Modeling our way out of this mess
As humans, we understand the world implicitly through models. Reality is intractably complex, and being complex means it cannot be distilled into neat equations and laws beyond its very basic levels. This is evident in the failures of classic economics to explain the chaotic motions of the economy, in our blindness to the ecological damage human interventions cause, and the countless governmental policies that have produced results counterintuitive to their own aims like the war on drugs. Jay Forrester put it best in The Counterintuitive Behavior of Social Systems:
Each of us uses models constantly. Every person in private life and in business instinctively uses models for decision making. The mental images in one’s head about one’s surroundings are models. One’s head does not contain real families, businesses, cities, governments, or countries. One uses selected concepts and relationships to represent real systems. A mental image is a model. All decisions are taken on the basis of models. All laws are passed on the basis of models. All executive actions are taken on the basis of models. The question is not to use or ignore models. The question is only a choice among alternative models.
The way we interact with the world is tied to our models of it - our actions are gauged on the potential consequences, whether when choosing what to say to someone based on our expectations of how they will react or more simply when trying to grab something in the dark. The field of robotics has generated many interesting insights into how the average human is still better at physical manipulation than state of the art machines, but when you give the machines a self-model that they can update, they can pick up new skills they weren’t explicitly trained for and even recover from damage. Of course, sometimes our intuitive spatial models fail us, as I like to remind myself when I knock something off a table gesticulating about this or that.
Beyond the physical, we form intuitive notions about complex systems into what is called expertise. Everyone has heard an expert on something try to explain why something is happening and fail utterly to our own amusement. Not that the expert has thrown away their whole life in some academic wormhole, but rather communicating our own internal models is very hard, especially to the unprimed listener. Maybe a 30 second sound bite is laughably incomprehensible, but a 20 page paper or even a 300 page book are much better media for transmitting these implicit understandings, gathered over years of intense study, to the lay reader. Indeed, this phenomenon, I would argue, is at the root of most of the bitter disagreements we see in the social sphere where antagonists have incompatible models but base their arguments on assumptions drawn from them, and thus get nowhere.
Our innate ability to model the world has gotten us this far, and human expert knowledge is still probably the most valuable resource on the planet, but the revolutionary advent of computers has given us another way to develop these intuitions, notably one that can be directly tested and checked against the growing troves of data collected from the world. The aforementioned Forrester paper is from 1971, and since then computer modelling methods have been refined into a whole slew of techniques for different types of systems and levels of uncertainty. System dynamics boils continuous processes into stocks, flows, delays, and feedback mechanisms that illuminate pesky systems like supply chains or collapsing fisheries. Agent-based modeling highlights the key role of the individual decision maker as the emergent basis of fractal-like complexity, the simplest examples being cellular automata. The techniques for modelling are as varied and flexible as programming allows, which is to say unboundedly so.
I would argue that video games are an interactive form of modelling that can be equally capable of encapsulating complex systems and studying their behaviors, in both entirely simulated worlds of interacting agents like in Dwarf Fortress or in virtual social interactions which resemble real world ones from history like Eve Online. While constructed to entertain and not to be studied, it turns out that we are often entertained by complex, unpredictable behavior, especially when it can be weaved into a narrative. When I play games I love the ones like Dwarf Fortress and Rimworld where the player is an architect of a virtual colony, responsible for building the structure of the world for agents to live and interact in, a world constrained by limited resources and persistent external threats. It is frequently out of such adversarial conditions that the most spectacular complexities emerge, namely that of life itself.
Currently, the ability to model the world in silico is accessible to only those with strong programming skills and patience for clunky tools. Part of the difficulty arises from the fact that creating a model is such an open ended task, where no framework can really handle everything that can be asked of it. A curious person attempting to create a model for the first time is very likely going to find themselves unsure of how to start translating their implicit model into an explicit one. Academically oriented tools assume a theoretical basis in modelling and are usually very old projects with outdated interfaces. Commercial ones are only a little better, but also cost in the thousands of dollars, leaving them only in the hands of larger institutions that can budget for such.
I’m working on a new software platform to solve some of these pain points. Hopefully, it will succeed in bringing modeling to a larger market and helping more people understand and wrangle complexity. Get in touch if you have any thoughts on this you would like to share.