{"title":"反一致性的去极化力量","authors":"Arkadiusz Lipiecki, Katarzyna Sznajd-Weron","doi":"10.1016/j.eswa.2025.127879","DOIUrl":null,"url":null,"abstract":"<div><div>Political polarization hinders collective decision-making across multiple domains, from public health to environmental policy. Therefore, depolarization strategies are crucial and have been increasingly studied. Anticonformity, responding to social influence by opposing the opinions of others, has been associated with increased polarization, while its potential role as a depolarizing force has been largely overlooked. Although psychologists point to different forms of anticonformity, most computational models focus solely on xenophobia, prejudice against outsiders, which radicalizes opinions. Our work addresses this gap by considering another type of anticonformity – asserting uniqueness. We propose the counterintuitive hypothesis that increasing the disagreement by anticonforming to the influence group can reduce issue-based polarization. Within a family of computational models, we show that a depolarizing intervention based on promoting uniqueness may be more effective than traditional interventions, such as decreasing in-group favoritism or enhancing tolerance. We discuss the relevance of our findings through the lens of recent psychological experiments on strategic anticonformity, which demonstrate the potential of applying the proposed depolarizing intervention in real-world settings.</div></div>","PeriodicalId":50461,"journal":{"name":"Expert Systems with Applications","volume":"285 ","pages":"Article 127879"},"PeriodicalIF":7.5000,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Depolarizing power of anticonformity\",\"authors\":\"Arkadiusz Lipiecki, Katarzyna Sznajd-Weron\",\"doi\":\"10.1016/j.eswa.2025.127879\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Political polarization hinders collective decision-making across multiple domains, from public health to environmental policy. Therefore, depolarization strategies are crucial and have been increasingly studied. Anticonformity, responding to social influence by opposing the opinions of others, has been associated with increased polarization, while its potential role as a depolarizing force has been largely overlooked. Although psychologists point to different forms of anticonformity, most computational models focus solely on xenophobia, prejudice against outsiders, which radicalizes opinions. Our work addresses this gap by considering another type of anticonformity – asserting uniqueness. We propose the counterintuitive hypothesis that increasing the disagreement by anticonforming to the influence group can reduce issue-based polarization. Within a family of computational models, we show that a depolarizing intervention based on promoting uniqueness may be more effective than traditional interventions, such as decreasing in-group favoritism or enhancing tolerance. We discuss the relevance of our findings through the lens of recent psychological experiments on strategic anticonformity, which demonstrate the potential of applying the proposed depolarizing intervention in real-world settings.</div></div>\",\"PeriodicalId\":50461,\"journal\":{\"name\":\"Expert Systems with Applications\",\"volume\":\"285 \",\"pages\":\"Article 127879\"},\"PeriodicalIF\":7.5000,\"publicationDate\":\"2025-05-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Expert Systems with Applications\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0957417425015015\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Expert Systems with Applications","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0957417425015015","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Political polarization hinders collective decision-making across multiple domains, from public health to environmental policy. Therefore, depolarization strategies are crucial and have been increasingly studied. Anticonformity, responding to social influence by opposing the opinions of others, has been associated with increased polarization, while its potential role as a depolarizing force has been largely overlooked. Although psychologists point to different forms of anticonformity, most computational models focus solely on xenophobia, prejudice against outsiders, which radicalizes opinions. Our work addresses this gap by considering another type of anticonformity – asserting uniqueness. We propose the counterintuitive hypothesis that increasing the disagreement by anticonforming to the influence group can reduce issue-based polarization. Within a family of computational models, we show that a depolarizing intervention based on promoting uniqueness may be more effective than traditional interventions, such as decreasing in-group favoritism or enhancing tolerance. We discuss the relevance of our findings through the lens of recent psychological experiments on strategic anticonformity, which demonstrate the potential of applying the proposed depolarizing intervention in real-world settings.
期刊介绍:
Expert Systems With Applications is an international journal dedicated to the exchange of information on expert and intelligent systems used globally in industry, government, and universities. The journal emphasizes original papers covering the design, development, testing, implementation, and management of these systems, offering practical guidelines. It spans various sectors such as finance, engineering, marketing, law, project management, information management, medicine, and more. The journal also welcomes papers on multi-agent systems, knowledge management, neural networks, knowledge discovery, data mining, and other related areas, excluding applications to military/defense systems.