On the Impact of Bounded Rationality in Strategic Data Gathering

TL;DR

This paper investigates how bounded rationality affects data collection strategies from strategic agents with different information levels, proposing models and algorithms for optimal estimation under these conditions.

Contribution

It introduces a framework modeling various levels of strategic rationality and information among survey respondents, along with a design algorithm for optimal data estimation.

Findings

Modeling of three agent types with different strategic behaviors

Development of a design algorithm for estimation under bounded rationality

Provision of numerical results and open-source code for research

Abstract

We consider the problem of estimation from survey data gathered from strategic and boundedly-rational agents with heterogeneous objectives and available information. Particularly, we consider a setting where there are three different types of survey responders with varying levels of available information, strategicness, and cognitive hierarchy: i) a non-strategic agent with an honest response, ii) a strategic agent that believes everyone else is a non-strategic agent and that the decoder also believes the same, hence assumes a naive estimator, i.e., level-1 in cognitive hierarchy, iii) and strategic agent that believes the population is Poisson distributed over the previous types, and that the decoder believes the same. We model each of these scenarios as a strategic classification of a 2-dimensional source (possibly correlated source and bias components) with quadratic distortion measures and provide a design algorithm. Finally, we provide our numerical results and the code to obtain them for research purposes at https://github.com/strategic-quantization/bounded-rationality.