Part 2 is here.
A very strong contender for an actor most similar to the schizophrenic is an investment or venture capital firm (or trader). Both receive large amounts of potentially specious data and must interpret it in order to predict the future – a high-entropy proposition. All finance and investment relies on complex models and practices. All these models and practices are false: if one was true, there would be no such thing as a failed investment. All these same models are, to at least some degree, descriptive of what the market does, because they would not be used if they did not have some kind of predictive power. It remains to be seen whether this is because the models evolve with the market or the market evolves with the models. The issue here is data: I can’t guarantee you that my interpretation of market data will be accurate or not, but I can guarantee you that I have far less market data than an investment firm. Leaving entropy completely out of this, because we are all victims of high entropy when trying to predict the future: who will do better at reading the market, the person with less or the person with more data? In general, the odds would favour the person with more, and that is our last nail in the coffin of irrationality.
Hindsight is 20/20 they say, and failed investments eventually make sense when fully analysed. The problem is that they have to fail in order to determine holes in the model. The humans who developed the model have to have the humility to make necessary changes to the model to preclude such failures in the future. The model must be disseminated accurately and applied appropriately to have a chance of improving its predictive capability. Do these things happen? Occasionally. Normally, the same well-worn model tends to go back out into the field; its shortcomings described as a 1-in-100-year anomaly or a failure of interpretation. Is this rational? Depends on the environment – how many times had the model succeeded before? Depends on the entropy – was bad data entered into the model? Depends on the data – was there enough to give the model a chance of success? Indeed, hindsight is 20/20, and what was a rational investment choice is now seen, in the light of new data, to have been “irrational”. But was it? With just a little more data, the benefit of hindsight (low entropy), and the ability to understand the whole environment, we can certainly say the decision was wrong, but was it irrational? Not at all.
Environment, entropy, and data can actually be boiled down into one simple concept: information. Environment is the source of all data; entropy is the factor that affects an actor’s interpretation of data; the concepts of data and information are more or less the same. We can truthfully say that “all actors are rational depending on their information”. As an illustration, consider what happens to you after you die. In reality, it doesn't matter what you think, what matters is that you realise you are dealing with a situation in which the information has high entropy. You can easily agree that many people who see these words will believe something different than you about the topic. They will, if they disagree with you, consider your views irrational, and you theirs. If you reflect back on the reasons for which your views are rational, you can see the environment in which you were brought up, the experiences that reinforced the ideas you hold, and the data that you inserted into your mental model of how the world works in order to come to your rational conclusion. You can also transpose that experience directly to all the other people who come to this page and disagree with you. While the result is different, the information was not; while the end decision is rational to everyone who reads these words, it is not, necessarily, right.