Market Incentives & Mechanism Design in Decentralized Distributed Ledger Systems and Meta-Platform Theory v Samuel M. Smith Ph.D.
BYU CIO Lecture Series 2019.03.05 sam@samuelsmith.org https://github.com/SmithSamuelM/Papers
Reading List J. Currier, “The NFX Archives: Foundations for Mastering Network Effects,” NFX.com, https:/fwww.nfx.com/post/network- effects-archives N. Ferguson, “The square and the tower: Networks and power, from the freemasons to Facebook,” Penguin Books, 2019. . Keen, “A dynamic monetary multi-sectoral model of production,” DebtWatch, 2011/05/16 http:/fwww.debtdeflation.com/blogs/2011/05/16/a-dynamic-monetary-multi-sectoral-model-of-production/ M. C. Munger, “Tomorrow 3.0: Transaction costs and the sharing economy,” Cambridge University Press, 2018. G.G. Parker, M. W. Van Alstyne and S. P. Choudary, “Platform Revolution: How Networked Markets Are Transforming the Economyand How to Make Them Work for You,” WW Norton & Company, 2016. E. A. Posner and E. G. Weyl, “Radical Markets: Uprooting Capitalism and Democracy for a Just Society,” Princeton University Press, 2018. R.G. Rajan and L. Zingales, “Saving capitalism from the capitalists: Unleashing the power of financial markets to create wealth and spread opportunity,” Princeton University Press, 2004. S. M. Smith, “Open Reputation Framework,” vol. Version 1.2, 2015/05/13. https://github.com/SmithSamuelM/Papers/blob/masterfwhitepapers/open-reputation-low-level-whitepaper.pdf B. Widrow and S. D. Stearns, “Adaptive signal processing,” 1985.
Mnemonic GitHub SmithSamuelM Papers
distributed = computation happens at multiple sites non-distributed = computation happens at one site centralized = computation controlled by a single entity decentralized = computation controlled by more than one entity computing system may be some combination of centralized it dsbtad (decentralized) and distributed (non-distributed). centalzed dacenirazed decentralization may occur to a degree. 52 system decentralization may lie on a spectrum of strongly § a | decentralized to weakly decentralized. 5z = ‘non-distributed. non-distributed it derntmized Nunberof Conolig Enics Decentalization
Mechanism design is a field in economics and game theory that takes an engineering approach to designing economic mechanisms or incentives, toward desired objectives, in strategic settings, where players act rationally.
Algorithmic mechanism design lies at the intersection of economic game theory and computer science. It combines ideas such as utility maximization and mechanism design from economics, rationality and Nash equilibrium from game theory, with such concepts as complexity and algorithm design from discrete mathematics and theoretical computer science. Control theory in control systems engineering is a subfield of mathematics that deals with the control of continuously operating dynamical systems in engineered processes and machines. The objective is to develop a control model for controlling such systems using a control action in an optimum manner.
(see https:flen.wikipedia.org/wiki/Mechanism_design, https:/en.wikipedia.org/wiki/Algorithmic_mechanism_design, https:/fen wikipedia.orgfwiki/Control_theory)
Platform Business Models Multi-Sided Platform (MSB), Two-sided Networks, N-sided Networks, Network Markets. Think Airbnb or Uber Primary advantage of a platform business model is value capture through network effects Aplatform is a business based on enabling value-creating interactions between external producers and consumers. A platform is an automated intermediary A platform provides an open, participative infrastructure and sets governance conditions for these interactions. A platform facilitates the exchange of goods, services, or social currency amongst participants thereby enabling value creation or co-creation for all participants. Primary activity of a platform is external orchestration/coordination of interactions between third parties. ( See Platform Revolution 2016, NFX Archives, Tomorrow 3.0)
Single-Sided Network Effect + Network + more consumers increases value which attracts more consumers demand side driven
Two-Sided Network D ,
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more consumers drive demand which attracts more producers more producers drive supply which attracts more consumers both demand and supply side driven
Example 7 s ™\ { h { i | |
Negative Cross-Side Network Effects + T 7 e — N\ 1 o+ Platfor O i / More supply choice increases friction e.g. customer confusion in producer selection thereby decreasing demand More demand choice increases friction e.g. producer failure in customer satisfaction thereby decreasing supply
Platform Business Model Participant Participant = =3 _ Core Interaction = Participant/Value unit + Filter Platform = Pull + Match + Facilitate Two-sided network effects Demand economies of scale (network effect multipliers of value) that eventually drive Supply economies of scale (production efficiency)
Transaction Costs
Triangulation:
Find, Filter, Match Transfer:
Transport, Deliver, Pay Trust:
Competency, Reliability, Honesty Platforms sell reductions in transaction costs To a consumer, all costs look like transaction costs (Michael Munger, Ronald Coase: Transaction Costs)
Enablement Distributed network computation enables platforms Distributed consensus enables more secure platforms Platform lock in induces exploitation via information asymmetry Decentralized distributed consensus enables more trustworthy platforms Distributed Al provides scalable super-efficient user controlled re-intermediation Platform governance matters
Decentralization Advantage
Although decentralization can reduce triangulation and transfer costs, its primary potential advantage is in reducing trust costs! Online interactions make trust harder = increased trust costs Hidden trust costs are still costs Conventional ways to reduce trust costs:
Branding & Reputation
Certification
Bonding
Regulation
Algorithmic Decentralized Governance autonomic system: self-regulating, self-governing, self-managing, self-certifying, self-protecting Distributed decentralized computing systems may provide trustworthy algorithmic market behavioral incentivization and regulation carrot and stick
Curation Match + Filter = Curation - | &porticpants | Reduces negative cross-side network effects o + Enhances positive cross side network effects 4 Al Platform . N Interaction
Essential enabling capability for any platform Curation is applied reputation ‘\
-
- -
Reputation Driven Interaction - \ 3 Sy SUr4 Graduated Participation Interfaces
Reputation Based Curation “\, /
Meta-Platform Platform that enables and fosters network effects across and amongst other platforms Enables network of network effects Enabled by contextual transitivity = value transfer between platforms Enables long-tail network effects P - [\ 1 / 7 — \ 7 S I N
Networks Effects Metcalf's Law for platform: platform value s proportional to the square of the number of participants using the platform Competing Networks sub sub sub network network B network S se=t0 S se=t0 S st S a2zt 0 vaexiog=i00 0 vae wto2=100
Big Networks Effects Winner Take All Network network S size = 100 . e value 10'2= 10,000 N
Network of Networks Effects Cooperating Meta-Network of Networks SEEEBEREEE e e i e e i i i O smensmmai D aesariore=to=tx B size =100 :
Meta-Network Effect The increase in value due to network effects of any platform participating on a meta-platform is proportionate to the ratio of the meta-platform’s size to the platform’s size. N= platform size V(N) = N2 M = meta-platform size V(M) « M2 V(N:M) = N/M-M2=N-M V(N:M)IV(N) « N-MIN? = M/N N=10, V(N) = 100, M=100, V(M) = 10,000, V(N:M) = 1000 V(N:M)/V(N) = M/N = 10 Meta-Platforms Will Eat Platforms
Contextual Transitivity and “The Long Tail” long-tail = value capture of contextual value = unique value from extreme customization meta-platform technology enables transfer of value between platforms meta-platforms may aggregate contextual value from multiple long-tails enables long-tail meta-network effects participant’s amplify their value across multiple platforms at minimal transaction costs maximum platform pull = fastest spin-up of network effects meta-platforms eat non-meta-platforms
Examples of Meta-Platforms concept of meta-platform is relative yesterday’s meta-platforms are today’s platforms key is identifying tomorrow’s meta-platforms Yesterday: Money Tomorrow: Public Markets Decentralized Identity and Reputation ‘Governments and Quasi-Government Institutions Decentralized Algorithmically Governed Institutions Today: Decentralized Algorithmically Regulated Marketplaces Internet Decentralized Autonomic Services
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