Reasoning about the Impacts of Information Sharing
This work addresses the challenge of strategic information sharing in multi-agent systems, but it appears incremental as it builds on existing concepts of trust and propagation models.
The paper tackles the problem of an agent deciding what information to share with neighbors in a communication graph to maximize its utility, considering that information can propagate and impact the agent positively or negatively. It results in a decision process framework that uses the agent's subjective beliefs and trust to modify messages for optimal benefit.
In this paper we describe a decision process framework allowing an agent to decide what information it should reveal to its neighbours within a communication graph in order to maximise its utility. We assume that these neighbours can pass information onto others within the graph. The inferences made by agents receiving the messages can have a positive or negative impact on the information providing agent, and our decision process seeks to identify how a message should be modified in order to be most beneficial to the information producer. Our decision process is based on the provider's subjective beliefs about others in the system, and therefore makes extensive use of the notion of trust. Our core contributions are therefore the construction of a model of information propagation; the description of the agent's decision procedure; and an analysis of some of its properties.