Propositional knowledge base revision and minimal change
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On the logic of iterated belief revision
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Quatum logic, Hilbert space, revision theory
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Reasoning about Information Change
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On a Linear Representation Theory for Quantitative Belief Change
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A Formal Model of Multi-Agent Belief-Interaction
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Minimum payments that reward honest reputation feedback
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Eliciting Informative Feedback: The Peer-Prediction Method
Management Science
Robust mechanisms for information elicitation
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Complexity of mechanism design
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Reputation-Based service level agreements for web services
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Enforcing truthful strategies in incentive compatible reputation mechanisms
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Robust solutions of uncertain linear programs
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Incentives for expressing opinions in online polls
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Truthful opinions from the crowds
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Mechanisms for making crowds truthful
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Reputation in multi agent systems and the incentives to provide feedback
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Peer prediction without a common prior
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Eliciting high quality feedback from crowdsourced tree networks using continuous scoring rules
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Crowdsourced judgement elicitation with endogenous proficiency
Proceedings of the 22nd international conference on World Wide Web
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In this paper, we discuss the dynamics of multi-agent belief change in the framework of linear algebra. We regard an epistemic state of each agent as an element in the vector space spanned by the basis of possible worlds, so that belief change corresponds to a linear transformation on this vector space. The compound belief states of multi-agents are treated by using the product tensor of the vector for each agent. In this formulation, the reasoning in the process of belief change can be reduced to the matrix and tensor calculation.