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Artificial Intelligence (AI) holds promise as a technology that can be used to improve government and economic policy-making.
Modelling bounded rationality in multi-agent interactions by generalized recursive reasoning
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Nash and dominant strategy implementation in economic environments
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Automated Mechanism Design: A New Application Area for Search Algorithms
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Measuring expressiveness in conceptual modeling
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Computing the optimal strategy to commit to
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Linear bilevel multi-follower programming with independent followers
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Leader-follower semi-Markov decision problems: Theoretical framework and approximate solution
Tharakunnel, K. and Bhattacharyya, S · 2007
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A general approach to environment design with one agent
Zhang, H., Chen, Y., and Parkes, D · 2009
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Good Rationalizations of Voting Rules
Elkind, E., Faliszewski, P., and Slinko, A · 2010
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Can approximation circumvent gibbard-satterthwaite?
Procaccia, A. D · 2010
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Arrow, K. J · 2012
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Shall we vote on values, but bet on beliefs?
Hanson, R · 2013
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Defender (mis) coordination in security games
Jiang, A. X., Procaccia, A. D., Qian, Y., Shah, N., and Tambe, M · 2013
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Advancing the Empirical Research on Lobbying
de Figueiredo, J. M. and Richter, B. K · 2014
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Voluntary participation in cyber-insurance markets
Naghizadeh, P. and Liu, M · 2014
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Economic reasoning and artificial intelligence
Parkes, D. C. and Wellman, M. P · 2015
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High-dimensional continuous control using generalized advantage estimation
Schulman, J., Moritz, P., Levine, S., Jordan, M., and Abbeel, P · 2015
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Handbook of computational social choice
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Cake cutting algorithms
Procaccia, A. D · 2016
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Introduction to the Theory of Fair Allocation , pp. 261–283
Thomson, W · 2016
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A multi-leader multi-follower Stackelberg game for resource management in lte unlicensed
Zhang, H., Xiao, Y., Cai, L. X., Niyato, D., Song, L., and Han, Z · 2016
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Methods for finding leader–follower equilibria with multiple followers
Basilico, N., Coniglio, S., and Gatti, N · 2017
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A multi-agent reinforcement learning algorithm based on Stackelberg game
Cheng, C., Zhu, Z., Xin, B., and Chen, C · 2017
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Multi-agent actor-critic for mixed cooperative-competitive environments
Lowe, R., Wu, Y., Tamar, A., Harb, J., Abbeel, O. P., and Mordatch, I · 2017
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A multi-agent reinforcement learning model of common-pool resource appropriation, 2017
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Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
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Counterfactual multi-agent policy gradients
Foerster, J. N., Farquhar, G., Afouras, T., Nardelli, N., and Whiteson, S · 2018
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Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning
Rashid, T., Samvelyan, M., Witt, C. S., Farquhar, G., Foerster, J., and Whiteson, S · 2018
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Value-decomposition networks for cooperative multi-agent learning based on team reward
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Automated Mechanism Design via Neural Networks, May 2021
Shen, W., Tang, P., and Zuo, S · 2021
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Agapiou, J. P., Vezhnevets, A. S., Duéñez-Guzmán, E. A., Matyas, J., Mao, Y., Sunehag, P., Köster, R., Madhushani, U., Kopparapu, K., Comanescu, R., et al · 2022
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Hcmd-zero: Learning value aligned mechanisms from data
Balaguer, J., Koster, R., Weinstein, A., Campbell-Gillingham, L., Summerfield, C., Botvinick, M., and Tacchetti, A · 2022
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Learning Stackelberg equilibria and applications to economic design games
Brero, G., Eden, A., Chakrabarti, D., Gerstgrasser, M., Li, V., and Parkes, D. C · 2022
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Learning expensive coordination: An event-based deep RL approach
Shi, Z., Yu, R., Wang, X., Wang, R., Zhang, Y., Lai, H., and An, B · 2019
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M 3 RL \text{M}^{3}\text{RL} : Mind-aware multi-agent management reinforcement learning
Shu, T. and Tian, Y · 2019
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Learning when to communicate at scale in multiagent cooperative and competitive tasks
Singh, A., Jain, T., and Sukhbaatar, S · 2019
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Qtran: Learning to factorize with transformation for cooperative multi-agent reinforcement learning
Son, K., Kim, D., Kang, W. J., Hostallero, D. E., and Yi, Y · 2019
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A trilevel model for best response in energy demand-side management
Aussel, D., Brotcorne, L., Lepaul, S., and von Niederhäusern, L · 2020
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Deep coordination graphs
Böhmer, W., Kurin, V., and Whiteson, S · 2020
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Curry, M. J., Lyi, U., Goldstein, T., and Dickerson, J. P · 2022
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Low-rank modular reinforcement learning via muscle synergy
Dong, H., Wang, T., Liu, J., and Zhang, C · 2022
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Optimal-er auctions through attention
Ivanov, D., Safiulin, I., Filippov, I., and Balabaeva, K · 2022
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Non-linear coordination graphs
Kang, Y., Wang, T., Yang, Q., Wu, X., and Zhang, C · 2022
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Human-centred Mechanism Design with Democratic AI
Koster, R., Balaguer, J., Tacchetti, A., Weinstein, A., Zhu, T., Hauser, O., Williams, D., Campbell-Gillingham, L., Thacker, P., Botvinick, M., et al · 2022
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Welfare maximization in competitive equilibrium: Reinforcement learning for Markov exchange economy
Liu, Z., Lu, M., Wang, Z., Jordan, M., and Yang, Z · 2022
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Multi-agent reinforcement learning is a sequence modeling problem
Wen, M., Kuba, J., Lin, R., Zhang, W., Wen, Y., Wang, J., and Yang, Y · 2022
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The surprising effectiveness of ppo in cooperative multi-agent games
Yu, C., Velu, A., Vinitsky, E., Gao, J., Wang, Y., Bayen, A., and Wu, Y · 2022
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The AI Economist: Taxation policy design via two-level deep multiagent reinforcement learning
Zheng, S., Trott, A., Srinivasa, S., Parkes, D. C., and Socher, R · 2022
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Fairness and Machine Learning: Limitations and Opportunities
Barocas, S., Hardt, M., and Narayanan, A · 2023
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Learning equilibria in asymmetric auction games
Bichler, M., Kohring, N., and Heidekrüger, S · 2023
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Learning solutions in large economic networks using deep multi-agent reinforcement learning
Curry, M., Trott, A., Phade, S., Bai, Y., and Zheng, S · 2023
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Symmetry-aware robot design with structured subgroups
Dong, H., Zhang, J., Wang, T., and Zhang, C · 2023
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A scalable neural network for DSIC affine maximizer auction design
Duan, Z., Sun, H., Chen, Y., and Deng, X · 2023
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Oracles & Followers: Stackelberg equilibria in deep multi-agent reinforcement learning
Gerstgrasser, M. and Parkes, D. C · 2023
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Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, October 2023
House, T. W · 2023
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Enabling first-order gradient-based learning for equilibrium computation in markets
Kohring, N., Pieroth, F. R., and Bichler, M · 2023
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Deep contract design via discontinuous networks
Wang, T., Duetting, P., Ivanov, D., Talgam-Cohen, I., and Parkes, D. C · 2023
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Optimal auctions through deep learning: Advances in Differentiable Economics
Dütting, P., Feng, Z., Narasimhan, H., Parkes, D. C., and Ravindranath, S. S · 2024
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Multi-sender persuasion–a computational perspective
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GemNet: Menu-Based, strategy-proof multi-bidder auctions through deep learning
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