An Application of Shapley Value Cost Allocation to Liquidity Savings Mechanisms

Garratt, Rodney

doi:10.1111/jmcb.12853

Cited by 3 publications

(3 citation statements)

References 18 publications

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“…With Y i said I in the case of a project I partners from the alliance profit maximum V ( I ) in income, namely, the income gained by the each participant in the I uses letters to represent Y , this model is a cost allocation which should not only meet the rationality of overall but also at the same time to meet the individual rationality [ 25 , 26 ], as shown below:

where V ( I ) represents the benefit that I, a participant, can obtain by completing the project alone, and V ( I ) represents the maximum benefit that can be obtained in many cooperative alliances [ 27 , 28 ]. At this point, we get the Shapley value we want to find, which is called Y i ( v ):

where S is a subset of all combinations of I , and | S | is the number of project-related stakeholders in a collaboration S , and W (| S |) is the weight.…”

Section: Basic Theory Of Air Pollution Control Cost Allocationmentioning

confidence: 99%

A Cost Allocation Decision Model for Air Pollution Control

Qin

Chang-yuan

2022

Computational Intelligence and Neuroscience

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Air pollution control as the background of a cost allocation method is based on the Shapley value to determine the core stakeholder, so fair pollution control projects and the establishment of the atmospheric pollution of governance cost allocation model are put forward for the solution of air pollution coordinated by the government supervision and the atmospheric pollution control collaborative group. The results show that the cost allocation model of air pollution control based on Shapley value is more reasonable, and the cost of stakeholders is reduced to a certain extent, and the risk of the participants is reduced so that it maximizes social benefits.

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where S is a subset of all combinations of I , and | S | is the number of project-related stakeholders in a collaboration S , and W (| S |) is the weight.…”

Section: Basic Theory Of Air Pollution Control Cost Allocationmentioning

confidence: 99%

A Cost Allocation Decision Model for Air Pollution Control

Qin

Chang-yuan

2022

Computational Intelligence and Neuroscience

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“…A newer strand of literature explores an alternative approach, by taking seriously the incentives of the participants arising from the costs they face for liquidity provision and the benefits of settling their payments. Garratt (2019) proposed a mechanism using the Shapley value cost allocation method. In their mechanism, to ensure that the welfare-maximizing netting proposals are always accepted, participants receive take-it-or-leave-it offers to contribute the needed liquidity.…”

Section: Literature Reviewmentioning

confidence: 99%

Improving the Efficiency of Payments Systems Using Quantum Computing

McMahon,

McGillivray,

Desai

et al. 2024

Management Science

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High-value payment systems (HVPSs) are typically liquidity intensive because payments are settled on a gross basis. State-of-the-art solutions to this problem include algorithms that seek netting sets and allow for ad hoc reordering of submitted payments. This paper introduces a new algorithm that explores the entire space of payments reordering to improve the liquidity efficiency of these systems without significantly increasing payment delays. Finding the optimal payment order among the entire space of reorderings is, however, an NP-hard combinatorial optimization problem. We solve this problem using a hybrid quantum annealing algorithm. Despite the limitations in size and speed of today’s quantum computers, our algorithm provides quantifiable liquidity savings when applied to the Canadian HVPS using a 30-day sample of transaction data. By reordering batches of 70 payments, we achieve an average of Canadian (C) $240 million in daily liquidity savings, with a settlement delay of approximately 90 seconds. For a few days in the sample, the liquidity savings exceed C$1 billion. Compared with classical computing and with current algorithms in HVPS, our quantum algorithm offers larger liquidity savings, and it offers more reliable and consistent solutions, particularly under time constraints. This paper was accepted by Chung Piaw Teo, optimization. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.00314 .

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“…Note that to treat Equation 7 as our benchmark, we implicitly assume the existence of a planner that implements the allocation of initial liquidity that minimizes total costs or some form of cooperation between the parties, for example via side payments to allow them to share the costs (Garratt 2019). Treated as a cooperative solution, if the sum of both agents costs are above the average costs, there would still be room for better learning performance, albeit one that might be hard to achieve when both agents learn independently.…”

Section: Searchmentioning

confidence: 99%