Economic Production as Chemistry II

Abstract

This chapter further develops an agent-based model of economic production from the previous chapter. It shows that certain limitations intrinsic to the original hypercycle model—in particular, complexity barriers and vulnerability to parasites—are overcome once autocatalysis takes place in a spatial context, rather than in random-topology liquids. Localized heterogeneity in spatial interaction induces the inscription of path dependencies into cells. This explains why life becomes enhanced once it is embodied. The model also demonstrates why altruism and stigmergy produce more complex rule-chemistries. Altruistic reproduction and stigmergy are superior to selfish reproduction and fixed environments, respectively, because of their superior capacities for self-repair. Beyond suggestive specifics, the hypercycle model and its extensions show how chemistry and economic production and trading in markets can be mapped onto each other, sparking insights for both sides.

Analysis of the Specialization Patterns of an Agricultural Innovation System: A Case Study on the Banana Production Chain (Colombia)

“…In order to better understand the effects of learning and specialization patterns regarding innovation in an AIS, the three main agent-based models reported in the literature were identified. These models were recognized as useful sources for formulating a new proposal to analyze learning in AIS: the simulating knowledge dynamics in innovation networks model (SKIN) [29], the chemistry-inspired economic production model or hypercycle model [30,31], and the Self-Sustaining Regional Innovation System model (SSRIS) [32].…”

Drawing impossible boundaries: field delineation of Social Network Science

“…However, it can only explain the emergence of up to four types of living chemical species from simple rules of production. Padgett, McMahan, and Zhong (2012) and Padgett (2012b) show that this complexity barrier can be transcended and that overlapping social networks as well as culture can emerge from simple interaction principles. We will return to this imagery in later sections.…”

“…These facts are concept symbols, like stories, words, or cited references. Our point of departure is the social-autocatalysis model of Padgett et al (2012), the only one that is capable of explaining the birth of multiple network domains needed for meaning to emerge. Recall that multiple overlapping domains are the result of free riding enabled by altruistic agent behavior and that the complexity barrier of just 1.3.…”

“…Autocatalysis is the (co-)reproduction of social facts (D. R. White et al, 2006;Padgett et al, 2012;Padgett, 2012b). This concept empirically addresses structure as a memory needed to reproduce ties.…”

“…The new nodes contextualize the core. They are the potential free riders that create overlaps with other network domains and maintain diversity and switching opportunities inside the public(Padgett et al, 2012). They are the necessary sources of innovation and new meaning that balance the path-dependent trajectory caused by autocatalysis.…”