Common sense is not common because like everything in Nature, human thinking is clustered. We don’t question what we are taught at school. This is why ML is Cargo cult, amplifying the replication crisis in Science.
Free lunch was a tradition once common in saloons in the US. It was a sales enticement that offered a meal at no cost to attract customers and build patronage. So popular was the scheme that a complete meal was offered with a 15 cents drink and people used to throng to such saloons, a few of them even complaining about the quality of food. On the other hand, it was Milton Friedman who popularized the term ‘No free lunch’ with his book. The term meant that in reality, a person or a society cannot get “something for nothing”. In mathematical finance, this was an informal synonym for the no-arbitrage principle.
Schrödinger’s, “What is Life?” asked questions around the functioning of life and how Nature extracted order from disorder relentlessly. This paper illustrates a [3N] model of life which attempts to reconcile the conflict between Quantum and Newtonian states and explains how Nature overcomes the challenges of the second law of thermodynamics using a perpetual motion machine, allowing it to extract order from disorder.
The $ 100 trillion managed globally as your pension and investment assets is primarily discretionary. The human does the job. With or without the crash, the pie will become bigger. The future is about machines managing your pension and investments. There are many problems to solve, starting from bias to teaching investors how and why to trust the machine. But before anything, we need to pose the right questions for the machines to solve.
“We learn from history that we do not learn from history,” these famous words of George Bernard Shaw are a befitting reality for mankind.
Let's first explore, why is it hard to Short?
First; Stocks can go up 100 times but they can fall by 90% or go bankrupt. So the payoff is asymmetric. The profit is capped on the downside but are significant on the upside.
There are many detractors of AI, a few are taking the field ahead by redefining cause and effect [1], a few have called AI dead, and believe it is nothing but an attempt to get computers to do cool stuff [2]. The web is full of garbage in garbage out [3] references when it comes to deep learning. Despite the naysayers, proteins keep getting folded [4], robots keep getting smarter [5] and the surpassing human intelligence timeline keeps getting closer [5].
Friends have started calling me, messaging me. Most of them, anxious about missing the 'doubling the returns in under a year' bus. This is different from the calls which I receive at market lows. I will be honest. I am not an advisor. I build intelligence machines, which manage money, can advise, and do a host of other intelligence things.
Psychology is mathematical and Socionomics is a subjective and non-quantifiable study.
Tech Tokenization is an effective way to distribute reward, maintain accounts, and fractionalize.
The beauty of AI is that it can enhance itself.
Nobel Prize Lecture of Eric Maskin
Followed by talk of blockchains and mechanism design by Jeff Coleman
AlphaBots is any SSR process that is open to validation on the AlphaBlock. The process can be alpha agents like prediction markets or data scientists, or simply machines open to competing on the AlphaBlock. Competition is important for the accountability of an SSR process. Without the validation accountability any alpha process is simply a subjective claim of alpha without an objective proof.
The verifier’s dilemma, which remains an open problem is another example of gaps in the current blockchain architecture and the consensus mechanism.
Modern finance is built on linear regression. Hence Asness’s textbook use of linear regression is brilliant because simplicity is powerful. He uses Aristotelian logic to elegantly bring out the information content buried in hedge fund performance.
Fred Ehrsam, I would frame the problem differently. The need is not just for creating most powerful artificial intelligences, it is about redesigning the marketplace that can nurture such an ecosystem. The marketplace cannot be taken for granted. A poorly designed marketplace means poor AI, even if we assume that the state of AI today is structurally stable.
As intelligence moves from arbitrary and erratic patterns of human discretionary knowledge-building toward a more systematic and organic AI, there is a need for a new market mechanism to validate, distribute, and reward intelligent processes.