Toward Real-world Models of Trust. "A theory, ultimately, must be judged for its accord with reality. " GENERATED GLOSSARY: Trust 1997 note: This is a work document for open peer-review and public discussion. It may change frequently. Foreword: Trust is the problem. Understanding human trust is exactly what brought me to that great IT question in 1997: how can I trust a set of bytes? My answer, given in this original paper draft, provided a framework that has been useful in the field of information security. The framework allows us to use the concept of trust in a common heterogeneous environment, comprising humans and machines, where trust is understood exactly as what we humans call trust (e.g., as expected fulfillment of behavior) and bridges to machines in terms of qualified information based on factors independent of that information.
We realize that trust is essentially communicable. I call this principle the Trust Induction Principle: to induce trust, every action needs both a trusted introducer and a trusted witness. Trust-metrics - Implementation and analysis of trust metrics. Levente Buttyán / Publications. Bnt - Bayes Net Toolbox for Matlab. Graphical Models. By Kevin Murphy, 1998. "Graphical models are a marriage between probability theory and graph theory. They provide a natural tool for dealing with two problems that occur throughout applied mathematics and engineering -- uncertainty and complexity -- and in particular they are playing an increasingly important role in the design and analysis of machine learning algorithms.
Fundamental to the idea of a graphical model is the notion of modularity -- a complex system is built by combining simpler parts. Probability theory provides the glue whereby the parts are combined, ensuring that the system as a whole is consistent, and providing ways to interface models to data. The graph theoretic side of graphical models provides both an intuitively appealing interface by which humans can model highly-interacting sets of variables as well as a data structure that lends itself naturally to the design of efficient general-purpose algorithms. This tutorial We will briefly discuss the following topics. Harrison McKnight, Accounting & Information Systems Department, Michigan State University.