In this work I will lend support to the theory of «dynamic efficien - cy», as outlined by Prof. Huerta de Soto in The Theory of Dynamic Efficiency (2010a). Whereas Huerta de Soto connects economics with ethics, I will take a different approach. Since I have a back-ground in Artificial Intelligence (A.I.), I will show that this and related fields have yielded insights that, when applied to the study of economics, may call for a different way of looking at the eco-nomy and its processes.
At first glance, A.I. and economics do not seem to have a lot in common. The former is thought to attempt to build a human being; the latter is supposed to deal with depressions, growth, inflation, etc. That view is too simplistic; in fact there are strong similarities.
First, economics is based on (inter-)acting individuals, i.e. on human action. A.I. tries to understand and simulate human (and animal) behavior. Second, economics deals with information pro-cessing, such as how the allocation of resources can best be orga-nized. A.I. also investigates information processing. This can be in specific systems, such as the brain, or the evolutionary process, or purely in an abstract form. Finally, A.I. tries to answer more philosophical questions like: what is intelligence? What is a mind? What is consciousness? Is there free will? These topics play a less prominent role in economics, but are sometimes touched upon, together with the related topic of the «entrepreneurial function».
The paradigm that was dominant in the early days of A.I. is static in nature. Reaching a solution is done in different steps. First: gathering all necessary information. Second: processing this in - formation. Finally: the outcome of this process, a clear conclusion. Each step in the process is entirely separate. During information gathering no processing is done, and during processing, no new information is added. The conclusion reached is final and cannot change later on. Logical problems are what is mostly dealt with, finding ways in which a computer can perform deductions based on the information that is represented as logical statements. Other applications are optimization problems, and so-called «Expert Systems», developed to perform the work of a judge reaching a verdict, or a medical doctor making a diagnosis based on the symptoms of the patient. This paradigm is also called «top-down», because information flows to a central point where it is processed, or «symbolic processing», referring to deduction in formal logic.1
In economics there is a similar paradigm, and it is still the do-minant one. This is the part of economics that deals with opti - mization of resources: given costs and given prices, what is the allocation that will lead to the highest profit? Also belonging to this paradigm are the equilibrium models. Demand and supply curves are supposed to be knowable and unchangeable, and the price is a necessary outcome. The culmination is central planning that supposes all necessary information, such as demand and supply curves and available resources to be known. Based on this, the central planner determines prices.
A Survey on Cat Swarm Optimization Algorithm
