首页
学习
活动
专区
工具
TVP
发布
社区首页 >专栏 >Anchoring & random 锚定/沉锚效应和随机

Anchoring & random 锚定/沉锚效应和随机

原创
作者头像
vanguard
修改2020-04-03 17:47:05
6770
修改2020-04-03 17:47:05
举报
文章被收录于专栏:vanguardvanguard

Anchoring or focalism is a cognitive bias where an individual depends too heavily on an initial piece of information offered (considered to be the "anchor") when making decisions.

Anchoring occurs when, during decision making, an individual depends on an initial piece of information to make subsequent judgments. Those objects near the anchor tend to be assimilated toward it and those further away tend to be displaced in the other direction. Once the value of this anchor is set, all future negotiations, arguments, estimates, etc. are discussed in relation to the anchor. This bias occurs when interpreting future information using this anchor. For example, the initial price offered for a used car, set either before or at the start of negotiations, sets an arbitrary focal point for all following discussions. Prices discussed in negotiations that are lower than the anchor may seem reasonable, perhaps even cheap to the buyer, even if said prices are still relatively higher than the actual market value of the car.

沉锚效应,心理学名词,指的是人们在对某人某事做出判断时,易受第一印象或第一信息支配,就像沉入海底的锚一样把人们的思想固定在某处。作为一种心理现象,沉锚效应普遍存在于生活的方方面面。第一印象和先入为主是其在社会生活中的表现形式。

通常来讲,人们在作决策时,思维往往会被得到的第一信息所左右,就像沉入海底的锚一样,把你的思维固定在某处。而用一个限定性的词语或规定作行为导向,达成行为效果的心理效应,被称为“沉锚效应”。

薪资福利锚定上家公司或者市场价格 -> 薪水过低工作失去动力 -> 为钱而被迫工作或辞职

第一印象很重要 -> 第一次见面举止得体 -> 如果不高兴假装高兴 -> 积极乐观的人容易被社会接纳

还有很多例子...

解决的最简单办法应该就是随机选择,比如找出治疗某种病毒的药物,这么想难点在于病毒样本和新药配方的随机范围和时间...

A randomized algorithm is an algorithm that employs a degree of randomness as part of its logic. The algorithm typically uses uniformly random bits as an auxiliary input to guide its behavior, in the hope of achieving good performance in the "average case" over all possible choices of random bits. Formally, the algorithm's performance will be a random variable determined by the random bits; thus either the running time, or the output (or both) are random variables.

随机化算法(randomized algorithm),是这样一种算法,在算法中使用了随机函数,且随机函数的返回值直接或者间接的影响了算法的执行流程或执行结果。就是将算法的某一步或某几步置于运气的控制之下,即该算法在运行的过程中的某一步或某几步涉及一个随机决策,或者说其中的一个决策依赖于某种随机事件。

Pseudo Random Distribution PRD|洗牌算法|组合随机

The Fisher–Yates shuffle is an algorithm for generating a random permutation of a finite sequence—in plain terms, the algorithm shuffles the sequence. The algorithm effectively puts all the elements into a hat; it continually determines the next element by randomly drawing an element from the hat until no elements remain. The algorithm produces an unbiased permutation: every permutation is equally likely. The modern version of the algorithm is efficient: it takes time proportional to the number of items being shuffled and shuffles them in place.

https://blog.csdn.net/sgbfblog/article/details/7917685

证明随机函数是不是真随机是不是一个NPC问题?

# pytohn
import random
# random.random()用于生成一个0到1的随机符点数: 0 <= n < 1.0
random.random()
# random.uniform的函数原型为:random.uniform(a, b),用于生成一个指定范围内的随机符点数。如果a > b,则生成的随机数n: b <= n <= a。如果 a <b, 则 a <= n <= b。
random.uniform(a, b)
# random.randint()的函数原型为:random.randint(a, b),用于生成一个指定范围内的整数。其中参数a是下限,参数b是上限,生成的随机数n: a <= n <= b
random.randint(0,10)
# random.shuffle(x[, random])
# Random.randrange([start], stop[, step])
# random.choice(sequence)
# random.sample(sequence, k)

https://blog.csdn.net/pipisorry/article/details/39086463

// javascript
// 获取从 1 到 10 的随机整数,取 0 的概率极小。
Math.ceil(Math.random()*10);
// 可均衡获取 0 到 1 的随机整数
Math.round(Math.random());
 // 可均衡获取 0 到 9 的随机整数。
Math.floor(Math.random()*10);   
 // 基本均衡获取 0 到 10 的随机整数,其中获取最小值 0 和最大值 10 的几率少一半。
Math.round(Math.random()*10);   

随机多次看看时间和随机值有没有关系

import time, random
[print(time.time(),random.random()) for a in range(10)]
[print(str(time.time())[-1],str(random.random())[-1]) for a in range(10)]
1585905362.369547 0.45203566891682945
1585905362.36959 0.06491357326071667
1585905362.369598 0.08881257660670305
1585905362.3696032 0.9419159971518406
1585905362.369608 0.2518668874878929
1585905362.3696132 0.9647548256817006
1585905362.369618 0.7210943511439714
1585905362.369623 0.22359143354273003
1585905362.369628 0.04308719400515337
1585905362.369633 0.35297085329847944
9 3
2 3
3 4
9 7
4 8
2 1
5 8
8 8
1 3
1 2
[Finished in 0.1s]

显然不够多,如果N次后时间和有些随机函数是否有线性或者某种分布的关系?

原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。

如有侵权,请联系 cloudcommunity@tencent.com 删除。

原创声明:本文系作者授权腾讯云开发者社区发表,未经许可,不得转载。

如有侵权,请联系 cloudcommunity@tencent.com 删除。

评论
登录后参与评论
0 条评论
热度
最新
推荐阅读
领券
问题归档专栏文章快讯文章归档关键词归档开发者手册归档开发者手册 Section 归档