Nonlinear optimization of revenue per unit of time in discrete Dutch auctions with risk-aware bidders
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| Název: | Nonlinear optimization of revenue per unit of time in discrete Dutch auctions with risk-aware bidders |
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| Autoři: | R.A. Shamim, M.K. Majahar Ali |
| Zdroj: | Iranian Journal of Numerical Analysis and Optimization, Vol 15, Iss Issue 4, Pp 1607-1638 (2025) |
| Informace o vydavateli: | Ferdowsi University of Mashhad, 2025. |
| Rok vydání: | 2025 |
| Sbírka: | LCC:Applied mathematics. Quantitative methods |
| Témata: | auctions, constant absolute risk aversion, discrete dutch auction, nonlinear programming, revenue per unit of time, Applied mathematics. Quantitative methods, T57-57.97 |
| Popis: | This study develops a computational framework to optimize the auctioneer’s revenue per unit of time in modified discrete Dutch auction by incorporating bidders’ risk preferences through the constant absolute risk aversion utility function. Bidders are categorized into three distinct risk profiles—risk-loving, risk-neutral, and risk-averse—allowing for a comprehensive analysis of how risk attitudes influence auction outcomes. A nonlinear programming methodology is utilized to ascertain the optimal revenue per unit time while incorporating discrete bid levels. The findings demonstrate that, at the outset, an increase in the number of bidders substantially boosts the revenue per unit time; nevertheless, after reaching a specific point, the incremental benefits decrease, resulting in a plateau. Additionally, the analysis suggests that, in auctions featuring larger pools of bidders, achieving maximum revenue per unit time necessitates fewer bid levels, as surplus bid levels do not yield further revenue improvements. Bidders exhibiting risk-averse tendencies tend to generate lower returns due to their cautious bidding patterns, whereas risk-seeking participants contribute to higher revenue per unit time by engaging in more assertive bidding. Collectively, these results highlight the significant influence of bidders’ risk preferences on auction design and establish a comprehensive mathematical framework that can be readily adapted to various algorithmic auction mechanisms. Behavioral interpretation via the prospect theory and alignment with published field evidence support the model’s external validity. |
| Druh dokumentu: | article |
| Popis souboru: | electronic resource |
| Jazyk: | English |
| ISSN: | 2423-6977 2423-6969 |
| Relation: | https://ijnao.um.ac.ir/article_47234_a773df0ffef29e46a6c7881f2776da25.pdf; https://doaj.org/toc/2423-6977; https://doaj.org/toc/2423-6969 |
| DOI: | 10.22067/ijnao.2025.93750.1656 |
| Přístupová URL adresa: | https://doaj.org/article/1f49d294df2144799f5b2e660cd09e3a |
| Přístupové číslo: | edsdoj.1f49d294df2144799f5b2e660cd09e3a |
| Databáze: | Directory of Open Access Journals |
Nájsť tento článok vo Web of Science | Abstrakt: | This study develops a computational framework to optimize the auctioneer’s revenue per unit of time in modified discrete Dutch auction by incorporating bidders’ risk preferences through the constant absolute risk aversion utility function. Bidders are categorized into three distinct risk profiles—risk-loving, risk-neutral, and risk-averse—allowing for a comprehensive analysis of how risk attitudes influence auction outcomes. A nonlinear programming methodology is utilized to ascertain the optimal revenue per unit time while incorporating discrete bid levels. The findings demonstrate that, at the outset, an increase in the number of bidders substantially boosts the revenue per unit time; nevertheless, after reaching a specific point, the incremental benefits decrease, resulting in a plateau. Additionally, the analysis suggests that, in auctions featuring larger pools of bidders, achieving maximum revenue per unit time necessitates fewer bid levels, as surplus bid levels do not yield further revenue improvements. Bidders exhibiting risk-averse tendencies tend to generate lower returns due to their cautious bidding patterns, whereas risk-seeking participants contribute to higher revenue per unit time by engaging in more assertive bidding. Collectively, these results highlight the significant influence of bidders’ risk preferences on auction design and establish a comprehensive mathematical framework that can be readily adapted to various algorithmic auction mechanisms. Behavioral interpretation via the prospect theory and alignment with published field evidence support the model’s external validity. |
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| ISSN: | 24236977 24236969 |
| DOI: | 10.22067/ijnao.2025.93750.1656 |