Fast decisions reflect biases; slow decisions do not

被引:0
|
作者
Linn, Samantha [1 ]
Lawley, Sean D. [1 ]
Karamched, Bhargav R. [2 ,3 ,4 ]
Kilpatrick, Zachary P. [5 ]
Josic, Kresimir [6 ,7 ]
机构
[1] Univ Utah, Dept Math, Salt Lake City, UT 84112 USA
[2] Florida State Univ, Dept Math, Tallahassee, FL 32306 USA
[3] Florida State Univ, Inst Mol Biophys, Tallahassee, FL 32306 USA
[4] Florida State Univ, Program Neurosci, Tallahassee, FL 32306 USA
[5] Univ Colorado, Dept Appl Math, Boulder, CO 80309 USA
[6] Univ Houston, Dept Math, Houston, TX 77004 USA
[7] Univ Houston, Dept Biol & Biochem, Houston, TX 77004 USA
关键词
EVIDENCE ACCUMULATION; SIMPLE-MODEL; CASCADES; DYNAMICS; CHOICE; SPEED; BRAIN;
D O I
10.1103/PhysRevE.110.024305
中图分类号
O35 [流体力学]; O53 [等离子体物理学];
学科分类号
070204 ; 080103 ; 080704 ;
摘要
Decisions are often made by heterogeneous groups of individuals, each with distinct initial biases and access to information of different quality. We show that in groups of independent agents who accumulate evidence the first to decide are those with the strongest initial biases. Their decisions align with their initial bias, regardless of the underlying truth. In contrast, agents who decide last make decisions as if they were initially unbiased and hence make better choices. We obtain asymptotic expressions in the large population limit quantifying how agents' initial inclinations shape early decisions. Our analysis shows how bias, information quality, and decision order interact in nontrivial ways to determine the reliability of decisions in a group.
引用
收藏
页数:18
相关论文
共 50 条
  • [31] Dopamine biases decisions by limiting temporal integration
    Gautham, Aditya K.
    Miner, Lauren E.
    Franco, Marco N.
    Thornquist, Stephen C.
    Crickmore, Michael A.
    NATURE, 2024, : 850 - 857
  • [32] Distinct beta frequencies reflect categorical decisions
    Elie Rassi
    Yi Zhang
    Germán Mendoza
    Juan Carlos Méndez
    Hugo Merchant
    Saskia Haegens
    Nature Communications, 14 (1)
  • [33] Desirability Biases Perceptual Decisions in the Aversive Domain
    Kim, Haena
    Liu, Alicia
    Leong, Yuan Chang
    EMOTION, 2025,
  • [35] Careful Choices: Parents Reflect on their Childcare Decisions
    Ferguson, Jesica
    Lampkins, Chanel
    Moody, Brandon
    Shpancer, Noam
    CHILD CARE IN PRACTICE, 2022, 28 (03) : 368 - 380
  • [36] Distinct beta frequencies reflect categorical decisions
    Rassi, Elie
    Zhang, Yi
    Mendoza, German
    Mendez, Juan Carlos
    Merchant, Hugo
    Haegens, Saskia
    NATURE COMMUNICATIONS, 2023, 14 (01)
  • [37] Decisions in the Delivery Room: How Do Obstetricians Come to Their Decisions?
    Hellmers, C.
    Krahl, A.
    Schuecking, B.
    GEBURTSHILFE UND FRAUENHEILKUNDE, 2010, 70 (07) : 553 - 560
  • [38] Do decision biases predict bad decisions? Omission bias, naturalness bias, and influenza vaccination
    DiBonaventura, Marco daCosta
    Chapman, Gretchen B.
    MEDICAL DECISION MAKING, 2008, 28 (04) : 532 - 539
  • [39] Evaluating Ventures Fast and Slow: Sensemaking, Intuition, and Deliberation in Entrepreneurial Resource Provision Decisions
    Fisher, Greg
    Neubert, Emily
    ENTREPRENEURSHIP THEORY AND PRACTICE, 2023, 47 (04) : 1298 - 1326
  • [40] Combining Fast and Slow Thinking for Human-like and Efficient Decisions in Constrained Environments
    Ganapini, M. Bergamaschi
    Campbell, M.
    Fabiano, F.
    Horesh, L.
    Lenchner, J.
    Loreggia, A.
    Mattei, N.
    Rossi, F.
    Srivastava, B.
    Venable, K. B.
    NEURAL-SYMBOLIC LEARNING AND REASONING, NESY 2022, 2022, : 171 - 185