Towards a Theory of Trust in Networks of Humans and Computers

  • Virgil Gligor
  • Jeannette M. Wing
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7114)


We argue that a general theory of trust in networks of humans and computers must be build on both a theory of behavioral trust and a theory of computational trust. This argument is motivated by increased participation of people in social networking, crowdsourcing, human computation, and socio-economic protocols, e.g., protocols modeled by trust and gift-exchange games [3,10,11], norms-establishing contracts [1], and scams [6,35,33]. User participation in these protocols relies primarily on trust, since on-line verification of protocol compliance is often impractical; e.g., verification can lead to undecidable problems, co-NP complete test procedures, and user inconvenience. Trust is captured by participant preferences (i.e., risk and betrayal aversion) and beliefs in the trustworthiness of other protocol participants [11,10]. Both preferences and beliefs can be enhanced whenever protocol noncompliance leads to punishment of untrustworthy participants [11,23]; i.e., it seems natural that betrayal aversion can be decreased and belief in trustworthiness increased by properly defined punishment [1]. We argue that a general theory of trust should focus on the establishment of new trust relations where none were possible before. This focus would help create new economic opportunities by increasing the pool of usable services, removing cooperation barriers among users, and at the very least, taking advantage of “network effects.” Hence a new theory of trust would also help focus security research in areas that promote trust-enhancement infrastructures in human and computer networks. Finally, we argue that a general theory of trust should mirror, to the largest possible extent, human expectations and mental models of trust without relying on false methaphors and analogies with the physical world.


Risk Aversion Recommendation System Social Preference Risk Preference Computer Security 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Virgil Gligor
    • 1
  • Jeannette M. Wing
    • 1
  1. 1.Carnegie Mellon UniversityPittsburghUSA

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