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arxiv: 1907.08828 · v1 · pith:JXVELQNVnew · submitted 2019-07-20 · 💻 cs.GT

Bidding for Preferred Timing: An Auction Design for Electric Vehicle Charging Station Scheduling

Luyang Hou , Chun Wang , Jun Yan This is my paper

Pith reviewed 2026-05-24 18:40 UTC · model grok-4.3

classification 💻 cs.GT
keywords electric vehicle chargingiterative auctionschedulingsocial welfare maximizationprivacy preservationgame theoryindividual rationalitypreference elicitation
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The pith

An iterative auction computes high-quality EV charging schedules by progressively eliciting user time preferences while preserving privacy and proving individual rationality.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper addresses the problem of scheduling electric vehicle charging slots where each user has private preferences over start times and charging durations, with the objective of maximizing the sum of all users' values subject to station capacity. It introduces an iterative auction that asks users for preference information only as needed to build a schedule, rather than requiring complete value functions at the outset. A game-theoretic analysis establishes that the mechanism is individually rational, so no user loses by participating, and that reporting true preferences is a best response. Experiments demonstrate that the resulting schedules reach high social welfare even when only a fraction of each user's preferences is revealed, and they quantify how efficiency changes with the amount of information elicited.

Core claim

In an electric vehicle charging station where users may strategically reserve time slots according to their preferences over start times and charging lengths, the iterative auction elicits those preferences only as required to form a socially welfare-maximizing schedule. The mechanism is shown to be individually rational and to make truthful reporting a best response for each agent. Simulations confirm that near-optimal welfare is attained with partial value information, and the dependence of efficiency on the volume of revealed preferences is characterized.

What carries the argument

Iterative auction that progressively elicits preferences over charging start times and durations only as necessary to compute welfare-maximizing schedules.

If this is right

  • High-efficiency schedules are obtained without requiring users to disclose full preference information.
  • Participation is individually rational: no user receives negative utility from joining the auction.
  • Truthful reporting of preferences is a best response for each strategic user.
  • Scheduling efficiency improves as more preference information is revealed during the iterative process.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The observed relationship between information volume and efficiency implies that designers can tune the number of elicitation rounds to balance privacy against welfare loss.
  • Because the mechanism works with partial information, it could be combined with statistical models of typical user preferences to further reduce the number of queries needed in practice.

Load-bearing premise

Users are self-interested agents who may behave strategically to advance their own benefits rather than the social welfare of all agents.

What would settle it

An experiment in which agents submit non-truthful bids, the auction still terminates, and the achieved social welfare is materially lower than the levels reported under truthful best-response play would falsify the efficiency and incentive claims.

Figures

Figures reproduced from arXiv: 1907.08828 by Chun Wang, Jun Yan, Luyang Hou.

Figure 1
Figure 1. Figure 1: Efficiency and information revelation between the iterative biddi [PITH_FULL_IMAGE:figures/full_fig_p009_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: (a) Accommodation level, (b) rounds and (c) running time o [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
read the original abstract

This paper considers an electric vehicle charging scheduling setting where vehicle users can reserve charging time in advance at a charging station. In this setting, users are allowed to explicitly express their preferences over different start times and the length of charging periods for charging their vehicles. The goal is to compute optimal charging schedules which maximize the social welfare of all users given their time preferences and the state of charge of their vehicles. Assuming that users are self-interested agents who may behave strategically to advance their own benefits rather than the social welfare of all agents, we propose an iterative auction which computes high quality schedules and, at the same time, preserve users' privacy by progressively eliciting their preferences as necessary. We conduct a game theoretical analysis on the proposed iterative auction to prove its individual rationality and the best response for agents. Through extensive experiments, we demonstrate that the iterative auction can achieve high-efficiency solutions with a partial value information. Additionally, we explore the relationship between scheduling efficiency and information revelation in the auction.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper proposes an iterative auction for scheduling EV charging slots at a station, where users bid on preferred start times and durations to maximize social welfare. The mechanism progressively elicits preferences to preserve privacy, includes a game-theoretic analysis proving individual rationality and best-response strategies for agents, and reports experimental results showing high-efficiency schedules achievable with only partial value information.

Significance. If the incentive and efficiency properties hold under strategic behavior, the work provides a practical, privacy-aware mechanism for a growing application domain. The explicit focus on partial information and iterative elicitation is a positive feature, though the analysis does not establish dominant-strategy incentive compatibility.

major comments (2)
  1. [Game theoretical analysis] Game-theoretical analysis (as described in the abstract): the proofs establish individual rationality and best responses but do not claim or demonstrate dominant-strategy incentive compatibility. This is load-bearing for the central efficiency claim, because non-myopic agents can shade or withhold bids across rounds to manipulate future allocations, potentially degrading social welfare below the levels reported in the experiments.
  2. [Experiments] Experiments section: the reported high-efficiency outcomes with partial value information are presented without explicit discussion of the bidding model used (myopic/truthful vs. fully strategic). If the simulations assume myopic play, they do not directly support the efficiency claim under the strategic-agent premise stated in the abstract.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. We address the two major comments below, acknowledging where the manuscript requires clarification or additional discussion while defending the scope of our claims based on the analysis and experiments presented.

read point-by-point responses

  1. Referee: [Game theoretical analysis] Game-theoretical analysis (as described in the abstract): the proofs establish individual rationality and best responses but do not claim or demonstrate dominant-strategy incentive compatibility. This is load-bearing for the central efficiency claim, because non-myopic agents can shade or withhold bids across rounds to manipulate future allocations, potentially degrading social welfare below the levels reported in the experiments.

    Authors: We agree that the analysis establishes individual rationality and identifies best-response strategies (truthful reporting of queried preferences in each round) but does not prove dominant-strategy incentive compatibility. The efficiency results are supported under the best-response behavior characterized in the game-theoretic section. We will add explicit discussion in the revised manuscript clarifying that the mechanism is not DSIC and noting the potential for non-myopic manipulation as a limitation, while emphasizing that the iterative design and privacy preservation remain valid under the proven properties. revision: partial

  2. Referee: [Experiments] Experiments section: the reported high-efficiency outcomes with partial value information are presented without explicit discussion of the bidding model used (myopic/truthful vs. fully strategic). If the simulations assume myopic play, they do not directly support the efficiency claim under the strategic-agent premise stated in the abstract.

    Authors: The experiments model agents as following the best-response strategy from the game-theoretic analysis, which involves truthful revelation of preferences when elicited by the mechanism. We will revise the experiments section to explicitly describe this bidding model, reference the best-response result, and discuss how the reported efficiency holds under the analyzed strategic behavior rather than assuming purely myopic play. revision: yes

Circularity Check

0 steps flagged

No significant circularity in the derivation chain.

full rationale

The paper proposes an original iterative auction mechanism for EV charging scheduling, supported by a game-theoretic analysis proving individual rationality and best-response properties, plus experimental validation of efficiency under partial information. No equations, fitted parameters, or self-citations are shown to reduce any central claim (such as the auction's IR or efficiency) to a definitional tautology or prior self-referential result. The derivation chain is self-contained as an independent construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The design rests on standard mechanism-design assumptions rather than new invented entities or fitted parameters.

axioms (1)
  • domain assumption Users are self-interested strategic agents who may misreport preferences
    This premise is invoked to justify the need for incentive-compatible auction design and is stated explicitly in the abstract.

pith-pipeline@v0.9.0 · 5698 in / 1156 out tokens · 25720 ms · 2026-05-24T18:40:05.373669+00:00 · methodology

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