Journal articles: '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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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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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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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 D -to-end mod e l m a rketp l ace with diff e rential p r 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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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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Reddy, Prashant, and Manuela Veloso. "Negotiated Learning for Smart Grid Agents: Entity Selection based on Dynamic Partially Observable Features." Proceedings of the AAAI Conference on Artificial Intelligence 27, no. 1 (June 29, 2013): 1313–19. http://dx.doi.org/10.1609/aaai.v27i1.8481.

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An attractive approach to managing electricity demand in the Smart Grid relies on real-time pricing (RTP) tariffs, where customers are incentivized to quickly adapt to changes in the cost of supply. However, choosing amongst competitive RTP tariffs is difficult when tariff prices change rapidly. The problem is further complicated when we assume that the price changes for a tariff are published in real-time only to those customers who are currently subscribed to that tariff, thus making the prices partially observable. We present models and learning algorithms for autonomous agents that can address the tariff selection problem on behalf of customers. We introduce 'Negotiated Learning', a general algorithm that enables a self-interested sequential decision-making agent to periodically select amongst a variable set of 'entities' (e.g., tariffs) by negotiating with other agents in the environment to gather information about dynamic partially observable entity 'features' (e.g., tariff prices) that affect the entity selection decision. We also contribute a formulation of the tariff selection problem as a 'Negotiable Entity Selection Process', a novel representation. We support our contributions with intuitive justification and simulation experiments based on real data on an open Smart Grid simulation platform.

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Kopányi, Dávid, Jean Paul Rabanal, Olga A. Rud, and Jan Tuinstra. "Can competition between forecasters stabilize asset prices in learning to forecast experiments?" Journal of Economic Dynamics and Control 109 (December 2019): 103770. http://dx.doi.org/10.1016/j.jedc.2019.103770.

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ALMEIDA, VINICIO DE SOUZA E., and RICARDO PEREIRA CÂMARA LEAL. "A JOINT EXPERIMENTAL ANALYSIS OF INVESTOR BEHAVIOR IN IPO PRICING METHODS." Revista de Administração de Empresas 55, no. 1 (February 2015): 14–25. http://dx.doi.org/10.1590/s0034-759020150103.

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This article jointly examines the differences of laboratory versions of the Dutch clock open auction, a sealed-bid auction to represent book building, and a two-stage sealed bid auction to proxy for the “competitive IPO”, a recent innovation used in a few European equity initial public offerings. We investigate pricing, seller allocation, and buyer welfare allocation efficiency and conclude that the book building emulation seems to be as price efficient as the Dutch auction, even after investor learning, whereas the competitive IPO is not price efficient, regardless of learning. The competitive IPO is the most seller allocative efficient method because it maximizes offer proceeds. The Dutch auction emerges as the most buyer welfare allocative efficient method. Underwriters are probably seeking pricing efficiency rather than seller or buyer welfare allocative efficiency and their discretionary pricing and allocation must be important since book building is prominent worldwide.

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Dziob, Daniel, Urszula Górska, and Tomasz Kołodziej. "Chain Experiment competition inspires learning of physics." European Journal of Physics 38, no. 3 (February 8, 2017): 034002. http://dx.doi.org/10.1088/1361-6404/38/3/034002.

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Sweeting, Andrew, Dun Jia, Shen Hui, and Xinlu Yao. "Dynamic Price Competition, Learning-by-Doing, and Strategic Buyers." American Economic Review 112, no. 4 (April 1, 2022): 1311–33. http://dx.doi.org/10.1257/aer.20202016.

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We examine how strategic buyer behavior affects equilibrium outcomes in a model of dynamic price competition where sellers benefit from learning-by-doing by allowing each buyer to expect to capture a share of future buyer surplus. Many equilibria that exist when buyers consider only their immediate payoffs are eliminated when buyers expect to capture even a modest share of future surplus, and the equilibria that survive are those where long-run market competition is more likely to be preserved. Our results are relevant for antitrust policy and our approach may be useful for future analyses of dynamic competition. (JEL C73, D21, D43, D83, K21, L13, L40)

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Lanzillotti, Robert F. "Collusion/Competition." Antitrust Bulletin 62, no. 3 (August 16, 2017): 591–602. http://dx.doi.org/10.1177/0003603x17719765.

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Ever since the U.S. Supreme Court opinion in Matsushita, various U.S. district courts have issued a series of rulings that appear to constitute a new learning on the economics of collusive behavior and to elevate the economic evidentiary bar for successful proof of price-fixing and bid-rigging. The rulings use game theory constructs expressed as pure, interdependent behavior that theoretically can result in supracompetitive prices in the absence of any agreement. The most recent explanation of this learning is contained in the 2016 titanium dioxide (TiO2) opinion Valspar v. E. I. DuPont, which raises the bar for proving a Sherman Act Sec. 1 violation. This and earlier rulings appear counterintuitive when their reasoning is tested against the context of Judge Richard Posner’s opinion on the value of circumstantial evidence in High Fructose Corn Syrup and In re Text Messaging. This article identifies market structure and behavioral features typically found in cartel arrangements, and tests the efficacy of what is perceived as a new learning on collusion/competition with empirical data from twelve alleged price-fixing conspiracies successfully litigated over the past two decades.

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Dimitrakakis, Christos, Guangliang Li, and Nikoalos Tziortziotis. "The Reinforcement Learning Competition 2014." AI Magazine 35, no. 3 (September 19, 2014): 61–65. http://dx.doi.org/10.1609/aimag.v35i3.2548.

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Reinforcement learning is one of the most general problems in artificial intelligence. It has been used to model problems in automated experiment design, control, economics, game playing, scheduling and telecommunications. The aim of the reinforcement learning competition is to encourage the development of very general learning agents for arbitrary reinforcement learning problems and to provide a test-bed for the unbiased evaluation of algorithms.

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Kogan, Konstantin, and Fouad El Ouardighi. "Autonomous and induced production learning under price and quality competition." Applied Mathematical Modelling 67 (March 2019): 74–84. http://dx.doi.org/10.1016/j.apm.2018.10.018.

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Lau, Jonas Sin-Heng, Michael B. Casale, and Harold Pashler. "Mitigating cue competition effects in human category learning." Quarterly Journal of Experimental Psychology 73, no. 7 (April 28, 2020): 983–1003. http://dx.doi.org/10.1177/1747021820915151.

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When people learn perceptual categories, if one feature makes it easy to determine the category membership, learning about other features can be reduced. In three experiments, we asked whether this cue competition effect could be fully eradicated with simple instructions. For this purpose, in a pilot experiment, we adapted a classical overshadowing paradigm into a human category learning task. Unlike previous reports, we demonstrate a robust cue competition effect with human learners. In Experiments 1 and 2, we created a new warning condition that aimed at eradicating the cue competition effect through top-down instructions. With a medium-size overshadowing effect, Experiment 1 shows a weak mitigation of the overshadowing effect. We replaced the stimuli in Experiment 2 to obtain a larger overshadowing effect and showed a larger warning effect. Nevertheless, the overshadowing effect could not be fully eradicated. These experiments suggest that cue competition effects can be a stubborn roadblock in human category learning. Theoretical and practical implications are discussed.

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Packheiser, Julian, Roland Pusch, Clara C. Stein, Onur Güntürkün, Harald Lachnit, and Metin Uengoer. "How competitive is cue competition?" Quarterly Journal of Experimental Psychology 73, no. 1 (August 14, 2019): 104–14. http://dx.doi.org/10.1177/1747021819866967.

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Cue competition refers to phenomena indicating that learning about the relationship between a cue and an outcome is influenced by learning about the predictive significance of other cues that are concurrently present. In two autoshaping experiments with pigeons, we investigated the strength of competition among cues for predictive value. In each experiment, animals received an overexpectation training (A+, D+ followed by AD+). In addition, the training schedule of each experiment comprised two control conditions—one condition to evaluate the presence of overexpectation (B+ followed by BY+) and a second one to assess the strength of competition among cues (C+ followed by CZ−). Training trials were followed by a test with individual stimuli (A, B, C). Experiment 1 revealed no evidence for cue competition as responding during the test mirrored the individual cue–outcome contingencies. The test results from Experiment 2, which included an outcome additivity training, showed cue competition in form of an overexpectation effect as responding was weaker for Stimulus A than Stimulus B. However, the test results from Experiment 2 also revealed that responding to Stimulus A was stronger than to Stimulus C, which indicates that competition among cues was not as strong as predicted by some influential theories of associative learning.

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Van Hanh, Nguyen. "The real value of experiential learning project through contest in engineering design course: A descriptive study of students' perspective." International Journal of Mechanical Engineering Education 48, no. 3 (November 19, 2018): 221–40. http://dx.doi.org/10.1177/0306419018812659.

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In the engineering design course, the experiential learning project is an effective approach to teach students about hands-on experiments as an engineer. However, the factor for motivation that was lacking in these projects was competition amongst student teams. We had incorporated project-based activities and engineering design competitions that would motivate the interest of engineering students. A theoretical model of creating the value in experiential learning project through contest was developed and applied at the Hung Yen University of Technology and Education. So, what was the real value of experiential learning project through a contest in engineering design course? This study used the questionnaire method with 107 students at the Hung Yen University of Technology and Education who participated in the ABU Robocon 2018 project. The new finding of this study provided some surprises: (1) the prize money of contest was not the major factor for the motivations in students' learning; (2) the attractiveness of a contest theme, more than one way of solving the problem and using high technology were the major factors that motivate students to learn; (3) the experiential learning project through contest had the greatest impact of development on the theoretical knowledge of engineering design, and the skills, experiences and abilities to use technologies, and the power of teamwork to make decisions.

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Bochet, Olivier, and Simon Siegenthaler. "Competition and Price Transparency in the Market for Lemons: Experimental Evidence." American Economic Journal: Microeconomics 13, no. 2 (May 1, 2021): 113–40. http://dx.doi.org/10.1257/mic.20170087.

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In markets with asymmetric information, where equilibria are often inefficient, bargaining can help promote welfare. We design an experiment to examine the impact of competition and price transparency in such settings. Consistent with the theoretical predictions, we find that competition promotes efficiency if bargainers cannot observe each other’s price offers. Contrary to the predictions, however, the efficiency-enhancing effect of competition persists even when offers are observable. We explore different behavioral explanations for the absence of a detrimental effect of price transparency. Remarkably, implementing the strategy method improves subjects’ conditional reasoning, delivering the predicted loss in efficiency when offers are observable. (JEL C78, D82, L15)

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Li, Chenchen, Xiang Yan, Xiaotie Deng, Yuan Qi, Wei Chu, Le Song, Junlong Qiao, Jianshan He, and Junwu Xiong. "Latent Dirichlet Allocation for Internet Price War." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 639–46. http://dx.doi.org/10.1609/aaai.v33i01.3301639.

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Current Internet market makers are facing an intense competitive environment, where personalized price reductions or discounted coupons are provided by their peers to attract more customers. Much investment is spent to catch up with each other’s competitors but participants in such a price cut war are often incapable of winning due to their lack of information about others’ strategies or customers’ preference. We formalize the problem as a stochastic game with imperfect and incomplete information and develop a variant of Latent Dirichlet Allocation (LDA) to infer latent variables under the current market environment, which represents preferences of customers and strategies of competitors. Tests on simulated experiments and an open dataset for real data show that, by subsuming all available market information of the market maker’s competitors, our model exhibits a significant improvement for understanding the market environment and finding the best response strategies in the Internet price war. Our work marks the first successful learning method to infer latent information in the environment of price war by the LDA modeling, and sets an example for related competitive applications to follow.

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Li, Jia, and Zhengying Luo. "A Quasi-Natural Experiment: Product Market Competition and Stock Price Crash Risk." Global Economic Review 51, no. 1 (January 2, 2022): 18–42. http://dx.doi.org/10.1080/1226508x.2022.2040043.

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Wellman, M. P., D. M. Reeves, K. M. Lochner, and Y. Vorobeychik. "Price Prediction in a Trading Agent Competition." Journal of Artificial Intelligence Research 21 (January 1, 2004): 19–36. http://dx.doi.org/10.1613/jair.1333.

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The 2002 Trading Agent Competition (TAC) presented a challenging market game in the domain of travel shopping. One of the pivotal issues in this domain is uncertainty about hotel prices, which have a significant influence on the relative cost of alternative trip schedules. Thus, virtually all participants employ some method for predicting hotel prices. We survey approaches employed in the tournament, finding that agents apply an interesting diversity of techniques, taking into account differing sources of evidence bearing on prices. Based on data provided by entrants on their agents' actual predictions in the TAC-02 finals and semifinals, we analyze the relative efficacy of these approaches. The results show that taking into account game-specific information about flight prices is a major distinguishing factor. Machine learning methods effectively induce the relationship between flight and hotel prices from game data, and a purely analytical approach based on competitive equilibrium analysis achieves equal accuracy with no historical data. Employing a new measure of prediction quality, we relate absolute accuracy to bottom-line performance in the game.

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Eckel, Catherine C., and Sascha C. Füllbrunn. "Thar SHE Blows? Gender, Competition, and Bubbles in Experimental Asset Markets." American Economic Review 105, no. 2 (February 1, 2015): 906–20. http://dx.doi.org/10.1257/aer.20130683.

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Do women and men behave differently in financial asset markets? Our results from an asset market experiment show a marked gender difference in producing speculative price bubbles. Mixed markets show intermediate values, and a meta-analysis of 35 markets from different studies confirms the inverse relationship between the magnitude of price bubbles and the frequency of female traders in the market. Women's price forecasts also are significantly lower, even in the first period. Implications for financial markets and experimental methodology are discussed. (JEL D14, D81, G01, G11, J16)

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Aoyagi, Masaki, Manaswini Bhalla, and Hikmet Gunay. "Social learning and delay in a dynamic model of price competition." Journal of Economic Theory 165 (September 2016): 565–600. http://dx.doi.org/10.1016/j.jet.2016.05.005.

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Xie, Feng Jie, and Jing Shi. "The Evolution of Price Competition Game on Complex Networks." Complexity 2018 (July 9, 2018): 1–13. http://dx.doi.org/10.1155/2018/9649863.

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The well-known “Bertrand paradox” describes a price competition game in which two competing firms reach an outcome where both charge a price equal to the marginal cost. The fact that the Bertrand paradox often goes against empirical evidences has intrigued many researchers. In this work, we study the game from a new theoretical perspective—an evolutionary game on complex networks. Three classic network models, square lattice, WS small-world network, and BA scale-free network, are used to describe the competitive relations among the firms which are bounded rational. The analysis result shows that full price keeping is one of the evolutionary equilibriums in a well-mixed interaction situation. Detailed experiment results indicate that the price-keeping phenomenon emerges in a square lattice, small-world network and scale-free network much more frequently than in a complete network which represents the well-mixed interaction situation. While the square lattice has little advantage in achieving full price keeping, the small-world network and the scale-free network exhibit a stronger capability in full price keeping than the complete network. This means that a complex competitive relation is a crucial factor for maintaining the price in the real world. Moreover, competition scale, original price, degree of cutting price, and demand sensitivity to price show a significant influence on price evolution on a complex network. The payoff scheme, which describes how each firm’s payoff is calculated in each round game, only influences the price evolution on the scale-free network. These results provide new and important insights for understanding price competition in the real world.

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Edwinarto, Dominicus. "Imperfect monitoring, cyclical, and learning model perspectives: Price war in the Indonesian lighting industry." International Journal of Engineering & Technology 7, no. 2.29 (May 22, 2018): 236. http://dx.doi.org/10.14419/ijet.v7i2.29.13323.

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In marketing terms, the phenomenon of price war is regarded as the result of over-competition and retaliatory reaction in order to win market share. Based on the available literature, three refined models of price war antecedents has been identified: the imperfect monitoring model, the cyclical model, and the learning model. This article was written as part of a recent empirical observation of four Indonesian lighting companies who consider themselves to be currently engaged in price war. Based on the proposition made earlier by Heil and Helsen (2001), this study was prepared as a qualitative survey using an open-ended questionnaire method. The study found that price war is a result of competitive interaction in periods where demands are declining and induced by intra-brand competition. In conclusion, propositions to manage activities in conditions of price war are presented.

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Liu, Yu-Jiao, Ying-Ge Zhou, Qi-Long Li, and Xin-Dong Ye. "Impact Study of the Learning Effects and Motivation of Competitive Modes in Gamified Learning." Sustainability 14, no. 11 (May 28, 2022): 6626. http://dx.doi.org/10.3390/su14116626.

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At a time when game-based learning has become a research hotspot, this study focused on the competition mechanism in gamified learning, aiming to explore the impact of different competition modes on students’ vocabulary learning effect and learning motivation. A group of 79 sixth grade students from China were randomly assigned to a non-competitive class, an individual competition class, and an inter-group competition class. The experiment was conducted in an English vocabulary course, and the game competition was carried out using the Quizlet Live game platform. The results indicated that: (1) the vocabulary learning effect and motivation of students in the competitive classes (individual competition and inter-group competition) were better than those in the non-competitive class; (2) the learning effect of students in the inter-group competitive class outperformed that of the individual competitive class, but there was no significant difference in learning motivation. Through the qualitative analysis of the students’ interviews, it was found that the results of inter-group competition may be related to the student’s perception of learning and emotional support. The findings of this study can provide relevant support for the subsequent game-based learning design.

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Bigoni, Maria, Margherita Fort, Mattia Nardotto, and Tommaso G. Reggiani. "Cooperation or Competition? A Field Experiment on Non-monetary Learning Incentives." B.E. Journal of Economic Analysis & Policy 15, no. 4 (October 1, 2015): 1753–92. http://dx.doi.org/10.1515/bejeap-2014-0109.

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Abstract We assess the effect of two antithetic non-monetary incentive schemes based on grading rules on students’ effort, using experimental data. We randomly assigned students to a tournament scheme that fosters competition between paired up students, a cooperative scheme that promotes information sharing and collaboration between students and a baseline treatment in which students can neither compete nor cooperate. In line with theoretical predictions, we find that competition induces higher effort with respect to cooperation, whereas cooperation does not increase effort with respect to the baseline treatment. Nonetheless, we find a strong gender effect since this result holds only for men while women do not react to this type of non-monetary incentives.

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TUINSTRA, JAN. "A PRICE ADJUSTMENT PROCESS IN A MODEL OF MONOPOLISTIC COMPETITION." International Game Theory Review 06, no. 03 (September 2004): 417–42. http://dx.doi.org/10.1142/s0219198904000289.

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We consider a price adjustment process in a model of monopolistic competition. Firms have incomplete information about the demand structure. When they set a price they observe the amount they can sell at that price and they observe the slope of the true demand curve at that price. With this information they estimate a linear demand curve. Given this estimate of the demand curve they set a new optimal price. We investigate the dynamical properties of this learning process. We find that, if the cross-price effects and the curvature of the demand curve are small, prices converge to the Bertrand-Nash equilibrium. The global dynamics of this adjustment process are analyzed by numerical simulations. By means of computational techniques and by applying results from homoclinic bifurcation theory we provide evidence for the existence of strange attractors.

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Walker, S., L. M. Henderson, F. E. Fletcher, V. C. P. Knowland, S. A. Cairney, and M. G. Gaskell. "Learning to live with interfering neighbours: the influence of time of learning and level of encoding on word learning." Royal Society Open Science 6, no. 4 (April 2019): 181842. http://dx.doi.org/10.1098/rsos.181842.

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New vocabulary is consolidated offline, particularly during sleep; however, the parameters that influence consolidation remain unclear. Two experiments investigated effects of exposure level and delay between learning and sleep on adults' consolidation of novel competitors (e.g. BANARA) to existing words (e.g. BANANA). Participants made speeded semantic decisions (i.e. a forced choice: natural versus man-made) to the existing words, with the expectation that novel word learning would inhibit responses due to lexical competition. This competition was observed, particularly when assessed after sleep, for both standard and high exposure levels (10 and 20 exposures per word; Experiment 1). Using a lower exposure level (five exposures; Experiment 2), no post-sleep enhancement of competition was observed, despite evidence of consolidation when explicit knowledge of novel word memory was tested. Thus, when encoding is relatively weak, consolidation-related lexical integration is particularly compromised. There was no evidence that going to bed soon after learning is advantageous for overnight consolidation; however, there was some preliminary suggestion that longer gaps between learning and bed-onset were associated with better explicit memory of novel words one week later, but only at higher levels of exposure. These findings suggest that while lexical integration can occur overnight, weaker lexical traces may not be able to access overnight integration processes in the sleeping brain. Furthermore, the finding that longer-term explicit memory of stronger (but not weaker) traces benefit from periods of wake following learning deserves examination in future research.

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Bilkova, Renata, and Hana Kopackova. "Enhancing E-commerce by Website Quality." International Journal of Systems Applications, Engineering & Development 15 (November 26, 2021): 99–106. http://dx.doi.org/10.46300/91015.2021.15.14.

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Competition among e-commerce sites challenges their providers to look for new ways of customer attraction. Website quality can be included among the fundamental tools (along with the information and service quality) to attract and retain customers. In this article is described experiment covering establishment of e-shop as the competitor to producer website. New e-shop has defined terms, services are assured by producer therefore the only way how to compete is through website quality. During the reporting period producer applied discount actions, which allow us to determine the influence of the price and non-price competition.

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Besanko, David, Ulrich Doraszelski, and Yaroslav Kryukov. "How Efficient Is Dynamic Competition? The Case of Price as Investment." American Economic Review 109, no. 9 (September 1, 2019): 3339–64. http://dx.doi.org/10.1257/aer.20180131.

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We study industries where the price that a firm sets serves as an investment into lower cost or higher demand. We assess the welfare implications of the ensuing competition for the market using analytical and numerical approaches to compare the equilibria of a learning-by-doing model to the first-best planner solution. We show that dynamic competition leads to low deadweight loss. This cannot be attributed to similarity between the equilibria and the planner solution. Instead, we show how learning-by-doing causes the various contributions to deadweight loss to either be small or partly offset each other. (JEL D21, D25, D43, D83, L13)

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Cheung, Yiu-Ming, Wai-Man Leung, and Lei Xu. "Adaptive Rival Penalized Competitive Learning and Combined Linear Predictor Model for Financial Forecast and Investment." International Journal of Neural Systems 08, no. 05n06 (October 1997): 517–34. http://dx.doi.org/10.1142/s0129065797000501.

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We propose a prediction model called Rival Penalized Competitive Learning (RPCL) and Combined Linear Predictor method (CLP), which involves a set of local linear predictors such that a prediction is made by the combination of some activated predictors through a gating network (Xu et al., 1994). Furthermore, we present its improved variant named Adaptive RPCL-CLP that includes an adaptive learning mechanism as well as a data pre-and-post processing scheme. We compare them with some existing models by demonstrating their performance on two real-world financial time series — a China stock price and an exchange-rate series of US Dollar (USD) versus Deutschmark (DEM). Experiments have shown that Adaptive RPCL-CLP not only outperforms the other approaches with the smallest prediction error and training costs, but also brings in considerable high profits in the trading simulation of foreign exchange market.

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Zhang, Xiaoyan, Ruifang Ye, Fengxian Hu, Yitao Zheng, Shuhong Gao, Yingping Zhuang, Qiyao Wang, and Yunpeng Bai. "Learning from Competition: An Outcome-Based Introductory Activity for First-Year Biotechnology Undergraduates." American Biology Teacher 81, no. 7 (September 1, 2019): 467–73. http://dx.doi.org/10.1525/abt.2019.81.7.467.

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In recent years, accreditation standards for international engineering education have led to a dramatic rise in the use of outcome-based education at universities. In this system, enticing new undergraduate students to science and engineering, although challenging, is the first important step toward building students' career competencies. An ongoing effort to attract students to biotechnology was initiated 13 years ago in the School of Biotechnology at the East China University of Science and Technology in Shanghai. We describe the design and organization of the Microbe Competition, a program attracting a total of nearly 6,500 students as of 2018. In the competition, students need to pass the microbiology knowledge test, provide a practical experiment proposal related to the topic of competition, and finish the experiment under the supervision of teachers before getting final prizes. The competition develops students' competencies in acquiring and applying knowledge, problem solving, teamwork, communication, and experimental skills. By investigating students' feedback, we have been continuously improving the quality of competition to attract more students from the biotechnology major. We hope that by sharing our experience, we can help educators at other universities organize similar introductory activities on their own campuses.

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MICHELITCH, KRISTIN. "Does Electoral Competition Exacerbate Interethnic or Interpartisan Economic Discrimination? Evidence from a Field Experiment in Market Price Bargaining." American Political Science Review 109, no. 1 (February 2015): 43–61. http://dx.doi.org/10.1017/s0003055414000628.

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Does political competition exacerbate economic discrimination between citizens on ethnic or partisan cleavages? Individuals often discriminate on group lines in ordinary economic activities, especially in low-income settings. Political competition, and thus mobilization of partisan and ethnic groups, waxes and wanes over the electoral cycle. This study therefore investigates discrimination over the electoral cycle in a commonplace yet consequential economic activity: market price bargaining. By conducting field experiments on taxi fare bargaining at three points in time around Ghana’s 2008 election, the research reveals that drivers accept lower prices from coethnics regardless of temporal proximity to the election. However, only at election time, drivers accept lower prices from copartisans and demand higher prices from noncopartisans. In sum, political competition affects commonplace economic transactions between citizens on the partisan cleavage. This study is the first to show evidence of interpartisan discrimination in everyday behavior and expands our knowledge of electoral cycle effects.

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Suwasono, Suwasono, Dwi Prihanto, Irawan Dwi Wahyono, and Andrew Nafalski. "Virtual Laboratory for Line Follower Robot Competition." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 4 (August 1, 2017): 2253. http://dx.doi.org/10.11591/ijece.v7i4.pp2253-2260.

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Laboratory serves as an important facility for experiment and research activity. The limitation of time, equipment, and capacity in the experiment and research undertaking impede both students and college students in undertaking research for competition preparation, particularly dealing with line follower robot competition which requires a wide space of the room with various track types. Unsettled competition track influences PID control setting of line follower robot. This study aims at developing Virtual Laboratory (V-Lab) for students or college students who are preparing for line follower robot competition with unsettled and changeable tracks. This study concluded that the trial data score reached 98.5%, the material expert score obtained 89.7%, learning model expert score obtained 97.9%, and the average score of small group learning model and field of 82.4%, which the average score of the entire aspects obtained 90.8%.

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Miller, Ralph R., and Helena Matute. "Competition Between Outcomes." Psychological Science 9, no. 2 (March 1998): 146–49. http://dx.doi.org/10.1111/1467-9280.00028.

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In both Pavlovian conditioning and human causal judgment, competition between cues is well known to occur when multiple cues are presented in compound and followed by an outcome. More questionable is the occurrence of competition between outcomes when a single cue is followed by multiple outcomes presented in compound. In the experiment reported here, we demonstrated blocking (a type of stimulus competition) between outcomes. When the cue predicted one outcome, its ability to predict a second outcome that was presented in compound with the first outcome was reduced. The procedure minimized the likelihood that the observed competition between outcomes arose from selective attention. The competition between outcomes that we observed is problematic for contemporary theories of learning.

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Li, Qiaoru, Zhe Zhang, Zixuan Chen, Liang Chen, Jingchun Zhang, Ke Chen, Yuanyuan Wang, and Yuechao Gao. "Evolutionary dynamics of competition among ports in networks." Modern Physics Letters B 34, no. 24 (June 26, 2020): 2050248. http://dx.doi.org/10.1142/s0217984920502486.

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The competitive relationships among ports become complicated as the consequence of the prosperity of international trade. Typical oligopoly competition models cannot be competent for the analysis of ports competition in real scenario. In this paper, the scale-free network is adopted to characterize the interactions among ports with various number of neighbors. For each port node, not only direct competition but also indirect influences exerted by its neighbor are taken into account. Following the hypothesis in evolutionary game theory, social learning behavior among competitors occurs generally in our model. Conforming to reality, strategies considering both price collusion and price competition are proposed to investigate evolutionary dynamics of competition among ports in the self-organization process of imitation. It shows that neighbors have the same goal but play different roles in affecting strategy transition during the evolution with two competitive means. We explore how evolutionary dynamics are influenced by different imitation means. Then this paper verifies that price collusion is more conducive to port development when abundant resources is provided. Our results obtained in this evolutionary framework with different imitation means may enhance port-operation efficiency.

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Bertini Junior, João Roberto, and Maria do Carmo Nicoletti. "Enhancing Constructive Neural Network Performance Using Functionally Expanded Input Data." Journal of Artificial Intelligence and Soft Computing Research 6, no. 2 (April 1, 2016): 119–31. http://dx.doi.org/10.1515/jaiscr-2016-0010.

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Abstract Constructive learning algorithms are an efficient way to train feedforward neural networks. Some of their features, such as the automatic definition of the neural network (NN) architecture and its fast training, promote their high adaptive capacity, as well as allow for skipping the usual pre-training phase, known as model selection. However, such advantages usually come with the price of lower accuracy rates, when compared to those obtained with conventional NN learning approaches. This is, perhaps, the reason for conventional NN training algorithms being preferred over constructive NN (CoNN) algorithms. Aiming at enhancing CoNN accuracy performance and, as a result, making them a competitive choice for machine learning based applications, this paper proposes the use of functionally expanded input data. The investigation described in this paper considered six two-class CoNN algorithms, ten data domains and seven polynomial expansions. Results from experiments, followed by a comparative analysis, show that performance rates can be improved when CoNN algorithms learn from functionally expanded input data.

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Lee, Doo Ho. "Pricing Decisions in a Competitive Closed-Loop Supply Chain with Duopolistic Recyclers." Mathematical Problems in Engineering 2020 (April 18, 2020): 1–22. http://dx.doi.org/10.1155/2020/5750370.

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In this study, we consider a three-echelon closed-loop supply chain consisting of a manufacturer, a collector, and two duopolistic recyclers. In the supply chain, the collector collects end-of-life products from consumers in the market. Then, both recyclers purchase the recyclable waste from the collector, and each recycler turns them into new materials. The manufacturer has no recycling facilities; therefore, the manufacturer only purchases the recycled and new materials for its production from the two recyclers. Under this scenario, price competition between recyclers is inevitable. With two pricing structures (Nash and Stackelberg) of the leaders group and three competition behaviors (Collusion, Cournot, and Stackelberg) of the followers group, we suggest six different pricing game models. In each of them, we establish a pricing game model among the members, prove the uniqueness of the equilibrium prices of the supply chain members, and discuss the effects of competition on the overall supply chain’s profitability. Our numerical experiment indicates that as the price competition between recyclers intensifies, the supply chain profitability decreases. Moreover, the greater the recyclability degree of the waste is, the higher the profits in the supply chain become.

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Soo, Alexander, and Bee Lan Oo. "The effect of construction demand on contract auctions: an experiment." Engineering, Construction and Architectural Management 21, no. 3 (May 13, 2014): 276–90. http://dx.doi.org/10.1108/ecam-01-2013-0010.

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Purpose – The purpose of this paper is to present an experiment to test the effect of construction demand on the mark-up price level in construction contract auctions. Design/methodology/approach – An experimental approach was adopted for this study. In a controlled laboratory environment, a first-price sealed bid auction was simulated with varying number of projects available over ten rounds to simulate changing construction demand. Two experimental treatments were run in parallel, one exhibiting a “booming” demand over time, and the other group with a “recession” scenario. The experiment involved student (inexperienced) bidders with a construction project management background. Findings – The results show that inexperienced bidders do behave differently when subjected to varying levels of construction demand. Variations in the bid price level are affected by varying levels of construction demand and the general mark-up level for the bidders experiencing a booming scenario was higher on average compared to bidders subjected to the recession scenario. Research limitations/implications – An identified limitation of this study is the use of student subjects in the experiment, thus the experiment results are limited in generalisation to inexperienced bidders. Further studies may be able to replicate the experiment with experienced industry practitioners to observe the results. Practical implications – The results allow for industry practitioners to gain a stronger understanding of the effects of varying levels of construction demand and the need to consider construction demand in construction contracting. For construction clients, the level of construction demand may be used as an indicator to assist in the timing to call tenders to achieve a desirable price. For contractors, increased awareness of how demand affects competition and the price level will allow additional optimisations to be achieved in the bid price. Originality/value – Construction demand has been widely known to be one of the key factors affecting contractors’ bidding decisions. However, there has been little empirical investigation of the changes in bidders’ behaviour due to varying levels of construction demand. This paper attempts to add to the empirical research knowledgebase through an experimental setting.

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Sung, Han-Yu. "A Competition-Based Problem-Posing Approach for Nursing Training." Healthcare 10, no. 6 (June 17, 2022): 1132. http://dx.doi.org/10.3390/healthcare10061132.

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Conventional nursing teaching usually adopts one-way teaching approaches. As such, students cannot think deeply and engage in learning, which results in lower learning motivation and learning achievement. Several studies have indicated that problem-posing is a learning process that has students think about problems and actively construct knowledge, which helps their in-depth thinking and promotes their learning achievement. However, problem-posing is a task with a higher difficulty level; in particular, with insufficient learning motivation, it is not easy for students to pose in-depth questions. Therefore, the present study introduced competition to a problem-posing activity to facilitate students’ motivation. This study adopted a quasi-experimental design and conducted an experiment in the unit of Care of Critically Ill Patients. The results showed that the proposed competition-based problem-posing mobile learning approach could significantly enhance students’ learning achievement and learning motivation and would not cause an excessive cognitive load. Moreover, competition increased students’ learning motivation, and fostered them to actively reflect on and revise their questions, thereby increasing their problem-posing quality and learning achievement. This study can serve as a reference for future clinical practice to enhance the quality and sustainability of apprenticeships.

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McNutt, Patrick A. "Taxonomy of Non-Market Economics for European Competition Policy The Search for the True Competitive Price." World Competition 26, Issue 2 (June 1, 2003): 303–32. http://dx.doi.org/10.54648/woco2003016.

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We argue that competition is a process, and as such can be described, rather than defined. No parameter of interest to competition assessment should be estimated in isolation without reference to the history of prices and quantities in a market and the strategic interactions of the firms. An alternative to the approach adopted by DG Competition for modelling tacit collusion and dynamic coordination would be to incorporate directly into competition assessment models some reason for history to matter. With a new package of reforms from DG Competition in Brussels, now may be the time for European competition analysis to embrace more fully elements of public choice, law and economics, and game theory. Collectively, they represent non-market economics. Nonmarket economics for the purposes of this article is therefore based on the conception of economics as the science of rational choice and strategic reaction rather than merely the study and analysis of economic markets. Understanding competition law and understanding non-market economics are challenges that go hand in hand. “The pleasures of the retrospective view; attractive though they are, are not for me—or you—since we always must renew; the habit of re-learning how to see.”

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Mahtab, Md Tanvir, A. G. M. Zaman, Montasir Rahman Mahin, Mohammad Nazim Mia, and Md. Tanjirul Islam. "Stock Price Prediction: An Incremental Learning Approach Model of Multiple Regression." AIUB Journal of Science and Engineering (AJSE) 21, no. 3 (December 31, 2022): 159–66. http://dx.doi.org/10.53799/ajse.v21i3.490.

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The endeavour of predicting stock prices using different mathematical and technological methods and tools is not new. But the recent advancements and curiosity regarding big data and machine learning have added a new dimension to it. In this research study, we investigated the feasibility and performance of the multiple regression method in the prediction of stock prices. Here, multiple regression was used on the basis of the incremental machine learning setting. The study conducted an experiment to predict the closing price of stocks of six different organizations enlisted in the Dhaka Stock Exchange (DSE). Three years of historical stock market data (2017-2019) of these organizations have been used. Here, the Multiple Regression, Squared Loss Function, and Stochastic Gradient Descent (SGD) algorithms are used as a predictor, loss function, and optimizer respectively. The model incrementally learned from the data of several stock-related attributes and predicted the closing price of the next day. The performance of prediction was then analysed and assessed on the basis of the rolling Mean Absolute Error (MAE) metric. The rolling MAE scores found in the experiment are quite promising.

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Bitsch, Linda, Jon Henrich Hanf, and Jens Rüdiger. "An innovative price-setting approach: a pay-what-you-want experiment." British Food Journal 122, no. 8 (March 3, 2020): 2481–96. http://dx.doi.org/10.1108/bfj-07-2019-0504.

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PurposeDue to high competition in the agricultural industries and heterogeneous products, the setting of prices for direct sales to consumers is difficult. In recent years, pay-what-you-want (PWYW) is discussed as an innovative pricing strategy. This study analyses whether the implementation of a pay-what-you-want strategy can be successful and if there is a willingness to pay from the consumers for wine touristic offers. Furthermore, the study analyses, in general, how suitable experiments are as a research tool.Design/methodology/approachA PWYW mechanism creates a situation of strategic decision- making that can be modelled as a game. This can be transferred to an experimental setting. The results were analysed with a two-sided MWU test (Stata, ranksum) in order to determine whether the differences are statistically significant.FindingsParticipants pay positive prices, although theory predicts the opposite. PWYW is a good strategy to analyse the willingness-to-pay for heterogeneous and homogenous services or products. Information or reference prices have no clear influence on the willingness to pay, confirming results of other studies. There is no influence of gender and age on the payments.Practical implicationsIn general, consumers have a willingness to pay positive prices for wine- touristic offers. The willingness to pay is not different for people with or without wine knowledge. For the chosen variable information and reference price, wine producers do not have to address target groups differently.Originality/valueIt is the first study which analyses the pay-what-you-want mechanism as a tool for wine touristic activities. In addition, an experimental approach was used to analyse an innovative, consumer-based price-setting strategy.

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Lin, Tse-Chun, Qi Liu, and Bo Sun. "Contractual Managerial Incentives with Stock Price Feedback." American Economic Review 109, no. 7 (July 1, 2019): 2446–68. http://dx.doi.org/10.1257/aer.20151310.

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We study the effect of financial market frictions on managerial compensation. We embed a market microstructure model into an otherwise standard contracting framework, and analyze optimal pay-for-performance when managers use information they learn from the market in their investment decisions. In a less frictional market, the improved information content of stock prices helps guide managerial decisions and thereby necessitates lower-powered compensation. Exploiting a randomized experiment, we document evidence that pay-for-performance is lowered in response to reduced market frictions. Firm investment also becomes more sensitive to stock prices during the experiment, consistent with increased managerial learning from the market. (JEL D83, G12, G14, G32, G34, M12, M52)

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Hamel, R., L. Dallaire-Jean, É. De La Fontaine, J. F. Lepage, and P. M. Bernier. "Learning the same motor task twice impairs its retention in a time- and dose-dependent manner." Proceedings of the Royal Society B: Biological Sciences 288, no. 1942 (January 13, 2021): 20202556. http://dx.doi.org/10.1098/rspb.2020.2556.

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Anterograde interference emerges when two differing tasks are learned in close temporal proximity, an effect repeatedly attributed to a competition between differing task memories. However, recent development alternatively suggests that initial learning may trigger a refractory period that occludes neuroplasticity and impairs subsequent learning, consequently mediating interference independently of memory competition. Accordingly, this study tested the hypothesis that interference can emerge when the same motor task is being learned twice, that is when competition between memories is prevented. In a first experiment, the inter-session interval (ISI) between two identical motor learning sessions was manipulated to be 2 min, 1 h or 24 h. Results revealed that retention of the second session was impaired as compared to the first one when the ISI was 2 min but not when it was 1 h or 24 h, indicating a time-dependent process. Results from a second experiment replicated those of the first one and revealed that adding a third motor learning session with a 2 min ISI further impaired retention, indicating a dose-dependent process. Results from a third experiment revealed that the retention impairments did not take place when a learning session was preceded by simple rehearsal of the motor task without concurrent learning, thus ruling out fatigue and confirming that retention is impaired specifically when preceded by a learning session. Altogether, the present results suggest that competing memories is not the sole mechanism mediating anterograde interference and introduce the possibility that a time- and dose-dependent refractory period—independent of fatigue—also contributes to its emergence. One possibility is that learning transiently perturbs the homeostasis of learning-related neuronal substrates. Introducing additional learning when homeostasis is still perturbed may not only impair performance improvements, but also memory formation.

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Journal articles on the topic 'Price competition; learning; experiment'

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