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Found 24 papers, 4 papers with code
Generative Social Choice
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.
The Distortion of Binomial Voting Defies Expectation
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.
Fair Sortition Made Transparent
Sortition is an age-old democratic paradigm, widely manifested today through the random selection of citizens' assemblies.
District-Fair Participatory Budgeting
On the other hand, decision making that only takes global social welfare into account can be unfair to districts: A social-welfare-maximizing solution might not fund any of the projects preferred by a district, despite the fact that its constituents pay taxes to the city.
Decision Making
Fairness
Computer Science and Game Theory
Data Structures and Algorithms
Axioms for Learning from Pairwise Comparisons
To be well-behaved, systems that process preference data must satisfy certain conditions identified by economic decision theory and by social choice theory.
The Phantom Steering Effect in Q&A Websites
Moreover, we conduct a qualitative survey of the users on Stack Overflow which provides further evidence that the insights from the model reflect the true behavior of the community.
Efficient and Thrifty Voting by Any Means Necessary
We take an unorthodox view of voting by expanding the design space to include both the elicitation rule, whereby voters map their (cardinal) preferences to votes, and the aggregation rule, which transforms the reported votes into collective decisions.
Paradoxes in Fair Machine Learning
Equalized odds is a statistical notion of fairness in machine learning that ensures that classification algorithms do not discriminate against protected groups.
Learning and Planning in the Feature Deception Problem
In order to formally reason about deception, we introduce the feature deception problem (FDP), a domain-independent model and present a learning and planning framework for finding the optimal deception strategy, taking into account the adversary's preferences which are initially unknown to the defender.
Envy-Free Classification
In classic fair division problems such as cake cutting and rent division, envy-freeness requires that each individual (weakly) prefer his allocation to anyone else's.
Loss Functions, Axioms, and Peer Review
The key challenge that arises is the specification of a loss function for ERM.
Fairly Allocating Many Goods with Few Queries
For two agents with arbitrary monotonic utilities, we design an algorithm that computes an allocation satisfying envy-freeness up to one good (EF1), a relaxation of envy-freeness, using a logarithmic number of queries.
Strategyproof Linear Regression in High Dimensions
This paper is part of an emerging line of work at the intersection of machine learning and mechanism design, which aims to avoid noise in training data by correctly aligning the incentives of data sources.
Collaborative PAC Learning
We introduce a collaborative PAC learning model, in which k players attempt to learn the same underlying concept.
The Provable Virtue of Laziness in Motion Planning
The Lazy Shortest Path (LazySP) class consists of motion-planning algorithms that only evaluate edges along shortest paths between the source and target.
Robotics Data Structures and Algorithms
A Voting-Based System for Ethical Decision Making
We present a general approach to automating ethical decisions, drawing on machine learning and computational social choice.
Why You Should Charge Your Friends for Borrowing Your Stuff
We consider goods that can be shared with k-hop neighbors (i. e., the set of nodes within k hops from an owner) on a social network.
Small Representations of Big Kidney Exchange Graphs
This growth results in more lives saved, but exacerbates the empirical hardness of the $\mathcal{NP}$-complete problem of optimally matching patients to donors.
Is Approval Voting Optimal Given Approval Votes?
Some crowdsourcing platforms ask workers to express their opinions by approving a set of k good alternatives.
Learning Cooperative Games
This paper explores a PAC (probably approximately correct) learning model in cooperative games.
Computer Science and Game Theory
Diverse Randomized Agents Vote to Win
We investigate the power of voting among diverse, randomized software agents.
Learning Optimal Commitment to Overcome Insecurity
Game-theoretic algorithms for physical security have made an impressive real-world impact.
