We empirically study the interplay between exploration and competition.
Systems that learn from interactions with users often engage in exploration:
making potentially suboptimal decisions in order to acquire new information for
future decisions. However, when multiple systems are competing for the same
market of users, exploration may hurt a system's reputation in the near term,
with adverse competitive effects. In particular, a system may enter a "death
spiral", when the short-term reputation cost decreases the number of users for
the system to learn from, which degrades its performance relative to
competition and further decreases its market share.
We ask whether better exploration algorithms are incentivized under
competition. We run extensive numerical experiments in a stylized duopoly model
in which two firms deploy multi-armed bandit algorithms and compete for myopic
users. We find that duopoly and monopoly tend to favor a primitive "greedy
algorithm" that does not explore and leads to low consumer welfare, whereas a
temporary monopoly (a duopoly with an early entrant) may incentivize better
bandit algorithms and lead to higher consumer welfare. Our findings shed light
on the first-mover advantage in the digital economy by exploring the role that
data can play as a barrier to entry in online markets.

通过对探索和竞争之间的相互作用进行实证研究，我们发现：在数字经济中，数据作为进入在线市场的一个障碍，可能促使更好的算法的产生，获得更高的消费者福利；在有竞争的市场中，探索可能会损害系统的声誉，进而降低市场份额，而第一个进入市场的公司则可能促进更好的算法的出现从而提高客户福利。

竞争环境下探索的危险：一种计算建模方法

The Perils of Exploration under Competition: A Computational Modeling  Approach

Video dissemination through sites such as YouTube can have widespread impacts
on opinions, thoughts, and cultures. Not all videos will reach the same
popularity and have the same impact. Popularity differences arise not only
because of differences in video content, but also because of other
"content-agnostic" factors. The latter factors are of considerable interest but
it has been difficult to accurately study them. For example, videos uploaded by
users with large social networks may tend to be more popular because they tend
to have more interesting content, not because social network size has a
substantial direct impact on popularity. In this paper, we develop and apply a
methodology that is able to accurately assess, both qualitatively and
quantitatively, the impacts of various content-agnostic factors on video
popularity. When controlling for video content, we observe a strong linear
"rich-get-richer" behavior, with the total number of previous views as the most
important factor except for very young videos. The second most important factor
is found to be video age. We analyze a number of phenomena that may contribute
to rich-get-richer, including the first-mover advantage, and search bias
towards popular videos. For young videos we find that factors other than the
total number of previous views, such as uploader characteristics and number of
keywords, become relatively more important. Our findings also confirm that
inaccurate conclusions can be reached when not controlling for content.

本文研究视频传播中的一些因素对视频受欢迎程度的影响，主要得出 “富者愈富”、视频年龄等因素是影响视频流行度的关键。同时，本文研究表明在没有控制视频内容的情况下，将会得出错误的结论。