[论文解读] Multilevel evolutionary developmental optimization (MEDO): A theoretical framework for understanding preferences and selection dynamics

Adam Safron|arXiv (Cornell University)|Oct 27, 2019

Evolutionary Game Theory and Cooperation参考文献 208被引用 9

一句话总结

MEDO 是一个理论框架，统一了进化生物学、发育系统和决策理论，以解释偏好和目标导向行为如何在基因、神经和文化层面涌现。通过在多个层级上建模选择压力——其中经验通过反馈回路塑造未来的动机——它为动机、目标形成和行为稳定性提供了一种广义达尔文主义的解释。

ABSTRACT

What is motivation and how does it work? Where do goals come from and how do they vary within and between species and individuals? Why do we prefer some things over others? MEDO is a theoretical framework for understanding these questions in abstract terms, as well as for generating and evaluating specific hypotheses that seek to explain goal-oriented behavior. MEDO views preferences as selective pressures influencing the likelihood of particular outcomes. With respect to biological organisms, these patterns must compete and cooperate in shaping system evolution. To the extent that shaping processes are themselves altered by experience, this enables feedback relationships where histories of reward and punishment can impact future motivation. In this way, various biases can undergo either amplification or attenuation, resulting in preferences and behavioral orientations of varying degrees of inter-temporal and inter-situational stability. MEDO specifically models all shaping dynamics in terms of natural selection operating on multiple levels--genetic, neural, and cultural--and even considers aspects of development to themselves be evolutionary processes. Thus, MEDO reflects a kind of generalized Darwinism, in that it assumes that natural selection provides a common principle for understanding the emergence of complexity within all dynamical systems in which replication, variation, and selection occur. However, MEDO combines this evolutionary perspective with economic decision theory, which describes both the preferences underlying individual choices, as well as the preferences underlying choices made by engineers in designing optimized systems. In this way, MEDO uses economic decision theory to describe goal-oriented behaviors as well as the interacting evolutionary optimization processes from which they emerge. (Please note: this manuscript was written and finalized in 2012.)

研究动机与目标

• 开发一个统一的理论框架，以解释生物和认知系统中偏好和目标的起源与动态。
• 解决个体和物种层面的动机模式如何从进化、发育和学习过程的相互作用中产生。
• 将自然选择原理整合到多个层级——基因、神经和文化——形成一个单一连贯的优化模型。
• 通过受奖励和惩罚历史塑造的反馈机制，解释偏好的跨时间与跨情境稳定性。
• 弥合进化理论与经济决策理论，以将生物行为和工程系统设计均建模为优化过程的结果。

提出的方法

• 将选择动态建模为广义达尔文主义，将复制、变异和选择应用于基因、神经和文化层级。
• 将偏好表示为选择压力，影响发展和进化轨迹中特定结果的可能性。
• 引入反馈回路，其中过去奖励和惩罚的经验改变未来的塑造过程，从而实现偏好的动态修改。
• 应用经济决策理论来描述个体选择以及工程师在优化系统时所作的设计选择。
• 将发育本身视为一个进化过程，其中发育约束和可塑性塑造了复杂的目标导向行为的出现。
• 使用多层级优化架构，模拟偏好和目标如何在生物和文化维度上共同进化。

实验结果

研究问题

• RQ1 偏好在个体和物种内部如何随时间和情境而涌现并稳定？
• RQ2 哪些机制使得奖励和惩罚的经验能够重塑未来的动机状态和行为偏差？
• RQ3 自然选择如何在基因、神经和文化层级上同时运作，以塑造目标导向行为？
• RQ4 将决策理论与进化理论整合，如何改善对复杂适应性行为的解释？
• RQ5 发育过程在多大程度上可被建模为自身独立的进化过程？

主要发现

• 偏好并非固定不变，而是源于选择压力与奖励和惩罚历史经验之间动态反馈的结果。
• 该框架通过递归反馈回路解释偏好的跨情境和跨时间稳定性，这些回路可放大或减弱行为偏差。
• 当从时间维度上考察复制、变异和选择时，发育过程可被理解为一种进化过程。
• 将经济决策理论与广义达尔文主义整合，可为生物行为和工程系统优化提供统一解释。
• MEDO 为理解复杂的目标导向系统如何从基因、神经和文化领域中多层级选择的共同作用中产生，提供了理论基础。
• 该模型支持动机和目标形成是分层优化过程的涌现属性，而非预先编程的特征。