Can We Leverage AI to Enhance Our Stewardship of Nature?

Bayesian Modeling and the Free Energy Principle

Yes, I know many of you are going to react strongly to the concept of “managing” nature. But bear with me. The ideas here do not stem from a perspective of controlling nature but rather from finding ways to co-evolve with it.

In the vast convergence of philosophy, computational science, neuroscience, artificial intelligence, mathematics, complexity science, and living system theory, we find a profound synthesis that allows us to apply the concepts of Bayesian inference, active inference, and the free energy principle in a meaningful way.

This approach enables us to understand living ecosystems, value ecosystem services, and find solutions through trustworthy MMVC processes that foster regenerative practices and biodiversity restoration processes.

Understanding Bayesian Inference and Active Inference

Bayesian inference is a method of statistical inference in which Bayes’ theorem is used to update the probability for a hypothesis as more evidence or information becomes available. It is a cornerstone of how we process and update our understanding of the world, allowing us to refine our beliefs with each new piece of data.

Active inference, a related concept, extends Bayesian inference by incorporating the idea that living beings don’t passively receive information but actively seek out data that minimizes the uncertainty in their model of the world. This aligns closely with the free energy principle.

The Free Energy Principle

The free energy principle posits that living systems maintain their order by minimizing the difference between their expectations (predictions) and the actual sensory inputs they receive from the environment. In essence, this principle describes the drive of biological systems to reduce uncertainty and maintain homeostasis.

This is how biological systems, such as human minds, animal behavior, and even artificial intelligence, use active inference to evolve. By continuously seeking out and processing new information, they aim to reduce the free energy principle, thereby minimizing the delta between prediction and reality.

For example, the human brain actively anticipates sensory inputs and adjusts its model of the world accordingly, learning from discrepancies to refine its understanding. Similarly, animals adapt their behaviors based on environmental feedback, enhancing their survival and reproductive success.

In the realm of artificial intelligence, machine learning algorithms dynamically update their predictions by incorporating new data, thus improving their performance over time. In all these cases, the active pursuit of reducing prediction errors leads to more accurate models and better adaptation to changing environments.

Valuing ecosystems According to the Free Energy Principle:

By applying these concepts, we can evaluate systems based on their ability to minimize free energy. Those that do this most effectively are the ones that evolve and adapt the best. This is not merely a biological observation but a profound insight into how knowledge itself evolves. Species, ideas, and systems that best align their predictions with reality are those that thrive and progress.

Applying Bayesian Modeling to Value Biodiversity

Now, let’s take a step further into the realm of application. Imagine we could harness the power of Bayesian modeling and the free energy principle to not just understand, but actively manage and restore our natural ecosystems. Here’s how this could unfold:

Step 1

a) Mesuring Biodiversity in an Underlying Plot of Land

We start with a specific plot of land, our canvas, where we develop a model of MMVC (Monitoring, Measuring, Verifying, and Certifying).

This involves experimenting at three levels:

1. Hardware and Sensory Technology for Land-Based Measurement: Deploying sensors and other devices to capture real-time data.
2. Complementary Satellite and Drone Measurement: These technologies provide an aerial perspective, offering broader data coverage.
3. Complementary eDNA Measurement: Environmental DNA sampling helps us understand the presence and interactions of various species at a genetic level.

b) Agregating data into an Integrated System

We create an integrated system that comprehends the interdependent, integral relationships among diverse species, crafting a holistic view.

c) Building Bayesian Active Inference Model

This integrated system is then transformed into a Bayesian active inference model.

This model appreciates or identifies the relationships between different species, understanding the intricate feedback loops that govern these interactions. It understands the patterns and principles of living systems, recognizing the dynamic processes that sustain ecosystems.

The model comprehensively grasps ecosystem services, acknowledging the benefits that these systems provide to humanity. It also takes into account the impact of human interventions, assessing how activities like land use changes or pollution affect ecological balance.

Furthermore, it understands troffic cascades, which are the chain reactions triggered by changes within the ecosystem.

Gauged by the reduction of the free energy principle, this model aligns predicted and real sensory information, ensuring that our understanding of the ecosystem remains accurate and adaptive. This principle reflects the continuous effort to minimize the discrepancy between expectations and reality, thereby fostering resilience and adaptability within the system.

Each plot of land, each context, has its own Bayesian model, but we aggregate these into an integral, comprehensive model that understands and measures the interdependency between different plots of land and even different regions and ecosystems.

Step 2

Creation of a Digital Twin

From the individual Bayesian models, a digital twin is created. This digital twin is the digital living representation of the underlying living ecosystem. The Bayesian model guarantees that we are continuously updating the information through the MMVC process.

Each inference gives us a better understanding of the healthiness of the underlying biodiversity system.

In the digital twin, we can visualize this ecosystem, understanding the healthiness of the system due to its diversity and the profoundness or deepness of relationships.

From the Bayesian active inference model of the MVP, we create a digital twin of this land, this Digital Twin MVP, and this digital twin is certified into a nature finance biodiversity Asset of “Living Capital”.

This can then be securitized to become a biodiversity credit or a land stewardship token. This process is called CAST: Certification, Assetization, Securitization, and Tradability, (making it tradable).

Thus, it becomes a financial security sought after in the offset market of biodiversity, aligning with the Global Biodiversity Framework

The Global Biodiversity Framework (GBF) aims to halt biodiversity loss by 2050, promoting ecosystem health, sustainable use, equitable benefits sharing, and closing a $700 billion annual biodiversity finance gap.

Step 3

Scalability Application to Multiple Plots

Our evolved model, tested and refined can now be applied to various other plots of land.
This first Bayesian MVP model and digital twin -that is continually tested and redefined- can now be applied to other plots as a Software as a Service (SaaS). Each new plot of land, referred to as plot N, goes through the MMVC process.

Its data is uploaded to a Bayesian Active Inference Model, which we will now call Model N, since it is scalable to innumerable N models. Each of these models will be digitized into its own digital twin. Each of these N models will evolve, improving through a virtuous MMVC process, where new inferences create a better understanding of the underlying ecosystem.

This digital twin is assetized as a certified nature biodiversity tradable security, aligning with the principles of nature finance.

By incorporating local data, we continuously enhance the model’s accuracy and relevance. From the learning of the first Bayesian plot of land, we can create individual Bayesian plots and digital twins for each plot of land.

Outcomes and Benefits:

1.Increased Productivity and Margins: (For Agroforestry Models) Greater biodiversity leads to increased productivity due to enhanced ecosystem services.

2. Resilience and Capability: The system fosters resilience and adaptability in the face of environmental changes.

3. Community Impact and Well-Being: This approach creates a co-evolving process between humans and nature, enhancing community impact and well-being. The Bayesian models will include social metrics, understanding that the healthiest ecosystems are those in a mutualistic symbiotic relationship between humans and nature.

As we evolve the Bayesian model from the MVP through multiple iterations, this model can be effectively applied as a Software as a Service (SaaS) for agricultural and forestry production areas. This advanced model is designed to maximize the outcomes of both production and biodiversity, comprehensively measuring the benefits of biodiversity in enhancing the results of forestry and agricultural harvesting. Furthermore, the model is applicable to grazing livestock management, where the richness of biodiversity significantly boosts pasture production.

It optimizes the mutualistic symbiotic relationships between livestock — such as Cattle and Ovine— and the biodiversity of natural pastures or managed pastures, thereby achieving the best possible outcomes in both productivity and ecological health.

Comprehensive Bayesian Active Inference Model

Above the individual Bayesian models, we introduce an overarching, comprehensive Bayesian active inference model.

Although each plot of land, each context, has its own Bayesian model, we aggregate these into an integral, comprehensive model that understands and measures the interdependency between different plots of land and even different regions and ecosystems.

As explained through diverse Bayesian models we guarantee that we are continuously updating the information through the MMVC process. Each inference gives us a better understanding of the healthiness of the underlying biodiversity systems, now at a integrated Comprehensive level.

We are creating a sort of mycelium network, where the experiences of each individual plot of land, aggregated, create eclectic knowledge. Through these individual iterations, the model becomes even more comprehensive and intelligent, able to understand how to diminish the gap between predictions and reality, eventually valuing the managed stewardship of the free energy principle.

A Vision for the Future

As we look to the future, the integration of Bayesian modeling with active inference and the free energy principle offers a groundbreaking approach to managing our natural resources.

Imagine a world where every plot of land has its own digital twin, continuously updated with real-time data and integrated into a global network of ecological intelligence.

This network, fueled by the processes of CAST, (Certification, Assetization, Securitization and Tradability) enables us to trade biodiversity credits and living capital, fostering an economy that values sustainability and resilience.

In this vision, technology and nature are not at odds but work in harmony, guided by the timeless principles that govern life itself. By aligning our economic systems with the natural world, we can create a future where both thrive, ensuring that the resources we depend on today are preserved for generations to come.

This is not just an idea; it’s a blueprint for a sustainable future, a call to rethink how we measure success, value our resources, and invest in the future. As we face unprecedented challenges such as climate change, biodiversity loss, and social inequality, regenerative finance offers a hopeful path forward, one that harmonizes economic activity with the well-being of our planet and its inhabitants.

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