%alphabet

a market making function

%alphabet allows you to make a market on anything, with anyone, on a decentralized front-end served from your own server, using a decentralized ledger.

%alphabet is a consumer-oriented decentralized application, targeted at the end-user. You can make money from each market you make, and you get reputation for doing so; reputation is valuable.

%alphabet is a market making function. The company aims to drive new forms of consensus in the broadening web3 and decentralized computing world. These forms of consensus will generate truth and reward a reputation for generating truth. %alphabet generates truth.

Traditionally, most markets of truth consensus are regulated markets such as option houses (CBOE), the stock market (NYSE), and prediction markets (IEM)[^1]. A collision point between trust, persistent reputation, and data management demands a new, sovereign market making function to explore the possibilities offered by zero-knowledge computational proofs and verification[^2], decentralized operating systems and self-contained software and hardware stacks[^3], and an increasing deference to the customer’s need for off-chain computation and data management.

What is a market making function? Markets are systems, protocols, institutions, and social and societal infrastructure that facilitate the exchange of goods or services. Markets usually require a currency in lieu of a direct bartering system. A key function of markets is to establish a price for the goods or services exchanged. A market making function allows for the creation of these markets.

A “global prediction market” is the rationalists’ epitome of markets: the abstraction of markets to decision making on markets themselves, allowing for the collective divination of decision making on any subject. In short, a market for everything, or, being able to see the market in everything. In parallel with this theory, %alphabet began as a prediction market.

Borrowing from Arrow, et. al.:[^4]

Prediction markets have been used by decision-makers in the U.S. Department of Defense[^5], the health care industry[^6], and multibillion-dollar corporations such as Eli Lilly, General Electric, Google, France Telecom, Hewlett-Packard, IBM, Intel, Microsoft, Siemens, and Yahoo[^7]. The prices in these markets reflect employees' expectations about the likelihood of a homeland security threat, the nationwide extent of a flu outbreak, the success of a new drug treatment, the sales revenue from an existing product, the timing of a new product launch, and the quality of a recently introduced software program. These markets could assist private firms and public institutions in managing economic risks, such as declines in consumer demand, and social risks, such as flu outbreaks and environmental disasters, more efficiently.

The more people that use %alphabet, the more reputation matters. The more people that use %alphabet, the better markets’ realize truth. The more people trust truth, the more valuable the market making function becomes. This literally is the best thing ever if you’re a fan of truth.

...

[^1]: The first two of these are option and stock exchanges, which is classically the most in demand form of market making. The third, the Iowa Electronic Markets, functions on a variety of topics. Cf. Berg, J., Forsythe, R. and Rietz, T., 1997. What makes markets predict well? Evidence from the Iowa Electronic Markets. Understanding strategic interaction: Essays in honor of Reinhard Selten, pp. 444-463, as well as Berg, J.E. and Rietz, T.A., 2019. Longshots, overconfidence and efficiency on the Iowa Electronic Market. International Journal of Forecasting, 35(1), pp. 271-287.

[^2]: Cf. Zorp (5m raise, 2023); Allen, L., Klatt, B., Quirk, P., and Shaikh, Y., 2023. EDEN - a practical, SNARK-friendly combinator VM and ISA. Preprint. https://ia.cr/2023/1021.

[^3]: Cf. Urbit, Kinode, and Plunder. All are indebted to Yarvin, C., Monk, P., Dyudin, A., and Pasco, R., 2016. Urbit: A Solid-State Interpreter. Originally presented at LambdaConf 2016. https://media.urbit.org/whitepaper.pdf.

[^4]: Arrow, K.J., Forsythe, R., Gorham, M., Hahn, R., Hanson, R., Ledyard, J.O., Levmore, S., Litan, R., Milgrom, P., Nelson, F.D. and Neumann, G.R., 2008. The promise of prediction markets. Science, 320 (5878), pp. 877-878. https://www.science.org/doi/10.1126/science.1157679.

[^5]: Hanson R., Ishikida T., Ledyard J., Polk C., Proceedings of the ACM International Conference on Electronic Commerce, Pittsburgh, PA, 30 September to 3 October 2003 [Association for Computing Machinery (ACM), New York, 2003], p. 272.

[^6]: Polgreen P. M., Nelson F., Neumann G., Clin. Infect. Dis. 44, 272 (2007).

[^7]: Cowgill B., Wolfers J., Zitzewitz E., “Using prediction markets to track information flows: Evidence from Google,” Dartmouth College (2008); www.bocowgill.com/GooglePredictionMarketPaper.pdf.