TY - GEN
T1 - Modeling self-interested information diffusion with game theory on graphs
AU - Hudack, Jeffrey
AU - Gemelli, Nathaniel
AU - Oh, Jae
PY - 2014
Y1 - 2014
N2 - We model information diffusion through social networks using a game-theoretic paradigm. Our work focuses on the pairwise interactions between individuals and their social contacts, allowing each agent to make local decisions to maximize individual gain. This fully distributed approach is driven only by local utility and differs from many existing models that treat diffusion as a network process that occurs passively. Agents are inherently selfish, acting only to benefit from obtaining new information and from providing contacts with information that is new to them. Framed using game theory on graphs, we present a model that allows for parameterization of individual preference and models of pairwise interaction. We observe the effects of graph structure, incomplete information, and sharing cost on the model. We show that spatially organized graphs, due to their degree distribution, are much more resilient to higher costs of sharing. Additionally, we show how incomplete information often leads to more active agents at the cost of individual payoff. Finally, we provide insight into a number of extensions to this model that will allow for simulation of various diffusion phenomenon.
AB - We model information diffusion through social networks using a game-theoretic paradigm. Our work focuses on the pairwise interactions between individuals and their social contacts, allowing each agent to make local decisions to maximize individual gain. This fully distributed approach is driven only by local utility and differs from many existing models that treat diffusion as a network process that occurs passively. Agents are inherently selfish, acting only to benefit from obtaining new information and from providing contacts with information that is new to them. Framed using game theory on graphs, we present a model that allows for parameterization of individual preference and models of pairwise interaction. We observe the effects of graph structure, incomplete information, and sharing cost on the model. We show that spatially organized graphs, due to their degree distribution, are much more resilient to higher costs of sharing. Additionally, we show how incomplete information often leads to more active agents at the cost of individual payoff. Finally, we provide insight into a number of extensions to this model that will allow for simulation of various diffusion phenomenon.
KW - Game theory
KW - Information diffusion
KW - Multi-agent systems
KW - Social networks
UR - http://www.scopus.com/inward/record.url?scp=84902320291&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84902320291&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84902320291
SN - 9789897580161
T3 - ICAART 2014 - Proceedings of the 6th International Conference on Agents and Artificial Intelligence
SP - 215
EP - 222
BT - ICAART 2014 - Proceedings of the 6th International Conference on Agents and Artificial Intelligence
PB - SciTePress
T2 - 6th International Conference on Agents and Artificial Intelligence, ICAART 2014
Y2 - 6 March 2014 through 8 March 2014
ER -