Reasoning about the impacts of information sharing

Shared information can benefit an agent, allowing others to aid it in its goals. However, such information can also harm, for example when malicious agents are aware of these goals, and can then thereby subvert the goal-maker's plans. 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 assess 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 with regards to the likelihood that a message will be passed on by the receiver, and the likelihood that an agent will use the information against the provider. 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

Risk, uncertainty and possible worlds

Risk is an important and ubiquitous concept that plays a crucial role in decision makings across domains. Risk is also a vague notion that carries different meaning under different domain context and perspectives. This paper aims to provide a formal generalised definition of risk based on the possible world paradigm and expected utility theory and the meanings of risk from both qualitative and quantitative level. This definition of risk is developed from the perspective an intelligent agent or information system. It provides a solid theoretical foundation upon which we can construct an intelligent generalised risk modelling and management framework using techniques from Artificial Intelligence research. This framework can be implemented as an integral part of an information system for better decision support and management of businesses. © 2011 IEEE