• Corpus ID: 218515922

Attention Overload

@inproceedings{Cattaneo2020AttentionO,
  title={Attention Overload},
  author={M. D. Cattaneo and Paul Kim-ming Cheung and Xinwei Ma and Yusufcan Masatlioglu},
  year={2020}
}

• M. D. Cattaneo, P. Cheung, +1 author Yusufcan Masatlioglu
• Published 20 October 2021
• Economics

We introduce an Attention Overload Model that captures the idea that alternatives compete for the decision maker’s attention, and hence the attention frequency each alternative receives decreases as the choice problem becomes larger. Using this nonparametric restriction on the random attention formation, we show that a fruitful revealed preference theory can be developed, and provide testable implications on the observed choice behavior that can be used to partially identify the decision maker’s preference. Furthermore, we provide novel partial identification results on the underlying attention frequency, thereby offering the first nonparametric identification result of (a feature of) the random attention formation mechanism in the literature. Building on our partial identification results, for both preferences and attention frequency, we develop econometric methods for estimation and inference. Importantly, our econometric procedures remain valid even in settings with large number of alternatives and choice problems, an important feature of the economic environment we consider. We also provide a software package in R implementing our empirical methods, and illustrate them in a simulation study.