“Financial markets don’t follow economic laws. Financial markets are a product of human evolution and follow biological laws instead”
- Andrew Lo, Adaptive Markets
In attempting to understand (and exploit) the operation of modern financial markets, academics and investors alike have long found comfort in the reductionism of all-encompassing equations. The problem is these equations can sometimes be wrong, and when they are wrong, they are destructively wrong. Richard Feynman once quipped “imagine how difficult physics would be if electrons had feelings,” pointing out with characteristic wit, the inappropriateness of translating physics to human financial markets. In trying to solve this problem there has been a recent shift towards recognizing the role that humans play in these markets (and the irrationality and unpredictability they create) and by extension the role that biological laws play in finance. From Herbert Simon’s bounded rationality, to Daniel Kahneman’s heuristics and biases, this focus on human biology in financial markets has been a long time coming.
Our focus here will be on how we incorporate (and exploit) these new techniques and tools as an investor in the venture capital market. In this context., we can see that attempting to incorporate the ‘physics’ of machine learning alone will be suboptimal. We need to leverage biological laws in optimizing our investments. We do this by using interpreting the venture capital market as a complex adaptive system, and draw on insights from machine learning, theoretical computer science, graph theory, and evolutionary game theory.
Why Machine Learning Alone is Not Enough
Building quantitative tools to support investment decisions is valuable in itself. Alan Turing once said “I believe that the attempt to make a thinking machine will help us greatly in finding out how we think ourselves.” I believe all venture investors, for every decision, invoke an internal ‘model’ that they’ve ‘learned’ over their career through the all companies reviewed, decisions made, successes and failures. In building a model we can learn more about how we make decisions, and how we can improve them. The is based on the problem that humans forget, are biased, and generally make sub-optimal, heuristic based short cut decisions. Also, how many sufficiently detailed deals could a human have possibly seen? 2,000, 10,000? And of these deals how many ‘features’ do they remember about each of the deals? Psychologist George Miller of Princeton famously found that humans can only hold 7 objects (plus or minus 2) in their working memory.
But a machine learning model does not forget, is not biased (as long as the training data is appropriate) and can evaluate all 30,000+ deals in making a decision. But what happens when the first Blockchain deal is reviewed by the model? What ‘market’ feature do we assign to this new market? Here we see the breakdown of using only machine learning to make decisions; it violates the invariance assumption (from Theoretical Computer Science Professor Leslie Valiant), the invariance assumption states that the context in which the generalization (prediction) is to be applied cannot be fundamentally different from that in which it was made.
But in almost every successful case, the entrepreneur is deliberately trying to violate this assumption. ‘We are doing something completely unique’, every entrepreneur is deliberately trying to break the current context and introduce something new, and if it sufficiently new that it is unrecognizable to the model that has learned over the past 10 years (like blockchain technology) the model is broken.
Incorporating Elements of Systems Biology
“I remember the first time I met Edsger Dijkstra. He was noted not only for his pioneering contributions to computer science, but also for having strong opinions and a stinging wit. He asked me what I was working on. Perhaps just to provoke a memorable exchange I said, “AI.” To that he immediately responded, ‘Why don’t you just on work on I.’ ”
- Harvard Professor Leslie Valiant, Probably Approximately Correct
So it is that highly complex, non-linear systems must be treated (at the time of writing) with more than just artificial intelligence. We are trying to get to a more complete understanding, and with that goal in mind we introduce elements of biology into the mental model.
The venture capital market lends itself naturally to biology; it is completely driven by human interactions, networks and relationships, it’s constantly evolving, and involves concepts of competition and survival analogous to evolutional biology. Indeed, with some (all?) companies it is inherently human; Sequoia Capital reportedly analyzes which of the ‘7 Deadly Sins’ the company under question exploits.
Applying X to venture capital:
- Evolutionary biology
- Graph theory
- Diversity and complexity
- Theory of ‘Ecorithms’
To be continued.