Leviticus 25:9
Once every 50 years comes the great day of the Jubilee, the day when all debts are forgiven.
For this special year and for the first time, the latest cryptographic technology can be used to obtain better debt jubilees.
Hallelujah!
וְעַתָּה, אִם‑תִּשָּׂא חַטָּאתָם; וְאִם‑אַיִן–מְחֵנִי נָא, מִסִּפְרְךָ אֲשֶׁר כָּתָבְתָּ
Exodus 32:31
Erecting false idols leads to the fall: you don’t build an enduring house over pseudo-anonymity, inefficient Sybil-resistance, insecure scripts, and unstable policies. And what is worse, with entry limited to only a very limited few.
Instead, when you build a great house, you invite everyone to enjoy it.
Rembrandt — Moses Breaking the Tablets of the Law
לֹא, תִּגְנֹבוּ; וְלֹא‑תְכַחֲשׁוּ וְלֹא‑תְשַׁקְּרוּ, אִישׁ בַּעֲמִיתוֹ
Lev. 19:11
As the human mind is inscrutable to others, so its elucubrations are the truly purest form of property. Raziel protects your secrets from the Adversary and provides proofs against its malicious machinations: you shall not be robbed neither of your data nor of your code, for they are your inalienable property.
Hallelujah!
Imagine devising a set of rules for a game such that the dominant strategy of every player is to truthfully reveal their valuations and/or strategies: this is just one of the ambitious goals of mechanism design, the science of rule-making and the most useful branch of game theory. Fifteen years ago, a pioneering paper of Nisam and Ronen (Algorithmic Mechanism Design) merged it with computer science by including the requisite that computations should also be reasonably tractable for every involved player: this created a fruitful field of research that contributed every tool of algorithmics and computational complexity, from combinatorial optimization and linear programming to approximation algorithms and complexity classes.
In practice, Algorithmic Mechanism Design is also behind the successes of the modern Internet economy: every ad-auctions uses it results, like Google’s DoubleClick auctions or Yahoo’s Auctions, and peer-to-peer networks and network protocols are being designed under its guiding principles. It has also contributed to spectrum auctions and matching markets (kidneys, school choice systems and medical positions) and it has also generated interesting models, like the first one that justifies the optimality of the fee-setting structure of real estate agents, stock brokers and auction houses (see Fee Setting Intermediaries).
Up until a decade ago, the only way to learn this fascinating field of research was by venturing to read papers dispersed between the areas of economics, game theory and computer science, but this changed in 2008 with the publication of the basic textbook of the field, [amazon_link id=“0521872820” target=“_blank” ]Algorithmic Game Theory[/amazon_link], also available online:
Now a bit dated, it has recently been complemented with some great resources:
And that’s enough to begin with: hundred of hours of learning insightful research with fantastic applications!
One the biggest mysteries of economics is how an academic discipline could have come so long without solid models of one of its most fundamental pieces of study: markets. They are assumed to exist, without any formal typology nor empirical tests of their properties. So it’s refreshing to find this recent paper to try to tackle this challenge:
It introduces the central property of decentralization to a model of markets, and proceeds to proof that this very property makes them resilient to manipulation, enhancing welfare and liquidity. Just like in the real world.
In a recent conversation with a friend, she lamented the hard time she was having in justifying the deployment of thousands of tablets within a company, especially given their high price. But it’s really the opposite: computers pay for themselves in a very short time, no matter what their format. A fact in direct contrast with the old Solow’s productivity paradox.
Just a little research to proof this: take the welfare gain of computers, measured by their compensating variation, that is, the amount of income a consumer would have to give up in order to attain the level of utility that would have realised if computers had never been invented. Most recent results show that is 3.8–4% of total consumption expenditure: in other words, >$1500 per year in a first world country (you can also play with the Matlab code behind this model!)
A high sum that completely justifies their price, but low if it’s compared with the compensating variation of the Internet (26.8%) or that of electricity (92%), ie. no one would live without electricity. And even though the variation of the Internet is much higher than that of computers, and also its contribution to economic growth, computers are Generally Purpose Technologies absolutely necessary to access the Internet, thus complementary to it and its compensating variation cannot be taken apart from them.