Stupid models are extremely useful. They are usefulbecause humans are boundedly rational and because language is imprecise. It is often only by formalizing a complex system that we can make progress in understanding it. Formal models should be a necessary component of the behavioral scientist’s toolkit. Models are stupid, and we need more of them.

Microeconomic models based on rational choice theory are useful for developing intuition, and may even approximate reality in a fewspecial cases, but the history of behavioral economics shows that standard economic theory has also provided a smorgasbord of null hypotheses to be struck down by empirical observation.

Where differences between conditions are indicated, avoid the mistake of running statistical analyses as if you were sampling from a larger population.

A model’s strength stemsfromits precision.

However, I want to be careful not to elevate modelers above those scientists who employ other methods.

Table 1.Twelve functions served by false models. Adapted with permissionfrom Wimsatt

To paraphrase Gunawardena (2014), a model is a logical engine for turning assumptions into conclusions.

the stupidity of a model is often its strength. By focusing on some key aspects of a real-world system(i.e., those aspectsinstantiated in the model), we can investigate how such a system would work if, in principle, we really couldignore everything we are ignoring. This only sounds absurd until one recognizes that, in our theorizing about the nature of reality –both as scientists and as quotidianhumans hopelessly entangled in myriad webs of connection and conflict –weignore thingsall the time.

The generalized linear model, the work horse ofthe social sciences, models data as being randomly drawn from a distribution whose mean varies according to some parameter. The linear model is so obviously wrong yet so useful that the mathematical anthropologist Richard McElreathhas dubbed it “the geocentric model of applied statistics,”in reference to the Ptolemaic model of the solar system that erroneously placed the earth rather than the sun at the center but nevertheless produced accurate predictions of planetary motion as they appeared in the night sky(McElreath 2015).

Unfortunately, my own experience working with complex systems and working among complexity scientistssuggests that we are hardly immune to such stupidity. Consider the case of Marilyn Vos Savantand the Monty Hall problem.

Mitch Resnick, in his book Turtles, Termites, and Traffic Jams, details his experiences teaching gifted high school students about the dynamics of complex systems using artificial life models (Resnick 1994). He showed them how organized behavior could emerge when individualsresponded only to local stimuli using simple rules, without the need for a central coordinating authority. Resnick reports that even after weeks spent demonstrating the principles of emergence,using computer simulations that the students programmed themselves, many students still refused to believe that what they were seeing could really work without central leadership.

Introduction to Sociology 2e

The fact that professionals prefer anecdotes from people like us over concerted efforts to understand a demographic as a whole is shameful. More importantly, it’s downright dangerous. It shapes what the tech industry builds and invests in, what gets promoted by journalists, and what gets legitimized by institutions of power. This is precisely why and how the tech industry is complicit in the increasing structural inequality that is plaguing our society.

If we want to understand how social structures, let alone put ourselves in a position to take control of them (that is, to “reassemble” them) we must dispose of the assumption that the local is explained by the global, and start tracing the process by which the local GENERATES global structures

Traditional sociologists start with a macro-scale entity or force–”society” or “culture”–that they then use to explain myriad local interactions.