Using Revealed Preferences to Infer Environmental Benefits:Evidence from Recreational Fishing Licenses

Using revealed preferences to infer environmental benefits, evidence from recreational fishing licenses

We develop and apply a new method for estimating the economic benefits of an environmental amenity. The method is based upon the notion of estimating the derived demand for a privately traded option to utilize an open access good. In particular, the demand for state fishing licenses is used to infer the benefits of recreational fishing. Using panel data on state fishing license sales and prices...

Revealed Indifference : Using Response Times to Infer Preferences

Revealed preference is the dominant approach for inferring preferences, but it relies on discrete, stochastic choices. The choice process also produces response times (RTs) which are continuous and can often be observed in the absence of informative choice outcomes. Moreover, there is a consistent relationship between RTs and strength-of-preference, namely that people make slower decisions as t...

Using an Ontologcial A-priori Score to Infer User’s Preferences

With the ever growing importance of the web in our life, people have become overwhelmed by information and are unable to find the object that matches their need. To help the users in this process, recommendation systems filter unnecessary objects and only show the most suitable ones. However, due to an incomplete preference model, today’s systems may fail to recommend the items that best match ...

Strategic Classification from Revealed Preferences

We study an online linear classification problem, in which the data is generated by strategic agents who manipulate their features in an effort to change the classification outcome. In rounds, the learner deploys a classifier, and an adversarially chosen agent arrives, possibly manipulating her features to optimally respond to the learner. The learner has no knowledge of the agents’ utility fun...

From P3P to Data Licenses