Self-fulfilling Prophecy(自验预言)

自我实现预言是指一种预测,它直接或间接地导致自身成为现实。这种现象最早见于古希腊和古印度文学,但直到20世纪由社会学家罗伯特·K·默顿正式定义并阐述了其结构和后果。自我实现预言开始于对情境的一种错误定义,这种定义引发的行为最终使原本错误的概念变为真实。



self-fulfilling prophecy is a prediction that directly or indirectly causes itself to become true, by the very terms of the prophecy itself, due to positive feedback between belief and behavior. Although examples of such prophecies can be found in literature as far back as ancient Greece and ancient India, it is 20th-century sociologist Robert K. Mertonwho is credited with coining the expression "self-fulfilling prophecy" and formalizing its structure and consequences. In his 1948 article Self-Fulfilling Prophecy, Merton defines it in the following terms:

The self-fulfilling prophecy is, in the beginning, a false definition of the situation evoking a new behavior which makes the original false conception come true. This specious validity of the self-fulfilling prophecy perpetuates a reign of error. For the prophet will cite the actual course of events as proof that he was right from the very beginning.[1]

In other words, a positive or negative prophecy, strongly held belief, or delusion—declared as truth when it is actually false—may sufficiently influence people so that their reactions ultimately fulfill the once-false prophecy.

Self-fulfilling prophecy are effects in behavioral confirmation effect, in which behavior, influenced by expectations, causes those expectations to come true.[2] It is complementary to the self-defeating prophecy.












from: https://en.wikipedia.org/wiki/Self-fulfilling_prophecy






内容概要:本文介绍了一个基于MATLAB实现的无人机三维路径规划项目,采用蚁群算法(ACO)与多层感知机(MLP)相结合的混合模型(ACO-MLP)。该模型通过三维环境离散化建模,利用ACO进行全局路径搜索,并引入MLP对环境特征进行自适应学习与启发因子优化,实现路径的动态调整与多目标优化。项目解决了高维空间建模、动态障碍规避、局部最优陷阱、算法实时性及多目标权衡等关键技术难题,结合并行计算与参数自适应机制,提升了路径规划的智能性、安全性和工程适用性。文中提供了详细的模型架构、核心算法流程及MATLAB代码示例,涵盖空间建模、信息素更新、MLP训练与融合优化等关键步骤。; 适合人群:具备一定MATLAB编程基础,熟悉智能优化算法与神经网络的高校学生、科研人员及从事无人机路径规划相关工作的工程师;适合从事智能无人系统、自动驾驶、机器人导航等领域的研究人员; 使用场景及目标:①应用于复杂三维环境下的无人机路径规划,如城市物流、灾害救援、军事侦察等场景;②实现飞行安全、能耗优化、路径平滑与实时避障等多目标协同优化;③为智能无人系统的自主决策与环境适应能力提供算法支持; 阅读建议:此资源结合理论模型与MATLAB实践,建议读者在理解ACO与MLP基本原理的基础上,结合代码示例进行仿真调试,重点关注ACO-MLP融合机制、多目标优化函数设计及参数自适应策略的实现,以深入掌握混合智能算法在工程中的应用方法。
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