Biases affect our judgments and decisions everywhere,because in our daily life,no matter where you are,what kind of occupation you are doing,every decision we make is more or less interfered by cognitive biases,which ...Biases affect our judgments and decisions everywhere,because in our daily life,no matter where you are,what kind of occupation you are doing,every decision we make is more or less interfered by cognitive biases,which even determines the outcome of things.In addition,with the development of the times,the progress of science and technology,and the change of social structure,we have experienced too many processes from rejection to acceptance,from stubbornness to change.However,it often takes time;especially in the commercial field,the timing when users accept products can better reflect this point.This article mainly aims at these phenomena,through the information,examples and data from the online sources,to explore how four kinds of cognitive biases:status quo bias,loss aversion bias,mere-exposure effect,and bounded-rationality that affect the smooth progress of innovation products in the fast consumer market,and how these biases can attack the confidence of merchants,so that the originally widely favored products will eventually end in failure.At the end of the article will also discuss the heuristics that can deal with the biases.展开更多
随着智能电网的快速发展,虚假数据注入(false data injection,FDI)攻击已经成为未来电力系统运行面临的主要威胁之一。攻击者通过篡改系统原始数据,导致电力系统失负荷(loss of load demand,LoLD),甚至引发级联失效。因此,有必要建立一...随着智能电网的快速发展,虚假数据注入(false data injection,FDI)攻击已经成为未来电力系统运行面临的主要威胁之一。攻击者通过篡改系统原始数据,导致电力系统失负荷(loss of load demand,LoLD),甚至引发级联失效。因此,有必要建立一种成本效益机制来减轻FDI攻击造成的LoLD。提出了一种多目标风险规避优化模型,在FDI攻击的防御成本、电力系统运行网损和LoLD之间进行权衡。采用多目标进化捕食策略对多目标模型进行求解,获取多目标优化Pareto最优解。仿真结果在IEEE 30节点电力系统证明了所提模型的有效性,并且揭示FDI攻击下电力系统运行中存在着较高的LoLD风险。展开更多
基金During this holiday,I participated in two subject project groups,and I would like to thank Dr.Kishore Sengupta for taking me to explore more in innovation fields.I would also like to thank Dr.E.Gallo for telling me a lot about the application of behavioral economics in business.I would also like to thank my supervisor Rick Boutcher,who helps me to do more critical thinking about innovation,and I also thank the teacher Yufan Huang for her guidance on the revision of my paper.Finally,I would like to thank all the publishers of the research materials quoted by me.It is absolutely impossible to have this article without you.
文摘Biases affect our judgments and decisions everywhere,because in our daily life,no matter where you are,what kind of occupation you are doing,every decision we make is more or less interfered by cognitive biases,which even determines the outcome of things.In addition,with the development of the times,the progress of science and technology,and the change of social structure,we have experienced too many processes from rejection to acceptance,from stubbornness to change.However,it often takes time;especially in the commercial field,the timing when users accept products can better reflect this point.This article mainly aims at these phenomena,through the information,examples and data from the online sources,to explore how four kinds of cognitive biases:status quo bias,loss aversion bias,mere-exposure effect,and bounded-rationality that affect the smooth progress of innovation products in the fast consumer market,and how these biases can attack the confidence of merchants,so that the originally widely favored products will eventually end in failure.At the end of the article will also discuss the heuristics that can deal with the biases.
文摘随着智能电网的快速发展,虚假数据注入(false data injection,FDI)攻击已经成为未来电力系统运行面临的主要威胁之一。攻击者通过篡改系统原始数据,导致电力系统失负荷(loss of load demand,LoLD),甚至引发级联失效。因此,有必要建立一种成本效益机制来减轻FDI攻击造成的LoLD。提出了一种多目标风险规避优化模型,在FDI攻击的防御成本、电力系统运行网损和LoLD之间进行权衡。采用多目标进化捕食策略对多目标模型进行求解,获取多目标优化Pareto最优解。仿真结果在IEEE 30节点电力系统证明了所提模型的有效性,并且揭示FDI攻击下电力系统运行中存在着较高的LoLD风险。
