Building Better Ecological Machines: Complexity Theory and Alternative Economic Models

Abstract

Computer models of the economy are regularly used to predict economic phenomena and set financial policy. However, the conventional macroeconomic models are currently being reimagined after they failed to foresee the current economic crisis, the outlines of which began to be understood only in [2007][2008]. In this article we analyze the most prominent of this reimagining: Agent-Based models (ABMs). ABMs are an influential alternative to standard economic models, and they are one focus of complexity theory, a discipline that is a more open successor to the conventional chaos and fractal modeling of the 1990s. The modelers who create ABMs claim that their models depict markets as ecologies, and that they are more responsive than conventional models that depict markets as machines. We challenge this presentation, arguing instead that recent modeling efforts amount to the creation of models as ecological machines. Our paper aims to contribute to an understanding of the organizing metaphors of macroeconomic models, which we argue is relevant conceptually and politically, e.g., when models are used for regulatory purposes.

Steps to an Ecology of Algorithms

“…For Benjamin, writing about the United States, this notion is expressed in a "New Jim Code" (Benjamin 2019a,b). Algorithms can appear as automatons of everyday ontological hierarchization in, for instance, discrimination in job hiring algorithms (Tufekci 2015), racial profiling and predictive policing (Scannell 2019), the racialization of health care decisions (Ledford 2019), the incorporation of white supremacy in facial recognition algorithms (Benjamin 2019b), or the algorithmic and biometric constitution of borders (Amoore 2013). One ominous recent introduction has been a supermodel created by artificial intelligence in the appearance of a black woman that has been featured, among other places, in Vogue (Yates 2022).…”