Beyond Pigouvian Taxes: A Worst Case Analysis
In the early $20^{th}$ century, Pigou observed that imposing a marginal cost tax on the usage of a public good induces a socially efficient level of use as an equilibrium. Unfortunately, such a "Pigouvian" tax may also induce other, socially inefficient, equilibria. We observe that this social inefficiency may be unbounded, and study whether alternative tax structures may lead to milder losses in the worst case, i.e. to a lower price of anarchy. We show that no tax structure leads to bounded losses in the worst case. However, we do find a tax scheme that has a lower price of anarchy than the Pigouvian tax, obtaining tight lower and upper bounds in terms of a crucial parameter that we identify. We generalize our results to various scenarios that each offers an alternative to the use of a public road by private cars, such as ride sharing, or using a bus or a train.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Secondary materials, Pigouvian taxes, and a monopsony
Secondary materials present promising opportunities for firms to repurpose emissions into marketable goods, aligning with circular economy principles. This paper examines conditions under which introducing a market for s…
Misinformation as Information Pollution
Social media feed algorithms are designed to optimize online social engagements for the purpose of maximizing advertising profits, and therefore have an incentive to promote controversial posts including misinformation. …
MisinformationTaxes and Market Power: A Principal Components Approach
Suppliers of differentiated goods make simultaneous pricing decisions, which are strategically linked. Because of market power, the equilibrium is inefficient. We study how a policymaker should target a budget-balanced t…
Beyond the Worst-Case Analysis of Algorithms (Introduction)
One of the primary goals of the mathematical analysis of algorithms is to provide guidance about which algorithm is the "best" for solving a given computational problem. Worst-case analysis summarizes the performance pro…
Efficient Tensor Decomposition
This chapter studies the problem of decomposing a tensor into a sum of constituent rank one tensors. While tensor decompositions are very useful in designing learning algorithms and data analysis, they are NP-hard in the…
Tensor Decomposition