Reinforcement Learning for Fair Dynamic Pricing

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works

Multilayer feedforward networks are universal approximators

Hornik, K., Stinchcombe, M., & White, H. (1989) · Neural Networks · doi:10.1016/0893-6080(89)90020-8 · Cited by 21,728 · see citing works

Technical Note: Q-Learning

Watkins, C., & Dayan, P. (1992) · Machine-mediated learning · doi:10.1023/a:1022676722315 · Cited by 14,277 · see citing works

Playing Atari with Deep Reinforcement Learning

Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471 · see citing works

Multilayer feedforward networks are universal approximators

HornikK., StinchcombeM., & WhiteH. (1989) · Neural Networks · doi:10.5555/70405.70408 · Cited by 9,266 · see citing works

Introduction to Reinforcement Learning

Sutton, R. S., & Barto, A. G. (1998) · The MIT Press eBooks · Cited by 6,855 · see citing works

How the machine “thinks”: Understanding opacity in machine learning algorithms

Burrell, J. (2016) · Big Data & Society · doi:10.1177/2053951715622512 · Cited by 3,196 · see citing works

Fairness as a Constraint on Profit Seeking: Entitlements in the Market

Kahneman, D. J., Knetsch, J. L., & Thaler, R. H. (2000) · Cambridge University Press eBooks · doi:10.1017/cbo9780511803475.019 · Cited by 3,127 · see citing works

Semantics derived automatically from language corpora contain human-like biases

Caliskan, A., Bryson, J. J., & Narayanan, A. (2017) · Science · doi:10.1126/science.aal4230 · Cited by 2,887 · see citing works

Equality of Opportunity in Supervised Learning

Hardt, M., Price, E., & Srebro, N. (2016) · arXiv · doi:10.48550/arxiv.1610.02413 · Cited by 2,536 · see citing works

Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V., & Kalai, A. (2016) · arXiv · doi:10.48550/arxiv.1607.06520 · Cited by 1,615 · see citing works

The price is unfair! A conceptual framework of price fairness perceptions

Xia, L., Monroe, K. B., & Cox, J. L. (2004) · Journal of Marketing · doi:10.1509/jmkg.68.4.1.42733 · Cited by 1,474 · see citing works

Evolution Strategies as a Scalable Alternative to Reinforcement Learning

Salimans, T., Ho, J., Chen, X., Sidor, S., & Sutskever, I. (2017) · arXiv · doi:10.48550/arxiv.1703.03864 · Cited by 1,027 · see citing works

Reinforcement learning for robots using neural networks

Lin, L.-J. (1992) · Defense Technical Information Center (DTIC) · Cited by 836 · see citing works

Neural Fitted Q Iteration – First Experiences with a Data Efficient Neural Reinforcement Learning Method

Riedmiller, M. (2005) · Lecture notes in computer science · doi:10.1007/11564096_32 · Cited by 771 · see citing works

Pricing and Revenue Optimization

Phillips, R. L. (2005) · Stanford University Press eBooks · doi:10.1515/9780804781640 · Cited by 621 · see citing works

Dynamic Pricing and Consumer Fairness Perceptions

Haws, K. L., & Bearden, W. O. (2006) · Journal of Consumer Research · doi:10.1086/508435 · Cited by 520 · see citing works

Throughput fairness index : An explanation

Jain, R. (1999) · Medical Entomology and Zoology · Cited by 472 · see citing works

Dynamic pricing and learning: Historical origins, current research, and new directions

den Boer, A. V. (2015) · Surveys in Operations Research and Management Science · doi:10.1016/j.sorms.2015.03.001 · Cited by 451 · see citing works

Advances in Intelligent Systems and Computing

Lie, T., & Liu, C. (2014) · Cited by 348 · see citing works

Detecting price and search discrimination on the internet

Mikians, J., Gyarmati, L., Erramilli, V., & Laoutaris, N. (2012) · Proceedings of the 11th ACM Workshop on Hot Topics in Networks · doi:10.1145/2390231.2390245 · Cited by 244 · see citing works

Dynamic pricing in internet retail: Effects on consumer trust

Garbarino, E., & Lee, O. F. (2003) · Psychology & Marketing · doi:10.1002/mar.10084 · Cited by 242 · see citing works

Privacy, economics, and price discrimination on the Internet

Odlyzko, A. M. (2003) · International Conference on Electronic Commerce · doi:10.1145/948005.948051 · Cited by 233 · see citing works

Reinforcement Learning in Continuous Action Spaces

van Hasselt, H. P., & Wiering, M. (2007) · IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning · doi:10.1109/adprl.2007.368199 · Cited by 210 · see citing works

Effects of price framing on consumers’ perceptions of online dynamic pricing practices

Weisstein, F. L., Monroe, K. B., & Kukar-Kinney, M. (2013) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-013-0330-0 · Cited by 146 · see citing works

Perceived price fairness of dynamic pricing

Lee, S., Illia, A., & Lawson‐Body, A. (2011) · Industrial Management & Data Systems · doi:10.1108/02635571111133533 · Cited by 92 · see citing works

Dynamic pricing models for electronic business

Narahari, Y., Raju, C. V. L., Ravikumar, K., & Shah, S. (2005) · Sadhana · doi:10.1007/bf02706246 · Cited by 88 · see citing works

Learning competitive pricing strategies by multi-agent reinforcement learning

Kutschinski, E., Uthmann, T., & Polani, D. (2003) · Journal of Economic Dynamics and Control · doi:10.1016/s0165-1889(02)00122-7 · Cited by 78 · see citing works

Not fair!: The typology of commonsense unfairness.

Finkel, N. J. (2001) · American Psychological Association eBooks · doi:10.1037/10431-000 · Cited by 74 · see citing works

Dynamic pricing policies for interdependent perishable products or services using reinforcement learning

Rana, R., & Oliveira, F. S. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.07.007 · Cited by 61 · see citing works

Transparent Data Mining for Big and Small Data

Cerquitelli, T., Cerquitelli, T., Quercia, D., & Pasquale, F. (2017) · Studies in big data · doi:10.1007/978-3-319-54024-5 · Cited by 61 · see citing works

Customizing Prices in Online Markets

Reinartz, W. (2002) · Symphonya Emerging Issues in Management · doi:10.4468/2002.1.05reinartz · Cited by 37 · see citing works

Dynamic pricing based on asymmetric multiagent reinforcement learning

Könönen, V. (2005) · International Journal of Intelligent Systems · doi:10.1002/int.20121 · Cited by 24 · see citing works

TCP Congestion Window Adaptation Through Contention Detection in Ad Hoc Networks

Zhang, X., Zhu, W. B., Li, N. N., & Sung, D. K. (2010) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2010.2070522 · Cited by 22 · see citing works

An Axiomatic Theory of Fairness

Lan, T., Kao, D., Chiang, M., et al. (2009) · Cited by 20 · see citing works

The Authority of "Fair" in Machine LearningPreprint

Skirpan, M. W., & Gorelick, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.09976 · Cited by 18 · see citing works

Dynamic Pricing and Its Forming Factors

Deksnytė, I., & Lydeka, Z. (2012) · Cited by 16 · see citing works

A retrospective commentary on “discounting in the hotel industry: a new approach”

Kimes, S. (2002) · Cornell Hotel and Restaurant Administration Quarterly · doi:10.1016/s0010-8804(02)80045-3 · Cited by 15 · see citing works

Improving Reinforcement Learning Speed for Robot Control

Matignon, L., Laurent, G. J., & Fort-Piat, N. L. (2006) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2006.282341 · Cited by 15 · see citing works

Improving Smiling Detection with Race and Gender Diversity

Ryu, H. J., Mitchell, M., & Adam, H. (2017) · Cited by 14 · see citing works

How network monitoring and reinforcement learning can improve tcp fairness in wireless multi-hop networks

Arianpoo, N., & Leung, V. C. M. (2016) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-016-0773-3 · Cited by 12 · see citing works

Adaptive strategies for price markdown in a multi-unit descending price auction: a comparative study

Gupta, M., Ravikumar, K., & Kumar, M. (2005) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2002.1168003 · Cited by 11 · see citing works

Organizational Integrity and Moral Climates

Bowie, N. E. (2013) · Issues in business ethics · doi:10.1007/978-94-007-6223-7_11 · Cited by 9 · see citing works

Online reinforcement learning in multi-agent systems for distributed energy systems

Menon, R. B., Menon, S. B., Srinivasan, D., et al. (2014) · IEEE Innovative Smart Grid Technologies-Asia · doi:10.1109/isgt-asia.2014.6873894 · Cited by 8 · see citing works

Techniques for Enhancing the Performance of TCP in Wireless Networks

Sirajuddin, M., Rupa, C., & Prasad, A. (2014) · Advances in intelligent systems and computing · doi:10.1007/978-81-322-2126-5_18 · Cited by 4 · see citing works

Tackling climate change with machine learning

Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works

Special Issue Papers: Revenue management performance in a low-fare airline environment: Insights from the Passenger Origin—Destination Simulator

Gorin, T., & Belobaba, P. P. (2004) · Journal of Revenue and Pricing Management · doi:10.1057/palgrave.rpm.5170110 · Cited by 26 · see citing works

Dynamic pricing in hospitality: overview and opportunities

Anderson, C. K., Xiaoqing, Kristine, N. A., et al. (2016) · International Journal of Revenue Management · doi:10.1504/ijrm.2016.077029 · Cited by 23 · see citing works

Dynamic booking forecasting for airline revenue management: A Kenya Airways Case Study

Ostaijen, T. H. T. V. (2017) · Research Repository (Delft University of Technology) · Cited by 2 · see citing works

Analytics for Revenue Management

McGuire, K. A. (2016) · doi:10.1002/9781119162308.ch8

On a semi-dynamic pricing and seat inventory allocation problem

Xiao, Y. B., Chen, J., & Chen, F. Y. (2006) · OR Spectrum · doi:10.1007/s00291-005-0017-0 · Cited by 7 · see citing works