Alex Douglas On Arches And Money

Alex Douglas just published a post on arches and money which I quite liked. It is an easy read, and I will just offer my spin on one of his points. It also leaks into a discussion of engineering, which I will digress into at the end of this post.

He draws on an excellent book — J.E. Gordon’s The New Science of Strong Materials: or, Why You Don’t Fall Through the Floor. (I have sentimental attachment to this book since I won a copy of it and “Structures: Or Why Things Don’t Fall Down” from a provincial high school physics contest. Both books gave me a good introduction to the non-electrical parts of engineering — possibly more than my undergrad electrical engineering degree, since Canadian accreditation bodies do not allow much space for non-EE content in undergraduate programmes.) In the book, an arch is termed an “apparent impossibility.”

Why You Should Avoid Using "Positive/Negative Feedback"

I ran into a discussion on Twitter about the use of the terms positive feedback and negative feedback. These phrases really should not be applied to discussions of things like economic dynamics. I have discussed this before, but I decided to give the full explanation.

The stand alone term feedback as it is commonly used is perfectly fine. (Whether it was turned into corporate manager-babble is one concern, but I will leave that aside.) The problem is adding the qualifier “positive” or “negative.” Someone could try to argue that “positive” is a synonym for “good,” and so “positive feedback” is equivalent to saying “good feedback.” The problem is that good feedback is a silly sounding phrase in most of the contexts where “positive feedback” shows up (but not in contexts where you compliment someone).

Electricity Options

The recent power failures in Texas led me to think about a topic I had not thought much about since my electrical engineering undergraduate days -- electricity transmission and generation. In this article, I discuss big picture options for electricity, both from a technology and electricity markets/regulation point of view.

Inherent Limitations Of Linear Economic Models

Linear models used to be a popular methodology in economics, such as the log-linearisations of dynamic stochastic general equilibrium (DSGE) models. Rather than look at particular models, it is simpler to examine the properties of linear models themselves to see why they are inherently unable to capture key features of recessions, or rely on “unforecastable shocks” that represent an absence of theory about recessions. Since the author is unaware of anyone putting forth linear models as being useful in this context, this discussion is kept brief, and is perhaps only of historical interest. For example, if one wants to examine why the Financial Crisis acted as a theoretical shock, we need to understand how it conflicted with the popular linear models of the time.

Robust Control Theory And Model Uncertainty

Mainstream mathematical economics has a very strong influence from optimal control theory. As I discussed previously, optimal control was abandoned as a modelling strategy decades ago by controls engineers; it only survives for path-planning problems, where you are relatively assured that you have an accurate model of the overall dynamics of the system. In my articles, I have referred to robust control theory as an alternative approach. In robust control theory, we are no longer fixated on a baseline model of the system, we incorporate model uncertainty. These concepts are not just the standard hand-waving that accompanies a lot of pop mathematics; robust control theory is a practical and rigorous modelling strategy. This article is a semi-popularisation of some of the concepts; there is some mathematics, but readers may be able to skip over it.

Why Parameter Uncertainty Is An Inadequate Modelling Strategy

We live in a world of uncertainty. One strategy used in economics is to incorporate the notion of parameter uncertainty: we have the correct model, but the parameters have some random variation from a baseline value. This strategy is highly inadequate, and has been rejected by robust control theory. The belief that we have the correct model was an underlying premise of optimal control theory, and the weakness of this premise in practice explains why optimal control theory was largely abandoned in controls engineering. (Interestingly enough, it persists in Dynamic Stochastic General Equilibrium (DSGE) models).

In this article, I give an example of an abject failure of parameter uncertainty as a notion of model uncertainty.