Merge branch 'pluralitybook:main' into main

Author: audreyt

ReadMe.md

@@ -16,7 +16,7 @@ To cite this text, you can use this bibtex as a sample
 
 @online{plurality2023,
   title={Plurality: The Future of Collaborative Technology and Democracy},
-  author={Tang, Audrey and Weyl, Glen and {the Plurality Community}},
+  author={Weyl, E. Glen and Tang, Audrey and {the Plurality Community}},
   year={2023},
   url={https://github.com/pluralitybook/plurality/blob/main/contents/english},
   publisher={GitHub},

contents/english/02-00-information-technology-and-democracy-a-widening-gulf.md

@@ -16,7 +16,7 @@ While a human rights operating system is the foundation, the point of the system
 
 While we have titled this section of the book "democracy", what we plan to describe goes well beyond many conventional descriptions of democracy as a system of governance of nations.  Instead, to build ⿻ on top of fundamental social protocols, we must explore the full range of ways in which applications can facilitate collaboration and cooperation, the working of several entities (people or groups) together towards a common goal.   Yet even these phrase miss something crucial that we focus on: the power that working together has to create something greater than the sum of what the parts working together could have created separately.
 
-Mathematically, this idea is known as "supermodularity" and captures the classic idea from Aristotle that "the whole is greater than the sum of the parts".  Given our emphasis on diversity, what "greater" means here is context specific, defined by the norms and values of the individuals and communities coming together.  Furthermore, our focus is less on people or groups *per se* than on the fabric running through and separating them, social difference.  Thus, what we will describe in this part of the book is, most precisely, the way how technology can empower supermodularity across social difference or, more colloquially, "collaboration across diversity".
+Mathematically, this idea is known as "supermodularity" and captures the classic idea from Aristotle that "the whole is greater than the sum of the parts".  An early example of the quantitative application of supermodularity is the idea of "comparative advantage", the first comprehensive description of which that we are aware of presented by the English economist David Ricardo in 1817.[^Ricardo]  "Comparative advantage" says, roughly, that overall welfare will be maximized when all trading partners specialize in making their most efficient product, even when some other partner can make *everything* more efficiently.  Comparative advantage is understood as an 'economic law' stating in effect that there are guaranteed gains from diversity that can be realized through the market mechanism.  This idea has been extremely influential in neoliberal economics (see Social Markets 05-07), although later iterations are more sophisticated than the Ricardian version.  Given our emphasis on diversity, however, what "gains" means here is context specific, and will be defined by the norms and values of the individuals and communities coming together.  Furthermore, our focus is less on people or groups *per se* than on the fabric running through and separating them, social difference.  Thus, what we will describe in this part of the book is, most precisely, the way how technology can empower supermodularity across social difference or, more colloquially, "collaboration across diversity".
 
 In this chapter, which lays out the framework for the rest of this part of the book, will highlight why collaboration across diversity is such a fundamental and ambitious goal.  We then define a range of different domains where it can be pursued based on a spectrum of depth and breadth.  Next we  highlight a framework for  design in the space that navigates between the dangers of premature optimization and chaotic experimentation.  Yet harnessing the potential of collaboration across diversity also holds the risk of reducing the diversity available for future collaboration.  To guard against this we discuss the necessity of *regenerating* diversity.  We round out this chapter by describing the structure followed in each subsequent chapter in this part.
 
@@ -118,6 +118,6 @@ None of this is inevitable and of course there are many stories of intersections
 
 In this part of the book we will (far from exhaustively) explore a range of approaches to collaboration across difference and how further advances to ⿻ can extend and build on them.  Each chapter will begin, as this one did, with an illustration of technology near the cutting edge of what is possible that is in use today.  It will then describe the landscape of approaches that are common and emerging in its area. Next it will highlight the promise of future developments that are being research, as well as risks these tools might pose to ⿻ (such as homogenization) and approaches to mitigating them, including by harnessing tools described in other chapters.  We hope that the wide range of approaches we highlight draws out not just the substance of ⿻, but also the consistency of its approach with its substance.  Only a ⿻ complementary and networked directions can support the development of a ⿻ future.
 
-
+[^Ricardo]: On the Principles of Political Economy and Taxation, London, John Murray, 1817. 
 [^Disanalogy]: One possible disanalogy is that the Second Law of Thermodynamics implies that in a long-term and broad scope sense, regeneration  can never succeed.  Whether the same applies to diversity is less clear, though given how long term the relevance of the Second Law is, the analogy is quite strong for practical purposes.  In the long run, we're all dead.
 [^Levi]: Cite Levi-Strauss here

contents/english/05-00-collaborative-technology-and-democracy.md

@@ -47,7 +47,7 @@ While many Americans look back with nostalgia on the history of the press, the e
 
 The "Deliberation" chapter above suggests a natural strategy.  Social media algorithms could "communities" based both on patterns of behavior internal to the platform (e.g. views, likes, responses, propagation, choices to join) and on external data such as social science or group explicit self-identification (more on this below).  For each such community, the algorithms could highlight "common content" (commonly agreed facts and values) of the group that span the divides internally, as well as important points of division within the community.  Content could then be highlighted to citizens of the communities within this social context, making clear which content is rough consensus in which communities that citizen is a member  and which content is divisive, as well as offering opportunities for the citizen to explore content that is consensus on the other side of each divide from the one she is on within that community. 
 
-Such a design would continue to offer individuals and communities the agency social media affords them to respectively shape their own intersectional identities and self-govern.  Yet at the same time it would avoid the rampant "false consensus" effect where netizens come to believe that extreme or idiosyncratic views are widely shared, fueling demonization of those who do not share them and a feeling of resentment when associated political outcomes are not achieved or "⿻istic ignorance" where netizens are unable to act collectively on "silent majority" views.  Furthermore, and perhaps most importantly, it would reshape the incentives of journalist and other creators away from divisive content and towards stories that bring us together.  Furthermore, it is relevant beyond "hard journalism" *per se* as many other cultural forms (e.g. music) benefit from audiences who want to share cultural objects and fandom with other.
+Such a design would continue to offer individuals and communities the agency social media affords them to respectively shape their own intersectional identities and self-govern.  Yet at the same time it would avoid the rampant "false consensus" effect where netizens come to believe that extreme or idiosyncratic views are widely shared, fueling demonization of those who do not share them and a feeling of resentment when associated political outcomes are not achieved or "⿻istic ignorance" where netizens are unable to act collectively on "silent majority" views.[^Note] Furthermore, and perhaps most importantly, it would reshape the incentives of journalists and other creators away from divisive content and towards stories that bring us together.  It is relevant beyond "hard journalism" *per se* as many other cultural forms (e.g. music) benefit from audiences who want to share cultural objects and fandom with others.
 
 
 ### ⿻ public media
@@ -65,6 +65,7 @@ This might play out in a variety of ways, but a simple one would be for particip
 
 Overall, the examples above show how ⿻ can empower a new pro-social, ⿻ media environment: one where we can connect deeply with others from very different from us, where people come together to tell their stories in authoritative and verifiable ways without compromising community or individual privacy and where we come to understand what unites and divides us in the interests of the dynamism and solidarity of all our communities.
 
+[^Note]: An example of false consensus is that many observers believe SARS-Cov-2 escaped from a laboratory ('lab leak' hypothesis).  The rationalist web site Rootclaim (https://www.rootclaim.com/) even assessed 'lab leak' at 89% probability (~8 to 1 in favour).  Subsequently, educated laypersons were exposed to the evidence (e.g. Pekar et al., Science 377, 960–966, 2022 and Worobey et al., Science 377, 951–959, 2022.) in over 18 hours of adversarial debate and found posterior probabilities on the order of ~800 to 1 *against* lab leak, implying a Bayes factor of ~100,000 to 1 against lab leak. Despite the strength of the evidence, the lab leak claim persists since not only does zoonosis lack emotional resonance but it also requires hard work to evaluate and offers no cathartic pay-off.  Similarly, due to ⿻istic ignorance, despite the fact that more than 81 million people in the United States voted for Joe Biden in 2020, a small crowd of several thousand highly motivated individuals almost succeeded in disrupting the Electoral College vote count on 6 January 2021.  
 [^Publicmedia]: https://reutersinstitute.politics.ox.ac.uk/sites/default/files/2017-11/Public%20support%20for%20Media.pdf
 [^Religiousmedia]: https://www.causeiq.com/directory/grants/grants-for-religious-media-organizations/
 [^Twitterrev]: https://www.statista.com/statistics/271337/twitters-advertising-revenue-worldwide/

contents/english/06-03-media.md

@@ -0,0 +1,8 @@
+# VC Deals: AI and Blockchain vs Other Tech
+
+Sources:
+- [NVCA](https://nvca.org/pitchbook-nvca-venture-monitor/) and [data](https://nvca.org/wp-content/uploads/2024/01/Q4_2023_PitchBook-NVCA_Venture_Monitor_Summary_XLS.xlsx)
+- [Pitchbook](https://pitchbook.com/news/reports/q4-2023-crypto-report)
+- [Galaxy Digital Research](https://www.galaxy.com/research/insights/2021-crypto-vcs-biggest-year-ever/)
+
+The data represents annual VC funding from the US for AI and other tech, and (global) VC funding for blockchain and crypto. No data is available prior to 2017 from these sources for AI/ML or blockchain/crypto.

figs/data/tech_vc_funding/crypto_vc.png

@@ -0,0 +1,12 @@
+Year,Crypto (Global),AI/ML (US),Tech (US)
+,,,38.00
+,,,59.00
+,,,68.80
+,,,69.70
+2017,0.92,15.50,72.00
+2018,5.30,26.90,125.50
+2019,3.20,31.80,120.70
+2020,4.20,39.10,143.40
+2021,24.80,80.20,304.80
+2022,29.20,54.40,208.60
+2023,9.30,62.60,146.50