Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Trust and Distrust Definitions: One Bite at a Time
Proceedings of the workshop on Deception, Fraud, and Trust in Agent Societies held during the Autonomous Agents Conference: Trust in Cyber-societies, Integrating the Human and Artificial Perspectives
A Computational Model of Trust and Reputation for E-businesses
HICSS '02 Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS'02)-Volume 7 - Volume 7
An integrated trust and reputation model for open multi-agent systems
Autonomous Agents and Multi-Agent Systems
A model of a trust-based recommendation system on a social network
Autonomous Agents and Multi-Agent Systems
JCAT: a platform for the TAC market design competition
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: demo papers
Learning to trust in the competence and commitment of agents
Autonomous Agents and Multi-Agent Systems
Trust Theory: A Socio-Cognitive and Computational Model
Trust Theory: A Socio-Cognitive and Computational Model
| Hi-index | 0.00 |
Traders that operate in markets with multiple competing marketplaces can use learning to choose in which marketplace they will trade, and how much they will shout in that marketplace. If traders are able to share information with each other about their shout price and market choice over a social network, they can trend towards the market equilibrium more quickly, leading to higher profits for individual traders, and a more efficient market overall. However, if some traders share false information, profit and market efficiency can suffer as a result of traders acting on incorrect information. We present the Trading Agent Trust Model (TATM) that individual traders employ to detect deceptive traders and mitigate their influence on the individual's actions. Using the JCAT double-auction simulator, we assess TATM by performing an experimental evaluation of traders sharing information about their actions over a social network in the presence of deceptive traders. Results indicate that TATM is effective at mitigating traders sharing false information, and can increase the profit of TATM traders relative to non-TATM traders.