The scientific method is the single method of knowledge acquisition and validation that across time and cultures has most transformed the human condition and the natural world. It has succeeded because it is composed of a set of principles that are independent of person, status, culture, and power, and is open, transparent, evidence-based, and self-correcting. It is also imperfect. It is never complete, but is a process of continuous conjecture, testing, and revision based upon evidence, prediction, and replication. Yet it is also a social construction, hence, subject to capture, suppression, and concentration. For that reason, the scientific method needs to be open, distributed, and autonomous in its funding and governance. By making the scientific process open, self-funding, self-governing and computational, it can be scaled and embedded in an unlimited number of societal processes- media, governance, economic, cultural, educational, medical, legal, financial, and ecological. As such, it is not just an embedding of the scientific processes in societal processes, but an accumulating building of a collective understanding and expertise grounded in the first principles of physics, biology, neuroscience, and computation.

An important goal of the First Principles First initiative is to develop an open source "Computational Science Kernel" (CSK) that interoperates with a rapidly evolving network of multi-agent frameworks, LLMs, vector databases, and secure local Small Language Model (SLM). From our current review the most promising and "first principles" approach is based upon The Free Energy Principle (FEP), a theoretical framework for describing how predictive systems self-organize into coherent, stable structures by minimizing a free energy mathematical function. Active Inference (AIF) is a computational Bayesian embodiment of the FEP that specifically details how systems can plan for future actions by minimizing free energy functions that incorporate information seeking components. A goal here would be to develop a domain and scale free version of CSK and the FEP that could be embedded in a variety of application domains: finance, economics, law, governance, medicine, and media. The most mature and scalable computational version of AIF to date is the open-source module, RxInfer developed by BIASlabs at Eindhoven University. (This module is being commercially developed by StanhopeAI and LazyDynamics.) One near term goal is to increase the accessibility of the tool by simplifying model specification by having natural language interface and visual UX and APIs tools to enable RxInfer to be programmable for domain specific data, models and forms of actions and interventions. For example, in Climate Finance, there is considerable uncertainty and hence, risk, lack of trust and fungibility around MRVs (Monitoring, Reporting and Verification) because of high variability in methods and reliability of metrics. FEP combined with CSK could provide a highly scalable solution by providing scale and domain free MRVs that are commensurable and intrinsically measure the degree of risk associated with MRVs across different domains and scales. These models could be integrated with "digital twins" of a bioregion whereby the resolution and accuracy of the model will be enhanced through the minimization of uncertainty of MRVs. In other words, the digital twin evolves through better modeling and learning to become a simulation of a geospatial region it is intended to impact.

Another example in the application of CSK is jurisprudence. The scientific method and jurisprudence have much in common, both having been developed by the same man, Sir Francis Bacon. To date, the magnitude of costs and procedural barriers for everyday citizens asserting and protecting their legitimate legal rights has effectively denied those rights as a public good. Much of the judicial system is mired in case and procedural backlogs and is woefully under-resourced without public transparency and accountability. There are no scientific metrics by which judicial processes and actions can be evaluated in terms of either their effectiveness or their fairness. The judicial process has been effectively gamed for decades by well-funded private and corporate interests to protect and service their own interests. The potential automation and explicit modeling of the evidence collection and admissibility processes, as well as the capacity to predict the likely success of different legal briefs under different scenarios, could dramatically expedite the highly costly and protracted legal processes. It could also give litigants effective representation in the adjudication of their basic rights and thereby reduce the time and cost between the declaration of rights and the enforcement of those rights. A related area of legal application is in the explication of the consistency, rigor, and neutrality of judicial reasoning and the extent to which textual interpretation is consistent and bound to evidence and precedent.