Two-layered fuzzy logic-based model for predicting court decisions in construction contract disputes
The dynamic nature and increasing complexity of the construction industry have led to increased conflicts in construction projects. An accurate prediction of the outcome of a dispute resolution in courts could effectively reduce the number of disputes that would otherwise conclude by spending more m...
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| Veröffentlicht in: | Artificial intelligence and law 2021-12, Vol.29 (4), p.453-484 |
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| container_title | Artificial intelligence and law |
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| creator | Bagherian-Marandi, Navid Ravanshadnia, Mehdi Akbarzadeh-T, Mohammad-R. |
| description | The dynamic nature and increasing complexity of the construction industry have led to increased conflicts in construction projects. An accurate prediction of the outcome of a dispute resolution in courts could effectively reduce the number of disputes that would otherwise conclude by spending more money through litigation. This study aims to introduce a two-layered fuzzy logic model for predicting court decisions in construction contract disputes. 100 cases of construction contract disputes are selected from the courts of Iran. A questionnaire survey is then conducted to extract a set of fuzzy rules for identifying important decision parameters and expert knowledge. Accordingly, a two-layered fuzzy logic-based decision-making architecture is proposed for the prediction model. Furthermore, the fuzzy system is trained based on 10-fold cross-validation. Analysis of results indicates that 51 out of the 100 cases are filed after the dissolution and termination of the contract show a significant impact of these clauses as the root cause in construction contract disputes. Our results present a proposed hierarchical fuzzy system that can correctly predict nearly 60% of the test data. Also, we demonstrate a methodology of using argument before ML to establish interpretable AI models. Based on our findings, a fuzzy model with a hierarchical structure may be used as a simple and efficient method for predicting court decisions in construction contract disputes. |
| doi_str_mv | 10.1007/s10506-021-09281-9 |
| format | Article |
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An accurate prediction of the outcome of a dispute resolution in courts could effectively reduce the number of disputes that would otherwise conclude by spending more money through litigation. This study aims to introduce a two-layered fuzzy logic model for predicting court decisions in construction contract disputes. 100 cases of construction contract disputes are selected from the courts of Iran. A questionnaire survey is then conducted to extract a set of fuzzy rules for identifying important decision parameters and expert knowledge. Accordingly, a two-layered fuzzy logic-based decision-making architecture is proposed for the prediction model. Furthermore, the fuzzy system is trained based on 10-fold cross-validation. Analysis of results indicates that 51 out of the 100 cases are filed after the dissolution and termination of the contract show a significant impact of these clauses as the root cause in construction contract disputes. Our results present a proposed hierarchical fuzzy system that can correctly predict nearly 60% of the test data. Also, we demonstrate a methodology of using argument before ML to establish interpretable AI models. Based on our findings, a fuzzy model with a hierarchical structure may be used as a simple and efficient method for predicting court decisions in construction contract disputes.</description><identifier>ISSN: 0924-8463</identifier><identifier>EISSN: 1572-8382</identifier><identifier>DOI: 10.1007/s10506-021-09281-9</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Artificial Intelligence ; Building ; Computer Science ; Construction contracts ; Construction industry ; Contracts ; Court decisions ; Decision making ; Fuzzy algorithms ; Fuzzy logic ; Fuzzy sets ; Fuzzy systems ; Information Storage and Retrieval ; Intellectual Property ; IT Law ; Judgments ; Legal Aspects of Computing ; Litigation ; Media Law ; Mediation ; Original Research ; Parameter identification ; Philosophy of Law ; Prediction models ; Structural hierarchy</subject><ispartof>Artificial intelligence and law, 2021-12, Vol.29 (4), p.453-484</ispartof><rights>The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature 2021</rights><rights>COPYRIGHT 2021 Springer</rights><rights>The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c386t-bc8ddf235a41802bc25b16182a1a542528164c11a49833c438de22b67e8dcb5e3</citedby><cites>FETCH-LOGICAL-c386t-bc8ddf235a41802bc25b16182a1a542528164c11a49833c438de22b67e8dcb5e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10506-021-09281-9$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10506-021-09281-9$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Bagherian-Marandi, Navid</creatorcontrib><creatorcontrib>Ravanshadnia, Mehdi</creatorcontrib><creatorcontrib>Akbarzadeh-T, Mohammad-R.</creatorcontrib><title>Two-layered fuzzy logic-based model for predicting court decisions in construction contract disputes</title><title>Artificial intelligence and law</title><addtitle>Artif Intell Law</addtitle><description>The dynamic nature and increasing complexity of the construction industry have led to increased conflicts in construction projects. An accurate prediction of the outcome of a dispute resolution in courts could effectively reduce the number of disputes that would otherwise conclude by spending more money through litigation. This study aims to introduce a two-layered fuzzy logic model for predicting court decisions in construction contract disputes. 100 cases of construction contract disputes are selected from the courts of Iran. A questionnaire survey is then conducted to extract a set of fuzzy rules for identifying important decision parameters and expert knowledge. Accordingly, a two-layered fuzzy logic-based decision-making architecture is proposed for the prediction model. Furthermore, the fuzzy system is trained based on 10-fold cross-validation. Analysis of results indicates that 51 out of the 100 cases are filed after the dissolution and termination of the contract show a significant impact of these clauses as the root cause in construction contract disputes. Our results present a proposed hierarchical fuzzy system that can correctly predict nearly 60% of the test data. Also, we demonstrate a methodology of using argument before ML to establish interpretable AI models. Based on our findings, a fuzzy model with a hierarchical structure may be used as a simple and efficient method for predicting court decisions in construction contract disputes.</description><subject>Artificial Intelligence</subject><subject>Building</subject><subject>Computer Science</subject><subject>Construction contracts</subject><subject>Construction industry</subject><subject>Contracts</subject><subject>Court decisions</subject><subject>Decision making</subject><subject>Fuzzy algorithms</subject><subject>Fuzzy logic</subject><subject>Fuzzy sets</subject><subject>Fuzzy systems</subject><subject>Information Storage and Retrieval</subject><subject>Intellectual Property</subject><subject>IT Law</subject><subject>Judgments</subject><subject>Legal Aspects of Computing</subject><subject>Litigation</subject><subject>Media Law</subject><subject>Mediation</subject><subject>Original Research</subject><subject>Parameter identification</subject><subject>Philosophy of Law</subject><subject>Prediction models</subject><subject>Structural hierarchy</subject><issn>0924-8463</issn><issn>1572-8382</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp9kc1qAyEUhaW00DTtC3Q10LWpXh3jLEPoHwS6SdfiqBMMkzHVGUry9DVJIRRKcSEev6PnchC6p2RCCZk-JkpKIjABikkFkuLqAo1oOQUsmYRLNMoqx5ILdo1uUloTQipRsRGyy6-AW71z0dmiGfb7XdGGlTe41ikrm2BdWzQhFtsMeNP7blWYMMS-sM745EOXCt9lqUt9HPJ9OB76qE1GfNoOvUu36KrRbXJ3P_sYfTw_LeevePH-8jafLbBhUvS4NtLaBlipOZUEagNlTQWVoKkuOZR5LsENpZpXkjHDmbQOoBZTJ62pS8fG6OH07jaGz8GlXq1z1C5_qaCUTFSEcX6mVrp1yndNOKTd-GTUbEoBgBMBmZr8QeVl3cbnCV3js_7LACeDiSGl6Bq1jX6j405Rog4lqVNJKpekjiWpKpvYyZQy3K1cPCf-x_UNRyCULQ</recordid><startdate>20211201</startdate><enddate>20211201</enddate><creator>Bagherian-Marandi, Navid</creator><creator>Ravanshadnia, Mehdi</creator><creator>Akbarzadeh-T, Mohammad-R.</creator><general>Springer Netherlands</general><general>Springer</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ILT</scope><scope>3V.</scope><scope>7SC</scope><scope>7XB</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ALSLI</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CNYFK</scope><scope>DWQXO</scope><scope>E3H</scope><scope>F2A</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0N</scope><scope>M1O</scope><scope>M2O</scope><scope>MBDVC</scope><scope>P5Z</scope><scope>P62</scope><scope>PADUT</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope></search><sort><creationdate>20211201</creationdate><title>Two-layered fuzzy logic-based model for predicting court decisions in construction contract disputes</title><author>Bagherian-Marandi, Navid ; 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An accurate prediction of the outcome of a dispute resolution in courts could effectively reduce the number of disputes that would otherwise conclude by spending more money through litigation. This study aims to introduce a two-layered fuzzy logic model for predicting court decisions in construction contract disputes. 100 cases of construction contract disputes are selected from the courts of Iran. A questionnaire survey is then conducted to extract a set of fuzzy rules for identifying important decision parameters and expert knowledge. Accordingly, a two-layered fuzzy logic-based decision-making architecture is proposed for the prediction model. Furthermore, the fuzzy system is trained based on 10-fold cross-validation. Analysis of results indicates that 51 out of the 100 cases are filed after the dissolution and termination of the contract show a significant impact of these clauses as the root cause in construction contract disputes. 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| source | HeinOnline Law Journal Library; SpringerLink Journals - AutoHoldings |
| subjects | Artificial Intelligence Building Computer Science Construction contracts Construction industry Contracts Court decisions Decision making Fuzzy algorithms Fuzzy logic Fuzzy sets Fuzzy systems Information Storage and Retrieval Intellectual Property IT Law Judgments Legal Aspects of Computing Litigation Media Law Mediation Original Research Parameter identification Philosophy of Law Prediction models Structural hierarchy |
| title | Two-layered fuzzy logic-based model for predicting court decisions in construction contract disputes |
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