DELIBERATIVE EXCHANGE, TRUTH, AND COGNITIVE DIVISION OF LABOUR: A LOW-RESOLUTION MODELING APPROACH

被引:19
|
作者
Hegselmann, Rainer [1 ]
Krause, Ulrich [2 ]
机构
[1] Univ Bayreuth, Bayreuth, Germany
[2] Univ Bremen, D-28359 Bremen, Germany
关键词
CONSENSUS;
D O I
10.3366/E1742360009000604
中图分类号
B [哲学、宗教];
学科分类号
01 ; 0101 ;
摘要
This paper develops a formal framework to model a process in which the formation of individual opinions is embedded in a deliberative exchange with others. The paper opts for a low-resolution modeling approach and abstracts away from most of the details of the social-epistemic process. Taking a bird's eye view allows us to analyze the chances for the truth to be found and broadly accepted under conditions of cognitive division of labour combined with a social exchange process. Cognitive division of labour means that only some individuals are active truth seekers, possibly with different capacities. Both mathematical tools and computer simulations are used to investigate the model. As an analytical result, the Funnel Theorem states that under rather weak conditions on the social process, a consensus on the truth will be reached if all individuals possess an arbitrarily small capacity to go for the truth. The Leading the pack Theorem states that under certain conditions even a single truth seeker may lead all individuals to the truth. Systematic simulations analyze how close agents can get to the truth depending upon the frequency of truth seekers, their capacities as truth seekers, the position of the truth (more to the extreme or more in the centre of an opinion space), and the willingness to take into account the opinions of others when exchanging and updating opinions.
引用
收藏
页码:130 / 144
页数:15
相关论文
共 50 条
  • [1] Modeling of Low-Resolution Face Imaging
    Lestriandoko, Nova Hadi
    Apriyanti, Diah Harnoni
    Prakasa, Esa
    [J]. 2020 INTERNATIONAL CONFERENCE ON RADAR, ANTENNA, MICROWAVE, ELECTRONICS, AND TELECOMMUNICATIONS (ICRAMET): FOSTERING INNOVATION THROUGH ICTS FOR SUSTAINABLE SMART SOCIETY, 2020, : 314 - 319
  • [2] Low-resolution structural modeling of protein interactome
    Vakser, Ilya A.
    [J]. CURRENT OPINION IN STRUCTURAL BIOLOGY, 2013, 23 (02) : 198 - 205
  • [3] Combining computational modeling with sparse and low-resolution data
    Habeck, Michael
    Nilges, Michael
    [J]. JOURNAL OF STRUCTURAL BIOLOGY, 2011, 173 (03) : 419 - 419
  • [4] Modeling Uncertainty for Low-Resolution Facial Expression Recognition
    Lo, Ling
    Ruan, Bo-Kai
    Shuai, Hong-Han
    Cheng, Wen-Huang
    [J]. IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, 2024, 15 (01) : 198 - 209
  • [5] Kernel Modeling Super-Resolution on Real Low-Resolution Images
    Zhou, Ruofan
    Susstrunk, Sabine
    [J]. 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019), 2019, : 2433 - 2443
  • [6] Truth and Cognitive Division of Labour First Steps towards a Computer Aided Social Epistemology
    Hegselmann, Rainer
    Krause, Ulrich
    [J]. JASSS-THE JOURNAL OF ARTIFICIAL SOCIETIES AND SOCIAL SIMULATION, 2006, 9 (03):
  • [7] Neural approach for the magnification of low-resolution document images
    Kezzoula, Zakia
    Faouci, Soumia
    Gaceb, Djamel
    [J]. 2018 IEEE/ACS 15TH INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS (AICCSA), 2018,
  • [8] A New Variational Approach to Deblurring Low-Resolution Images
    Shao, Wen-Ze
    Ge, Qi
    Wang, Li-Qian
    Lin, Yun-Zhi
    Deng, Hai-Song
    [J]. TENTH INTERNATIONAL CONFERENCE ON GRAPHICS AND IMAGE PROCESSING (ICGIP 2018), 2019, 11069
  • [9] Low-Resolution Modeling of Dense Drainage Networks in Confining Layers
    Pauw, P. S.
    Van der Zee, S. E. A. T. M.
    Leijnse, A.
    Delsman, J. R.
    De Louw, P. G. B.
    De Lange, W. J.
    Essink, G. H. P. Oude
    [J]. GROUNDWATER, 2015, 53 (05) : 771 - 781
  • [10] SURPASS Low-Resolution Coarse-Grained Protein Modeling
    Dawid, Aleksandra E.
    Gront, Dominik
    Kolinski, Andrzej
    [J]. JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 2017, 13 (11) : 5766 - 5779