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Yuezhou Lv

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AAMAS Conference 2023 Conference Paper

Node Conversion Optimization in Multi-hop Influence Networks

Jie Zhang
Yuezhou Lv
Zihe Wang

In this paper, we study scenarios such as diffusion of innovations in a social system and belief propagation in social choice decisionmaking, which can be captured by a social influence network. In such networks, nodes are distributed and are connected by links between them. Nodes have two different states, 𝑠 and 𝑟. They can change from state 𝑠 to state 𝑟, but not backward [24]. Nodes are interested in changing to state 𝑟 only if a sufficient number of their neighbors change to state 𝑟. In many scenarios, it is desired to design local decision algorithms that guarantee this feature, termed as the safety of node conversion. We design optimal algorithms that maximize the number of nodes that change to state 𝑟. In particular, we assume that each node can observe its neighbors up to a distance of 𝑘 from itself, which introduces complexity to the setting that each node can only observe its immediate neighbors, i. e. , 𝑘 = 1. Moreover, we consider the models that nodes have the same threshold or different thresholds under which their conversion from 𝑠 to 𝑟 is safe. We first present the optimal algorithm for the uniform threshold model and establish its optimality by characterizing a monotonicity property. We then generalize the algorithm to maximize node conversion when they have different threshold values. The monotonicity properties and insights on nodes’ recursive reasoning of their neighbors’ status may be of independent interest.

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IJCAI Conference 2016 Conference Paper

To Give or Not to Give: Fair Division for Single Minded Valuations

Simina Br
acirc; nzei
Yuezhou Lv
Ruta Mehta

Single minded agents have strict preferences, in which a bundle is acceptable only if it meets a certain demand. Such preferences arise naturally in scenarios such as allocating computational resources among users, where the goal is to fairly serve as many requests as possible. In this paper we study the fair division problem for such agents, which is complex due to discontinuity and complementarities of preferences. Our solution concept - the competitive allocation from equal incomes (CAEI) - is inspired from market equilibria and implements fair outcomes through a pricing mechanism. We study existence and computation of CAEI for multiple divisible goods, discrete goods, and cake cutting. Our solution is useful more generally, when the players have a target set of goods, and very small positive values for any bundle other than their target set.

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AAAI Conference 2015 Conference Paper

Incentive Networks

Yuezhou Lv
Thomas Moscibroda

In a basic economic system, each participant receives a (financial) reward according to his own contribution to the system. In this work, we study an alternative approach – Incentive Networks – in which a participant’s reward depends not only on his own contribution; but also in part on the contributions made by his social contacts or friends. We show that the key parameter effecting the efficiency of such an Incentive Networkbased economic system depends on the participant’s degree of directed altruism. Directed altruism is the extent to which someone is willing to work if his work results in a payment to his friend, rather than to himself. Specifically, we characterize the condition under which an Incentive Network-based economy is more efficient than the basic ”pay-for-your-contribution” economy. We quantify by how much incentive networks can reduce the total reward that needs to be paid to the participants in order to achieve a certain overall contribution. Finally, we study the impact of the network topology and various exogenous parameters on the efficiency of incentive networks. Our results suggest that in many practical settings, Incentive Network-based reward systems or compensation structures could be more efficient than the ubiquitous ’pay-for-your-contribution’ schemes.

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