<p>This paper presents a novel axiomatic approach to measuring and comparing hierarchical structures. Hierarchies are fundamental across a range of disciplines—from ecology to organizational science—yet existing measures of hierarchical degree often lack systematic criteria for comparison. We introduce a mathematically rigorous framework based on a simple partial pre-order over hierarchies, denoted as <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="355_2025_1582_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="31" /> </InlineMediaObject> <EquationSource Format="TEX">\(\succcurlyeq _H,\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <msub> <mo>≽</mo> <mi>H</mi> </msub> <mo>,</mo> </mrow> </math></EquationSource> </InlineEquation> and demonstrate its equivalence to intuitively appealing axioms for hierarchy comparisons. Our analysis yields three key results. First, we establish that for fixed-size hierarchies, one hierarchy is strictly more hierarchical than another according to <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="355_2025_1582_Article_IEq2.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="27" /> </InlineMediaObject> <EquationSource Format="TEX">\(\succcurlyeq _H\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mo>≽</mo> <mi>H</mi> </msub> </math></EquationSource> </InlineEquation> if the latter can be derived from the former through a series of subordination removals. Second, we fully characterize the hierarchical pre-orders that align with <InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="355_2025_1582_Article_IEq2.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="27" /> </InlineMediaObject> <EquationSource Format="TEX">\(\succcurlyeq _H\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mo>≽</mo> <mi>H</mi> </msub> </math></EquationSource> </InlineEquation> using two fundamental axioms: Anonymity and Subordination Removal. Finally, we extend our framework to varying-size hierarchies through the introduction of a Replication Principle, which enables consistent comparisons across different scales.</p>

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Measuring hierarchy

  • Oriol Carbonell-Nicolau

摘要

This paper presents a novel axiomatic approach to measuring and comparing hierarchical structures. Hierarchies are fundamental across a range of disciplines—from ecology to organizational science—yet existing measures of hierarchical degree often lack systematic criteria for comparison. We introduce a mathematically rigorous framework based on a simple partial pre-order over hierarchies, denoted as \(\succcurlyeq _H,\) H , and demonstrate its equivalence to intuitively appealing axioms for hierarchy comparisons. Our analysis yields three key results. First, we establish that for fixed-size hierarchies, one hierarchy is strictly more hierarchical than another according to \(\succcurlyeq _H\) H if the latter can be derived from the former through a series of subordination removals. Second, we fully characterize the hierarchical pre-orders that align with \(\succcurlyeq _H\) H using two fundamental axioms: Anonymity and Subordination Removal. Finally, we extend our framework to varying-size hierarchies through the introduction of a Replication Principle, which enables consistent comparisons across different scales.