Mhatre Natasha, Robert Daniel
Department of Biological Sciences, University of Toronto at Scarborough, Scarborough, ON, Canada.
School of Biological Sciences, University of Bristol, Bristol, United Kingdom.
Front Psychol. 2018 Jun 21;9:1015. doi: 10.3389/fpsyg.2018.01015. eCollection 2018.
Insects have small brains and heuristics or 'rules of thumb' are proposed here to be a good model for how insects optimize the objects they make and use. Generally, heuristics are thought to increase the speed of decision making by reducing the computational resources needed for making decisions. By corollary, heuristic decisions are also deemed to impose a compromise in decision accuracy. Using examples from object optimization behavior in insects, we will argue that heuristics do not inevitably imply a lower computational burden or lower decision accuracy. We also show that heuristic optimization may be driven by certain features of the optimization problem itself: the properties of the object being optimized, the biology of the insect, and the properties of the function being optimized. We also delineate the structural conditions under which heuristic optimization may achieve accuracy equivalent to or better than more fine-grained and onerous optimization methods.
昆虫的大脑较小，本文提出启发式方法或“经验法则”是昆虫优化其所制造和使用物体的良好模型。一般来说，启发式方法被认为通过减少决策所需的计算资源来提高决策速度。由此推论，启发式决策也被认为会在决策准确性上有所妥协。通过昆虫物体优化行为的例子，我们将论证启发式方法并不必然意味着更低的计算负担或更低的决策准确性。我们还表明，启发式优化可能由优化问题本身的某些特征驱动：被优化物体的属性、昆虫的生物学特性以及被优化功能的属性。我们还描绘了启发式优化可能实现与更精细、更繁琐的优化方法相当或更好准确性的结构条件。
