TY - GEN
T1 - Using hierarchical bayesian models to learn about reputation
AU - Hendrix, Philip
AU - Gal, Ya'akov
AU - Pfeffer, Avi
PY - 2009/12/4
Y1 - 2009/12/4
N2 - This paper addresses the problem of learning with whom to interact in situations where obtaining information about others is associated with a cost, and this information is potentially unreliable. It considers settings in which agents need to decide whether to engage in a series of interactions with partners of unknown competencies, and can purchase reports about partners' competencies from others. The paper shows that Hierarchical Bayesian models offer a unified approach for (1) inferring the reliability of information providers, and (2) learning the competencies of individual agents as well as the general population. The performance of this model was tested in experiments of varying complexity, measuring agents' performance as well as error in estimating others' competencies. Results show that agents using the hierarchical model to make decisions outperformed other probabilistic models from the recent literature, even when there was a high ratio of unreliable information providers
AB - This paper addresses the problem of learning with whom to interact in situations where obtaining information about others is associated with a cost, and this information is potentially unreliable. It considers settings in which agents need to decide whether to engage in a series of interactions with partners of unknown competencies, and can purchase reports about partners' competencies from others. The paper shows that Hierarchical Bayesian models offer a unified approach for (1) inferring the reliability of information providers, and (2) learning the competencies of individual agents as well as the general population. The performance of this model was tested in experiments of varying complexity, measuring agents' performance as well as error in estimating others' competencies. Results show that agents using the hierarchical model to make decisions outperformed other probabilistic models from the recent literature, even when there was a high ratio of unreliable information providers
UR - http://www.scopus.com/inward/record.url?scp=70849092710&partnerID=8YFLogxK
U2 - 10.1109/CSE.2009.349
DO - 10.1109/CSE.2009.349
M3 - Conference contribution
AN - SCOPUS:70849092710
SN - 9780769538235
T3 - Proceedings - 12th IEEE International Conference on Computational Science and Engineering, CSE 2009
SP - 208
EP - 214
BT - Proceedings - 12th IEEE International Conference on Computational Science and Engineering, CSE 2009 - 2009 IEEE International Conference on Social Computing, SocialCom 2009
T2 - 2009 IEEE International Conference on Social Computing, SocialCom 2009
Y2 - 29 August 2009 through 31 August 2009
ER -