A paper “Learning decision trees with flexible constraints and objectives using integer optimization” has been accepted by CPAIOR 2017!
Abstract: Task allocation problems have focused on achieving one-shot optimality. In practice, many task allocation problems are of repeated nature, where the allocation outcome of previous rounds may influence the participation of agents in subsequent rounds, and consequently, the quality of the allocations in the long term. We investigate how allocation influences agents’ decision to participate using prospect theory, and simulate how agents’ participation affects the system’s long term social welfare. We compare two task allocation algorithms in this study, one only considering optimality in terms of costs and the other considering optimality in terms of primarily fairness and secondarily costs. The simulation results demonstrate that fairness incentivizes agents to keep participating and consequently leads to a higher social welfare.
Qing Chuan Ye and Yingqian Zhang. Participation behavior and social welfare in repeated task allocations. IEEE International Conference on Agents. 2016. to appear.
(Download paper here)
Keywords: Agent based modelling and simulation; Repeated task allocation; Prospect theory; Fairness;Participation behavior.