Abstract
In a bankruptcy situation individuals are not equally affected since each one has its own specific characteristics. These aspects cannot be ignored and may justify an allocation bias in favor of or against some individuals. This paper develops a theory of differentiation in claims problems that considers not only the vector of claims, but also some justified differentiating criteria based on other characteristics (wealth, net-income, GDP, etc.). Accordingly, we propose some progressive transfers from richer to poorer claimants with the purpose of distributing the damage as evenly as possible. Finally, we characterize our solution by means of the Lorenz criterion. Endogenous convex combinations between solutions are also considered.

Abstract
Agents involved in a conflicting claims problem may be concerned with the proportion of their claims that is satisfied, or with the total amount they get. In order to relate both perspectives, we associate to each conflicting claims problem a bargaining-in-proportions set. Then, we obtain a correspondence between classical bargaining solutions and usual claims rules. In particular, we show that the constrained equal losses, the truncated constrained equal losses and the contested garment (Babylonian Talmud) rules can be obtained throughout the Nash bargaining solution. © 2015 by the authors.

Abstract
In the present paper we characterize the optimal use of Poisson signals to establish incentives in the "bad" and "good" news models of Abreu et al. (1991). In the former, for small time intervals the signals' quality is high and we observe a "selective" use of information; otherwise there is a "mass" use. In the latter, for small time intervals the signals' quality is low and we observe a "fine" use of information; otherwise there is a "non-selective" use.

Abstract
In many economic situations, individuals with different bargaining power must agree on how to divide a given resource. For instance, in the dictator game the proposer has all the bargaining power. In spite of it, the majority of controlled experiments show that she shares an important amount of the resource with the receiver. In the present paper I consider how behavioural and psychological internal conflicting aspects, such as self-interest and equity concerns, determine the split of the resource. The individual allocation proposals are aggregated in terms of altruism and value for the resource under dispute to obtain a single allocation. The resulting allocation rule is generalized to the n-individuals case through efficiency and consistency. Finally, I show that it satisfies a set of desirable properties. The obtained results are of practical interest for a number of situations, such as river sharing problems, sequential allocation and rationing problems. © 2017 Elsevier B.V.

Abstract
In this paper, we consider a sequential allocation problem with n individuals. The first individual can consume any amount of a resource, leaving the remainder for the second individual, and so on. Motivated by the limitations associated with the cooperative or non-cooperative solutions, we propose a new approach from basic definitions of representativeness and equal treatment. The result is a unique asymptotic allocation rule for any number of individuals. We show that it satisfies a set of desirable properties. © 2016, Springer Science+Business Media Dordrecht.

Abstract
The complexity and subjectivity of the judgement task conceals the existence of biases that undermines the quality of the process. This paper presents a weighted aggregation function that attempts to reduce the influence of biased judgements on the final score. We also discuss a set of desirable properties. The proposed weighted aggregation function is able to correct the “nationalism bias” found by Emerson et al. (Am Stat 63(2):124–131, 2009) in the 2000 Olympic Games diving competition and suggest the possibility of a “reputation bias”. Our results can be applied to judgement sports and other activities that require the aggregation of several personal evaluations. © 2016, Springer Science+Business Media New York.