The impact of artificial intelligence design on pricing

IF 1.2 4区 管理学 Q3 ECONOMICS
John Asker, Chaim Fershtman, Ariel Pakes
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引用次数: 0

Abstract

The behavior of artificial intelligence (AI) algorithms is shaped by how they learn about their environment. We compare the prices generated by AIs that use different learning protocols when there is market interaction. Asynchronous learning occurs when the AI only learns about the return from the action it took. Synchronous learning occurs when the AI conducts counterfactuals to learn about the returns it would have earned had it taken an alternative action. The two lead to markedly different market prices. When future profits are not given positive weight by the AI, (perfect) synchronous updating leads to competitive pricing, while asynchronous can lead to pricing close to monopoly levels. We investigate how this result varies when either counterfactuals can only be calculated imperfectly and/or when the AI places a weight on future profits. Lastly, we investigate performance differences between offline and online play.

人工智能设计对定价的影响
人工智能(AI)算法的行为受其学习环境的影响。我们比较了使用不同学习协议的人工智能在市场互动时产生的价格。当人工智能只了解它所采取的行动的回报时,就会进行异步学习。同步学习是指人工智能进行反事实学习,了解如果它采取另一种行动会获得的回报。这两种情况会导致明显不同的市场价格。当人工智能不赋予未来利润正权重时,(完全)同步更新会导致竞争性定价,而异步更新则会导致定价接近垄断水平。我们研究了当反事实只能以不完全的方式计算和/或人工智能对未来利润进行加权时,这一结果有何不同。最后,我们研究了离线游戏和在线游戏的性能差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
3.20
自引率
5.30%
发文量
43
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