Tag: evolutionary psychology

Economics, psychology and blockchain systems

Blockchain platforms are economic systems. And just like any economy, a blockchain requires that its designers define monetary policy (inflation), fiscal policy (block size), taxation (fees), voting (governance/upgrades), and provide for the common defence (securing the network). Yet, unlike traditional economies, they offer the possibility of greater freedom and transparency because they avoid the problems of centralisation and concentration of power.

So the blockchain is great for academic economists, because it is a kind of living economic laboratory. Economists have plenty of tools for designing such systems. Does it end there? No, because the blockchain does not evolve randomly but by attempts at designing new, and better, models for money, ownership, control, trade, lending, licensing, and investment. In other words, many of the key innovations of the blockchain are economic innovations, and that means we need economists to help design them.

The problem with traditional economics is that it has two major assumptions, based on which the entire economic system is built. First, it assumes humans are rational and always and consistently optimise their utility (happiness, satisfaction). Second, economic theory assumes that on macro economic level there is or tends to be an equilibrium or balance which economics systems need to attain. In other words, we assume wisdom of the crowds and efficient market hypothesis. But what if neither is true, at least some times?

Humans are irrational by design. Just look at some of the decisions each of us make on a daily basis. We may vote for policies that go against our own economic interests. We make food selections that are at odds with our physical health. So there’s no clear, codeable logic in much of our behaviour.

Blockchain systems are, by design, difficult to change once deployed. Repairs and improvements to these systems are difficult. Protocols with billion-dollar valuations could disappear overnight. Things can get very acrimonious. Check the infamous Bitcoin block size debate. And one of the most difficult aspects for blockchain platform creators is accurately predicting people’s behaviour. That would be a relatively easy task if humans had a consistency, rational behaviour or an overarching logic in how they go about their lives.

Good news is that there is an entire field studying this – human irrationality and how to incentivise humans – very phenomenon, including Nobel laureates Daniel Kahneman and Amos Tversky and former Clinton advisor Cass Sunstein, who discovered that changing the default setting from “opt-in” to “opt-out” on things such as organ donation on a driver’s license and 401k contributions at work could dramatically improve uptake. This seemingly little change tapped on our Default Bias, enabling individuals to adjust their contribution levels (to retirement), but even the flummoxed novice who did nothing is at least socking some money away for retirement and taking advantage of company matching payments.

It turns out that we have a huge number of cognitive biases and knowing these and knowing how to go around these or nudge or incentivise us to act in desired ways is the key to understanding and predicting human behaviour accurately. One of most commonly-observed cognitive biases is loss aversion. Loss aversion derives from our innate motive to prefer avoiding losses rather than achieving similar gains. Kahneman and Tversy conducted an experiment asking people if they would accept a bet based on the flip of a coin. If the coin came up tails the person would lose $100, and if it came up heads, they would win $200. The results of the experiment showed that on average people needed to gain about twice (1.5x – 2.5x) as much as they were willing to lose in order to proceed forward with the bet (meaning the potential gain must have been at least twice as much as the potential loss). However, from traditional economic theory perspective, one’s risk appetite to losing or gaining $100 is the same.

The most tangible of incentives on blockchain platforms are digital tokens. Tokens usually represent currency, digital asset or some form of value in given blockchain system. Getting incentives right is fundamental to network growth, reflected in increased token adoption that yields positive network effects. Once this flywheel gets started, it serves as the ongoing funding mechanism for future development. Without it, the network cannot achieve self-sustainability. The value of the community and the token is what incentivises new members to join initially. If that value is off, new people don’t join and a death spiral begins. Once members are in, there needs to be a sustainable and growing value in the system to keep them using the tokens, participating in the community and helping the system grow. While token was the allure, the incentive system needs to account for conflicting interests of different types of users, system changes and perceived value and expectations from the system and, most importantly, it cannot necessarily based on the value of token itself. There are a few systems that help evaluate blockchain projects, including T3CG framework which is pretty solid.

Cryptoeconomics is hard as it requires expertise and mastery of mechanism design, contract theory, game theory, behavioral economics, public policy, macro-economics, and a solid understanding of decentralized technology in order to the design robust, sustainable and valuable blockchain economies. Hence is boils down to designing  incentive systems based on known biases – default bias, endowment effect, bandwagon effect, etc –  and other factors, including cultural values, public policies, system-specific goals.

I am very excited by potential of blockchain systems but also humbled by the realization that we have just scratched the surface on how to build optimal blockchain systems.

Failures of the theory of Darwin (part 1)

Evolution theory devised by Darwin is generally considered one of the most important intellectual achievements of the modern age. The theory allegedly put an end to hitherto existing speculations purporting to explain evolution of humanity and life on earth. In 1859, when the Origin of Species was first published, it did not directly reference humans nor made any claims of our common ancestry with other mammals. Ever since and with increasing knowledge in spheres of anthropology, genetics and biology, modern scientists came to hold it not as a possible conjecture (a sound theory with many explanations of empiric data) but as universal truth about the human life on earth. Currently, two main version of evolution theory exist: phyletic gradualism (uniformity and gradual transformation) and punctuated equilibrium (slight changes with final leap).

However till now, the theory failed to exhaustively explain or address a number of open questions and and issues:

1. Darwin, in The Descent of Man, considered it  logical to extend the theory to cognition, when he considered human characteristics such as morality or emotions to have been evolved, introducing evolutionary psychology. It holds that human nature was designed by natural selection in the Pleistocene epoch and aims to apply evolutionary theory to the human mind. It proposes that the mind consists of cognitive modules that evolved in response to selection pressures faced by our Stone Age ancestors. In the recent research conducted by authorities on the topic, Buller (in his book Adapting Minds) and  Richardson (in his book Evolutionary Psychology as Maladapted Psychology) show that neither the methodology nor the results of evolutionary psychology can be justified scientifically.

2. An apparent lack of “evolutionary” effect on bacteria (new generation: 12 mins to 24 hours) and fruit flies (new generation: 9 days) with unlimited number of genetic mutations and variations. Evolution theory must have had even a bigger effect on those because of a recently introduced model, which suggests that body size and temperature combine to control the overall rate of evolution through their effects on metabolism (smaller organisms evolve faster and are more diverse than larger organisms).

3. On rare and random occasions a mutation in DNA improves a creature’s ability to survive, so it is more likely to reproduce (natural selection). But it is widely known that there are very few human treats, which were tracked to one gene (sicknesses like the Dracula Gene and the Cheeseburger Gene). Modern science currently holds that most of even simplest of human treats, features and behavioral patterns have underlying sophisticated molecular and genetic mechanisms. Therefore it is doubtful natural selection could favor parts that did not have all their components existing in place, connected, and regulated because the parts would not work.

4. The Cambrian/Precambrian time period does not support Darwinian evolution. There are no intermediate (transitional forms) found during this period. There appear to be no fossil ancestors for complex invertebrates or fish.

5. The theory of evolution seems to be in violation of two fundament laws: second law of thermodynamics (things fall apart over time, they do not get more organized) and law of biogenesis (living cells divide to make new cells, and fertilized eggs and seeds develop into animals and plants, but chemicals don’t fall together and life appears).

To be continued some time soon..