Explore Truthful Incentives for Tasks with Heterogenous Levels of Difficulty in the
Sharing Economy
Abstract
Incentives are explored in the sharing economy to
inspire users for better resource allocation. Previous works build a budget-feasible incentive mechanism to learn users’ cost distribution. However,
they only consider a special case that all tasks are
considered as the same. The general problem asks
for finding a solution when the cost for different
tasks varies. In this paper, we investigate this general problem by considering a system with k levels
of difficulty. We present two incentivizing strategies for offline and online implementation, and formally derive the ratio of utility between them in different scenarios. We propose a regret-minimizing
mechanism to decide incentives by dynamically adjusting budget assignment and learning from users’
cost distributions. Our experiment demonstrates
utility improvement about 7 times and time saving
of 54% to meet a utility objective compared to the
previous works