英文标题：
《Dealing with the Dimensionality Curse in Dynamic Pricing Competition:
  Using Frequent Repricing to Compensate Imperfect Market Anticipations》
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作者：
Rainer Schlosser and Martin Boissier
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最新提交年份：
2018
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英文摘要：
  Most sales applications are characterized by competition and limited demand information. For successful pricing strategies, frequent price adjustments as well as anticipation of market dynamics are crucial. Both effects are challenging as competitive markets are complex and computations of optimized pricing adjustments can be time-consuming. We analyze stochastic dynamic pricing models under oligopoly competition for the sale of perishable goods. To circumvent the curse of dimensionality, we propose a heuristic approach to efficiently compute price adjustments. To demonstrate our strategy\'s applicability even if the number of competitors is large and their strategies are unknown, we consider different competitive settings in which competitors frequently and strategically adjust their prices. For all settings, we verify that our heuristic strategy yields promising results. We compare the performance of our heuristic against upper bounds, which are obtained by optimal strategies that take advantage of perfect price anticipations. We find that price adjustment frequencies can have a larger impact on expected profits than price anticipations. Finally, our approach has been applied on Amazon for the sale of used books. We have used a seller\'s historical market data to calibrate our model. Sales results show that our data-driven strategy outperforms the rule-based strategy of an experienced seller by a profit increase of more than 20%.
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中文摘要：
大多数销售应用程序的特点是竞争激烈，需求信息有限。对于成功的定价策略，频繁的价格调整以及对市场动态的预期至关重要。这两种影响都具有挑战性，因为竞争市场非常复杂，优化定价调整的计算可能非常耗时。我们分析了寡头垄断竞争下易腐商品销售的随机动态定价模型。为了避免维数灾难，我们提出了一种启发式方法来有效地计算价格调整。为了证明我们的战略的适用性，即使竞争对手的数量很大，并且他们的战略未知，我们考虑了不同的竞争环境，其中竞争对手经常战略性地调整价格。对于所有设置，我们验证了我们的启发式策略产生了有希望的结果。我们将我们的启发式算法的性能与上界进行了比较，上界是通过利用完美价格预期的最优策略获得的。我们发现，价格调整频率比价格预期对预期利润的影响更大。最后，我们的方法已应用于亚马逊的二手书销售。我们使用了卖方的历史市场数据来校准我们的模型。销售结果表明，我们的数据驱动策略优于经验丰富的卖家基于规则的策略，利润增长超过20%。
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Science and Game Theory        计算机科学与博弈论
分类描述：Covers all theoretical and applied aspects at the intersection of computer science and game theory, including work in mechanism design, learning in games (which may overlap with Learning), foundations of agent modeling in games (which may overlap with Multiagent systems), coordination, specification and formal methods for non-cooperative computational environments. The area also deals with applications of game theory to areas such as electronic commerce.
涵盖计算机科学和博弈论交叉的所有理论和应用方面，包括机制设计的工作，游戏中的学习（可能与学习重叠），游戏中的agent建模的基础（可能与多agent系统重叠），非合作计算环境的协调、规范和形式化方法。该领域还涉及博弈论在电子商务等领域的应用。
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一级分类：Quantitative Finance        数量金融学
二级分类：Trading and Market Microstructure        交易与市场微观结构
分类描述：Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构，流动性，交易和拍卖设计，自动化交易，基于代理的建模和做市
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