-19 dataset. To assess the proposed algorithm performance, the dataset used for this work includes 87 CXR images acquired from 25 cases, all of which were confirmed with COVID-19. The dataset preprocessing is needed to facilitate the role of convolutional neural networks (CNN). Thus, hybrid decomposition and fusion of Nonsubsampled Contourlet Transform (NSCT) and CNN_VGG19 as feature extractor was used. Results Our experimental results show that imbalanced COVID-19 datasets can be reliably generated by the algorithm established here. Compared to the COVID-19 dataset used, the fuzed images have more features and characteristics. In evaluation performance measures, six metrics are applied, such as QAB/F, QMI, PSNR, SSIM, SF, and STD, to determine the evaluation of various medical image fusion (MIF). In the QMI, PSNR, SSIM, the proposed algorithm NSCT + CNN_VGG19 achieves the greatest and the features characteristics found in the fuzed image is the largest. We can deduce that the proposed fusion algorithm is efficient enough to generate CXR COVID-19 images that are more useful for the examiner to explore patient status. Conclusions A novel image fusion algorithm using DL for an imbalanced COVID-19 dataset is the crucial contribution of this work. Extensive results of the experiment display that the proposed algorithm NSCT + CNN_VGG19 outperforms competitive image fusion algorithms.
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Dugar, Subhasish, and Todd Sorensen. "Hassle Costs, Price-Matching Guarantees and Price Competition: An Experiment." Review of Industrial Organization 28, no. 4 (June 2006): 359–78. http://dx.doi.org/10.1007/s11151-006-9103-y.

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Дисертації з теми "Price competition; learning; experiment"

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Collins, Andrew. "Evaluating reinforcement learning for game theory application learning to price airline seats under competition." Thesis, University of Southampton, 2009. https://eprints.soton.ac.uk/69751/.

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Анотація:
Applied Game Theory has been criticised for not being able to model real decision making situations. A game's sensitive nature and the difficultly in determining the utility payoff functions make it hard for a decision maker to rely upon any game theoretic results. Therefore the models tend to be simple due to the complexity of solving them (i.e. finding the equilibrium). In recent years, due to the increases of computing power, different computer modelling techniques have been applied in Game Theory. A major example is Artificial Intelligence methods e.g. Genetic Algorithms, Neural Networks and Reinforcement Learning (RL). These techniques allow the modeller to incorporate Game Theory within their models (or simulation) without necessarily knowing the optimal solution. After a warm up period of repeated episodes is run, the model learns to play the game well (though not necessarily optimally). This is a form of simulation-optimization. The objective of the research is to investigate the practical usage of RL within a simple sequential stochastic airline seat pricing game. Different forms of RL are considered and compared to the optimal policy, which is found using standard dynamic programming techniques. The airline game and RL methods displays various interesting phenomena, which are also discussed. For completeness, convergence proofs for the RL algorithms were constructed.
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Wu, Hang. "Essays on pricing and learning in Bertrand markets." Thesis, 2013. http://hdl.handle.net/2440/83734.

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The thesis studies sellers’ pricing and learning behaviour in Bertrand oligopoly markets using a bounded rational approach. It consists of four chapters. Chapter 1 develops a quantal response adaptive learning model which combines sellers’ bounded rationality with adaptive belief learning in order to explain price dispersion and dynamics in laboratory Bertrand markets with perfect information. In the model, sellers hold beliefs about their opponents’ strategies and play quantal best responses to these beliefs. After each round, sellers update their beliefs based on the information learned from previous play. Maximum likelihood estimation suggests that when sellers have full past price information, the learning model explains price dispersion within periods and the dynamics across periods. The fit is particularly good if one allows for sellers being risk averse. In contrast, Quantal Response Equilibrium does not organize the data well. Chapter 2 proposes a generalized payoff assessment learning model of Sarin & Vahid (1999) for the perfect information Bertrand experiments we studied in Chapter 1. The model contains the quantal-response adaptive learning model of Chapter 1 and the original payoff assessment learning model as special cases. A main feature of the model is that it stresses the importance of forgone payoffs for unselected prices in driving the price adjustments. Maximum likelihood estimation shows that the model substantially outperforms the quantal-response adaptive learning model with respect to fitting the data. Chapter 3 studies the effects of increasing number of sellers on Quantal Response Equilibrium (QRE) prices in homogeneous product Bertrand oligopoly markets. We show that the comparative statics properties of QRE can be very sensitive to the specification of the quantal response function. With the power-function specification, an increase in the number of competing sellers leads to a decrease in the average QRE market price. In stark contrast, with logistic specification, having more sellers may increase the equilibrium market price, which is at odds with the general intuition that competition should lead to lower prices. Chapter 4 proposes an extended payoff-assessment learning model to explain the pricing and learning behaviour observed in a repeated Bertrand market experiment with limited feedback. In the experiments, sellers’ only feedback after a period was their own payoff. Sellers were not able to observe the prices set by their competitors. The data show that pricing behaviour is strongly influenced by past sales. Sellers on average increase prices after being successful at selling, while they reduce prices after failing to sell. We show that by explicitly incorporating the sellers inferences from the sale history, our learning model manages to explain the data on both the aggregate and individual level.
Thesis (Ph.D.) -- University of Adelaide, School of Economics, 2013
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Частини книг з теми "Price competition; learning; experiment"

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Wicaksono, Hendro, Tina Boroukhian, and Atit Bashyal. "A Demand-Response System for Sustainable Manufacturing Using Linked Data and Machine Learning." In Dynamics in Logistics, 155–81. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88662-2_8.

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AbstractThe spread of demand-response (DR) programs in Europe is a slow but steady process to optimize the use of renewable energy in different sectors including manufacturing. A demand-response program promotes changes of electricity consumption patterns at the end consumer side to match the availability of renewable energy sources through price changes or incentives. This research develops a system that aims to engage manufacturing power consumers through price- and incentive-based DR programs. The system works on data from heterogeneous systems at both supply and demand sides, which are linked through a semantic middleware, instead of centralized data integration. An ontology is used as the integration information model of the semantic middleware. This chapter explains the concept of constructing the ontology by utilizing relational database to ontology mapping techniques, reusing existing ontologies such as OpenADR, SSN, SAREF, etc., and applying ontology alignment methods. Machine learning approaches are developed to forecast both the power generated from renewable energy sources and the power demanded by manufacturing consumers based on their processes. The forecasts are the groundworks to calculate the dynamic electricity price introduced for the DR program. This chapter presents different neural network architectures and compares the experiment results. We compare the results of Deep Neural Network (DNN), Long Short-Term Memory Network (LSTM), Convolutional Neural Network (CNN), and Hybrid architectures. This chapter focuses on the initial phase of the research where we focus on the ontology development method and machine learning experiments using power generation datasets.
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Posada, Marta. "Emissions Permits Auctions." In Social Simulation, 180–91. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-522-1.ch014.

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In this chapter the authors demonstrate with three relevant issues that Agent Based Modeling (ABM) is very useful to design emissions permits auctions and to forecast emission permits prices. They argue that ABM offers a more efficient approach to auction design than the usual mechanistic models. The authors set up the essential components of any market institution far beyond supply and demand. They build an ABM for the emissions permits auction of the Environment Protection Agency (EPA), and demonstrate why the EPA failed. In the second experiment they show that in a competitive and efficient auction, the Continuous Double Auction, there is room for traders learning and strategic behavior, thus clearing the perfect market paradox. In the third experiment they build an ABM of the Spanish electricity market to get CO2 emissions prices forecasts that are more accurate than those obtained with econometric or mechanistic models.
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Sisodia, Dilip Singh, and Sagar Jadhav. "Machine Learning Models for Forecasting of Individual Stocks Price Patterns." In Advances in Systems Analysis, Software Engineering, and High Performance Computing, 111–29. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3870-7.ch008.

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Stock investors always consider potential future prices before investing in any stock for making a profit. A large number of studies are found on the prediction of stock market indices. However, the focus on individual stock closing price predictions well ahead of time is limited. In this chapter, a comparative study of machine-learning-based models is used for the prediction of the closing price of a particular stock. The proposed models are designed using back propagation neural networks (BPNN), support vector regression (SVR) with SMOReg, and linear regression (LR) for the prediction of the closing price of individual stocks. A total of 37 technical indicators (features) derived from historical closing prices of stocks are considered for predicting the future price of stock in a time window of five days. The experiment is performed on stocks listed on Bombay Stock Exchange (BSS), India. The model is trained and tested using feature values extracted from the past five-year closing price of stocks of different sectors including aviation, pharma, banking, entertainment, and IT.
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Cesar, Nuša. "A Trip to Opatija." In Exercises in Travel Writing and Literary Tourism: A Teaching and Learning Experiment, 17–22. University of Maribor Press, 2020. http://dx.doi.org/10.18690/978-961-286-393-7.3.

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Анотація:
In the assignment we focused on the Croatian destination Opatija. We visited Opatija at the end of September and the trip was wonderful . We would like to show you the journey through our eyes, and also stimulate your interest in visiting this destination. If you want a break from reality and do not want to drive too far, Opatija is the right destination for you. If you like to go for a walk, enjoy the environment of pampering, you will get everything in one place. It may seem like a more expensive destination at first sight, but we willll show you tricks on how you can afford Opatija at a very low price and enjoy the luxury. We hope this destination will pique your interest and make it into your wish list to visit.
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Khoo, Li Jing. "Design and Develop a Cybersecurity Education Framework Using Capture the Flag (CTF)." In Design, Motivation, and Frameworks in Game-Based Learning, 123–53. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-6026-5.ch005.

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The rise of cyber threats is projecting the growth of cybersecurity education. Malaysian students who are interested in studying computing and information technologies suffer from knowledge and skill gaps because the earliest exposure of formal computer knowledge happens only at tertiary level education. In addition, the ever-evolving cyber landscape complicated the gaps and exposure. This chapter reveals the learner's motivation factor through an exploratory study in a national level cybersecurity competition. By simulating a real-world cyber landscape, a customized cybersecurity game, Capture the Flag was designed, developed, and validated as an experiment to study the relationship between learners' motivation and achievement level.
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Yermish, Ira. "A Case for Case Studies via Video-Conferencing." In Distance Learning Technologies, 208–17. IGI Global, 2000. http://dx.doi.org/10.4018/978-1-878289-80-3.ch015.

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Анотація:
Demands are being placed on educational institutions to provide course content in new and complex forms to address the needs of an ever more mobile student body. This chapter explores the issues of delivering a normally highly interactive graduate level course using these new technologies within the demands of organizational missions and constraints. We will argue that a course covering topics of organizational technology assimilation is the ideal place to begin this process. It will describe the problems and issues that were faced in one typical course. We will also suggest that this is an ideal area to focus future research in organizational adoption of new technologies that address missions and strategies. The “passing of remoteness” is how one commentator described the phenomenon of the rise of the Internet and other distance-shrinking technologies. Ever since the advent of television, educators have wrestled with the viability of using this technology to reach wider audiences. Educational television facilitated the distribution of high-quality program content in a one-directional fashion. Yet for many educators, this approach lacked the interactive give-and-take so important to the educational process. Video-conferencing has been used heavily in industry to reduce the costs of travel within far-flung organizations. This technology made it possible to meet “face-to-face,” even if the faces were a little blurry and movements were jumpy at best. The visual cues so often considered important in determining if messages were being properly communicated were now available. Immediate visual feedback leads to more productive dialog. Educational institutions have always lagged behind industry in adopting these technologies for two critical reasons. First, there is the psychological barrier that faculty must cross adapting new technologies. One could argue that despite the popular view of “radical academia”, the reality is much more conservative. Changes in curriculum or program delivery can be glacial. Second, and perhaps more critically, the investment in the infrastructure to support these technologies was beyond the means of the organization. Yet these same constraints are tipping the balance toward the requirements to adopt these technologies. Resource constraints, particularly in the area of a scarce, high-quality faculty, competition among educational institutions for market share, and the declining technology costs and improvements in transmission quality are combining to drive experiments in this area. In graduate business education, there has always been an emphasis on the interactive approach to education. Universities pride themselves on, and like to print, glossy brochures about the interactive classrooms where the faculty and students conduct highly charged dialogues on topics of immediacy. One popular form of this dialogue is the case study approach. Similar to the kinds of activities one might find in a law school moot-court experience, potential managers must, with often limited and yet at the same time overwhelming data, process situations, explore options and develop recommendations. The instructor may provide a gentle push based upon the direction the class takes but shouldn’t, assuming good case study pedagogy, be dominating a one-sided presentation. Unlike a lecture in nuclear physics, there is no way to predict the exact direction of the class interests - a very dynamic approach is required. How can the video-conferencing technologies address the needs of this very complex form of the educational experience? This chapter will review our experiences and organizational issues surrounding this issue and raise some future research questions that should be addressed to improve the quality and efficiency of this specific form of education.
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Nuninger, Walter. "Common Scenario for an Efficient Use of Online Learning." In Handbook of Research on Innovative Pedagogies and Technologies for Online Learning in Higher Education, 331–66. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1851-8.ch015.

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Анотація:
Training efficiency required for Higher Education (quality, accessibility, bigger groups with heterogeneous prior experience, funding, competition…) encourages providers to find new ways to facilitate access to knowledge and enhance skills. In this scope, the use of digital pedagogical devices has increased with innovative solutions; the ones based on an LMS to support a blended course or MOOCS design for self-education. This evolution has impacted teaching practices, learning and organizations leading to a new paradigm for trainers and a new business model to be found for online and distance learning. The innovation mostly relies on the use of learner-centered digital learning solutions in a comprehensive way for the commitment of more active and independent learners and their skills recognition. Based on a 3-year experiment (hybridized course for CVT) and continuous improvement in the WIL, a common scenario is proposed to address the issue for distance training.
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Rahman, Moksadur, Amare Desalegn Fentaye, Valentina Zaccaria, Ioanna Aslanidou, Erik Dahlquist, and Konstantinos Kyprianidis. "A Framework for Learning System for Complex Industrial Processes." In AI and Learning Systems - Industrial Applications and Future Directions. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.92899.

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Анотація:
Due to the intense price-based global competition, rising operating cost, rapidly changing economic conditions and stringent environmental regulations, modern process and energy industries are confronting unprecedented challenges to maintain profitability. Therefore, improving the product quality and process efficiency while reducing the production cost and plant downtime are matters of utmost importance. These objectives are somewhat counteracting, and to satisfy them, optimal operation and control of the plant components are essential. Use of optimization not only improves the control and monitoring of assets, but also offers better coordination among different assets. Thus, it can lead to extensive savings in the energy and resource consumption, and consequently offer reduction in operational costs, by offering better control, diagnostics and decision support. This is one of the main driving forces behind developing new methods, tools and frameworks. In this chapter, a generic learning system architecture is presented that can be retrofitted to existing automation platforms of different industrial plants. The architecture offers flexibility and modularity, so that relevant functionalities can be selected for a specific plant on an as-needed basis. Various functionalities such as soft-sensors, outputs prediction, model adaptation, control optimization, anomaly detection, diagnostics and decision supports are discussed in detail.
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Goeree, Jacob K., Charles A. Holt, and Thomas R. Palfrey. "Applications to Economics." In Quantal Response Equilibrium. Princeton University Press, 2016. http://dx.doi.org/10.23943/princeton/9780691124230.003.0009.

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Анотація:
This chapter explores whether the equilibrium effects of noisy behavior can cause large deviations from standard predictions in economically relevant situations. It considers a simple price-competition game, which is also partly motivated by the possibility of changing a payoff parameter that has no effect on the unique Nash equilibrium, but which may be expected to affect quantal response equilibrium. In the minimum-effort coordination game studied, any common effort in the range of feasible effort levels is a Nash equilibrium, but one would expect that an increase in the cost of individual effort or an increase in the number of players who are trying to coordinate would reduce the effort levels observed in an experiment. The chapter presents an analysis of the logit equilibrium and rent dissipation for a rent-seeking contest that is modeled as an “all-pay auction.” The final two applications in this chapter deal with auctions with private information.
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Saam, N. J., and W. Kerber. "Knowledge Accumulation in hayekian Market Process Theory." In Handbook of Research on Nature-Inspired Computing for Economics and Management, 352–66. IGI Global, 2007. http://dx.doi.org/10.4018/978-1-59140-984-7.ch024.

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Анотація:
This simulation model is an example of theory-driven modeling that aims at developing new hypotheses on mechanisms that work in markets. The central aim is to model processes of knowledge accumulation in markets on the theoretical basis of Hayek’s concept of “competition as a discovery procedure,” in which firms experiment with innovations that are tested in the market, and the superior innovations are imitated by other firms through mutual learning. After an overview on the structure of these simulation models and important results of previous research, we focus on the analysis of the severe negative effects that limited imitability has for this Hayekian process of knowledge accumulation. We show that limited imitability can hamper this process through the emergence of a certain kinds of lock-in situations which reduces the number of changes in the position of the leading firm.
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Тези доповідей конференцій з теми "Price competition; learning; experiment"

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Kheradmand, Shakiba, and Mohammad Reza Meybodi. "Price and QoS competition in cloud market by using cellular learning automata." In 2014 4th International eConference on Computer and Knowledge Engineering (ICCKE). IEEE, 2014. http://dx.doi.org/10.1109/iccke.2014.6993349.

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Ding, Qianggang, Sifan Wu, Hao Sun, Jiadong Guo, and Jian Guo. "Hierarchical Multi-Scale Gaussian Transformer for Stock Movement Prediction." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/640.

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Анотація:
Predicting the price movement of finance securities like stocks is an important but challenging task, due to the uncertainty of financial markets. In this paper, we propose a novel approach based on the Transformer to tackle the stock movement prediction task. Furthermore, we present several enhancements for the proposed basic Transformer. Firstly, we propose a Multi-Scale Gaussian Prior to enhance the locality of Transformer. Secondly, we develop an Orthogonal Regularization to avoid learning redundant heads in the multi-head self-attention mechanism. Thirdly, we design a Trading Gap Splitter for Transformer to learn hierarchical features of high-frequency finance data. Compared with other popular recurrent neural networks such as LSTM, the proposed method has the advantage to mine extremely long-term dependencies from financial time series. Experimental results show our proposed models outperform several competitive methods in stock price prediction tasks for the NASDAQ exchange market and the China A-shares market.
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Chang, Shao-Chen, Gwo-Jen Hwang, Chin-Chung Tsai, and Jyh-Chong Liang. "An Experiment of a Mobile Competition Game for Investigating Students' Interests in Learning Local Culture." In 2014 IEEE 14th International Conference on Advanced Learning Technologies (ICALT). IEEE, 2014. http://dx.doi.org/10.1109/icalt.2014.53.

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Shorokhov, S. "ON DEEP LEARNING FOR OPTION PRICING IN LOCAL VOLATILITY MODELS." In 9th International Conference "Distributed Computing and Grid Technologies in Science and Education". Crossref, 2021. http://dx.doi.org/10.54546/mlit.2021.17.84.001.

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We study neural network approximation of the solution to boundary value problem for Black-ScholesMerton partial differential equation for a European call option price, when model volatility is afunction of underlying asset price and time (local volatility model). Strike-price and expiry day of theoption are assumed to be fixed. An approximation to option price in local volatility model is obtainedvia deep learning with deep Galerkin method (DGM), making use of the neural network of specialarchitecture and stochastic gradient descent on a sequence of random time and underlying price points.Architecture of the neural network and the algorithm of its training for option pricing in local volatilitymodels are described in detail. Computational experiment with DGM neural network is performed toevaluate the quality of neural network approximation for hyperbolic sine local volatility model withknown exact closed form option price. The quality of the neural network approximation is estimatedwith mean absolute error, mean squared error and coefficient of determination. The computationalexperiment demonstrates that DGM neural network approximation converges to a European calloption price of the local volatility model with acceptable accuracy.
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GUO, YAN, LIAN ZHANG, JING YANG, and XIAO-QIAN HU. "THE REFORM AND EXPLORATION OF MICROCONTROLLER CURRICULUM SYSTEM COMBINING THEORY TEACHING WITH EXPERIMENT AND PRACTICE TEACHING." In 2021 International Conference on Education, Humanity and Language, Art. Destech Publications, Inc., 2021. http://dx.doi.org/10.12783/dtssehs/ehla2021/35684.

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In view of the paper, combining with the institutions of higher learning the electricity class, specialized single-chip computer technology course teaching reform and practice of cultivating students' innovative entrepreneurial ability of the new course teaching system, effectively the organic combination of classroom teaching, experiment teaching and training, outstanding teaching feedback function, to realize the integration of teaching, experiment and competition teaching mode. This can not only improve the teaching quality of SCM course, but also cultivate students' engineering practice ability.
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Pérez Poch, Antoni, Jordi Torner Ribé, Daniel Ventura González Alonso, Laura González Llamazares, Maria Josep Martí, Rosa Maria Pasquets Pérez, Francesc Alpiste Penalba, Miguel Ángel Brigos Hermida, and Gloria García Cuadrado. "Challenge-based learning and the Barcelona ZeroG Challenge: A space education case study." In Symposium on Space Educational Activities (SSAE). Universitat Politècnica de Catalunya, 2022. http://dx.doi.org/10.5821/conference-9788419184405.001.

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Challenge-Based Learining is a STEM Education methodology that has been used as a collaborative and hands-on approach to encourage students to put their knowledge in practice by addressing real-life problems. Space Education is a field particularly suited to apply it, with hands-on research projects which require students to take actions and communicate their efforts in a multicultural, international scenario in order to produce an optimal response a specific goal. We herein present a successful Challenge-Based Learning Case Study which involves designing, implementing, and actually flying a microgravity experiment in parabolic flight. The Barcelona ZeroG Challenge is an international competition addressed to University students worldwide. It challenges students to build a team with a mentor, propose, design, build and fly their experiment in microgravity and finally communicate their findings. The experiment has to meet the requirements of a unique microgravity research platform available in Barcelona for educational and research purposes. More than fifty students have flown their experiments on board an aerobatic CAP10B aircraft in Barcelona in previous educational campaigns; having published their results in relevant symposiums and scientific journals. These campaigns have always attracted media attention. The current edition is underway with the winner team expected to fly their experiment before the end of 2022. This edition is jointly organized by Universitat Politècnica de Catalunya, the Barcelona-Sabadell Aviation Club and the Space Generation Advisory Council. Up to fifteen projects have been submitted to this edition, an unprecedent number so far. A panel of experts from the European Space Agency Academy conducted the selection of the winner team, who receives a 2500 euros grant to develop its experiment, aside from the opportunity to fly it in parabolic flight. Furthermore, students from our own University have also the opportunity of designing and testing their microgravity experiments during their studies. Principles of Challenge-Based Learning are herein described as well as how this methodology is applied to this Case Study. Results from our experience are very satisfactory as most of the students who have been involved in it perceive this experience as a boost for their careers. Three key factors to success have been identified: a strong involvement from students' associations, a need for international cooperation and the quality of the students’ mentoring. The experience can be of interest for other organizations to conduct a successful CBL educational project
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Sendra Pons, Pau, and Lucía Pinar García. "Experimental macroeconomics: a role-playing experience among bachelor students." In INNODOCT 2020. Valencia: Editorial Universitat Politècnica de València, 2020. http://dx.doi.org/10.4995/inn2020.2020.11793.

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This current innovative education project has the main goal of introducing students to experimental economics to help them better understand complex macroeconomic concepts. For this purpose, it is used an online experimental platform to develop a role-playing dynamic with which students become real economic agents. This gamified technique allows students to interact with each other in the goods and production factors markets and, thus, generate a circular flow studied as one of the main macroeconomic principles. The online platform is conceived as a two-sided website: on one hand, students are assigned a role and asked to make decisions; and, on the other, professors can instantaneously access results in order to explain participants the consequences of their choices. This innovation had a three-step approach. In the first place, students participated in the internet-based experiment according to the instructions provided by the teaching team. Subsequently, there was a discussion around the main results and their connection with macroeconomic theory. Secondly, students were asked to analyze both the experience and the learning outcomes through a report following well-defined guidelines. Lastly, students evaluated themselves as a co-evaluation practice. This horizontal evaluation promotes students’ understanding of the topic due to empathy development and raising awareness of other fellows’ efforts. To evaluate the effectiveness of the activity, a survey using a Likert scale was conducted as well as an examination of co-evaluation procedures. Results show high levels of engagement, enhanced motivation due to role-playing and satisfaction due to this gamified experience that raises students’ levels of attention by incorporating competition and reward-based mechanisms.
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Tatt Cheah, Yeok, Ka Wing Frances Wan, and Joanne Yip. "Prediction of Muscle Fatigue During Dynamic Exercises based on Surface Electromyography Signals Using Gaussian Classifier." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1002597.

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Muscle fatigue is shown to be associated with incidence of musculoskeletal injuries found with sports training and competition. The real-time detection of fatigue onset allows preventative measures to be taken in time to minimize injuries. In this paper, we aim to provide a framework that classifies muscle fatigue based on surface electromyography (sEMG) features extracted during dynamic exercises. This includes the use of data segmentation, real-time-compatible data normalization, a principal component analysis (PCA) based feature reduction and Gaussian classifier methods.An experiment has been carried out to acquire the sEMG signals of the upper two pairs of rectus abdominis muscles of four healthy adult volunteers during weighted decline and bench-assisted sit-ups. The collected sEMG signals are then segmented into concentric and eccentric segments by using the inertial measurement unit (IMU) data. Eight commonly used sEMG features are extracted from each segment. We fit two Gaussian models (GMs) on the distribution of fatigued and non-fatigued data samples and show that the GM can utilize this information to predict the number of repetitions possible before task failure. We fit another set of GM on a reduced feature space by projecting the data onto principal component axes obtained through singular value decomposition (SVD). By projecting the features onto the first two principal axes, we achieve similar accuracy and f1-scores compared to the GM by using 6 handpicked features. This reduction in the feature space greatly reduces the training samples necessary for such class-imbalanced datasets. This classifier can also be directly used in the real-time detection of muscle fatigue during dynamic movements, which can be adopted in applications in sports, workplaces, and rehabilitation sciences. These frequency-time characteristics also provide insight into the function of low-level feature extractors when developing deep learning models to identify muscle fatigue.
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Звіти організацій з теми "Price competition; learning; experiment"

1

Sweeting, Andrew, Dun Jia, Shen Hui, and Xinlu Yao. Dynamic Price Competition, Learning-By-Doing and Strategic Buyers. Cambridge, MA: National Bureau of Economic Research, December 2020. http://dx.doi.org/10.3386/w28272.

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