서브메뉴
검색
Learning and Pricing With Reference Effects Under Logit Model
Learning and Pricing With Reference Effects Under Logit Model
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103129
- ISBN
- 9798288863424
- DDC
- 658
- 저자명
- Guo, Mengzi Amy.
- 서명/저자
- Learning and Pricing With Reference Effects Under Logit Model
- 발행사항
- [Sl] : University of California, Berkeley, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 211 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-01, Section: B.
- 주기사항
- Advisor: Shen, Zuo-Jun Max.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Berkeley, 2025.
- 초록/해제
- 요약Modern marketplaces are experiencing significant transformations driven by the greater availability and easier access to historical transaction data, which reshapes consumer habits and either enhances existing strategic behaviors or catalyzes new ones during decision-making processes. Motivated by key behavioral phenomena arising in online retailing, my dissertation primarily concentrates on designing effective pricing policies with theoretical guarantees in the presence of reference effects, which refers to a phenomenon where consumer evaluations depend not only on the absolute price but also on how it relates to their price expectations.The incorporation of reference effects introduces significant complexity to decision-making due to their intertemporal and asymmetric nature, fundamentally altering the structure of pricing problems. This necessitates the development of novel analytical approaches. To this end, I utilize methodologies drawn from various branches of Operations Operations Research---including optimization, online learning, game theory, and sequential decision-making---to provide fresh perspectives and valuable insights for pricing strategies in the modern retail industry.The dissertation comprises three papers, each exploring effective pricing policies within distinct frameworks. In Chapter 2, we investigate multi-product dynamic pricing with reference effects in a monopolistic setting, focusing on the characteristics of heuristic and optimal pricing policies. In Chapter 3, we extend the problem to a competitive setting and investigate equilibrium properties under various reference price models. In Chapter 4, we address a realistic competitive scenario in which firms lack information about competitors. We develop effective pricing algorithms that achieve no-regret learning under this partial information setting.
- 일반주제명
- Industrial engineering
- 키워드
- Dynamic pricing
- 키워드
- Reference effect
- 기타저자
- University of California, Berkeley Industrial Engineering & Operations Research
- 기본자료저록
- Dissertations Abstracts International. 87-01B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008260126s2025 us c eng d■001000017357089
■00520260202103129
■006m o d
■007cr#unu||||||||
■020 ▼a9798288863424
■035 ▼a(MiAaPQ)AAI31939270
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a658
■1001 ▼aGuo, Mengzi Amy.
■24510▼aLearning and Pricing With Reference Effects Under Logit Model
■260 ▼a[Sl]▼bUniversity of California, Berkeley▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a211 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-01, Section: B.
■500 ▼aAdvisor: Shen, Zuo-Jun Max.
■5021 ▼aThesis (Ph.D.)--University of California, Berkeley, 2025.
■520 ▼aModern marketplaces are experiencing significant transformations driven by the greater availability and easier access to historical transaction data, which reshapes consumer habits and either enhances existing strategic behaviors or catalyzes new ones during decision-making processes. Motivated by key behavioral phenomena arising in online retailing, my dissertation primarily concentrates on designing effective pricing policies with theoretical guarantees in the presence of reference effects, which refers to a phenomenon where consumer evaluations depend not only on the absolute price but also on how it relates to their price expectations.The incorporation of reference effects introduces significant complexity to decision-making due to their intertemporal and asymmetric nature, fundamentally altering the structure of pricing problems. This necessitates the development of novel analytical approaches. To this end, I utilize methodologies drawn from various branches of Operations Operations Research---including optimization, online learning, game theory, and sequential decision-making---to provide fresh perspectives and valuable insights for pricing strategies in the modern retail industry.The dissertation comprises three papers, each exploring effective pricing policies within distinct frameworks. In Chapter 2, we investigate multi-product dynamic pricing with reference effects in a monopolistic setting, focusing on the characteristics of heuristic and optimal pricing policies. In Chapter 3, we extend the problem to a competitive setting and investigate equilibrium properties under various reference price models. In Chapter 4, we address a realistic competitive scenario in which firms lack information about competitors. We develop effective pricing algorithms that achieve no-regret learning under this partial information setting.
■590 ▼aSchool code: 0028.
■650 4▼aIndustrial engineering
■653 ▼aDynamic pricing
■653 ▼aMultinomial logit
■653 ▼aReference effect
■690 ▼a0796
■690 ▼a0546
■71020▼aUniversity of California, Berkeley▼bIndustrial Engineering & Operations Research.
■7730 ▼tDissertations Abstracts International▼g87-01B.
■790 ▼a0028
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17357089▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
