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The Collective Trust Game: An Online Group Adaptation of the Trust Game Based on the HoneyComb Paradigm
Published on: October 20, 2022
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Trust and distrust in contradictory information transmission
Giuseppe Primiero1, Franco Raimondi1, Michele Bottone1
11Department of Computer Science, Middlesex University, the Borroughs, London, NW4 4BT UK.
Applied Network Science
|November 17, 2018
Summary
This study models information spread in trust networks using SecureNDsim. It reveals how agent trust, confirmation costs, and ranking influence consensus and information accuracy.
Area of Science:
- Artificial Intelligence
- Network Science
- Epistemology
Background:
- Information distribution in networks with positive and negative trust is complex.
- Agents possess varying epistemic attitudes and hierarchical rankings.
- Trust dynamics impact information verification and consensus.
Purpose of the Study:
- To model and analyze contradictory information distribution in agent networks.
- To investigate the role of positive and negative trust in information propagation.
- To evaluate epistemic costs and consensus-reaching conditions.
Main Methods:
- Development of a natural deduction system, SecureNDsim.
- Implementation of SecureNDsim in a NetLogo simulation.
- Experimental testing of formal properties and agent interactions.
Main Results:
- Identified conditions for consensus-reaching transmissions.
- Quantified epistemic costs associated with confirmation and rejection.
- Analyzed the impact of agent ranking on consensus and costs.
Conclusions:
- SecureNDsim provides a formal framework for trust network analysis.
- Agent ranking significantly influences network consensus and epistemic costs.
- The study offers insights into managing information flow and trust in decentralized systems.
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