Jessie is an environmental and human systems scientist who has been doing advanced research on emergent organization in nature for over 30 years. Her innovative work produced a practical new general scientific method for studying uncontrolled systems and led to numerous important findings.
One of those is the remarkable finding that the standard sustainability metrics used around the world are very unscientifically defined. They generally count the impacts we can trace and quite ignore the ones we can’t. It’s as if, culturally, we were all thinking “out of sight, out of mind”, as if that would work in nature! We can joke, but it also implies major changes in what we do, that the community is largely in denial of..
Her subjects include all kinds of lively complex systems such as, organisms, ecologies, cultures, communities, languages, technologies, weather, etc. Such systems generally originate with an initial seed of local organization and a process of accumulative development, forming a cell of organization by growth in an open environment. Jessie’s methods are based on using physics principles as diagnostic tools. She does not use physics to represent environmental systems with equations, but for investigating them, considered as self-defined objects of the environment. The method relies on data for displaying authentic images and behaviors of nature with "fidelity", not for making equations. The technique allows one to historically reconstruct the flow of innovation within a system’s own internal development, and then to anticipate the succession of other developments that are likely, or certain, in its future.
Jessie has published important papers, under the pen name "P.F. Henshaw", though many of her best discoveries aren’t well understood by others yet. She now lives in New York City, has a B.S. in physics from St. Lawrence University, an MFA in environmental design from the Univ. of Pennsylvania, stated her independent research living in Denver in the 1970’s, made her initial important discoveries studying the natural micro-climates of homes, and has accumulated a substantial body of useful work.
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To study natural systems one considers them as self-defining, “found objects” in the environment. They’re recognized by the organization of their parts, and how that organization is continually changing, and located within the self-defining boundary of their own internal organization. The lasting rules they follow are very general, such as that their organization is invariably accumulative and built on what went before, and develops by a succession of organizational level, ascending and declining. The features of that predictable succession of accumulating and dissipating processes are what one studies to understand how it works and is changing. The rule for nearly all other rules is that the rule will be temporary. That becomes quite useful, as finding a "formula" exposes where nature has created complex organization that appears to work simply. Then you can begin to ask questions about how that state of complex order developed and will change and why.
The most common reason for systems to break their own rules is changing scale, either getting too big for their prior internal working to work, or too small. Then the resilience of the former design disappears, and something has to change. That most often occurs when the rule the system is following is for either repeated expansion (growth) or contraction (decay) in the system itself. Growth and decay are nature's mechanisms of beginning and ending systems, and precipitate change in the system at their natural limits.
This approach is unusual, then, for actually abandoning the idea that nature follows scientific theories. It instead uses theory as a tool for exploring how nature works by itself. So the interest is less in imitating nature, and more in exploring nature, seeking "high fidelity" data and patterns to help raise leading questions about individual behaviors, rather than consistent data for making statistical predictions for defined classes of behaviors.
The conventional scientific method uses numerical data and rules to represent nature, and cannot actually even refer to "things" of nature in their natural form, except reduced to numbers. Particularly for subjects like economics, where all the parts of an economy are actively engaged in learning new ways of working together, it is just more accurate to represent the system as a learning process, rather than a set of fixed rules as with an equation. What does not change are the more basic elements of science, such as that science is always a matter of discovering questions you can answer with high confidence.