On approximately fair allocations of indivisible goods

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Handbook of Computational Social Choice (2016, methodology): “Any deterministic algorithm would require an exponential number of queries to compute any finite approximation for the minimal envy allocation (Lipton et al., 2004) or the maxmin allocation (Golovin, 2005).”
On the Complexity of Efficiency and Envy-Freeness in Fair Division of Indivisible Goods with Additive Preferences (2009, background): “Another solution is to relax envy-freeness or Pareto-efficiency, such as in [7], where envy-freeness is replaced by a measure of envy, and where allocations are only required to be complete (that is, all objects must be allocated) instead of being Pareto-efficient.”
The Santa Claus problem (2006, neutral): “Tocopyotherwise,torepublish,topostonserversortoredistributetolists,requirespriorspeci.cpermissionand/orafee.”
A Short Introduction to Computational Social Choice (2007, background): “[65], who study approximation schemes for envy-freeness, and Bouveret et al.”
Max-min fair allocation of indivisible goods (2005, neutral): “[20].”
Fair Public Decision Making (2016, background): “One starting point to achieving Prop1 and PO is the maximum Nash welfare (MNW) solution, which, for private goods division, is known to satisfy the similar guarantee of EF1 and PO [14].”