General Systems Theory and Economics

In trying to model a socioeconomic system that is generally coherent, General Systems Theory seems to be the obvious choice for a basis. A multi-disciplinary study towards the goal appears to be the route. The issues that we have faced with Socioeconomics has generally been due to either lack of understanding or more recently, lack of implementation. The accrued entropy has made the need for fundamental change not only more evident and accepted but also more dire. The attempts at developing contingencies have been a long line of partial solutions. Though at this stage such a change could produce large effects, it may still be too little too late.  General Systems Theory may provide a conceptual basis for a more coherent socioeconomic system. Realizing that socioeconomic systems are most likely to be coherent if they are inherently self-organizing systems, the model would require that particular property. This may be possible by modeling the system to be intrinsically dynamic, abstract and of course decentralized. This can only function properly if the inputs are considered with a high degree of coherence themselves. Rather than relying on the failed attempts at incentives that civilized society has defaulted to for thousands of years, mathematical attractors might be employed by the scientific disciplines that are relevant to the specific use case; be it Physical Science, Botany, Psychology etc.

The dichotomy of “Particular” and “Archetype” distinguish the theoretical (Archetype) and practical (Particular) usage of the tuple in the image above. The Particular represents the practical applications of the economic theory for business models and the Archetype supplies the modeler with axioms to ensure coherence in individual application. It’s important that admissible input (omega) be well researched across the relevant disciplines to ensure that the transition (delta) produce the intended observed output (lambda). This being the case, the input (U) can be checked against the axioms associated with the admissible input (omega) to ensure that the output (Y) brings about the desired state (Q).

Inclusion of all variables is extremely important due to the fact that there really are no closed systems. The fact the consideration of input and output relies upon a created boundary doesn’t suggest that it is an accurate representation of the physical state of the system. Entropy can more easily arise when variables aren’t accounted for as unexpected effects are likely whether or not the intended function exists. This doesn’t necessarily mean that destructive effects are probable in any certain instance; however they become more probable as instances accrue in multitude. Inclusion of as many variables as practical is essentially a preemptive solution to risk management in a more scientific sense.