Asymptotic Optimality of Myopic Optimization in Trial-Offer Markets with Social Influence
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 2458-2463.
Abstract
We study dynamic trial-offer markets, in which participants first try a product and later decide whether to purchase it or not. In these markets, social influence and position biases have a greater effect on the decisions taken in the sampling stage than those in the buying stage. We consider a myopic policy that maximizes the market efficiency for each incoming participant, taking into account the inherent quality of products, position biases, and social influence. We prove that this myopic policy is optimal and predictable asymptotically.
@inproceedings{ijcai2016p350,
title = {Asymptotic Optimality of Myopic Optimization in Trial-Offer Markets with Social Influence},
author = {Abeliuk, Andres and Berbeglia, Gerardo and Maldonado, Felipe and Van Hentenryck, Pascal},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {2458--2463},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/350},
}
TY - CPAPER TI - Asymptotic Optimality of Myopic Optimization in Trial-Offer Markets with Social Influence AU - Abeliuk, Andres AU - Berbeglia, Gerardo AU - Maldonado, Felipe AU - Van Hentenryck, Pascal T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 2458 EP - 2463 UR - https://ijcai.org/proceedings/2016/350 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
