Analysis of Conflicting Claims
Average-of-awards rule
Summary of the division rules
Adjusted proportional rule
Average rule
Properties of the rules
Concede-and-divide rule
Constrained egalitarian rule
Constrained equal awards rule
Constrained equal losses rule
Coalitional game associated with a claims problem
Cumulative awards curve
Deviation index
Dominguez-Thomson rule
Dynamic plot
Gini index
Index path
The Lorenz curve
Lorenz-dominance relation
Minimal overlap rule
The path of awards for two claimants
The path of awards for three claimants
Piniles' rule
Plot of an awards vector
Proportional rule
Claims problem data
Random arrival rule
Reverse Talmud rule
Schedules of awards of a rule
Schedules of awards of several rules
Set of awards vectors for a claims problem
Talmud rule
Vertical rule plot
Volume of the set of awards vectors
The analysis of conflicting claims arises when an amount has to be divided among a set of agents with claims that exceed what is available. A rule is a way of selecting a division among the claimants. This package computes the main rules introduced in the literature from ancient times to the present. The inventory of rules covers the proportional and the adjusted proportional rules, the constrained equal awards and the constrained equal losses rules, the constrained egalitarian, the Piniles’ and the minimal overlap rules, the random arrival and the Talmud rules. Besides, the Dominguez and Thomson and the average-of-awards rules are also included. All of them can be found in the book by W. Thomson (2019), How to divide when there isn't enough. From Aristotle, the Talmud, and Maimonides to the axiomatics of resource allocation', except for the average-of-awards rule, introduced by Mirás Calvo et al. (2022), <doi:10.1007/s00355-022-01414-6>. In addition, graphical diagrams allow the user to represent, among others, the set of awards, the paths of awards, the schedules of awards of a rule, and some indexes. A good understanding of the similarities and differences between the rules is useful for better decision-making. Therefore, this package could be helpful to students, researchers, and managers alike. For a more detailed explanation of the package, see Mirás Calvo et al. (2023), <doi:10.1016/j.dajour.2022.100160>.