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Shortlisting is the process of selecting a subset of alternatives from a larger pool for further consideration or final decision-making. It is widely applied in social choice and multi-agent system scenarios. The growing demand for…
Most computer science conferences rely on paper bidding to assign reviewers to papers. Although paper bidding enables high-quality assignments in days of unprecedented submission numbers, it also opens the door for dishonest reviewers to…
This paper studies one emerging procurement auction scenario where the market is constructed over the social networks. In a social network composed of many agents, smartphones or computers, one requester releases her requirement for goods…
We present a quantum auction protocol using superpositions to represent bids and distributed search to identify the winner(s). Measuring the final quantum state gives the auction outcome while simultaneously destroying the superposition.…
This research examines how Artificial Intelligence (AI) can improve participatory budgeting processes within smart cities. In response to challenges like declining civic participation and resource allocation conflicts, the study explores…
We live in an age of consumption with an ever-increasing demand of already scarce resources and equally fast growing problems of waste generation and climate change. To tackle these difficult issues, we must learn from mother nature. Just…
The football transfer market is a complex, dynamic environment in which clubs compete to acquire players who strengthen their squads. While several frameworks estimate a player's worth, a comprehensive approach that captures both squad…
Funding is a cost to trading desks that they see as an input. Current FVA-related literature reflects this by also taking funding costs as an input, usually constant, and always risk-neutral. However, this funding curve is the output from a…
Simulating long-term hydrothermal bid-based markets considering strategic agents is a challenging task. The representation of strategic agents considering intertemporal constraints within a stochastic framework brings additional complexity…
This paper examines the impact of government procurement in social welfare programs on consumers, manufacturers, and the government. We analyze the U.S. infant formula market, where over half of the total sales are purchased by the Women,…
We analyze the optimal strategy for a government to promote large-scale investment projects under information frictions. Specifically, we propose a model where the government collects information on probability of each investment and…
Procuring agricultural inputs (agri-inputs for short) such as seeds, fertilizers, and pesticides, at desired quality levels and at affordable cost, forms a critical component of agricultural input operations. This is a particularly…
Advertisement auctions play a crucial role in revenue generation for e-commerce companies. To make the bidding procedure scalable to thousands of auctions, the automatic bidding (autobidding) algorithms are actively developed in the…
This paper deals with the market-bidding problem of a cluster of price-responsive consumers of electricity. We develop an inverse optimization scheme that, recast as a bilevel programming problem, uses price-consumption data to estimate the…
Competitive Influence Maximization (CIM) has been studied for years due to its wide application in many domains. Most current studies primarily focus on the micro-level optimization by designing policies for one competitor to defeat its…
We consider budget feasible mechanisms for procurement auctions with additive valuation functions. For the divisible case, where agents can be allocated fractionally, there exists an optimal mechanism with approximation guarantee $e/(e-1)$…
Participatory budgeting is a democratic innovation that empowers citizens to propose and vote on public investment projects. While researchers in computer science focused on improving the voting phase of this process, in this work we aim to…
Online bidding is a classical problem in online decision-making, with applications in resource allocation, hierarchical clustering, and the analysis of approximation algorithms. We study its randomized learning-augmented variant, where an…
Real-time bidding (RTB) has become one of the largest online advertising markets in the world. Today the bid price per ad impression is typically decided by the expected value of how it can lead to a desired action event (e.g., registering…
Motivated by the problem of market power in electricity markets, we introduced in previous works a mechanism for simplified markets of two agents with linear cost. In standard procurement auctions, the market power resulting from the…