Generalizing Trust: Inferencing Trustworthiness from Categories

  • Rino Falcone
  • Cristiano Castelfranchi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5396)

Abstract

In this paper, starting from a socio-cognitive model of trust, we analyze how it is possible to predict how/when an agent who trusts something/ someone will therefore trust something/someone else, before and without a direct experience. On the contrary of models of trust just based on (or reduced to) a probability index or a simple measure of experience and frequency, we are interested to analyze the trust concept so that we are able to cope with problems like: a) given X’s evaluation about Y’s trustworthiness on a specific task τ, what can we say onX’s evaluation about Y’s trustworthiness on a different but analogous task τ’? What would we intend for an analogous task? b) Given X’s evaluation about Y’s trustworthiness on a specific task τ, what can we say on X’s evaluation about the trustworthiness of a different agent Z on the same task τ? In fact, in our view only a cognitive model of trust, with its analytical power, seems able to account for the inferential generalization of trustworthiness from task to task and from agent to agent not just based on specific experience and/or learning.

Keywords

Trust Class and Task Generalizations Inference Reasoning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Rino Falcone
    • 1
  • Cristiano Castelfranchi
    • 1
  1. 1.National Research Council– Institute of Cognitive Sciences and TechnologiesRomaItaly

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