Ashby’s Law of Requisite Variety: If a system is to be stable, the number of states of its control mechanism must be greater than or equal to the number of states in the system being controlled. Or: Only variety can absorb variety. Variety is a…
Requisite variety and its implications for the control of complex systems
Recent work on the fundamental processes of regulation in biology (Ashby, 1956) has shown the importance of a certain quantitative relation called the law of requisite variety. After this relation had been found, we appreciated that it was related to a theorem in a world far removed from the biological—that of Shannon on the quantity of noise or error that could be removed through a correction-channel (Shannon and Weaver, 1949; theorem 10). In this paper I propose to show the relationship between the two theorems, and to indicate something of their implications for regulation, in the cybernetic sense, when the system to be regulated is extremely complex. Since the law of requisite variety uses concepts more primitive than those used by entropy, I will start by giving an account of that law.
Variety (cybernetics)
In cybernetics, the term variety denotes the total number of distinguishable elements of a set, most often the set of states, inputs, or outputs of a finite-state machine or transformation, or the binary logarithm of the same quantity.[1] Variety is used in cybernetics as an information theory that is easily related to deterministic finite automata, and less formally as a conceptual tool for thinking about organization, regulation, and stability. It is an early theory of complexity in automata, complex systems,[1]: 6  and operations research.[2]
Viability theory
Viability theory is an area of mathematics that studies the evolution of dynamical systems under constraints on the system state.[1][2] It was developed to formalize problems arising in the study of various natural and social phenomena, and has close ties to the theories of optimal control and set-valued analysis.
Conway's Law | Laws of Software Engineering
Organizations design systems that mirror their own communication structure.
What Functional Programmers Get Wrong About Systems - Ian Duncan
Type systems verify properties of programs. Production correctness is a property of systems. The gap between these is where the interesting failures live.
Stigmergy
Stigmergy is a mechanism of indirect coordination, through the environment, between agents or actions. The principle is that the trace left in the environment by an individual action stimulates the performance of a succeeding action by the same or different agent. Agents that respond to traces in the environment receive positive fitness benefits, reinforcing the likelihood of these behaviors becoming fixed within a population over time.
A sufficiently detailed spec is code
Specifications do not address the limitations of agentic coding
Knowing When to Stop: The Art of Making a Loop Converge | Andreessen Horowitz
The systems that matter will not be the ones that can keep going. They all can.
Laws of Software Engineering
A collection of principles and patterns that shape software systems, teams, and decisions.
The Theory of Moral Sentiments
The foundation for a general system of morals, this 174…
Web Tiles
Trust has been the defining constraint on the Web's evolution towards more powerful, more applicative capabilities. In a Web context, the user must be able to safely load any arbitrary URL, to safely click on any arbitrary link. The way in which this is achieved is that the runtime places strict limits on what a Web page can do, which in turn necessarily limits powerful capabilities. Could we get more power using a primitive that places more stringent constraints in what pages can do?
All you need is links
When Tim Berners Lee talks about how he designed the web, he uses an analogy from physics, describing his process as a quest to find “fundamental laws” which can generate a desired system:
Russian Revolution becomes the property relations that are common to.
Anatomy of a Lean Proof for Software Engineers
Intro I recently worked through a problem from a theory of computation textbook that asked me to prove a property of a language using finite automata. The informal proof is a simple constructive proof where you build an automaton and show that it recognizes the language. This is kind of similar to program verification, so I thought it’d be interesting to see what it takes to formalize the proof. Lean is a good choice for this, because its Mathlib has all the theorems for the problem.