The refinement paradox and cumulative cultural evolution: Complex products of collective improvement favor conformist outcomes, blind copying, and hyper-credulity.

IF 3.8 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

PLoS Computational Biology Pub Date : 2024-09-26 eCollection Date: 2024-09-01 DOI:10.1371/journal.pcbi.1012436

Elena Miu, Luke Rendell, Sam Bowles, Rob Boyd, Daniel Cownden, Magnus Enquist, Kimmo Eriksson, Marcus W Feldman, Timothy Lillicrap, Richard McElreath, Stuart Murray, James Ounsley, Kevin N Lala

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Abstract

Social learning is common in nature, yet cumulative culture (where knowledge and technology increase in complexity and diversity over time) appears restricted to humans. To understand why, we organized a computer tournament in which programmed entries specified when to learn new knowledge and when to refine (i.e. improve) existing knowledge. The tournament revealed a 'refinement paradox': refined behavior afforded higher payoffs as individuals converged on a small number of successful behavioral variants, but refining did not generally pay. Paradoxically, entries that refined only in certain conditions did best during behavioral improvement, while simple copying entries thrived when refinement levels were high. Cumulative cultural evolution may be rare in part because sophisticated strategies for improving knowledge and technology are initially advantageous, yet complex culture, once achieved, favors conformity, blind imitation and hyper-credulity.

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完善悖论与累积性文化进化：集体改进的复杂产物有利于顺应潮流、盲目复制和过度信用。

社会学习在自然界很常见，但累积文化（知识和技术的复杂性和多样性随着时间的推移而增加）似乎仅限于人类。为了了解其中的原因，我们组织了一场计算机比赛，在比赛中，参赛者通过编程指定何时学习新知识，何时完善（即改进）现有知识。比赛揭示了一个 "精炼悖论"：当个体趋同于少数成功的行为变体时，精炼行为会带来更高的回报，但精炼行为一般不会带来回报。矛盾的是，在行为改进过程中，仅在特定条件下进行精炼的参赛者表现最佳，而当精炼水平较高时，简单复制的参赛者则会茁壮成长。累积性文化进化可能是罕见的，部分原因是改进知识和技术的复杂策略最初是有利的，但复杂文化一旦实现，就会倾向于顺从、盲目模仿和过度笃信。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY

CiteScore

7.10

自引率

4.70%

发文量

820

审稿时长

2.5 months

期刊介绍： PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.