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Intell. Syst. Technol."],"published-print":{"date-parts":[[2025,2,28]]},"abstract":"<jats:p>\n            In this article, we explore the Mechanism Design aspects of the Maximum Vertex-Weighted\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(b\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            -matching (MVbM) problem on bipartite graphs\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\((A\\cup T,E)\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            . The set\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(A\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            comprises agents, while\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(T\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            represents tasks. The set\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(E\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            , which connects\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(A\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            and\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(T\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            , is the private information of either agents or tasks. In this framework, we investigate three mechanisms\u2014\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathbb{M}_{BFS}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            ,\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathbb{M}_{DFS}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            , and\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathbb{M}_{G}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            . We examine scenarios in which either agents or tasks are strategic and report their adjacent edges to one of the three mechanisms. In both cases, we assume that the strategic entities are bounded by their statements: They can hide edges, but they cannot report edges that do not exist. First, we consider the case in which agents can manipulate. In this framework,\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathbb{M}_{BFS}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            and\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathbb{M}_{DFS}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            are optimal but not truthful. By characterizing the Nash Equilibria induced by\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathbb{M}_{BFS}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            and\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathbb{M}_{DFS}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            , we reveal that both mechanisms have a Price of Anarchy (\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(PoA\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            ) and Price of Stability (\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(PoS\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            ) of\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(2\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            . These efficiency guarantees are tight; no deterministic mechanism can achieve a lower\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(PoA\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            or\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(PoS\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            . In contrast, the third mechanism,\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathbb{M}_{G}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            , is not optimal, but truthful and its approximation ratio is\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(2\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            . We demonstrate that this ratio is optimal; no deterministic and truthful mechanism can outperform it. We then shift our focus to scenarios where tasks can exhibit strategic behavior. In this case,\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathbb{M}_{BFS}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            ,\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathbb{M}_{DFS}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            , and\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathbb{M}_{G}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            all maintain truthfulness, making\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathbb{M}_{BFS}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            and\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathbb{M}_{DFS}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            truthful and optimal mechanisms. In conclusion, we investigate the manipulability of\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathbb{M}_{BFS}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            and\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathbb{M}_{DFS}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            through experiments on randomly generated graphs. We observe that (i)\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathbb{M}_{BFS}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            is less prone to be manipulated by the first agent than\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathbb{M}_{DFS}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            , and (ii)\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathbb{M}_{BFS}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            is more manipulable on instances in which the total capacity of the agents is equal to the number of tasks.\n            <jats:xref ref-type=\"fn\">\n              <jats:sup>1<\/jats:sup>\n            <\/jats:xref>\n          <\/jats:p>","DOI":"10.1145\/3702650","type":"journal-article","created":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T15:24:19Z","timestamp":1730906659000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Edge Manipulations for the Maximum Vertex-Weighted Bipartite\n            <i>b<\/i>\n            -matching"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4285-8887","authenticated-orcid":false,"given":"Gennaro","family":"Auricchio","sequence":"first","affiliation":[{"name":"University of Bath, Bath, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8697-3089","authenticated-orcid":false,"given":"Jun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Beijing Normal University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5661-9875","authenticated-orcid":false,"given":"Qun","family":"Ma","sequence":"additional","affiliation":[{"name":"Tianjin University, Tianjin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5348-7671","authenticated-orcid":false,"given":"Jie","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Bath, Bath, United Kingdom"}]}],"member":"320","published-online":{"date-parts":[[2025,1,20]]},"reference":[{"issue":"4","key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1007\/s00182-005-0215-7","article-title":"College admissions with affirmative action","volume":"33","author":"Abdulkadiro\u011flu Atila","year":"2005","unstructured":"Atila Abdulkadiro\u011flu. 2005. 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