Zobrazeno 1 - 10
of 365
pro vyhledávání: '"Procaccia, Ariel"'
Autor:
Summerfield, Christopher, Argyle, Lisa, Bakker, Michiel, Collins, Teddy, Durmus, Esin, Eloundou, Tyna, Gabriel, Iason, Ganguli, Deep, Hackenburg, Kobi, Hadfield, Gillian, Hewitt, Luke, Huang, Saffron, Landemore, Helene, Marchal, Nahema, Ovadya, Aviv, Procaccia, Ariel, Risse, Mathias, Schneier, Bruce, Seger, Elizabeth, Siddarth, Divya, Sætra, Henrik Skaug, Tessler, MH, Botvinick, Matthew
Advanced AI systems capable of generating humanlike text and multimodal content are now widely available. In this paper, we discuss the impacts that generative artificial intelligence may have on democratic processes. We consider the consequences of
Externí odkaz:
http://arxiv.org/abs/2409.06729
We consider the problem of online fair division of indivisible goods to players when there are a finite number of types of goods and player values are drawn from distributions with unknown means. Our goal is to maximize social welfare subject to allo
Externí odkaz:
http://arxiv.org/abs/2407.01795
A citizens' assembly is a group of people who are randomly selected to represent a larger population in a deliberation. While this approach has successfully strengthened democracy, it has certain limitations that suggest the need for assemblies to fo
Externí odkaz:
http://arxiv.org/abs/2405.19129
Is it possible to understand or imitate a policy maker's rationale by looking at past decisions they made? We formalize this question as the problem of learning social welfare functions belonging to the well-studied family of power mean functions. We
Externí odkaz:
http://arxiv.org/abs/2405.17700
We introduce and study the problem of detecting whether an agent is updating their prior beliefs given new evidence in an optimal way that is Bayesian, or whether they are biased towards their own prior. In our model, biased agents form posterior bel
Externí odkaz:
http://arxiv.org/abs/2405.17694
Autor:
Ge, Luise, Halpern, Daniel, Micha, Evi, Procaccia, Ariel D., Shapira, Itai, Vorobeychik, Yevgeniy, Wu, Junlin
In the context of reinforcement learning from human feedback (RLHF), the reward function is generally derived from maximum likelihood estimation of a random utility model based on pairwise comparisons made by humans. The problem of learning a reward
Externí odkaz:
http://arxiv.org/abs/2405.14758
Rent division is the well-studied problem of fairly assigning rooms and dividing rent among a set of roommates within a single apartment. A shortcoming of existing solutions is that renters are assumed to be considering apartments in isolation, where
Externí odkaz:
http://arxiv.org/abs/2403.08051
Autor:
Fish, Sara, Gölz, Paul, Parkes, David C., Procaccia, Ariel D., Rusak, Gili, Shapira, Itai, Wüthrich, Manuel
Traditionally, social choice theory has only been applicable to choices among a few predetermined alternatives but not to more complex decisions such as collectively selecting a textual statement. We introduce generative social choice, a framework th
Externí odkaz:
http://arxiv.org/abs/2309.01291
In computational social choice, the distortion of a voting rule quantifies the degree to which the rule overcomes limited preference information to select a socially desirable outcome. This concept has been investigated extensively, but only through
Externí odkaz:
http://arxiv.org/abs/2306.15657
The design of algorithms for political redistricting generally takes one of two approaches: optimize an objective such as compactness or, drawing on fair division, construct a protocol whose outcomes guarantee partisan fairness. We aim to have the be
Externí odkaz:
http://arxiv.org/abs/2305.12079