Budding Men

Men die,
grass dies,
men are grass.
G.Bateson (“Mind and Nature”, 1979)

Taking its cue from the morphogenesis of Alan Turing, who in 1952 described the chemical processes responsible for the formation of patterns (such as zebra stripes), the experiment conducted in January 2025 led to the digital reproduction and visualisation of data relating to the growth and decay phases of grass stems in a garden. The experiment made it possible to simulate the life cycles of a digital garden and observe the emerging patterns, thus deepening the understanding of the dynamics underlying the generation of natural forms.

In the context of complexity theory, Gregory Bateson, British anthropologist and thinker, developed the concept of ‘budding syllogism’, an idea that intertwines nature, life and information in an interconnected dance. Bateson’s ‘budding syllogism’, in which ‘men’ become, according to the rules of Socratic syllogism, ‘grass’, offers a powerful insight into our connection with nature and universal evolutionary mechanisms.
Our existence, our evolution and our growth are shaped by laws and dynamics similar to those that govern plant growth. Just as grass feeds on light, water and soil to grow, human beings also transform themselves through continuous interaction with the environment and the forces that surround them. Bateson emphasises that this interaction is crucial and that it can be applied to our human context as well as to the plant world.

Morphogenesis (the process through which living structures are formed) is a field that is closely linked to Bateson’s complex and interconnected vision. Alan Turing, in his 1952 work, described how the laws that govern the growth of organisms are analogous to those that regulate complex systems. These laws are based on the interaction between two fundamental chemical agents: an activator and an inhibitor.
The form taken by a living being (be it a plant, an animal or a human being) is the result of a process of self-organisation. This process follows patterns deriving from the chemical interaction of the above-mentioned agents. In the context of complexity theory, the information that regulates these interactions becomes the basis of all reality, in which even minimal variations can lead to complex and unpredictable dynamics.

The experiment involved simulating the life cycle of a digital garden using Turing’s laws of morphogenesis as a guide. The mathematical equations behind the code reproduce the processes of growth and decay of blades of grass, offering a dynamic representation of a complex biological system.

Key elements of the code:

Turing morphogenesis equations: defined to model the interaction between activators and inhibitors in the system.
Initial parameters: fertiliser in blue (positive factors) and weeds in red (negative factors) are set as dynamic inputs that influence growth processes.
Graphic visualisation: rendered using Processing, allows us to observe the emerging patterns in real time.

Thanks to the implementation of these elements, it was possible to simulate the evolution of the system and analyse how the interactions between the various factors determined the final results. The code, in fact, allows us to explore the way in which complex patterns emerge from simple rules, providing a tool to better understand the dynamics of natural morphogenesis.

The Code: a colour description:

ùThe code produces an animation that, by means of 4 different sliders, is manipulated to allow or prevent the plants (in green) from spreading.

The 4 parameters are: the spread of fertiliser (blue), the spread of weeds (red), the growth rate of healthy plants (green) and the rate of decay and disease (black). Below is a detailed explanation of the part of the code that determines the colours of the programme once it has been started.

Extract Code, detail

if (wValue > fValue) {

fill(lerpColor(color(0, 0, 0), color(255, 0, 0), wValue)); // Weeds: from black to red

} else {

fill(lerpColor(color(0, 255, 0), color(0, 0, 255), fValue)); // Fertiliser: from green to blue

} rect(x * cellWidth, y * cellHeight, cellWidth, cellHeight);

Weeds:

fill(lerpColor(color(0, 0, 0), color(255, 0, 0), wValue)); // Weeds: black to red

Here, the colour of the weeds (wValue) is determined by the value of wValue, which varies between 0 and 1:

When wValue is 0, the resulting colour will be black (color(0, 0, 0)). When wValue is 1, the resulting colour will be red (color(255, 0, 0)). For intermediate values of wValue, the colour will be a gradation from black to red. For example, a value of 0.5 will produce a colour that is closer to the middle between black and red.

Fertiliser:

fill(lerpColor(color(0, 255, 0), color(0, 0, 255), fValue)); // Fertiliser: from green to blue

Here, the colour of the fertiliser (fValue) is determined by the value of fValue, which also varies between 0 and 1:

When fValue is 0, the resulting colour will be green (color(0, 255, 0)). When fValue is 1, the resulting colour will be blue (color(0, 0, 255)). For intermediate values of fValue, the colour will be a gradation of green towards blue. For example, a value of 0.5 will produce a colour that is closer to the middle between green and blue.

Colour interpretation in relation to the simulation:

Weeds:

Cells with a high wValue (close to 1) will appear red, indicating abundant weed growth. Grillo, M. (2025). Uomini in Erba: Esperimenti di Morfogenesi Digitale. www.manuelgrillo.com

Cells with a low wValue (close to 0) will appear black, indicating the absence of weeds.

A transition between black and red suggests the dynamics of weed growth based on their spread and influencing factors.

Fertiliser:

Cells with a high fValue (close to 1) will appear blue, suggesting a high presence of fertiliser.

Cells with a low fValue (close to 0) will appear green, indicating a low presence of fertiliser.

The gradation between green and blue represents the interaction between fertiliser and plants, with the intensification of the fertiliser potentially favouring the growth of vegetation.

Global interpretation of the simulation:

The simulation is constructed as a visual representation of two concurrent processes: The growth of the weeds, which is represented by the transition from black to red.

The distribution of fertiliser, which is represented by the transition from green to blue. Each cell in the grid changes colour according to the values of fertiliser and weeds, creating an animation that simulates the dynamic interaction between these two elements.

A higher weed value could overwhelm the fertiliser, while an increase in fertiliser could favour plant growth, influencing the dynamics of the simulation.

Interpretation in terms of evolutionary game theory:

Each grid cell represents a small game ‘arena’ where the growth of plants and weeds depends on the ‘strategy’ that each group adopts.

Parameters such as growthRate and decayRate are the ‘strategies’ that determine the behaviour of each group, while the limited ‘resource’ is the fertiliser (which the plants try to exploit to grow), while the weeds compete for the same nourishment.

The simulation can be seen as a series of ‘players’ that dynamically change their behaviour (growth or decay rate) in response to the behaviour of the other group, just like in an evolutionary game.

In summary:

The simulation described in the code can be interpreted through the model of game theory, in which plants and weeds compete for limited resources, with strategies that evolve over time in response to environmental conditions.

Although it is not a formal ‘game’ in the mathematical sense of the term, the dynamics of competition and adaptation between plants and weeds offers an interesting representation of competitive and strategic interactions, key concepts in game theory.

Gardens, people, cities: An interactive metaphor

The digital garden experiment is a powerful, albeit simplified, metaphor for understanding life as a complex system in which countless variables continuously interact. In this specific case, although the programme is suitable for the objectives of this experiment, it lacks those variables typical of a complex system, such as stochastic noise and periodic oscillations.

In this context, a parallel can easily be drawn: just as fertiliser nourishes a garden, education, good nutrition and positive experiences symbolically represent the healthy behaviour of a human being. On the contrary, if weeds were understood as diseases, stress and destructive habits, these would lead to a progressive deterioration of the ‘human’ system.
In the same way, if healthy behaviour were replaced by public funding and harmful habits by abandonment and urban decay, the system would no longer be a human organism, but a city or a neighbourhood. Its state of health would depend on the care and investment it received or, vice versa, on the neglect and degradation to which it was left.
From this analogy it emerges that a garden, a human being and a city can be considered equivalent systems. If in the first the basic unit is the blade of grass, in the second it is the cells and in the third the single housing unit. If in a garden we talk about the health of the plants, in the human body we talk about physical well-being and in a city we talk about the quality of urban life, we can therefore understand urbanisation as the equivalent of maintaining a healthy system.

This experiment, which deserves further investigation, achieves its objective in demonstrating how complex systems, apparently distant, can share the same functioning rules, the same diffusion and growth patterns.

Understanding these dynamics encourages us to reflect on our role within wider and interconnected systems. It is a step forward in the search for that connecting structure, a concept that, through the notion of copy, helps us to identify universal patterns and relationships.

Manuel Grillo

note to images 1-3-5-6-7): Some patterns derived from the interaction of morphogens. In blue, the fertiliser represents the activating agent, the presence of which leads to healthy plants (in green), while in red, weeds, represent the inhibiting agent. In black, the arid soil.

References
Bateson, G. (1972). Steps to an Ecology of Mind. Chandler Publishing Company
Turing, A. M. (1952). “The Chemical Basis of Morphogenesis”. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 237(641), 37-72.