Factoring the human decision-making limitations in mobile crowdsensing

In this paper we address the way crowds of mobile end users are matched with crowdsensing (MCS) tasks. We challenge two practices/assumptions that are dominant in the related literature. The first one consists in approaching the problem as an instance of optimal centralized assignment of users to tasks. We argue instead that it is more plausible to view this as a task selection problem on the user side, i.e., users select tasks, and cast it as a multi-attribute decision problem with many alternatives. The second monotonously repeated assumption confronted in this paper is that end users, either when bidding for a specific task or in the few cases that themselves select tasks, behave as fully rational agents (strategically) seeking to make optimal choices. We rather explicitly acknowledge the bounded rationality of human decision making as this results from the human cognition processes and their inherent limitations. We first summarize long research threads on cognitive decision-making in the fields of economics and psychology and iterate on models(heuristics) that mobile end users possibly activate when selecting of crowdsensing tasks. Then, we describe what these heuristics imply for the end-user characterization (profiling) process and how their activation can be inferred out of past choices. Finally, we draw on empirical data collected through an online questionnaire to conduct a comparative study of different models for MCS task selection.