Auctions vs. fixed pricing: Competing for budget constrained buyers

Selcuk, Cemil

doi:10.1016/j.geb.2016.01.003

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…The possibility for a seller to use different selling mechanisms in online markets has provoked interdisciplinary interest to determine which mix of mechanisms is efficient and optimal and to provide rationale for the observed patterns. For example, Chen et al (2017) argue that the choice of a selling mechanism may be attributed to the seller's time preference, while Selcuk (2017) suggests that such a decision is rooted in the buyers' budget constraints.…”

Section: Related Literaturementioning

confidence: 99%

Auctions versus posted prices in the revenue management of limited inventory

Maslov

2022

Manage Decis Econ

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The paper examines a monopolistic seller with a limited inventory, who can sell it by auctions or posted prices in an online market. Previous studies have investigated a seller with unlimited inventory and found that selling by auctions and posted prices is the profit‐maximizing strategy. Contrary to this finding, for limited inventory, mixing selling mechanisms is not always optimal. In particular, when the number of risk‐neutral buyers is high, listing the inventory by auctions generates the highest profit. When faced with risk‐averse buyers, selling by posted prices and auctions is the best strategy. Posted prices are almost always inferior to other listing strategies.

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Section: Related Literaturementioning

confidence: 99%

Auctions versus posted prices in the revenue management of limited inventory

Maslov

2022

Manage Decis Econ

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“…Experimental results suggest that Internet auctions require a few more participants and more time to achieve operational efficiency levels than centralized markets. Selcuk [4] investigates price mechanism selection and characterizes a possible equilibrium. Greenstein-Messica [5] introduces a novel approach for a personal price aware multi-seller recommender system (PMSRS), which could improve recommendation systems' prediction accuracy.…”

Section: Related Workmentioning

confidence: 99%

Learning to Predict Price based on E-commerce Online Auction Machine

Li¹

2018

IJPE

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In this paper, we put forward a novel optimization framework entitled the E-commerce Online Auction Machine. Considering all the characteristics that affect online auction prices, the algorithms are applied to calculate the best fitting line to predict online auction prices by ordinary least squares. After that, regression weights are optimized using the local weighted method. Finally, using the shrinkage method, each characteristic optimal weight is obtained through the EOAM-RR algorithm. We have identified the key characteristics that affect auction prices as well as those that are not important.

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“…In these industries, the market outcome is not only driven by the omnipotent power of overall supply and demand, but also the revenue-seeking behaviors of individual sellers. Seller competition is an ongoing topic in the literature of game theory, from the classic models of competition on the basis of prices and production quantities (see, e.g., Chapter 6 of the book by Dixon [2017]), to the more complex ones like the model by Selcuk [2017] and Paes Leme et al [2020] where sellers compete for buyers' money through selecting suitable trading mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Building Chinese interdisciplinary research centers : the case of Tsinghua University

Li¹,

李蓉暉²

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Graph machine learning has been extensively studied in both academia and industry. However, in the literature, most existing graph machine learning models are designed to conduct training with data samples in a random order, which may suffer from suboptimal performance due to ignoring the importance of different graph data samples and their training orders for the model optimization status. To tackle this critical problem, curriculum graph machine learning (Graph CL), which integrates the strength of graph machine learning and curriculum learning, arises and attracts an increasing amount of attention from the research community. Therefore, in this paper, we comprehensively overview approaches on Graph CL and present a detailed survey of recent advances in this direction. Specifically, we first discuss the key challenges of Graph CL and provide its formal problem definition. Then, we categorize and summarize existing methods into three classes based on three kinds of graph machine learning tasks, i.e., node-level, linklevel, and graph-level tasks. Finally, we share our thoughts on future research directions. To the best of our knowledge, this paper is the first survey for curriculum graph machine learning.

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Auctions vs. fixed pricing: Competing for budget constrained buyers

Cited by 5 publications

References 24 publications

Auctions versus posted prices in the revenue management of limited inventory

Auctions versus posted prices in the revenue management of limited inventory

Learning to Predict Price based on E-commerce Online Auction Machine

Building Chinese interdisciplinary research centers : the case of Tsinghua University

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