Abstract
Legal and regulatory understandings of information provision miss the importance of the exploration-exploitation dynamic. This Essay argues that is a mistake and seeks to bring this perspective to the debate about information provision and competition. A general, ongoing problem for an individual or an organization is whether to stay with a familiar solution to a problem or try new options that may yield better results. Work in organizational learning describes this problem as the exploration-exploitation dilemma. Understanding and addressing that dilemma has become a key part of an algorithmic approach to computation, machine learning, as it is applied to information provision. In simplest terms, even if one achieves success with one path, failure to try new options means one will be stuck in a local equilibrium while others find paths that yield better results and displace one’s original success. This dynamic indicates that an information provider has to provide new options and information to users, because a provider must learn and adapt to users’ changing interests in both the type of information they desire and how they wish to interact with information.
Put differently, persistent concerns about the way in which news reaches users (the so-called “filter bubble” concern) and the way in which online shopping information is found (a competition concern) can be understood as market failures regarding information provision. The desire seems to be to ensure that new information reaches people, because that increases the potential for new ideas, new choices, and new action. Although these desired outcomes are good, current criticisms and related potential solutions misunderstand the nature of information users and especially information provision, and miss an important point. Both information users and providers sort and filter as a way to enable better learning, and learning is an ongoing process that requires continual changes to succeed. From an exploration-exploitation perspective, a user or an incumbent may remain isolated or offer the same information provision but neither will learn. In that case, whatever short-term success either enjoys is likely to face leapfrogging by those who experiment through exploration and exploitation.
First Page
541
Recommended Citation
Deven
Desai,
Exploration and Exploitation: An Essay on (Machine) Learning, Algorithms, and Information Provision,
47
Loy. U. Chi. L. J.
541
(2015).
Available at:
https://lawecommons.luc.edu/luclj/vol47/iss2/7