Consumers interact with firms across multiple devices, browsers, and
machines; these interactions are often recorded with different identifiers for
the same consumer. The failure to correctly match different identities leads to
a fragmented view of exposures and behaviors. This paper studies the identity
fragmentation bias, referring to the estimation bias resulted from using
fragmented data. Using a formal framework, we decompose the contributing
factors of the estimation bias caused by data fragmentation and discuss the
direction of bias. Contrary to conventional wisdom, this bias cannot be signed
or bounded under standard assumptions. Instead, upward biases and sign
reversals can occur even in experimental settings. We then compare several
corrective measures, and discuss their respective advantages and caveats.

研究了消费者行为在多设备、浏览器和机器上交互时所面临的身份识别偏差，解构了数据碎片化造成的估计偏差因素并讨论了其方向，提出了几项纠正措施及其各自的优缺点。

身份碎片化偏差

The Identity Fragmentation Bias

Evolutionary algorithm research and applications began over 50 years ago.
Like other artificial intelligence techniques, evolutionary algorithms will
likely see increased use and development due to the increased availability of
computation, more robust and available open source software libraries, and the
increasing demand for artificial intelligence techniques. As these techniques
become more adopted and capable, it is the right time to take a perspective of
their ability to integrate into society and the human processes they intend to
augment. In this review, we explore a new taxonomy of evolutionary algorithms
and resulting classifications that look at five main areas: the ability to
manage the control of the environment with limiters, the ability to explain and
repeat the search process, the ability to understand input and output causality
within a solution, the ability to manage algorithm bias due to data or user
design, and lastly, the ability to add corrective measures. These areas are
motivated by today's pressures on industry to conform to both societies
concerns and new government regulatory rules. As many reviews of evolutionary
algorithms exist, after motivating this new taxonomy, we briefly classify a
broad range of algorithms and identify areas of future research.

本文提出了五个新的分类来研究进化算法，这些分类主要关注环境控制、搜索过程的解释和重复性、解决方案中输入和输出因果关系的理解、算法偏差的管理以及添加纠正措施的能力，同时也列举了现有算法的分类和未来研究方向。