Walker R
Università di Palermo, Viale delle Scienze, Palermo, Italy.
Artif Life. 1999 Summer;5(3):271-89. doi: 10.1162/106454699568782.
One of the key problems in theoretical biology is the identification of the mechanisms underlying the evolution of complexity. This paper suggests that some difficulties in current models could be avoided by taking account of"niche selection"as proposed by Waddington [21] and subsequent authors [2]. Computer simulations, in which an evolving population of artificial organisms"selects"the niche(s) that maximize their fitness, are compared with a Control Model in which"Niche Selection"is absent. In the simulations the Niche Selection Model consistently produced a greater number of"fit"organisms than the Control Model; although the Niche Selection Model tended, in general, to produce organisms occupying simple niches, it was nonetheless more effective than the Control Model in producing well-adapted organisms inhabiting complex niches. It is shown that the production of these organisms is critically dependent on the rate of environmental change: Slow change leads to fit but undifferentiated populations, dominated by organisms occupying simple niches; differentiated populations, including well-adapted organisms living in complex niches, require rates of environmental change lying just beyond a mathematically well-defined critical value. In simulation"Niche Selection,"unlike conventional"Natural Selection,"provides a permanent selective bias in favor of simplicity. This tendency is counterbalanced by statistical forces favoring shifts from rare"simple niches"to commoner niches of greater complexity. Fit organisms inhabiting complex niches only emerge in conditions where the rate of environmental change is high enough to avoid the concentration of the population in very simple niches, but slow enough to permit step-by-step adaptation to niches of gradually increasing complexity. This result appears to be robust to changes in simulation parameters and assumptions, and leads to interesting conjectures about the real world behavior of biological organisms (and other complex adaptive systems). It is suggested that some of these conjectures might be relatively easy to test.
理论生物学的关键问题之一是确定复杂性进化背后的机制。本文表明，通过考虑沃丁顿[21]及后续作者[2]提出的“生态位选择”，可以避免当前模型中的一些困难。在计算机模拟中，不断进化的人工生物群体“选择”使其适应性最大化的生态位，并与不存在“生态位选择”的控制模型进行比较。在模拟中，生态位选择模型始终比控制模型产生更多“适应”的生物；尽管生态位选择模型总体上倾向于产生占据简单生态位的生物，但在产生适应复杂生态位的适应性良好的生物方面，它比控制模型更有效。结果表明，这些生物的产生严重依赖于环境变化的速率：缓慢变化导致适应但未分化的群体，由占据简单生态位的生物主导；分化的群体，包括生活在复杂生态位中的适应性良好的生物，需要环境变化速率刚好超过一个数学上明确界定的临界值。在模拟“生态位选择”中，与传统的“自然选择”不同，它提供了一种有利于简单性的永久选择偏差。这种趋势被有利于从罕见的“简单生态位”向更复杂的常见生态位转变的统计力量所抵消。只有在环境变化速率足够高以避免群体集中在非常简单的生态位，但又足够慢以允许逐步适应逐渐增加复杂性的生态位的条件下，生活在复杂生态位中的适应性良好的生物才会出现。这一结果似乎对模拟参数和假设的变化具有鲁棒性，并引发了关于生物有机体（以及其他复杂适应系统）在现实世界行为的有趣推测。有人认为其中一些推测可能相对容易检验。
