The influence maximisation game

Chakraborty, Sukankana, Stein, Sebastian, Swami, Ananthram, Jones, Matthew and Hill, Lewis (2023) The influence maximisation game. In AAMAS '23: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). pp. 3059-3061 .

Abstract

The problem of influence maximisation investigates efficient ways in which external influence (typically limited by resources) can be applied to a social network to maximise control over the global behaviours of a population. It is an effective tool that finds its application in many real-world scenarios, for instance it can be used to gather intelligence in crowdsourcing activities and to incentivise people to adopt desirable public policies. While the problem has been studied extensively in theoretical settings, many of these approaches can be expensive and inefficient to apply in the real world, particularly when considering an unknown or irrational competitor. The influence maximisation game was designed to bridge this gap between theory and the practical application of this knowledge. In this experiment, human subjects are presented with networks where they can employ their own tactics to maintain maximum influence against a competitor (which in this case is an AI agent). We aim to determine how people strategise to spread influence in the real world. In particular, we determine if people always act rationally in these settings or if their strategies are inherently biased \textemdash in which case we aim to identify inexpensive, yet effective strategies that can outperform these biased strategies. Observing how people strategise in the real world can help us modify our theoretical results for more efficient practical applications.

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Contributors

Author: Sukankana Chakraborty

Author: Sebastian Stein

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