Thirty years ago, psychologist Daniel Kahneman and economists Jack Knetsch, and Richard Thaler (1986a & b) proposed that fairness matters in the marketplace. They presented evidence-based community rules of fairness that are encapsulated in the “principle of dual entitlement”; consumers are entitled to the reference transaction (e.g., price) and, similarly, the firm is entitled to its reference...

Without appropriately fair buffer management in the network, TCP encounters inherent unfairness towards connections with long round-trip times. This paper proposes a proportional fairness algorithm to correct the bias against these long connections. The algorithm is proven to converge to the fair point using Lyapunov Function. The basic algorithm is further modified to smooth the bursts of traf...

The objective of this paper is to bridge the gap between ACP and TCSP. To this end, ACP is extended with two non-deterministic choice operators in a setting of bisimulation semantics. With these operators, we can express safety properties of systems without the use of silent steps, and we can verify safety properties in a setting in which no assumption on fairness (or unfairness) has been made.

Experimental manipulation of serotonin (5-HT) availability has been shown to modulate social behavior. For instance, serotonin depletion increased the rejection rates of unfair offers in the ultimatum game (UG), whereas a single dose of the serotonin reuptake inhibitor (citalopram) decreased rejection rates. These effects were observed immediately after the manipulation. The aim of this study w...

The potential for machine learning systems to amplify social inequities and unfairness is receiving increasing popular academic attention. Much recent work has focused on developing algorithmic tools assess mitigate such unfairness. However, there little enhancing fairness in graph algorithms. Here, we develop a simple, effective general method, CrossWalk, that enhances of various algorithms, i...

Our research is a variant of the third party punishment game. In particular, we want to test whether players have heterogeneous preferences; the levels of the sanction and of the transfer are proportional to the unfairness of the Dictator; the change of the role influences the Observer’s reaction to unfair behavior; players’ decision to punish the Dictator and/or to help the Receiver depends on...

Abstract The increasing use of data-driven decision support systems in industry and governments is accompanied by the discovery a plethora bias unfairness issues outputs these systems. Multiple computer science communities, especially machine learning, have started to tackle this problem, often developing algorithmic solutions mitigate biases obtain fairer outputs. However, one core underlying ...