日時：2026年4月16日（木）15：10～16：40

 場所：豊中キャンパス　法経研究棟7階　小会議室

 講師：森田 公之氏（専修大学経済学部）

タイトル：The Allocation of Decision Authority in Three-Stage Decision Processes with Applications to Artificial Intelligence in Organizations

アブストラクト：We study a three-stage decision process that consists of information acquisition,

project choice, and execution of the selected project. A principal wants to choose and implement a proactive project, and hires an agent who chooses a costly effort at the information acquisition stage as well as a costly effort at the execution stage. What the principal can do at the beginning is the allocation of the formal decision authority over project choice, either to herself or the agent. We show that the principal may choose to delegate decision authority to the agent, however unlikely the interest of the agent is to be congruent with her interest, or however competent and experienced she is. We provide several testable predictions. (i) Delegation is more likely as the manager has discretion over both information acquisition and implementation. (ii) Delegation is less likely as opportunities for compromising improve. (iii) Whether or not the parties agree about the status quo matters: In particular, if their preferences about the default decisions differ, the organization is more likely to be decentralized for new project development as their interests are less likely to be congruent. We further discuss the extent to which our results on optimal delegation survive when artificial intelligence (AI) is deployed, distinguishing autonomous and nonautonomous AI. If AI can fully automate information acquisition or execution, delegation cannot be optimal, but it

can be optimal if the agent remains responsible for execution. If AI instead supports execution by lowering its cost, delegation can survive.

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 Time and Date:  15：10～16：40, Thursday, April, 16th

 Seminar Venue: 法経研究棟7階小会議室 ,Toyonaka Campus

 Presenters: Kimiyuki Morita(Senshu University)

 Title: The Allocation of Decision Authority in Three-Stage Decision Processes with Applications to Artificial Intelligence in Organizations

 Abstract:We study a three-stage decision process that consists of information acquisition,

project choice, and execution of the selected project. A principal wants to choose and implement a proactive project, and hires an agent who chooses a costly effort at the information acquisition stage as well as a costly effort at the execution stage. What the principal can do at the beginning is the allocation of the formal decision authority over project choice, either to herself or the agent. We show that the principal may choose to delegate decision authority to the agent, however unlikely the interest of the agent is to be congruent with her interest, or however competent and experienced she is. We provide several testable predictions. (i) Delegation is more likely as the manager has discretion over both information acquisition and implementation. (ii) Delegation is less likely as opportunities for compromising improve. (iii) Whether or not the parties agree about the status quo matters: In particular, if their preferences about the default decisions differ, the organization is more likely to be decentralized for new project development as their interests are less likely to be congruent. We further discuss the extent to which our results on optimal delegation survive when artificial intelligence (AI) is deployed, distinguishing autonomous and nonautonomous AI. If AI can fully automate information acquisition or execution, delegation cannot be optimal, but it

can be optimal if the agent remains responsible for execution. If AI instead supports execution by lowering its cost, delegation can survive.

日時：2026年4月23日(木)13：30～15：00

会場：豊中キャンパスOSIPP棟6階　会議室

講師：Alan Searot氏（University of California-Santa Cruz） 

タイトル：TBA

日時：2026年5月7日(木)13：30～15：00

会場：豊中キャンパスOSIPP棟6階　会議室

講師：柳本　和春氏（神戸大学） 

タイトル：TBA

日時：2026年5月28日(木)13：30～15：00

会場：豊中キャンパスOSIPP棟6階　会議室

講師：白糸 裕輝氏(University of Michigan) 　 

タイトル：TBA

2026年度　第1回　大阪大学経済学研究会　のご案内（IPP研究会共催）

日時：2026年4月8日（水）13：00～15：00（日本時間）☆Zoomでのオンラインミーティングで開催です☆

報告者：Undral Byambadalai 氏　(National University of Mongolia)

議題：Beyond the Average: Distributional Causal Inference under Imperfect Compliance

アブストラクト：We study the estimation of distributional treatment effects in randomized experiments with imperfect compliance. When participants do not adhere to their assigned treatments, we leverage treatment assignment as an instrumental variable to identify the local distributional treatment effect—the difference in outcome distributions between treatment and control groups for the subpopulation of compliers. We propose a regression-adjusted estimator based on a distribution regression framework with Neyman-orthogonal moment conditions, enabling robustness and flexibility with high-dimensional covariates. Our approach accommodates continuous, discrete, and mixed discrete-continuous outcomes, and applies under a broad class of covariate-adaptive randomization schemes, including stratified block designs and simple random sampling. We derive the estimator's asymptotic distribution and show that it achieves the semiparametric efficiency bound. Simulation results demonstrate favorable finite-sample performance, and we demonstrate favorable finite-sample perfomance, and we demonstrate the method's practical relevance in an appication to the Oregon Health Insurance Experiment.