Motivation-Aware Aggregation: Personalized Truth Inference Based on Participant Psychology

Current aggregation techniques reviewed by Difallah and Checco (2021) largely assume uniform participant motivation or use simple quality metrics, while qualitative studies like Manton et al. (2019) reveal that participant motivations are complex and varied, ranging from altruism to financial gain to social approval. This research challenges the core aggregation assumption that all participants are equally motivated to provide accurate information. I propose developing a motivation-aware truth inference system that models how different psychological profiles affect contribution reliability. Drawing from the Health Belief Model constructs identified by Dillon et al. (2020) and the theory of planned behavior framework from Manton et al., the system would infer participant motivation types through behavioral patterns and adjust aggregation weights accordingly. For example, financially-motivated participants might be more reliable on objective tasks but biased on subjective evaluations, while altruistically-motivated participants might show opposite patterns. This represents a fundamental shift from treating participants as interchangeable data points to recognizing them as heterogeneous agents whose psychological characteristics systematically influence their contributions. The approach could dramatically improve aggregation accuracy, especially for complex tasks where motivation and task type interact in non-obvious ways.

References:

1. Aggregation Techniques in Crowdsourcing: Multiple Choice Questions and Beyond. D. Difallah, Alessandro Checco (2021). International Conference on Information and Knowledge Management.
2. Qualitative study investigating the underlying motivations of healthy participants in phase I clinical trials. K. Manton, C. Gauld, K. White, P. Griffin, S. Elliott (2019). BMJ Open.
3. Motivations for Treatment Engagement in a Residential Substance Use Disorder Treatment Program: A Qualitative Study. Patrick J. Dillon, Satish K. Kedia, Oluwaseyi O. Isehunwa, M. Sharma (2020). Substance abuse : research and treatment.