Steering the Nexus: iZindaba Zokudla and Governing for Sustainability

“iZindaba Zokudla” means we talk about the food that we eat. iZindaba Zokudla is a public innovation lab that uses stakeholder-engagement methods to create “opportunities for urban agriculture in a sustainable food system.” iZindaba Zokudla is presented as an extra-institutional means to govern the water, land, energy, and waste nexus. This reflective essay critically describes iZindaba Zokudla and applies this to the design of institutional steering mechanisms to govern the food, water, land, and energy nexus towards sustainability. Governance is an intersubjective and interactive process between the subjects of governance and governance itself. Sustainability, as an interactive process, implies the creation of autocatalytic and symbiotic communities in society that integrates diverse actors and stakeholders, inclusive of scientific and lay actors, and ecosystems. iZindaba Zokudla is a means to govern and create such communities, and this article describes and reflects on how iZindaba Zokudla has created and managed such symbiotic communities or autocatalytic networks in the food system. The article generalises how the activities conducted in iZindaba Zokudla can be used to govern the water, land, energy, and waste nexus for sustainability. The article shows how iZindaba Zokudla has realised a progressive governance through the facilitation of its Farmers' Lab and website; how it has created opportunities for participation; and how it enables critical reflection in society.

Darwinian properties and their trade-offs in autocatalytic RNA reaction networks

Discovering autocatalytic chemistries that can evolve is a major goal in systems chemistry and a critical step towards understanding the origin of life. Autocatalytic networks have been discovered in various chemistries, but we lack a general understanding of how network topology controls the Darwinian properties of variation, differential reproduction, and heredity, which are mediated by the chemical composition. Using barcoded sequencing and droplet microfluidics, we establish a landscape of thousands of networks of RNAs that catalyze their own formation from fragments, and derive relationships between network topology and chemical composition. We find that strong variations arise from catalytic innovations perturbing weakly connected networks, and that growth increases with global connectivity. These rules imply trade-offs between reproduction and variation, and between compositional persistence and variation along trajectories of network complexification. Overall, connectivity in reaction networks provides a lever to balance variation (to explore chemical states) with reproduction and heredity (persistence being necessary for selection to act), as required for chemical evolution.

The Expected Number of Viable Autocatalytic Sets in Chemical Reaction Systems

The emergence of self-sustaining autocatalytic networks in chemical reaction systems has been studied as a possible mechanism for modeling how living systems first arose. It has been known for several decades that such networks will form within systems of polymers (under cleavage and ligation reactions) under a simple process of random catalysis, and this process has since been mathematically analyzed. In this paper, we provide an exact expression for the expected number of self-sustaining autocatalytic networks that will form in a general chemical reaction system, and the expected number of these networks that will also be uninhibited (by some molecule produced by the system). Using these equations, we are able to describe the patterns of catalysis and inhibition that maximize or minimize the expected number of such networks. We apply our results to derive a general theorem concerning the trade-off between catalysis and inhibition, and to provide some insight into the extent to which the expected number of self-sustaining autocatalytic networks coincides with the probability that at least one such system is present.

The structure of autocatalytic networks, with application to early biochemistry

Metabolism across all known living systems combines two key features. First, all of the molecules that are required are either available in the environment or can be built up from available resources via other reactions within the system. Second, the reactions proceed in a fast and synchronized fashion via catalysts that are also produced within the system. Building on early work by Stuart Kauffman, a precise mathematical model for describing such self-sustaining autocatalytic systems (RAF theory) has been developed to explore the origins and organization of living systems within a general formal framework. In this paper, we develop this theory further by establishing new relationships between classes of RAFs and related classes of networks, and developing new algorithms to investigate and visualize RAF structures in detail. We illustrate our results by showing how it reveals further details into the structure of archaeal and bacterial metabolism near the origin of life, and provide techniques to study and visualize the core aspects of primitive biochemistry.

A model of the transition to behavioural and cognitive modernity using reflexively autocatalytic networks

This paper proposes a model of the cognitive mechanisms underlying the transition to behavioural and cognitive modernity in the Upper Palaeolithic using autocatalytic networks. These networks have been used to model life’s origins. More recently, they have been applied to the emergence of cognitive structure capable of undergoing cultural evolution. Mental representations of knowledge and experiences play the role of catalytic molecules, the interactions among them (e.g. the forging of new associations or affordances) play the role of reactions, and thought processes are modelled as chains of these interactions. We posit that one or more genetic mutations may have allowed thought to be spontaneously tailored to the situation by modulating the degree of (i) divergence (versus convergence), (ii) abstractness (versus concreteness), and (iii) context specificity. This culminated in persistent, unified autocatalytic semantic networks that bridged previously compartmentalized knowledge and experience. We explain the model using one of the oldest-known uncontested examples of figurative art: the carving of the Hohlenstein–Stadel Löwenmensch, or lion man. The approach keeps track of where in a cultural lineage each innovation appears, and models cumulative change step by step. It paves the way for a broad scientific framework for the origins of both biological and cultural evolutionary processes.

A Model of the Transition to Behavioral and Cognitive Modernity using Reflexively Autocatalytic Networks

This paper proposes a model of the cognitive mechanisms underlying the transition to behavioral and cognitive modernity in the Upper Paleolithic using autocatalytic networks. These networks have been used to model life’s origins. More recently, they have been applied to the emergence of cognitive structure capable of undergoing cultural evolution. Mental representations of knowledge and experiences play the role of catalytic molecules, the interactions among them (e.g., the forging of new associations or affordances) play the role of reactions, and thought processes are modeled as chains of these interactions. We posit that one or more genetic mutations may have allowed thought to be spontaneously tailored to the situation by modulating the degree of (1) divergence (versus convergence), (2) abstractness (versus concreteness), and (3) context-specificity. This culminated in persistent, unified autocatalytic semantic networks that bridged previously compartmentalized knowledge and experience. We explain the model using one of the oldest-known uncontested examples of figurative art: the carving of the Hohlenstein-Stadel Löwenmensch, or lion-man. The approach keeps track of where in a cultural lineage each innovation appears, and models cumulative change step by step. It paves the way for a broad scientific framework for the origins of both biological and cultural evolutionary processes.

The structure of autocatalytic networks, with application to early biochemistry

Metabolism across all known living systems combines two key features. First, all of the molecules that are required are either available in the environment or can be built up from available resources via other reactions within the system. Second, the reactions proceed in a fast and synchronised fashion via catalysts that are also produced within the system. Building on early work by Stuart Kauffman, a precise mathematical model for describing such self-sustaining autocatalytic systems (RAF theory) has been developed to explore the origins and organisation of living systems within a general formal framework. In this paper, we develop this theory further by establishing new relationships between classes of RAFs and related classes of networks, and developing new algorithms to investigate and visualise RAF structures in detail. We illustrate our results by showing how it reveals further details into the structure of archaeal and bacterial metabolism near the origin of life, and provide techniques to study and visualise the core aspects of primitive biochemistry.