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Gerrymandering personnel selection systems is defined as those practices explicitly intended to improve the outcomes of individual applicants or groups of applicants to the detriment of other individuals and groups. Gerrymandering can serve a variety of purposes, such as enhancing demographic diversity, rewarding political allies, or giving hiring preference to specific individuals. This paper reviews eleven strategies for gerrymandering personnel selection systems. The strategies fall into three categories of decisions: decisions in choosing or designing personnel selection tools; decisions in scoring personnel selection tools; decisions in selecting among passing applicants. Perspectives on the appropriateness and ethics of gerrymandering are then reviewed.  相似文献   
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We show that optimal partisan redistricting with geographical constraints is a computationally intractable (NP-complete) problem. In particular, even when voter's preferences are deterministic, a solution is generally not obtained by concentrating opponent's supporters in “unwinnable” districts (“packing”) and spreading one's own supporters evenly among the other districts in order to produce many slight marginal wins (“cracking”).  相似文献   
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We study axioms which define “representative democracy” in an environment in which agents vote over a finite set of alternatives. We focus on a property that states that whether votes are aggregated directly or indirectly makes no difference. We call this property representative consistency. Representative consistency formalizes the idea that a voting rule should be immune to gerrymandering. We characterize the class of rules satisfying unanimity, anonymity, and representative consistency. We call these rules “partial priority rules.” A partial priority rule can be interpreted as a rule in which each agent can “veto” certain alternatives. We investigate the implications of imposing other axioms to the list specified above. We also study the partial priority rules in the context of specific economic models.  相似文献   
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