Contribution-Based Grouping under Noise
AbstractMany real-world mechanisms are “noisy” or “fuzzy”, that is the institutions in place to implement them operate with non-negligible degrees of imprecision and error. This observation raises the more general question of whether mechanisms that work in theory are also robust to more realistic assumptions such as noise. In this paper, in the context of voluntary contribution games, we focus on a mechanism known as “contribution-based competitive grouping”. First, we analyze how the mechanism works under noise and what happens when other assumptions such as population homogeneity are relaxed. Second, we investigate the welfare properties of the mechanism, interpreting noise as a policy instrument, and we use logit dynamic simulations to formulate mechanism design recommendations. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Nax, H.H.; Murphy, R.O.; Duca, S.; Helbing, D. Contribution-Based Grouping under Noise. Games 2017, 8, 50.
Nax HH, Murphy RO, Duca S, Helbing D. Contribution-Based Grouping under Noise. Games. 2017; 8(4):50.Chicago/Turabian Style
Nax, Heinrich H.; Murphy, Ryan O.; Duca, Stefano; Helbing, Dirk. 2017. "Contribution-Based Grouping under Noise." Games 8, no. 4: 50.