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We study the potential of large language models (LLMs) as proxies for humans to simplify preference elicitation (PE) in combinatorial assignment.
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Preference elicitation and query learning
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Applying learning algorithms to preference elicitation
Lahaie, S. M. and Parkes, D. C · 2004
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Evidence from the synthetic laboratory: Language models as auction participants, 2024
Zhu, K., Horton, J. J., Jiang, Y., Parkes, D. C., and Shah, A. V · 2004
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The clock-proxy auction: A practical combinatorial auction design
Ausubel, L. M., Cramton, P., and Milgrom, P · 2006
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The communication requirements of efficient allocations and supporting prices
Nisan, N. and Segal, I · 2006
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Eliciting single-peaked preferences using comparison queries
Conitzer, V · 2007
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Strategy-proof, efficient, and nonbossy quota allocations
Hatfield, J. W · 2009
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Course bidding at business schools
Sönmez, T. and Ünver, M. U · 2010
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The combinatorial assignment problem: Approximate competitive equilibrium from equal incomes
Budish, E · 2011
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Learning valuation functions
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Making risk minimization tolerant to label noise
Ghosh, A., Manwani, N., and Sastry, P · 2014
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A practical guide to the combinatorial clock auction
Ausubel, L. M. and Baranov, O · 2017
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Probably approximately efficient combinatorial auctions via machine learning
Brero, G., Lubin, B., and Seuken, S · 2017
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Course match: A large-scale implementation of approximate competitive equilibrium from equal incomes for combinatorial allocation
Budish, E., Cachon, G. P., Kessler, J. B., and Othman, A · 2017
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Combinatorial auctions via machine learning-based preference elicitation
Brero, G., Lubin, B., and Seuken, S · 2018
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Deep learning for revenue-optimal auctions with budgets
Feng, Z., Narasimhan, H., and Parkes, D. C · 2018
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Deep learning for multi-facility location mechanism design
Golowich, N., Narasimhan, H., and Parkes, D. C · 2018
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Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
Zhang, Z. and Sabuncu, M · 2018
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Optimal auctions through deep learning: Advances in differentiable economics
Using gpt for market research
Brand, J., Israeli, A., and Ngwe, D · 2023
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Large language models as simulated economic agents: What can we learn from homo silicus?
Horton, J. J · 2023
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Data market design through deep learning
Ravindranath, S. S., Jiang, Y., and Parkes, D. C · 2023
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Bayesian optimization-based combinatorial assignment
Weissteiner, J., Heiss, J., Siems, J., and Seuken, S · 2023
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Data-driven mechanism design: Jointly eliciting preferences and information, 2024
Bergemann, D., Bojko, M., Dütting, P., Leme, R. P., Xu, H., and Zuo, S · 2024
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Dütting, P., Feng, Z., Narasimhan, H., Parkes, D., and Ravindranath, S. S · 2019
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A neural architecture for designing truthful and efficient auctions
Tacchetti, A., Strouse, D., Garnelo, M., Graepel, T., and Bachrach, Y · 2019
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Proportionnet: Balancing fairness and revenue for auction design with deep learning
Kuo, K., Ostuni, A., Horishny, E., Curry, M. J., Dooley, S., Chiang, P.-y., Goldstein, T., and Dickerson, J. P · 2020
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Deep learning—powered iterative combinatorial auctions
Weissteiner, J. and Seuken, S · 2020
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Learning the valuations of a k k -demand agent
Zhang, H. and Conitzer, V · 2020
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Machine learning-powered iterative combinatorial auctions
Brero, G., Lubin, B., and Seuken, S · 2021
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Can market participants report their preferences accurately (enough)?
Budish, E. and Kessler, J. B · 2021
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Daskalakis, C., Gemp, I., Jiang, Y., Leme, R. P., Papadimitriou, C., and Piliouras, G · 2024
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Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., Fan, A., et al · 2024
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Mechanism design for large language models
Dütting, P., Mirrokni, V., Paes Leme, R., Xu, H., and Zuo, S · 2024
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Efficient exploration for llms, June 2024
Dwaracherla, V., Asghari, S. M., Hao, B., and Roy, B. V · 2024
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Algorithmic collusion by large language models
Fish, S., Gonczarowski, Y. A., and Shorrer, R. I · 2024
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Ad auctions for llms via retrieval augmented generation, 2024
Hajiaghayi, M., Lahaie, S., Rezaei, K., and Shin, S · 2024
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Automated social science: Language models as scientist and subjects
Manning, B. S., Zhu, K., and Horton, J. J · 2024
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Generative agent simulations of 1,000 people
Park, J. S., Zou, C. Q., Shaw, A., Hill, B. M., Cai, C., Morris, M. R., Willer, R., Liang, P., and Bernstein, M. S · 2024
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Gemnet: Menu-based, strategy-proof multi-bidder auctions through deep learning
Wang, T., Jiang, Y., and Parkes, D. C · 2024
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Evaluation of openai o1: Opportunities and challenges of agi
Zhong, T., Liu, Z., Pan, Y., Zhang, Y., Zhou, Y., Liang, S., Wu, Z., Lyu, Y., Shu, P., Yu, X., et al · 2024
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From natural language to extensive-form game representations
Deng, S., Wang, Y., and Savani, R · 2025
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Accelerated Preference Elicitation with LLM-Based Proxies, January 2025
Huang, D., Marmolejo-Cossío, F., Lock, E., and Parkes, D · 2025
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Prices, bids, values: One ml-powered combinatorial auction to rule them all, 2025
Soumalias, E., Heiss, J., Weissteiner, J., and Seuken, S · 2025
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