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Found 11 papers, 2 papers with code
First-Choice Maximality Meets Ex-ante and Ex-post Fairness
The generalized probabilistic Boston mechanism is also ex-post EF1, and satisfies ex-ante efficiency instead of fairness.
Anti-Malware Sandbox Games
We develop a game theoretic model of malware protection using the state-of-the-art sandbox method, to characterize and compute optimal defense strategies for anti-malware.
Favoring Eagerness for Remaining Items: Designing Efficient, Fair, and Strategyproof Mechanisms
In the assignment problem, the goal is to assign indivisible items to agents who have ordinal preferences, efficiently and fairly, in a strategyproof manner.
Sequential Mechanisms for Multi-type Resource Allocation
We study the relationship between properties of the local mechanisms, each responsible for assigning all of the resources of a designated type, and the properties of a sequential mechanism which is composed of these local mechanisms, one for each type, applied sequentially, under lexicographic preferences, a well studied model of preferences over multiple types of resources in artificial intelligence and economics.
Fair and Efficient Allocations under Lexicographic Preferences
Envy-freeness up to any good (EFX) provides a strong and intuitive guarantee of fairness in the allocation of indivisible goods.
Fairness
Computer Science and Game Theory
Necessarily Optimal One-Sided Matchings
We study the classical problem of matching $n$ agents to $n$ objects, where the agents have ranked preferences over the objects.
Probabilistic Serial Mechanism for Multi-Type Resource Allocation
For MTRAs with divisible items, we show that the existing multi-type probabilistic serial (MPS) mechanism satisfies the stronger efficiency notion of lexi-efficiency, and is sd-envy-free under strict linear preferences, and sd-weak-strategyproof under lexicographic preferences.
Multi-type Resource Allocation with Partial Preferences
We propose multi-type probabilistic serial (MPS) and multi-type random priority (MRP) as extensions of the well known PS and RP mechanisms to the multi-type resource allocation problem (MTRA) with partial preferences.
Minimizing Time-to-Rank: A Learning and Recommendation Approach
Consider the following problem faced by an online voting platform: A user is provided with a list of alternatives, and is asked to rank them in order of preference using only drag-and-drop operations.
Practical Algorithms for Multi-Stage Voting Rules with Parallel Universes Tiebreaking
STV and ranked pairs (RP) are two well-studied voting rules for group decision-making.
Practical Algorithms for STV and Ranked Pairs with Parallel Universes Tiebreaking
We also propose novel ILP formulations for PUT-winners under STV and RP, respectively.
