Who Were the Sea Peoples?

The World in the Model , is now available at The Review of Austrian Economics .

As I am finishing up my review of Mary Morgan's book , as well as reflecting back on several months of fairly intensive agent-based modeling, I find myself thinking that the use of models in the social sciences is much like its use in engineering: the model allows us to answer questions such as, "If the assumptions that went into building the model are true, then what will happen if we introduce this change?" In either field, the assumptions are always only more-or-less true, and may be wildly off: the engineer, for instance, may be mistaken about how strong the effect of the wind will be on a bridge he is building: To understand the analogy I am making, consider that the above can also be taken as a visual representation of what happened to Murphy's macroeconomic model following 2007 . (We kid because we love, Bob!)

Mistaking statistics, which are merely our summaries of goings-on in the world, with causal factors in the world, is a confusion that pops up too regularly. For instance, here is Gregory Clark suffering from it. Mary Morgan understands this point: "such statistical or probabilistic laws can be said to govern the behaviour of our population. We individual people know better -- we know that the births, marriages, and debts are determined by a whole realm of social, economic, medical, physiological, and other laws, which determine whom we fall in love with, whether we have children, why we die, and when any of these happen to us." -- The World in the Model , pp. 336-337 In fact, I think Morgan hasn't gone far enough here: the "laws" she cites are just our names for the regularities produced by concrete causal factors, and don't themselves cause anything. (The proposition in the title of this post admits of exceptions, such as when a statistic about hou...

"But it is worth remembering that inferences from laboratory experiments also lack formal decision rules. Laboratory scientists, like modellers, depend upon both tacit and articulated knowledge in making sense of their experimental findings and judging their relevance within the laboratory. And. like model work, laboratory scientists face the same question of whether their experimental results can form the basis for inference beyond the laboratory..." -- Mary Morgan, The World in the Model , p. 34

"First, simulation is a kind of experiment, and as such brings with it problems of creating experimental artefacts, raising questions about how to distinguish genuine characteristics of behaviour from artefeactual ones created by the technology of manipulation." -- Mary Morgan, The World in the Model , p. 331 This distinction makes perfect sense when considering something like a telescope. So, there was nothing nutty about Galileo's doubters wondering whether those little blobs that appeared near Jupiter were really up in the sky, or were just productions of the telescope itself ("the technology of manipulation"). Galileo, in fact, in one of his observations, wound up drawing a moon that wasn't there: early telescopes were not easy to use! And I believe I recall reading that one of the recent "false positives" for creating cold fusion was due to just such an instrumental artifact. But in a computer simulation, everything is a product of the ...

"It is a nice paradox of the way models are used that a humanistic notion -- narrative or storytelling -- is critical to the way that models are used as a mode of inquiry in economic science whether the model narrative is a story about the world portrayed in the model or a correspondence story about the real world, past, present, or future." -- Mary Morgan, The World in the Model , p. 251

"It seems that some models are considered better than others because they can be used to tell better stories, so that the judgment of models relies on judging their narratives." -- Mary Morgan, The World in the Model , 246

Mary Morgan's The World in the Model obviously is giving me a lot of food for thought, as you know if you have been reading here recently. It attempts to understand the role of modeling in economics by looking at the history of modeling in economics. One thing it reinforces for me is something I learned from Collingwood, which is that to fully understand some subject, one must understand its history: you can know all about the state-of-the-art for some field, but until you grasp why it became the state-of-the-art, and what the competing alternatives were, you only know half the story. But I do have a couple of minor gripes. One is about her use of the word "naturalize." (Or "naturalise": a related, even more minor annoyance is that the book switches back-and-forth between the American and the British spelling, probably a fault of the publisher, and not of Morgan.) Let me offer some examples of her usage, along with my comments: * "Yet, its ambition i...

"Step 1: Create or Construct a model relevant for a topic of interest. "Step 2: Question that model world: the 'external dynamic'. "Step 3: Demonstrate the answer to the questions using the model's resources: the 'internal dynamic'. "Step 4: Narrative accompanies the demonstration to link the answers back to the questions and to their domains: both to the world in the model and the world that the model represents." -- Mary Morgan, The World in the Model , p. 225

I formulated this principle when I was programming mathematical models of financial markets. I noticed that my colleague who generated the models (that I then implemented) was never particularly attached to them. He would run them so long as they were indicating profitable trades, and then abandon them when they stopped. I was also writing Economics for Real People at that time, and grappling with the issue of the relevance of mathematical models in the economic world. Noticing the modelers attitude, one day I asked him, "Would you say that our practice is to use our models, rather than to believe in them?" "Absolutely," he replied. Mary Morgan's book reaches a similar conclusion.

"Writing down a model and manipulating it allows economists to think through in a consistent and logical way how a number of variables might interrelate, and to find solutions to questions about such systems. This habit of making and using models extends the powers of the mind to ask questions and explore the answers in complicated cases." -- Mary Morgan, The World in the Model , p. 258

I am currently reviewing Mary Morgan's book, The World in the Model . (An excellent book, by the way, and one that shows the value of the history of economic thought for the practice of economics: economists who read it will, I think, have a much better understanding of what modeling is all about.) At the same time, I am busy building agent-based models. It is propitious that these two things are happening at the same time: my own modeling makes me appreciate Morgan's insights much better. In particular, she notes that models are away to explore how the world possibly works by exploring how the model works. The nature of models as something to explore has, I think, been underappreciated. I am fascinated, in working with my model of Adam Smith's theory of fashion, to see how much the results coming out of the model change based on tweaking the assumptions going into it. For instance, changing the amount of time that agents will tolerate a fashion scene not to their liking...