Behavioural economists have shown that people are often averse to inequality and will make choices to avoid unequal outcomes. In this paper, we consider how to allocate indivisible goods fairly to agents with additive utilities, so as to minimize inequality. We consider how this interacts with axiomatic properties such as envy-freeness, Pareto efficiency and strategy-proofness. We also consider the computational complexity of computing allocations minimizing inequality. Unfortunately, this is computationally intractable in general so we consider several tractable mechanisms that minimize greedily the inequality. Finally, we run experiments to explore the performance of these mechanisms.
Criteria for measuring economic inequality, such as the Lorenz curve and the Gini index, are widely used in the social sciences but have hardly been explored in Multiagent Systems, even though the significance of other concepts from fair division is widely accepted in the field. In a departure from the standard model used in Economics, we apply inequality criteria to allocation problems with indivisible goods, i.e., to the kind of problem typically analysed in Multiagent Systems. This gives rise to the combinatorial optimisation problem of computing an allocation that reduces inequality with respect to an initial allocation (and the closely related problem of minimising inequality), for a chosen inequality measure. We define this problem, we discuss the computational complexity of various aspects of it, and we formulate a generic approach to designing modular algorithms for solving it using integer programming. We analyse the problem of finding an allocation of resources in a multiagent system that is as fair as possible in terms of minimising inequality between the utility levels enjoyed by the individual agents. We use the well-known Atkinson index to measure inequality and we focus on the distributed approach to multiagent resource allocation, where new allocations emerge as the result of a sequence of local deals between groups of agents agreeing on an exchange of some of the items in their possession. Our results show that it is possible to design systems that provide theoretical guarantees for optimal outcomes that minimise inequality, but also that in practice there are significant computational hurdles to be overcome: finding an optimal allocation is computationally intractable---independently of the approach chosen---and large numbers of potentially highly complex deals may be required under the distributed approach. From a methodological point of view, while much work in multiagent resource allocation relies on combinatorial arguments, here we use insights from basic calculus.