Incentive Networks

AAAI 2015: 29th AAAI Conference on Artificial Intelligence, Austin, Texas |

Published by AAAI - Association for the Advancement of Artificial Intelligence

In a basic economic system, each participant receives a (financial) reward according to his own contribution. In this work, we study an alternative approach – Incentive Networks – in which a participant’s reward depends not only on his own contribution; but also in part on the contributions made by his social contacts or friends. We show that the key parameter effecting the efficiency of such an Incentive Network-based economic system depends on the participant’s degree of directed altruism. Directed altruism is the extent to which someone is willing to work if his work results in a payment to his friend, rather than to himself. Specifically, we characterize the condition under which an Incentive Network-based economy is more efficient than the basic ”pay-for-your-own-contribution” economy. We quantify by how much incentive networks can reduce the total reward that needs to be paid to the participants. Finally, we study the impact of the network topology and various exogenous parameters on the efficiency of Incentive Networks. Our results suggest that in many practical settings, Incentive Network-based reward systems can be more efficient than the ubiquitous ’pay-for-your-own-contribution’ schemes.