IN THE WINTER of 2020 there was a fair bit going on, so not many people noticed what was happening with the whales in the far north of Australia. But Elders noted the unusual behaviour – the whales swam inland along freshwater rivers – and contacted Bunnarong Elders thousands of kilometres to the south in Melbourne, where I’m camping while I write these words. That northern whale story connects all the way down here, along trade routes that have been managed across diverse bioregions by interdependent, autonomous peoples for millennia. The landscape has shifted recently, so the whale story has changed with it. I’m shifting with it too, because I know I can trust this ‘proper story’ as a predictive modelling process during a summer of uncertainty, in which ti-trees have bloomed way too early and lychees have arrived through disrupted supply chains way too late.
Proper story is a living landscape model that allows you to make accurate predictions, shows you the limits and obligations of your relationship with the land, and teaches you how to move with it as it transforms over time. It is collective, an aggregate of the knowledge of many people who speak for different aspects and diverse bioregions. This approach gives humans traditionally non-centralised governance structures, distributing power, knowledge and resources throughout social systems in patterns that align with the complex ecosystems we have inhabited over hundreds of millennia.
In this way, story speaks the law that is in the land. This is right story, which regenerates every entity of the landscape in perpetuity, including our own species, the custodial species of the Earth.
Wrong story is gossip, curse, illusion, bad faith, denial. When it becomes the baseline data for modelling, it can only result in self-termination algorithms in every landscape, even digital ones.
Wrong story is made by blending things that should not be blended. Nature takes care of these lawless combinations eventually. Father–daughter mating will result in birth defects that terminate species attempting those wrong syntheses over time. Economic models attempting to combine limitless growth and extraction with a limited resource base will also be terminated under the law of the land, historically within a thousand-year period. Synthetic combinations of elements produce fertilisers for intensive agriculture, rare earth metals for solar panels and devices, fuel for vehicles and so forth, all resulting in poisonous air and water, essential resource depletion, and population explosions and extinctions as consequences of wrong story and wrong models applied to landscapes. The law of the land imposes these limits.
This natural law acts as a kind of immune system response to multipolar traps in which bad actors seek to misuse landscape for personal advantage, forcing others to adopt the same behaviours at scale or be outcompeted. The systems of perverse incentives that arise from such lawless games are always eradicated by the law of the land, although the collective memory of right story (including cautionary tales) should prevent communities from having to repeat this process of correction over and over.
This is the purpose of knowledge. Populations who lose that knowledge are forced to undergo re-education beneath the tutelage of a sentient landscape until they learn the lesson again and recover right story. Smarter communities recover this knowledge by listening to those who do remember it.
THERE ARE NEW layers of landscape now. It has been divided into a stack of environmental, digital, financial, social and legal mappings that are all based on wrong story, wrong models. This stack demands that a pluriverse of interdependent bioregional systems and stories be unified into one meta-story seeking meta-stability – but this only produces an ongoing state of meta-crisis.
The financial layer of land as capital is the one that shapes the rest, even the true ground beneath our feet. Land is surveyed, enclosed and assigned a value for an elite minority who can own it and leverage it as capital against debt. It can only have value through limitability and excludability, so the majority must be denied access to the land that most people on Earth still lived upon until just a century ago. This land-based capital, which makes up two thirds of all global capital, can then be leveraged into infinity through a fantastical system of derivatives, giving rise to an illusory financial landscape. This is the simulated reality within which we are all attempting to live our real lives today.
There is good story, true story in the disciplines of that Enlightenment-inspired stack too, but this good science is grounded in viral foundations of pseudo-scientific theories about who we are as human beings, forcing us into a game-theoretical nightmare of scarcity and competition that forms the financial landscape.
There are also accurate multidisciplinary understandings of evolutionary fitness landscapes within the informational layers of this stack, and these may eventually provide the predictive power to design solutions to complex problems and existential threats. However, these models are all broken due to a little bug of wrong story about our Palaeolithic community origins. You can see evidence of that virus in pseudo-scientific assertions such as ‘In Stone Age communities, 30 per cent of all deaths were homicides’, or ‘Humans are driven by fight-or-flight responses because of the hypervigilance of our ancestors, who wandered in the wilderness without knowing when predators might attack’. These kinds of ‘facts’ do not come from any real dataset, but have been synthesised from wrong code, wrong story.
THE BEST PLACE to see this story bug is in agent-based modelling software: artificially intelligent social simulations such as ‘Sugarscape’, which are becoming essential tools in dealing with vastly complex issues such as pandemics, global financial crises and climate change. These models are designed to evolve from simplicity to complexity, with software ‘people’ (agents) moving and interacting autonomously in a digital landscape to mimic real-world systems and simulate how they might change over time.
The initial programming of agent-based modelling software began with a ‘hunter-gatherer’ phase, in which an environment of infinitely respawning resources was developed and basic software beings were let loose on that physics-defying landscape. Those simple beings had operating protocols that kept them moving, consuming resources, metabolising and interacting. The settings were different for each digital being, with different metabolic rates, different radiuses for sensing resource locations, different protocols for interaction and reproduction.
The landscape had zones of varying resource richness, so the software beings moved chaotically over it, formed competitive groups, killed each other, fought over territories, traded, stole and reproduced. New generations were created as they mated and murdered randomly in an environment of arbitrary abundance and scarcity. This ‘hunter-gatherer’ phase ended when powerful groups emerged to dominate the landscape and monopolise mating, trade, resource access and violence.
When Indigenous people see this ‘hunter-gatherer’ model, we do not recognise it as our story. We do, however, recognise it as the frontier story of pioneering settlers. So for accuracy and better functionality, we might suggest some different operating protocols for the ‘primitive’ stage in the development of future agent-based modelling software.
For a start, the landscape would require that agents put energy and resources back into it before it respawns new resources. Different agents would be assigned to different regions of the landscape for extractive and regenerative activities, and would then trade with agents from other areas. Within each region, ‘families’ would be assigned to different sections and agents would not be able to mate with others from the same section. They would have to mate across the other side of their region, or mate with agents from other regions. You would then begin to see very different, interdependent social systems emerge that are far more stable, complex and productive than the chaotic 1.0 version of the primitive landscape model.
This may offer some solutions for sustainable models of governance and trade into the future.
You might then introduce the lawless agents from that 1.0 pioneering chaos model, and watch them deplete and disrupt the entire system. This would generate an accurate systems model based on right story. And it would be a step towards agent-based modelling software of adequate complexity and sophistication to make some accurate predictions and try some helpful experiments in realistic simulations of change-making.
Getting the story right is not about social justice, or cultural sensitivity, or reconciliation, or identity politics and culture wars. It’s about survival. Without right story, we can have no accurate models to stabilise our landscapes – the ecological, digital, economic and political ones that currently form our habitat as a species. At the base of every evolutionary stack is the inalienable law of the land, telling us our limits, obligations and privileges. In evolutionary stacks, you can mess with any of the upper layers and survive, but once you knock the bottom block out of the Jenga tower, it’s all over.
To avoid this, we need to have good story and good models from that foundational layer of land and our place in it. At the moment, we have wrong story, throwing up viral pathologies and self-termination algorithms. And so all our landscapes are broken – although some are still being rebooted with right story from entities such as the whales, and the custodial humans who remember how to listen to them.