Empirical evidence indicates that people are inequity averse. However, it is unclear whether and how suffering unfairness impacts subsequent behavior. We investigated the consequences of unfair treatment in subsequent interactions with new interaction partners and the associated neural mechanisms. Participants were experimentally manipulated to experience fair or unfair treatment in the ultimat...

Previous research has shown that receiving an unfair monetary offer in economic bargaining elicits also-called feedback negativity (FN). This scalp-recorded brain potential probably reflects a bad-vs-good evaluation in the medial frontal cortex and has been linked to fundamental processes of reinforcement learning. In the present study, we investigated whether the evaluative mechanism indexed b...

In multiagent systems interaction protocols are usually enforced by law Enforcement is prob lematic among computational agents because they may operate under incomplete or di erent laws the laws may not be uniformly enforced and the agents can vanish easily This paper presents an enforcement free method for car rying out exchanges so that both agents are motivated to abide to their contract Thi...

In multiagent systems, interaction protocols are usually enforced by law. Enforcement is problematic among computational agents, because they may operate under incomplete or diierent laws, the laws may not be uniformly enforced, and the agents can vanish easily. This paper presents an enforcement free method for carrying out exchanges so that both agents are motivated to abide to their contract...

Punishing norm violations is considered an important motive during rejection of unfair offers in the ultimatum game (UG). The present study investigates the impact of the power to punish norm violations on people's responses to unfairness and associated neural correlates. In the UG condition participants had the power to punish norm violations, while an alternate condition, the impunity game (I...

This paper presents a novel context-based approach to filter out unfair recommendations for trust model in ubiquitous environments. Context is used in our approach to analyze the user’s activity, state and intention. Incremental learning based neural network is used to dispose the context in order to find doubtful recommendations. This approach has distinct advantages when dealing with randomly...

By 15 months of age infants are sensitive to violations of fairness norms as assessed via their enhanced visual attention to unfair versus fair outcomes in violation-of-expectation paradigms. The current study investigated whether 15-month-old infants select social partners on the basis of prior fair versus unfair behavior, and whether infants integrate social selections on the basis of fairnes...

The problem of unfair testimonies has to be addressed effectively to improve the robustness of reputation systems. We propose an integrated CLUstering-Based approach called iCLUB to filter unfair testimonies for reputation systems using multi-nominal testimonies, in multiagent-based electronic commerce. It adopts clustering and considers buying agents’ local and global knowledge about selling a...