Scholarlink

<p>The sciences divide into those that discover laws and those that reconstruct histories. We argue that this division does not reflect a difference in subject matter, but a difference in <i>epistemic regime</i>. Law-based sciences operate under <i>episodic closure</i>: systems are idealized so that the outcomes of prior interactions do not alter the rules governing future ones. This <i>regime-defining idealization</i> (distinguished from <i>pragmatic idealization</i>) underlies the predictive successes of physics, but creates a systematic blind spot for evolutionary dynamics. We formalize this distinction using Stability-Driven Assembly (SDA), a minimal non-equilibrium framework in which differential persistence couples episodes into population-level evolutionary dynamics without genes, replication, or predefined fitness functions. Representing compositional objects as <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\lambda\)</EquationSource> </InlineEquation>-calculus terms, we show that episodic science studies isolated <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\lambda\)</EquationSource> </InlineEquation>-reductions under fixed rules, while evolutionary science studies populations of <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\lambda\)</EquationSource> </InlineEquation>-instantiations whose outputs re-enter the space of operators. The resulting dynamics are self-modifying and irreducibly sequential: each step rewrites the conditions for the next. A four-quadrant taxonomy locates episodic science, evolutionary science, and two commonly conflated intermediate cases: formal possibility and constructive potential, within a single framework. From this analysis we derive the “No Free Telos” constraint: in constructive systems where population feedback reshapes the effective dynamics at each step, the cost of predicting future states cannot in general be reduced below the cost of simulating the generative history. The resulting framework bridges episodic and historical sciences, not by reducing one to the other, but by identifying population-level memory as the structural condition that transforms law-governed episodes into open-ended evolutionary processes.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

From episodes to populations: evolutionary explanation requires a constructive epistemology

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月26日

查看全文
  • Dan Adler

摘要

The sciences divide into those that discover laws and those that reconstruct histories. We argue that this division does not reflect a difference in subject matter, but a difference in epistemic regime. Law-based sciences operate under episodic closure: systems are idealized so that the outcomes of prior interactions do not alter the rules governing future ones. This regime-defining idealization (distinguished from pragmatic idealization) underlies the predictive successes of physics, but creates a systematic blind spot for evolutionary dynamics. We formalize this distinction using Stability-Driven Assembly (SDA), a minimal non-equilibrium framework in which differential persistence couples episodes into population-level evolutionary dynamics without genes, replication, or predefined fitness functions. Representing compositional objects as \(\lambda\) -calculus terms, we show that episodic science studies isolated \(\lambda\) -reductions under fixed rules, while evolutionary science studies populations of \(\lambda\) -instantiations whose outputs re-enter the space of operators. The resulting dynamics are self-modifying and irreducibly sequential: each step rewrites the conditions for the next. A four-quadrant taxonomy locates episodic science, evolutionary science, and two commonly conflated intermediate cases: formal possibility and constructive potential, within a single framework. From this analysis we derive the “No Free Telos” constraint: in constructive systems where population feedback reshapes the effective dynamics at each step, the cost of predicting future states cannot in general be reduced below the cost of simulating the generative history. The resulting framework bridges episodic and historical sciences, not by reducing one to the other, but by identifying population-level memory as the structural condition that transforms law-governed episodes into open-ended evolutionary processes.