生产总成本包括土地、劳动和资本等资源投入。这些投入同时关系着转换物品的物理性质(大小、重量、颜色、位置、化学组成等等),以及交易─ 定义、保护和执行物品的财产权(使用的权利、获取收入的权利、排他的权利和交换的权利)。

Channel
别的书摘与政治不正确玩梗
@whatbiereading
On this record: Growth · Engagement · Reactions · Posts · Citations · Cite this entry
3,028subscribers
+6 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 1,000–3,162.
Register entry
| Telegram ID | -1001628712613 |
|---|---|
| Type | Channel |
| Username | @whatbiereading |
| Created | Between 1 December 2021 and 31 March 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 10 August 2026 |
| Measurements held | 3 |
| Confirmed unchanged | 1 time, most recently 10 August 2026 |
| On Telegram | t.me/whatbiereading |
Growth
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 10 Aug 2026, 11:21 | 3,028 | +7 |
| 7 Aug 2026, 14:50 | 3,021 | -1 |
| 6 Aug 2026, 14:49 | 3,022 | first reading |
Engagement
20 posts held, back to 3 August 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 3 pagesof Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 5.82%
- avg views ÷ 3,028 subscribers
- Avg views / post
- 176
- 20 posts measured
- Reaction rate
- 1.18%
- reactions ÷ views · ER floor
- Posts in window
- 20
- of 20 held
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views— note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate. It is computed over the 1 of 20 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 5 August 2026 |
|---|---|
| Posts held | 20 (3 August 2026 – 5 August 2026) |
| Views total | 3,522 |
| Reactions total | 3 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 8 Aug 2026, 00:11 UTC |
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
Reaction mix
3 reactions across 1 post, in 1 kind.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| 👍 | 3 | 100.0% |
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 1 of the 20 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 3reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 3 August 2026 to 5 August 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
Recent posts
两百年以来经济理论的基石是专业化与分工造成之交易利益。扩大市场的规模能促成专业化,而且随着世界经济成长,分工变得更细密,经济成果所涉及的交换次数也随之扩张。然而,长串的经济学家将这种研究方法发展成很精致的经济理论,却并末考虑这种交换过程的成本。
讯息的成本是交易成本的关键。交易的成本包括衡量交换事物之价值成分的成本及保护权利、监督与执行合约的成本。这些衡量和执行成本乃是社会、政治和经济制度的来源。
基本上制度改变了个人付出的代价,因而导致观念、意识形态与教条常常在个人选择中扮演重要的角色。
有关意识形态、利他心和自我约束之行为标准的证据告诉我们,财富和这些其它的价值之间的取舍形成一个负相关的函数(negatively sloped function)。也就是说,当个人表达自己的价值和利益的代价很低时,它们就成了影响选择的重要因素;但是当个人要付很高的代价才能表达自己的意识形态、规范和偏好,则它们就较无法解释人类行为(见Nelson and Si1berberg,1987)。
我们必须深入两项人类行为的特定层面进行分析:(1)动机;与(2)诠释环境。人类行为看起来比经济学家设定模型时用个人效用函数所表示的更复杂。许多情况的人类行为并非财富极大化的行为,而是基于利他心以及自我约束。就人们实际的选择而言,那些不同的动机大大地改变选择的结果。同样地,我们发现人们诠释环境的方式是透过已有的心理构造来处理讯息。现成的心理构造是让他们用来了解环境以及解决面临的问题。要了解主题所在就必须考虑当事人的计算能力以及问题的复杂程度。我们在此先探讨人的动机。
合作的基本理论问题可以视为:在一个既定环境下,一个人至少必须具备多少关于他人想法与欲望的知识,才能够对他人的行为产生整体概念。以及能够用这种知识与他人沟通?
特别当存有不对称的关系让人们在重复游戏中去探索他人的动机与能力时,习俗(conventions)(引导某种社会秩序)可能会出现。哈定也说到,习俗也会发生于当人们采取有条件的策略(conditional strategy)时。不过,这些有条件的策略涉及纠察与(运用威胁来)强制执行。
制度改变的过程之所以复杂乃是由于边际的变化可能来自规则改变、非正规制约的改变,与执行方式和效果的改变。进而言之,制度通常是逐步渐进地改变,而非以不连续的方式改变,至于制度是如何逐步地改变与为何如此?以及为何即使不连续的改变(例如革命与征服)也绝非完全不连续的?这些原因都在于深植社会中的非正规制约。虽然正式规则可能经由政治或司法决策而在一夕之间改变,但是存在于习俗、传统和行为准则中的非正规制约却是普通政策所无法影响的。
制度在一个社会中的主要作用是建立人们互动的稳定结构(未必是有效率的),以降低不确定性。制度的演变可以经由习惯、行为准则、社会规范,乃至成文法、不成文法以及个人契约来达成。因此,制度不断地改变我们所能做的选择,不过,虽然在我们周遭明显可见制度的快速变化,但是实质上制度演变的进度可能宛如冰河移动般缓慢,以至我们必须用历史学家的眼光才能察觉。
制度和被利用的技术合起来决定了交易成本(transaction costs)与转换(transformation)(生产)成本。该两项成本构成总成本。
制度限制包括了两种:一种是什么行为个人不准去做,另一种是何种条件下个人可以从事某些行为。
Showing the 12 most recent of 20 posts we hold for @whatbiereading. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Citation-graph rank
Citation-graph rank — 639,792 of 1,160,990entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
Forward network
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Mentions
Named by 1 registered channel — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
Cite this entry
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 10 August 2026 — this entry's latest reading, not the date you are reading this.
“别的书摘与政治不正确玩梗” (@whatbiereading), 3,028 subscribers as measured 10 August 2026. Telegram Register, tgregister.com/channel/whatbiereading.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.