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Social choice theory is the study of preference aggregation across a population, used both in mechanism design for human agents and in the democratic alignment of language models.
Social choice and individual values
Arrow, K. J. (1951) · 1951
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Probability of error, equivocation, and the chernoff bound
Hellman, M. and Raviv, J. (1970) · 1970
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Manipulation of voting schemes: a general result
Gibbard, A. (1973) · 1973
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Strategy-proofness and arrow’s conditions: Existence and correspondence theorems for voting procedures and social welfare functions
Satterthwaite, M. A. (1975) · 1975
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Social choice scoring functions
Young, H. P. (1975) · 1975
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Pluralism and social choice
Miller, N. R. (1983) · 1983
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Democracy and social choice
Coleman, J. and Ferejohn, J. (1986) · 1986
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Algorithmic mechanism design
Nisan, N. and Ronen, A. (1999) · 1999
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An overview of statistical learning theory
Vapnik, V. N. (1999) · 1999
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A model of inductive bias learning
Baxter, J. (2000) · 2000
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Scoring rules, condorcet efficiency and social homogeneity
Lepelley, D., Pierron, P., and Valognes, F. (2000) · 2000
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Introduction to statistical learning theory
Bousquet, O., Boucheron, S., and Lugosi, G. (2003) · 2003
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Social choice and the mathematics of manipulation
Taylor, A. D. (2005) · 2005
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Welfare economics and social choice theory
Feldman, A. M. and Serrano, R. (2006) · 2006
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Rademacher complexity bounds for non-iid processes
Mohri, M. and Rostamizadeh, A. (2008) · 2008
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The theory of judgment aggregation: an introductory review
List, C. (2012) · 2012
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Dynamic social choice with evolving preferences
Parkes, D. and Procaccia, A. (2013) · 2013
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The theory of social choice
Fishburn, P. C. (2015) · 2015
Cited alongside, same era.
Handbook of computational social choice
Brandt, F., Conitzer, V., Endriss, U., Lang, J., and Procaccia, A. D. (2016) · 2016
Cited alongside, same era.
Discovering language model behaviors with model-written evaluations
Perez, E., Ringer, S., Lukošiūtė, K., Nguyen, K., Chen, E., Heiner, S., Pettit, C., Olsson, C., Kundu, S., Kadavath, S., et al. (2022) · 2022
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Generative social choice
Fish, S., Gölz, P., Parkes, D. C., Procaccia, A. D., Rusak, G., Shapira, I., and Wüthrich, M. (2023) · 2023
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Ai alignment and social choice: Fundamental limitations and policy implications
Mishra, A. (2023) · 2023
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Whose opinions do language models reflect?
Santurkar, S., Durmus, E., Ladhak, F., Lee, C., Liang, P., and Hashimoto, T. (2023) · 2023
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Position: Social choice should guide ai alignment in dealing with diverse human feedback
Conitzer, V., Freedman, R., Heitzig, J., Holliday, W. H., Jacobs, B. M., Lambert, N., Mossé, M., Pacuit, E., Russell, S., Schoelkopf, H., et al. (2024) · 2024
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Axioms for ai alignment from human feedback
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Kauderer-Abrams, E. (2017) · 2017
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Social choice and the value alignment problem
Prasad, M. (2018) · 2018
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The pitfalls of simplicity bias in neural networks
Shah, H., Tamuly, K., Raghunathan, A., Jain, P., and Netrapalli, P. (2020) · 2020
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Predicting winner and estimating margin of victory in elections using sampling
Bhattacharyya, A. and Dey, P. (2021) · 2021
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Goal misgeneralization in deep reinforcement learning
Di Langosco, L. L., Koch, J., Sharkey, L. D., Pfau, J., and Krueger, D. (2022) · 2022
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Locality guidance for improving vision transformers on tiny datasets
Li, K., Yu, R., Wang, Z., Yuan, L., Song, G., and Chen, J. (2022) · 2022
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Ge, L., Halpern, D., Micha, E., Procaccia, A. D., Shapira, I., Vorobeychik, Y., and Wu, J. (2024) · 2024
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Collective constitutional ai: Aligning a language model with public input
Huang, S., Siddarth, D., Lovitt, L., Liao, T. I., Durmus, E., Tamkin, A., and Ganguli, D. (2024) · 2024
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What are human values, and how do we align ai to them?
Klingefjord, O., Lowe, R., and Edelman, J. (2024) · 2024
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Openassistant conversations-democratizing large language model alignment
Köpf, A., Kilcher, Y., von Rütte, D., Anagnostidis, S., Tam, Z. R., Stevens, K., Barhoum, A., Nguyen, D., Stanley, O., Nagyfi, R., et al. (2024) · 2024
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Language models, like humans, show content effects on reasoning tasks
Lampinen, A. K., Dasgupta, I., Chan, S. C., Sheahan, H. R., Creswell, A., Kumaran, D., McClelland, J. L., and Hill, F. (2024) · 2024
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Investigating the properties of neural network representations in reinforcement learning
Wang, H., Miahi, E., White, M., Machado, M. C., Abbas, Z., Kumaraswamy, R., Liu, V., and White, A. (2024) · 2024
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Beyond preferences in ai alignment
Zhi-Xuan, T., Carroll, M., Franklin, M., and Ashton, H. (2024) · 2024
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