Kampis George, Gulyás László
History and Philosophy of Science, Eötvös University, Budapest, Hungary.
Artif Life. 2008 Summer;14(3):375-86. doi: 10.1162/artl.2008.14.3.14310.
This is a position paper on phenotype-based evolution modeling. It argues that evolutionary complexity is essentially a functional kind of complexity, and for it to evolve, a full body, or, in other words, a dynamically defined, deeply structured, and plasticity-bound phenotype is required. In approaching this subject, we ask and answer some key questions, which we think are interrelated. The questions we discuss and the answers we propose are: (a) How should complexity growth be measured or operationalized in natural and artificial systems? Evolutionary complexity is akin to that of machines, and to operationalize it, we need to study how machinelike organismic functions work and develop. Inspired by studies on causality, we propose the notion of mechanism. A mechanism is a simplified causal system that carries out a function. A growth of functional complexity involves interconversions between a deep (or unused) process and that of a mechanism. (b) Are the principles of natural selection, as they are currently understood, sufficient to explain the evolution of complexity? Our answer is strongly negative. Natural selection helps adapting mechanisms to carry out a given task, but will not generate a task. Hence there is a tradeoff between available tasks and mechanisms fulfilling them. To escape, we argue that competition avoidance is required for new complexity to emerge. (c) What are the environmental constraints on complexity growth in living systems? We think these constraints arise from the structure of the coevolving ecological system, and the basic frames are given by the niche structure. We consider the recently popular idea of niche construction and relate it to the plasticity of the phenotype. We derive a form of phenotype plasticity from the hidden (unused) and explicit (functional) factors discussed in the causality part. (d) What are the main hypotheses about complexity growth that can actually be tested? We hypothesize that a rich natural phenotype that supports causality-function conversions is a necessary ingredient of complexity growth. We review our work on the FATINT system, which incorporates similar ideas in a computer simulation, and shows that full-body phenotypes are sufficient for achieving functional evolution. (e) What language is most appropriate for speaking about the evolution of complexity in living systems? FATINT is developed using advanced agent-based modeling techniques, and we discuss the general relevance of this methodology for understanding and simulating the phenomena discussed.
这是一篇关于基于表型的进化建模的立场文件。它认为进化复杂性本质上是一种功能复杂性，要使其进化，需要一个完整的身体，或者换句话说，一个动态定义、结构深度且受可塑性约束的表型。在探讨这个主题时，我们提出并回答了一些我们认为相互关联的关键问题。我们讨论的问题和提出的答案如下：（a）在自然和人工系统中，应如何衡量或操作复杂性增长？进化复杂性类似于机器的复杂性，要对其进行操作，我们需要研究类似机器的机体功能如何运作和发展。受因果关系研究的启发，我们提出了机制的概念。机制是一个执行功能的简化因果系统。功能复杂性的增长涉及深度（或未使用）过程与机制过程之间的相互转换。（b）按照目前的理解，自然选择的原则是否足以解释复杂性的进化？我们的答案是否定的。自然选择有助于使机制适应执行给定任务，但不会产生任务。因此，在可用任务与执行这些任务的机制之间存在权衡。为了摆脱这种情况，我们认为新复杂性的出现需要避免竞争。（c）生命系统中复杂性增长的环境限制是什么？我们认为这些限制源于共同进化的生态系统的结构，其基本框架由生态位结构给出。我们考虑了最近流行的生态位构建概念，并将其与表型的可塑性联系起来。我们从因果关系部分讨论的隐藏（未使用）和显式（功能）因素中推导出一种表型可塑性形式。（d）关于复杂性增长的主要假设中，哪些实际上可以进行检验？我们假设支持因果关系 - 功能转换的丰富自然表型是复杂性增长的必要因素。我们回顾了我们在FATINT系统上的工作，该系统在计算机模拟中纳入了类似的想法，并表明完整身体表型足以实现功能进化。（e）用哪种语言来谈论生命系统中复杂性的进化最为合适？FATINT是使用先进的基于智能体的建模技术开发的，我们讨论了这种方法对于理解和模拟所讨论现象的一般相关性。
