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Friday, January 25, 2019

The Paradigm Shift in FinTech Computation and the need for a Computational Toolkit (Guest Post by Evangelos Georgiadis)

The Paradigm Shift in FinTech Computation and the need for a Computational Toolkit

(Guest Post by Evangelos Georgiadis)

We are experiencing a paradigm shift in finance as we are entering the era of algorithmic FinTech computation. (**And another yet to come. See **Future** below.)  This era is marked by a shift in the role played by the theoretical computer scientist. In the not so distant past, the (financial) economist had the ultimate stamp of approval  for how to study financial models, pricing models, mechanism design, etc. The economist was the ultimate gatekeeper of ideas and models, whereas the main role of the computer scientist was to turn these ideas or models into working code; in a sense, an obedient beaver/engineer. (In finance, the theoretical computer scientist more often than not wears the hat of the quant.)

In today's era, the role of the theoretical computer scientist has been elevated from the obedient engineer to the creative architect not only of models and mechanism designs but also of entire ecosystems. One example is blockchain based ecosystems. In the light of this promotion from obedient engineer to architect, we might need to re-hash the notion of 'sharing blame', as originally and elegantly voiced in On Quants by Professor Daniel W. Stroock, when things go wrong.)

The role change is also coupled by a shift in emphasis of computation that in turn necessitates a deeper understanding of (what this author would refer to as) distributed yet pragmatic complexity based crypto systems' that attempt to redefine 'trust' in terms of distributed computation.

This change necessitates an ability to think in terms of approximation (and lower/upper bounds)  or other good-enough solutions that work on all inputs,  rather than merely easy instances of  problem types that usually lead to clean, exact formulas or solutions.  Additionally, looking through the lens of approximation algorithms enables a different and often more insightful metric for dealing with intrinsically hard problems (for which often no exact or clean solutions exist.) Computer Scientists are trained in this way; however, financial economists are not.   Might the economists actually get in the way?

Our tentative response: The economists are valuable and the solution to the dilemma is to equip them with the right 'computational toolkit'. Ideally, such a toolkit comprises computational tools and algorithms that enable automation of certain computational tasks which otherwise would necessitate more granular understanding at the level of a theoretical computer scientist (or mathematician)
OR be too cumbersome to perform by hand even for the expert.

Essentially, a toolkit even for the theoretical computer scientist that frees her from clerical work and enables computation to scale from clean cases, such as n=1, to pathological (yet far more realistic) cases, such as n=100000, all the way to the advanced and rather important (agnostic case or) symbolic case when n=k -- without much pain or agony.

The existence of such a toolkit would in turn do justice to the definition of FinTech Computation, which entails applying advanced computational techniques not necessarily information techniques) to financial computation. in fact, this author is part of building such an infrastructure solution which
necessitates the underlying programming language [R-E-CAS-T] to have intrinsic hybrid capabilities -- symbolic as well as numeric.

One step towards this  "automation" conquest is shown in A combinatorial-probabilistic analysis of bitcoin attacks with Doron Zeilberger.  The work illustrates an algorithmic risk analysis of the bitcoin protocol via symbolic computation, as opposed to the meticulous, yet more laborious by hand conquest shown by the European duo in Double spend races Heavy usage of the "Wilf-Zeilberger algorithmic proof theory" one of the cornerstones in applied symbolic computation, enabled automated recurrence discovery and algorithmic derivation of higher-order asymptotics. For example, in terms of asymptotics tools: the ability to internalize a very dense body of mathematics, such as the G.D. Birkhoff and W.J. Trjitzinsky method, symbolically, automates the process of computing asymptotics of solutions of recurrence equations; a swiss army knife for any user.

<**Future**>

What does the future entail for FinTech Computation ?

[My two satoshis on this]

Where are we headed in terms of type of computation ?

Blockchain based systems, even though some of us (including this author) have noticed fundamental flaws, seem to still have momentum, at least, judging from recent news articles about companies becoming blockchain technology friendly.  Ranging from (of course) exchanges such as our friends at Binance and BitMEX, we have major smartphone makers such as SamsungHuawei, and HTC. The favorable sentiment towards blockchain technology is shared even amongst top tier U.S. banks.
 Can one deduce success or failure momentum from the citation count distribution of the paper that laid grounds to this technology ? Bitcoin: A Peer-to-Peer Electronic Cash System)

If we look at crypto(currencies), one of many challenges for these blockchain based systems is the high maintenance cost.  Certainly in terms of energy consumption when it comes to the process of mining -- whether Proof-of-Work (PoW) is replaced by Proof-of-Stake (PoS) or some other more energy efficient consensus variant. (This author is aware of various types of optimizations that have been used.)
A few questions that have bugged this author every since ...

a) Is there a natural way to formalize the notion of energy consumption for consensus mechanisms?

b) What about formalizing an energy-efficient mechanism design ?)

(The idea of savings when PoW is replaced by PoS as intended by our friends at the Ethereum Foundation has been around for some time but the point of this author is, the value of 0.99*X (where X is a supernatural number  [a la Don E. Knuth style]), is still a big quantity; too big for an environmentalist ?)

So, what comes next ?

[... the satoshis are still on the table.]

Daniel Kane has brought to my attention that quantum computation -- the seemingly next paradigm shift in which again the role of TCS seem  inextricably interwoven --  may lead to blockchain based systems being replaced by less expensive (at least in terms of energy consumption) quantum based systems. (Crypto might get replaced by Quantum (money). :-)) One such pioneering approach is masterfully articulated by Daniel Kane in "Quantum Money from Modular Forms.

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