6 research outputs found
Civic Crowdfunding for Agents with Negative Valuations and Agents with Asymmetric Beliefs
In the last decade, civic crowdfunding has proved to be effective in
generating funds for the provision of public projects. However, the existing
literature deals only with citizen's with positive valuation and symmetric
belief towards the project's provision. In this work, we present novel
mechanisms which break these two barriers, i.e., mechanisms which incorporate
negative valuation and asymmetric belief, independently. For negative
valuation, we present a methodology for converting existing mechanisms to
mechanisms that incorporate agents with negative valuations. Particularly, we
adapt existing PPR and PPS mechanisms, to present novel PPRN and PPSN
mechanisms which incentivize strategic agents to contribute to the project
based on their true preference. With respect to asymmetric belief, we propose a
reward scheme Belief Based Reward (BBR) based on Robust Bayesian Truth Serum
mechanism. With BBR, we propose a general mechanism for civic crowdfunding
which incorporates asymmetric agents. We leverage PPR and PPS, to present PPRx
and PPSx. We prove that in PPRx and PPSx, agents with greater belief towards
the project's provision contribute more than agents with lesser belief.
Further, we also show that contributions are such that the project is
provisioned at equilibrium.Comment: Accepted as full paper in IJCAI 201
Combinatorial Civic Crowdfunding with Budgeted Agents: Welfare Optimality at Equilibrium and Optimal Deviation
Civic Crowdfunding (CC) uses the ``power of the crowd'' to garner
contributions towards public projects. As these projects are non-excludable,
agents may prefer to ``free-ride,'' resulting in the project not being funded.
For single project CC, researchers propose to provide refunds to incentivize
agents to contribute, thereby guaranteeing the project's funding. These funding
guarantees are applicable only when agents have an unlimited budget. This work
focuses on a combinatorial setting, where multiple projects are available for
CC and agents have a limited budget. We study certain specific conditions where
funding can be guaranteed. Further, funding the optimal social welfare subset
of projects is desirable when every available project cannot be funded due to
budget restrictions. We prove the impossibility of achieving optimal welfare at
equilibrium for any monotone refund scheme. We then study different heuristics
that the agents can use to contribute to the projects in practice. Through
simulations, we demonstrate the heuristics' performance as the average-case
trade-off between welfare obtained and agent utility.Comment: To appear in the Proceedings of the Thirty-Seventh AAAI Conference on
Artificial Intelligence (AAAI '23). A preliminary version of this paper
titled "Welfare Optimal Combinatorial Civic Crowdfunding with Budgeted
Agents" also appeared at GAIW@AAMAS '2
Differentially Private Federated Combinatorial Bandits with Constraints
There is a rapid increase in the cooperative learning paradigm in online
learning settings, i.e., federated learning (FL). Unlike most FL settings,
there are many situations where the agents are competitive. Each agent would
like to learn from others, but the part of the information it shares for others
to learn from could be sensitive; thus, it desires its privacy. This work
investigates a group of agents working concurrently to solve similar
combinatorial bandit problems while maintaining quality constraints. Can these
agents collectively learn while keeping their sensitive information
confidential by employing differential privacy? We observe that communicating
can reduce the regret. However, differential privacy techniques for protecting
sensitive information makes the data noisy and may deteriorate than help to
improve regret. Hence, we note that it is essential to decide when to
communicate and what shared data to learn to strike a functional balance
between regret and privacy. For such a federated combinatorial MAB setting, we
propose a Privacy-preserving Federated Combinatorial Bandit algorithm, P-FCB.
We illustrate the efficacy of P-FCB through simulations. We further show that
our algorithm provides an improvement in terms of regret while upholding
quality threshold and meaningful privacy guarantees.Comment: 12 pages, 4 Figures, A version of this paper has appeared in the
Proceedings of the ECML PKDD '2
A Truthful, Privacy-Preserving, Approximately Efficient Combinatorial Auction For Single-minded Bidders
Combinatorial auctions are widely used to sell resources/items. The challenges in such auctions are multi-fold. We need to ensure that bidders, the strategic agents, bid their valuations truthfully to the auction mechanism. Besides, the agents may desire privacy of their identities as well as their bidding information. We consider three types of privacies: agent privacy, the identities of the losing bidders must not be revealed to any other agent except the auctioneer (AU), bid privacy, the bid values must be hidden from the other agents as well as the AU and bid-topology privacy, the items for which the agents are bidding must be hidden from the other agents as well as the AU. In this paper, we address whether can we solve the allocation and payment determination problems, which are NP-hard, approximately for single-minded bidders while preserving privacy. In the literature, root m-approximation, where m is the number of items auctioned, and a strategy-proof mechanism is available for this, which we refer to as ICA-SM. To implement ICA-SM with privacy, we propose a novel cryptographic protocol TPACAS. We show that TPACAS achieves these privacy guarantees with high probability. To accomplish this, we use notaries who are semi-trusted third parties. We show that, in TPACAS, notaries do not learn any information about the agents and their bidding information