Search costs matter and are reflected in many areas of law. For example, most disclosure requirements economize on search costs. A homeowner who must disclose the presence of termites saves a potential buyer, and perhaps many such buyers, from spending money to search, or inspect, the property. Similarly, requirements to reveal expected miles per gallon, or risks posed by a drug, economize on search costs. But these examples point to simple strategies and costs that can be minimized or entirely avoided with some legal intervention. Law can do better and take account of more subtle things once sophisticated search strategies are understood. This Essay introduces such search strategies and their implications for law.
Saul Levmore
We benefited from discussions with colleagues at a University of Chicago Law School workshop and with Concetta Balestra Fagan and Eliot Levmore.
When the past is thought to predict the future, it is unsurprising that machine learning, with access to large data sets, wins prediction contests when competing against an individual, including a judge. Just as computers predict next week’s weather better than any human working alone, at least one study shows that machine learning can make better decisions than can judges when deciding whether or not to grant bail.
I owe thanks to Thomas Gallanis, Claire Horrell, and Julie Roin for conversations, corrections, and ideas with regard to this project.
I am grateful to Lee Fennell, Daniel Hemel, Ariel Porat, and Claire Horrell for rewarding conversations and suggestions.
Richard Posner was certainly the most able judge in the history of tort law and in the development and deployment of law and economics.
I benefited greatly from conversations with Zak Rosenfield, Rosalind Dixon, and Julie Roin.
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