Why farmpunk?

A farmpunk could be described as a neo-agrarian who approaches [agri]culture, community development and/or design with an anarchistic hacker ethos. "Cyber-agrarian" could supplant neo-agrarian, indicating a back-to-the-land perspective that stands apart from past movements because it is heavily informed by conceptual integration in a post-industrial information society (thus "forward to the land" perhaps?) The art and science of modern ecological design—and ultimately, adapting to post-collapse contexts—will be best achieved through the combined arts of cybermancy and geomancy. In other words: the old ways of bushcraft and woodlore can be combined with modern technoscience (merely another form of lore) in open and decentralized ways that go beyond pure anarcho-primitivism. This blog is an example of just that. Throughout, natural ecologies must be seen as the original cybernetic systems.

**What we call for at the farmpunk headquarters**
°Freedom of information
°Ground-up action + top-down perspectives
°Local agricultural systems (adhering to permaculture/biodynamic principles) as the nuclei of economies
°Bioregional autonomy
°Computers are optional but can be used for good—see peer to peer tech, social media for direct popular management of natural or political disasters (e.g. Arab Spring), or the mission of the hacker collective Anonymous
°You

"Municipal liberty is the first and most important [principle] of democratic institutions, since nothing is more natural or worthy of respect then the right which citizens of any settlement have of arranging themselves the affairs of their common life and of resolving as best suits them in the interests and the needs of the locality." - Emilio Zapata

Sunday, February 8, 2009

Complex systems 101

I know some of you who drop by here are wise to these matters, but I decided to post this little primer for anyone who perhaps didn't realize how much they wanted to know about this stuff. :P
This is a FAQ from Computerworld.com that very succinctly conceptualizes complex adaptive systems (as they are relevant to software-based modeling).

"January 27, 2003 (Computerworld) -- What are "complex systems" in this context? These are noncomputer systems, such as a company's supply chain. A system is "complex" when it has so many variables and interacting forces that it can't be understood in its entirety or optimized by traditional, top-down approaches.

How can you tame this complexity? Although these systems are complex overall, they use a few simple rules at local levels. For example, in a supply chain system, a rule in a warehouse might be, "Fill orders on a first-in, first-out basis," or "Don't send this truck out on delivery until it is full." Dozens or hundreds of these local "agents" - truck dispatchers, say - acting autonomously produce complex behavior by the system as a whole. It's possible to simulate this complex behavior by programming software agents with a few rules and letting them interact with one another. By optimizing the agents' activities at a local level, it's possible to improve the performance of the system as a whole.

Why are these systems called "adaptive," and why are they sometimes likened to ant colonies? Ants individually have extremely primitive brains, yet collectively they run surprisingly sophisticated and efficient operations. With no central direction, they divide responsibilities among themselves, find food, build and maintain their nests, tend to their young and respond to attacks. And the colonies adapt; if you block access to a source of food, ants will find an alternate route to the food. Complex adaptive systems do the same. For example, if Plant A can't satisfy a customer order because it's temporarily out of a raw material, Plant B may fill the order. Plant B may do this "automatically," based on simple local rules without direction from a central authority.

What is meant by "emerging behavior"? Like ants, individual agents can modify their rules to adapt to changing circumstances, and this can alter the global behavior of the system, often in unpredictable ways. Sometimes small, local changes can have big system impacts, just as a tiny disturbance in the atmosphere over Africa can lead to a hurricane days later in the Gulf of Mexico. Agent-based modeling can help us understand and predict these emerging behaviors and help us devise new rules for the local agents that will improve the performance of the system as a whole. "

Source

3 comments:

Thirtyseven said...

My all-time favorite read on complex systems:
Complexity Rising
, which makes the counter-intuitive case that the more complex a system is, the simpler it's behavior is on a large scale.

the faun said...
This comment has been removed by the author.
the faun said...

Thanks for the link! This is fascinating.