Pareto principle

The Pareto principle is the principle, discovered by the Italian economist Vilfredo Pareto, that '20 percent of inputs lead to 80 percent of outputs'; in the incelosphere, and the manosphere in general, it refers to the observation that dating prospects are not always distributed evenly. While there is not necessarily evidence that human sexual endeavors follow a strict Pareto distribution, it is used a 'rule of thumb' in the incelosphere that illustrates the concept of female hypergamy. This does not mean that advocates of applying this principle to dating are implying that only 20 percent of men have sex. It is only claimed that sexual access (and therefore copulation frequency) is unevenly distributed among the male population
See the 80/20 Rule for more.
==80/20 Rules in Pure Mathematical Reasoning==
===80/20 Rules as Statistics of Inequality===
The Theil Indexhttps://www.wikiwand.com/en/Theil_index is an alternative index for measuring inequality, where 0 is perfect equality, 1 is the "platonic equilibrium" of approximately 82/18, and an index larger than 1 suggests inequality.https://www.wikiwand.com/en/Income_inequality_metric It can also be inverted to an equivalent Atkinson indexhttps://www.wikiwand.com/en/Generalized_entropy_indexhttps://www.wikiwand.com/en/Atkinson_index. Also in a pool of 80/20 split of women, the chances of any three-women clique agreeing that a man is hot are slightly higher than 50/50, and with any decrease of the agreement pool, the chances of clique consensus become lower than 50%.
=== Extensions of the Rule ===
The extension of the 80/20 Rule, is the 64/4 Rule and the 51/1 Rule, or "Two-thirds of women will chase the top 4% of men" and "Half of all women will chase the top 1% of men". This is borrowed from research on management optimization and Agile Developmenthttps://www.timewax.com/blog/the-80-20-rule-in-project-resource-planning/https://www.vthink.com.au/single-post/2017/08/18/the-644-principle, where work characteristics are ranked based on impact. To paraphrase FastCompanyhttps://www.fastcompany.com/3069049/beyond-the-80-20-rule-this-formula-might-make-you-rethink-time-management, "A Time" is 0.8% of effort for 51.2% of results (256XD), "B Time" is 3.2% effort for 12.8% of time (16XD), "C Time" is 16% of effort for 16% of time (4XD), "D Time" is 20% of the work for 80% of the time (1 XD). In the case of the Blackpill, Face and Height would be A Time (Genetics), Looks and Frame (Things that can be Maxxed) would be B Time, Status and Game (Redpill) would be C Time, Bluepill strategies would be D Time.
==Relationship to other laws==
===Zipf's Law on Attention Distribution===
Zipf's Lawhttps://www.wikiwand.com/en/Zipf%27s_law is an idea that the number of times a word (or phrasehttps://arxiv.org/pdf/1406.5181) is used is inversely correlated to its frequency rankings. It can also be seen as the amount of attention an item receives over another. Repeated experiments conducted when given 8 Alien names still demonstrate this pattern (albeit less strong at a slope of 0.31 instead of linguistic corpus platonic slope of 1).https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4176592 Newsgroup reply networkshttps://link.springer.com/article/10.1007/s13278-016-0334-0 also follow in similar fashion, however the newsgroup's size is only in the 4-digit range. Its extension is the Pareto–Zipf Lawhttps://www.wikiwand.com/en/Zipf%E2%80%93Mandelbrot_law, where low-rank items have more variance in distribution.
==== Alternative Hypothesis: False Power Law ====
False Power Lawhttps://arxiv.org/ftp/arxiv/papers/1507/1507.03408.pdf (e.g. Log-Normal, Gamma, Weibull, and Log-Log distributions) is used for modeling wealth and income distribution, human resource distribution per occupation, and Subreddit usage distributionhttps://cdanfort.w3.uvm.edu/research/kelly-gothard-undergraduate-thesis.pdf. Shalizihttp://www.stat.cmu.edu/~cshalizi/2010-10-18-Meetup.pdf and othershttps://arxiv.org/ftp/arxiv/papers/1011/1011.1533.pdf warned that most distribution do not follow the Zipf's Law if lower ranks were to be observed, and instead proposed replacing them with false power laws. 
YouTube Datahttps://artoftroubleshooting.com/2015/11/04/tube-of-plenty-analyzing-youtubes-first-decade/ has demonstrated that Log-Normal Distribution fits for views and comments, where the lower ranks have more sensitive variance compared to Pareto-Zipf Law. The same cannot be said for likes/dislikes, which more closely follow Pareto-Zipf Law. The 10% of videos matches Zipf's Law, whilst the bottom 90% approximates the power ratio of about one and a half, an average between Zipf's Law and Lotka's Law. This suggests that 90% of the time, merit is only accounted for half of the views. This is effectively Sturgeon's Lawhttps://www.wikiwand.com/en/Sturgeon%27s_law. 
Twitter reply networkshttps://doi.org/10.1111/j.1083-6101.2008.01435.x and academic citationshttps://link.springer.com/article/10.1007/s11192-014-1524-z also follows similar patterns. Zipf's Law and its extensions is sensitive against larger group size when it comes to attention, whilst False Power Law is sensitive against ratings. Note: Double Pareto Distribution cannot fully simulate Log-Normal Distributionhttps://www.eecs.harvard.edu/~michaelm/postscripts/im2004a.pdf. Another side effect of this discrepancy is the King Effecthttps://www.wikiwand.com/en/King_effect, where sufficiently large data will have the extremally ranked item to not follow the power law against the majority in the middle. 
=== Lotka's Law on Communication Distribution ===
Lotka's Lawhttps://www.wikiwand.com/en/Lotka%27s_law relates to social communication, it states that the number of times a person would post, is squared inversely correlated to its communication rankings. This is proven by Wikipedia authorship distributions (slope between 1.88 and 2.3)https://www.nature.com/articles/srep01783.pdfhttps://doi.org/10.1007/978-3-540-88655-6_19. This can be boiled further to Price's Lawhttps://dariusforoux.com/prices-law/https://learnrepeatacademy.com/price-law/, or that "50% of the work is done by the square root of the group size". This implies that "bloat" exists (see Corporate Blackpill). This effect explain why in social activismhttps://uniteyouthdublin.files.wordpress.com/2015/01/direct-action-an-ethnography-david-graeber.pdfhttps://philosophybear.substack.com/p/movements-are-always-a-distorted that the proportion of women increases along side the size of the group, and why underdeveloped nations have more female STEM majorshttps://dynomight.net/gender-equality-paradox/ (see Corporate Blackpill). Further experimentationhttps://journals.sagepub.com/doi/pdf/10.1177/0146167220912621 demonstrates that positional safety, team size and proportions of females are major factors in Misandry in the workplace.
==== Application: Effectiveness in Smaller Groups ====
This law can be extended into the idea of group size for effective work, which is demonstrated in Ringelmann's Rope Pulling Experimenthttps://blog.nuclino.com/two-pizza-teams-the-science-behind-jeff-bezos-rulehttp://blogs.quovantis.com/ringelmann-effect-why-small-teams-are-a-strategic-advantage/, where average personal effort is inversely and linearly correlated to group size, and that an 8-person team can only exert as much effort as 4 individuals, projecting that a 15-person team is practically useless (see Dunbar's Circles). This experiment is applied to many applications, including Amazon's Two Pizza Rule of team-building (based on Agile Teams), and Clandestine Cellhttps://www.wikiwand.com/en/Clandestine_cell_system utilized in warfare. The logic of applying this rule to non-athletic purposes is the Brook's Lawhttps://www.wikiwand.com/en/Brooks%27s_law, or that social links scales quadratically whilst membership scales linearly, with 5 links per person being the limit on team cohesion (see Dunbar's Circles).
===Synthesis of The Two Phenomena===
These two laws, when observed in aggregate, leads to an interesting result: Low-ranked persons' effort needed for increasing attention decreases as the group size increases, but the effort needed for increasing positive feedback stays relatively constant quadratic rate. The former is optimized for viral content, whilst the latter is optimized for quality content. 
==== The Chad Index ====
Since the attention ranking (Zipf's Law and False Power Law) and communication ranking (Lotka's Law) does not often match, one equation can be made to measure its distance. Chad Index can be introduced to measure one's attempt at being important compared to their actual importance. The variables for the equation below can vary from platform to platform, for example if it is Facebook or Twitter, it can be post and reply count (personal communication) versus like and retweet count (perceived attention). If Chad Index is negative, it might imply that the person in question is Overgaming, but if the Chad Index is positive, the person in question is likely an influencer who is Mogging. 
attention_ranking_deviation = LOG2( median_response_count / personal_response_count )
communication_ranking_deviation = LOG2( ( median_post_count ) / ( personal_post_count ) ) / 2
chad_index = attention_ranking_deviation - communication_ranking_deviation
The Chad index may be useful only insofar as to determining fame but not necessarily infamy or controversiality. For Facebook, there is the Angry emojis and replies (when compared to likes and hearts). For Twitter, there is the "ratio"https://www.dataforprogress.org/the-ratio-richter-scalehttps://fivethirtyeight.com/features/the-worst-tweeter-in-politics-isnt-trump/https://arxiv.org/pdf/2006.03526.pdf. For Reddit, there is the downvote. A further application of this is to see whether there is correlation between attention and communication deviations. TBD 
==== Possible complications ====
The major issue against the Chad index is that the False Power Law distribution (prestige, positive feedback) can sway closer to Lotka's Law (communication) OR Zipf's Law (attention, negative feedback) depending on whether the ranking is closer to the head or the long tail. The Chad index ignores prestige entirely as it is population-dependent, and prestige can only be compared rank-wise and within deciles post-hoc. An alternative may be needed to quantify prestige deviations in relation to communication and attention deviations, to identify major "hot topics". TBD.
=== The 90-9-1 Rule and the 70-20-10 Rule ===
This divide between quality and quantity can best be exemplified by the 90-9-1 Rulehttps://stangarfield.medium.com/90-9-1-rule-of-thumb-fact-or-fiction-2377c12f3a79 and the 70-20-10 Rulehttps://palavitsinis.wordpress.com/2015/08/26/user-generated-content-the-1-9-90-rule/, which states that 70% are Lurkers, 20% are Commenters, 9% are active, and 1% contributes.
===Media Gravity Theory===
The media gravity theory, is the idea that attention-centric mediums and communication-centric mediums garners opposite ends of the Lookism spectrum. There are two classification axis: McLuhan'shttps://www.wikiwand.com/en/Understanding_Media "Hot v Cold" axis (which is fiercely under debate), and Huffington'shttps://www.wikiwand.com/en/Slow_media "Fast v Slow" axis of prescriptive information (Hume's "ought") vs descriptive information (Hume's "is"). Two major example of this is Instagram Mogginghttps://knowyourmeme.com/memes/sites/instagram (attention-centric) and Discord Mod Nepotismhttps://knowyourmeme.com/memes/discord-mods-please-keep-memes-out-of-general (communication-centric), where both instances are fast media in opposite directions of the Hot-Cold Axis. In the case of Instagram (a photo/video platform), its algorithm prefers social media virality and visual attractiveness (similar to Tinder preference on Chad), thus content that are more controversial or thought-provoking are pushed out. In the case of Discord (an instant messaging platform), its algorithm prefers conversation frequency over quality, thus spamming and deceptive language is prioritized over tangible project work.
Researchhttps://doi.org/10.1016/j.procs.2020.03.433https://doi.org/10.1007/s11002-012-9179-0 has been done to quantify network characteristics for the classification of media typology. Assortativity and Homophily has been identified. In general Assortativity is correlated to information depth and cognition over instincts, whilst Homophily is correlated to slower and less emotionally-charged media consumption, and less need for less need for social participation. There are three major classes of media identified through this system:
# "Cold Reasoning" (Assortative-Homophilic) e.g. Facebook, Forums and Blogs
# "Heuristic and Automaticity" (Disassortative-Homophilic) e.g. YouTube, TikTok, and Instagram
# "Visceral Reaction Media" (Disassortative-Heterophilic) e.g. Twitter, Snapchat, and Discord
Contrasting media topology and media gravity one can see two extra patterns: "Cool Media" (instant messaging) are more likely to be textual, visceral, reactive (operant/instrumental) and heterophilic, spamming and soliciting is key to success in expanding ones group, leading to Overgaming being interpreted as a legitimate way of success; "Hot Media" (video and visual press) is more likely to be visual, satiating, Pavlovian, and homophilic, Chad and Lordosis is the norm, and that Lookism "like attracts like" happens often in these platforms.
Conway's Law (corporate structure dictates product structure) and Jakob's Law (UX converges on user demographics) applies here.
==See also==
*Briffault's law
category:theories