Many who read about the Resource-Based Economic (RBE) model wonder how such a thing would be possible ever, especially when many consider it to be possible with today’s technology. There is already a well-established science on resource management and efficient decision-making. Computer science has a significant requirement for the wise management of resources and efficient decisions regarding their use. Much of the hard work of understanding and implementing a Resource-Based Economic Model, or more generally a Natural Economic Model (one which does not use price or ownership systems), has already been considered, implemented, and tested by computer scientists.
First, we have to define an “economic system”. According to mainstream economics, it is “The large set of inter-related economic production and consumption activities which aid in determining how scarce resources are allocated.”[i] This is an adequate definition for us to work with, and it is further refined into sectors. There is the primary sector, which takes care of extraction of raw materials. The secondary sector processes the raw materials into products, and the tertiary sector provides services with those goods, such as transportation[ii].
Now, let us examine the definition of a “kernel”. It is “the main component of most computer operating systems”[iii]. This does not provide much information about it, so we need to break it down into its specific functions. The lowest-level function of the kernel is device management, in other words, the basic input/output of the computer. Next, it does memory management, which takes the I/O and stores it in memory to work with. It also decides how to allocate the resources to memory, and often has to work within strict limitations. Finally, it does process management, which maintains the execution of multiple processes, often in systems that can only manage a single process running at a time[iv].
It may not seem immediately obvious, but these functions are analogous, if not identical to one another. Extraction of basic input is analogous to extraction of basic materials, and the others respectively analogous to the other two sectors. The key is the use of real information, and the adaption of behavior to it. Computer science possesses existing solutions to long-standing problems in economics, requiring minimal change to exactly match economic functions. Despite the supposed problem[v], “economic calculation” is possible with computers.
The list of adaptations needed is so terse it can be laid out here in its entirety. To reiterate, a computer takes input (raw materials), stores the relevant data in memory (processes it into ‘products’), and uses it to run programs (services). Thus, in order for a computer kernel to function as an economic system, its input must be raw materials, its memory must be products, and its programs must be economic services.
First, a system to quantify supply and demand into input is needed. On the supply side, this can be done using a rigorous inventory of all resources and energy production capacity. From there, existing inventory and data management solutions can track supplies differentially until more accurate surveys may be completed. It is important to note that supply and demand must be separate values. This is because changes in supply are not reliably associated with changes in demand. The price system treats an increase in supply as equivalent to a decrease in demand, though each tells us more than their conflated change in price does. Not only that, but there is no agreed upon way that a price is composed, nor what information is conveyed in a price[vi][vii]. To contrast, there is no ambiguity over what is in a TCP packet[viii].
The second adaptation required is a memory management system of sorts, similar to a load-balancing or inventory management system. Different types of resources must be distinguished from one another in order to manage them effectively. Again, the price system is too primitive here, because while it may reveal whether there is a change in supply or demand, it provides no other information. The necessary information must be tracked according to their absolute values. This allows rates of change to predict changes of state in the abundance of a resource, such as approximately when a given resource will be exhausted.
An interface containing data and control hooks for industrial processes is the third major adaptation necessary. In the current model, these “hooks” exist, but they are people in a company, providing no certainty or automaticity to any attempted interaction with them. Efficient response to changing societal conditions requires computer control, and there is little reason for people to be in charge of this. It would allow, for example, construction of a new bridge to trigger automatically the production of all the bolts, beams, cables, and aggregates required to build a bridge. This means no need for someone to sit there and call a person that relays information to another person that tells other people what to make at a factory.
To summarize the list, converting a human-based global market economy into a science-based economic kernel requires a short list of changes: Encoding physical inventory into data, the adaptation of existing computational resource management techniques to physical resources, and an interface with productive facilities. This takes care of basic I/O, memory management, and conversion of that data into useful output. The only difficult item on the list is the first; It would require a high-end data center and large-scale, active surveying and data entry. The second is already largely fulfilled by logistics management systems, which track, for example, large retail chains’ product inventory[ix] or a military’s supply chains[x], outside of the price system. The final change can be implemented gradually as a standard, through the already-occurring process of replacing obsolete equipment.
There is already a type of software for managing large corporations’ logistics, called “Enterprise Resource Planning”. This is a computer system with automated tools for customer service, manufacturing scheduling and testing, project management, accounting, and supply chain management. These type of systems could provide a strong basis for an economic kernel. With this information, there emerges a list of equivalent resource-based economic structures to those in a market economy. Many of these have been described by The Venus Project and The Zeitgeist Movement in the past, but not all. The most obvious replacement is that of the price system with the above described “I/O” control system.
It is hard to find a decent definition of anything in economics, so I will attempt to concisely define “price system” myself. A price system is, “an emergent, stochastic, irreversible encoding scheme for data about resources based on supply, demand, and other factors.” The definition makes it obvious enough what its primary function is, which is a message-passing interface for economic data. Considering the major and well-known problems with the price system, such as the paradox of value and the ubiquity of negative externalities[xi][xii], it seems that it serves this function poorly. A price cannot even provide absolute data, only relative, and there are many competing “theories” on what a price actually is, none of which are falsifiable[xiii].
The replacement for the price system is called Direct Resource Tracking (DRT). It is an empirical, deterministic, unencoded (or at least, reversibly-encoded) scheme for data about resources. There would be a “central” server (in reality a reverse proxy) so that all the data is in one place. This ensures the data is falsifiable and can be audited at any time by any entity. The determinism ensures that it is an easily computable function of the kernel. In combination with a human interface, it would provide feedback to everyone regarding the sustainability of current economic activity, as well as the equity of resource distribution.
Ownership can be defined as a relationship between two entities in which one maintains exclusive access and control over the other. This is one of the utmost issues with current economic solutions and the essential cause of deprivation. It is somewhat important to have mutual exclusion over resources, but only when they are actually in use. Mutual exclusion is used frequently in computing, especially in recent years, where multi-core processors must share common resources in a computer. It is utilized to ensure that two processes operating on the same data does not lead to an invalid state. Typically, mutual exclusion from a computer resource is implemented through the use of semaphores, which are simply counters that only allow a certain number of concurrent processes to access memory at a given time[xiv].
The replacement for ownership, therefore, is the semaphore. A sedan, for example, would have a semaphore with a maximum value of 5. A mobile phone would have a binary semaphore, also known as a mutex, so that it has only one user at a time. Unlike ownership, the semaphore is not acquired permanently, only to be released when the holder sells the product or dies. It only provides mutual exclusion for use, not possession. Even if a product is scarce relative to the demand for it, those trying to acquire a lock on the semaphore can be queued, so that everyone who wants can access the product in a fair manner. A coincidental advantage of this method is its provision for highly-accurate, real-time tracking of demand. Money, on the other hand, cannot actually track demand, since those who cannot afford something do not provide “price signals” to affect the state of the economy[xv].
A market is “a structure that allows the exchange of goods and services”. Since there is no ownership, there is little purpose in exchange, so again a replacement is needed. In the RBE case it is fulfilled by two new structures: For one, a Lock Acquisition System (LAS), which manages semaphores for resources and exposes the data about their demand. The other is the Access Center (AC), which is a physical location for using, taking, and allocating resources. These can easily be made to interface automatically with the LAS for convenience. The market system is supposed to distribute resources according to expressed preferences. By using things at the AC, one expresses their preferences, which are encoded as data. For products that are consumed on use, this is measured in discrete units of products or mass of bulk resources. Those that remain after being used are measured in use-time. This data can then be used by the kernel to manage the distribution of resources on the large scale.
Finally, speculation is “a form of risky investment intended to provide [stochastic] protection against shortages.” Speculation can be rather destructive, being a form of hoarding, and possibly resulting in price changes that make the speculated-on resource harder for some to acquire. It relies on both price and private property, and thus would not be able to exist in a non-market economy. Instead, an RBE would use something called Selective Overproduction and Load Balancing. This would be a planned overproduction of certain resources in order to ensure the prevention of shortages. It uses computable functions to determine the class and magnitude of the excess production, and automatically moves the excess to regions experiencing a shortage. Load balancing is already used as a sort of disaster prevention[xvi].
It seems that the development requirement to adapt existing computational methods to economic functions are minimal. With the development of a small number of key projects, these systems could be realized on the timescale of around a decade. Several systems already exist, while the rest have the framework already laid out, or at least proven and tested methods. Finally, unlike economic solutions, all of these are computable and falsifiable, sustainable and equitable. The development of an economic kernel would be one of the most revolutionary projects in human history.
[i] “Economy Definition,” Wiki, Investopedia, n.d., http://www.investopedia.com/terms/e/economy.asp.
[ii] Zoltan Kenessey, “The Primary, Secondary, Tertiary and Quaternary Sectors of the Economy,” The Review of Income and Wealth (April 20, 2012).
[iii] The Linux Information Project, “Kernel Definition,” LINFO, May 31, 2005, http://www.linfo.org/kernel.html.
[iv] “HowStuffWorks ‘Processor Management’,” HowStuffWorks, n.d., http://computer.howstuffworks.com/operating-system.htm.
[v] “XXVI. THE IMPOSSIBILITY OF ECONOMIC CALCULATION UNDER SOCIALISM: The Problem,” Ludwig Von Mises Institute, n.d., http://mises.org/humanaction/chap26sec1.asp.
[vi] Louis O. Scott, “The Information Content of Prices in Derivative Security Markets,” Staff Papers - International Monetary Fund 39, no. 3 (September 1992): 596.
[vii] Kenneth D. Garbade, Jay L. Pomrenze, and William L. Silber, “On the Information Content of Prices,” American Economic Review 69, no. 1 (1979): 50–59.
[viii] J.H. Young, “TCP Packet Structure,” Computer Science Now, August 23, 2006, http://www.comsci.us/datacom/tcppacket.html.
[ix] B. Booen, “The Top 10 Automated Warehouses,” Warehousing, March 8, 2011, http://www.supplychaindigital.com/warehousing_storage/top-10-automated-warehouses.
[x] G. R. Gustafson, Logistics Management Systems in Desert Shield/Desert Storm-How Well Did They Do? (DTIC Document, 1992), http://oai.dtic.mil/oai/oai?verb=getRecord&metadataPrefix=html&identifier=ADA251296.
[xi] Nicholas Stern, The Economics of Climate Change: The Stern Review (Cambridge University Press, 2007).
[xii] Rob Dietz, “Negative Externalities Are the Norm,” The Daly News, April 2012, http://steadystate.org/negative-externalities/.
[xiii] Mark Thoma, “Economist’s View: ‘Science’ Without Falsification”, June 29, 2012, http://economistsview.typepad.com/economistsview/2012/06/science-without-falsification.html.
[xiv] Dave Marshall, “IPC:Semaphores,” Academic, Programming in C: UNIX System Calls and Subroutines Using C, March 1999, http://www.cs.cf.ac.uk/Dave/C/node26.html.
[xv] Roger W. Garrison and Israel Kirzner, “FRIEDRICH A. HAYEK,” Auburn University, n.d., http://www.auburn.edu/~garriro/e4hayek.htm.
[xvi] Pablo Valerio, “Load Balancing for Disaster Recovery,” Dell, February 14, 2011, http://content.dell.com/us/en/enterprise/d/large-business/load-balancing-disaster.