• 日　時：2025年7月24日（木）16：10-17：40
• 講演テーマ：『オークション理論研究の最先端－より「良い」公共工事のための入札制度設計ー』
• 会　場：滋賀大学彦根キャンパス　士魂商才館セミナー室Ⅰ
• 開催様式：対面及びZoom開催
• 参加対象：学内（学部生・院生・教職員）
• 司　会：石井利江子　教授（滋賀大学経済学部）

講演概要：***

【第1部】講演会（16：10-**：**）

表題1：『Beyond  Quasilinearity: Exploring Nonlinear Scoring Rules in Procurement Auctions』

　　　講演者：佐野　隆司　先生(横浜国立大学大学院国際社会科学研究院 准教授)

概　要：This study examines procurement auctions in which bidders submit price and quality, and bids are evaluated using a price-per-quality-ratio (PQR) scoring rule. Equilibrium bidding behavior is characterized for the first-score and second-score auctions in which bidder cost is determined by a unidimensional type and quality. In contrast to well-known quasilinear scoring rules, we show that the second-score auction yields a lower expected score than the first-score auction. We also provide a set of conditions under which expected quality and price are higher in the first-score auction. Finally, we show how these results can be extended to other non-quasilinear scoring rules.

https://www.mdc.e.u-tokyo.ac.jp/en/news-en/2788/

－－－－－－休憩（10分） 16:40-16:**－－－－－－

【第2部】対談（16：**-17：40）

表題2：「Uncertain Quality Evaluation in Procurement Auctions」

        講演者：鶴岡　昌徳　先生(横浜国立大学大学院国際社会科学研究院　准教授)

概　要

This study examines competition among bidders over price and quality under uncertain quality evaluations in procurement auctions. We formulate a model of scoring auctions where bidders face uncertainty generated by the buyer's subjective evaluations on quality. Under certain conditions, we derive the symmetric Bayesian Nash equilibrium for a first-score auction, in which bidders submit a cautious bid because the winner determination depends on the element of luck. In contrast, in a second-score auction, the truth-telling equilibrium exists and evaluation uncertainty does not affect bidding behavior. As the quality evaluation becomes more precise, the expected score and quality improve, and the expected price decreases under further conditions. Furthermore, the secondscore auction is more efficient and achieves a higher expected score and quality than the first-score auction.