# Complex Systems Complex systems is an interdisciplinary field that uses mathematical and computational modeling to analyze systems composed of many interacting components. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Palantir and NVIDIA Deploy a Sovereign Nemotron Supply Chain Stack, Starting With the 1.3 Million Parts in Every Vera Rubin Rack DevFeed: [Palantir and NVIDIA Deploy a Sovereign Nemotron Supply Chain Stack, Starting With the 1.3 Million Parts in Every Vera Rubin Rack]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-10T20:56:11Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Nemotron](), [Nvidia](), [Artificial Intelligence](), [Vera Rubin](), [AI Infrastructure](), [AI Platform](), [cuOpt](), [Complex Systems](), [Optimization]() Tags: [ai](), [ai-infrastructure](), [artificial-intelligence](), [complex-systems](), [cuopt](), [enterprise](), [models](), [nemotron](), [nvidia](), [open](), [optimization](), [supply-chain](), [systems](), [vera-rubin]() ### AI overview Palantir and NVIDIA have deployed a sovereign AI stack for supply chain operations, initially using NVIDIA's own Vera Rubin supply chain as the first customer. The system combines Nemotron open models with Palantir Foundry and AIP, NVIDIA NeMo Data Libraries, and cuOpt to support materials allocation, scenario planning, optimization, and risk detection while keeping final decisions with supply chain experts. ### Source excerpt Palantir and NVIDIA have built a sovereign AI stack for supply chain operations and are running it first inside NVIDIA's own supply chain, the one that has to line up 1.3 million parts for every Vera Rubin rack. The stack brings NVIDIA Nemotron open models into Palantir Foundry and its Artificial Intelligence Platform (AIP), grounded The post Palantir and NVIDIA Deploy a Sovereign Nemotron Supply Chain Stack, Starting With the 1.3 Million Parts in Every Vera Rubin Rack appeared first on StorageReview.com. ## How an MIT research project became a global programming language DevFeed: [How an MIT research project became a global programming language]() Original publisher: [Read original article]() Author: Zach Winn | MIT News Published: 2026-08-31T04:00:00Z Content type: news Language: en Sources: [MIT AI News]() Topics: [The Julia Language](), [Programming language](), [Data Science](), [Complex Systems](), [Data analysis](), [Open Source]() Tags: [ai](), [ai-platform](), [alan-edelman](), [analysis](), [applications](), [artificial-intelligence](), [chris-rackauckas](), [complex-systems](), [computer-science-and-artificial-intelligence-laboratory-csail](), [deshpande-center](), [jeff-bezanson](), [julia-programming-language](), [juliahub](), [language](), [mit-schwarzman-college-of-computing](), [open-source](), [programming](), [programming-language](), [software](), [startups](), [stefan-karpinski](), [viral-shah]() ### AI overview An MIT research project created Julia, a free and open-source programming language for scientific research, data analysis, and complex-systems modeling. The article describes Julia's adoption by researchers, engineers, companies, and universities, and introduces JuliaHub's Dyad 3.0 AI platform. ### Source excerpt With millions of users across the world, Julia has been used to conduct cutting-edge research and to design new drugs, jet engines, heat pumps, and more. ## The Real Python Podcast - Episode #309: Exploring Complex Systems & Maintainable Data Science Pipelines DevFeed: [The Real Python Podcast - Episode #309: Exploring Complex Systems & Maintainable Data Science Pipelines]() Original publisher: [Read original article]() Author: Real Python Published: 2026-08-28T12:00:00Z Content type: article Language: en Sources: [Real Python]() Topics: [Complex Systems](), [Data Science](), [Python](), [systems](), [configuration](), [ci](), [YAML](), [releases](), [Django](), [VS Code Extension]() Tags: [code](), [complex-systems](), [configuration](), [data-science](), [django](), [podcast](), [python](), [release](), [systems](), [vs-code](), [workflow]() ### AI overview This Real Python podcast episode explores complex versus complicated coding problems and practical patterns for designing maintainable systems. It also covers repeatable data science pipelines, DataFrame validation with Pointblank, configuration-driven modular workflows, Python releases, PEPs, Django's release cycle, and several Python community projects. ### Source excerpt What are the key characteristics of complex systems, and what are practical patterns for tackling complex coding problems? Christopher Trudeau is back on the show this week with another batch of PyCoder's Weekly articles and projects. ## The search for quantum advantage in differential equations DevFeed: [The search for quantum advantage in differential equations]() Original publisher: [Read original article]() Author: Robert Davis Published: 2026-08-03T13:00:00Z Content type: article Language: en Sources: [IBM Research]() Topics: [Quantum Computing](), [Algorithms, Complexity](), [Complex Systems]() Tags: [algorithms](), [complex-systems](), [ibm](), [mathematical-sciences](), [physics](), [q-a](), [quantum](), [quantum-algorithms](), [research](), [systems]() ### AI overview IBM researcher Hari Krovi discusses quantum algorithms for solving certain differential equations and their potential to scale beyond classical methods in selected applications. ### Source excerpt New quantum algorithms could unlock faster ways to model the complex systems behind circuits, fluids, finance, and more. ## How Data Scientists Create Impact in Complex Billing Systems DevFeed: [How Data Scientists Create Impact in Complex Billing Systems]() Original publisher: [Read original article]() Author: Madison Kohls Published: 2026-02-18T05:00:00Z Content type: opinion Language: en Sources: [Figma Blog]() Topics: [Data Science](), [Complex Systems](), [data-modeling](), [Instrumentation]() Tags: [complex-systems](), [data-modeling](), [data-science](), [instrumentation](), [systems]() ### AI overview The article argues that impactful data science is not limited to experiments, optimization, forecasting, or inferential modeling. In Figma's Billing infrastructure, it emphasizes modeling event lifecycles, reconciling data across systems, instrumentation, and building tools that make complex system behavior observable and verifiable. ### Source excerpt Not all impactful data science work involves experiments or optimization. Sometimes it's about making complex systems legible, correct, and safe to operate. ## Reliability lessons from the 2025 AWS DynamoDB outage DevFeed: [Reliability lessons from the 2025 AWS DynamoDB outage]() Original publisher: [Read original article]() Author: Gavin Cahill Published: 2025-11-07T00:00:00Z Content type: article Language: en Sources: [Gremlin Blog]() Topics: [Amazon DynamoDB](), [Amazon Web Services](), [Complex Systems](), [systems](), [Cloud]() Tags: [2025](), [amazon-dynamodb](), [applications](), [aws](), [cloud](), [industry](), [outage](), [outages](), [systems]() ### AI overview The article examines reliability lessons from the October 2025 AWS DynamoDB outage in US-EAST-1, which began with a DNS issue and was followed by an Amazon EC2 outage. It recommends mapping service dependencies, testing how applications behave when dependencies are unavailable, and distinguishing critical from non-critical dependencies. ### Source excerpt In October 2025, Amazon DynamoDB had a massive outage that took down hundreds of systems. Find out what your team can do to minimize the impact of similar outages in the future. ## How to test the reliability of a Point of Sale (POS) system DevFeed: [How to test the reliability of a Point of Sale (POS) system]() Original publisher: [Read original article]() Author: Gavin Cahill Published: 2025-10-20T00:00:00Z Content type: tutorial Language: en Sources: [Gremlin Blog]() Topics: [Chaos Engineering](), [Resilience](), [autoscaling](), [Microservice](), [Complex Systems](), [systems]() Tags: [autoscaling](), [best-practices](), [chaos-engineering](), [cpu](), [engineering](), [gremlin](), [how-to](), [infrastructure](), [memory](), [microservices](), [outage](), [reliability-management](), [resilience](), [retail](), [testing]() ### AI overview This tutorial explains how to test the reliability of retail Point of Sale systems using Gremlin and Chaos Engineering. It focuses on resilience testing for microservice-based checkout systems, including autoscaling, CPU, memory, and disk I/O capacity, to identify failure conditions and reduce outages. ### Source excerpt Find out how to use Gremlin and Chaos Engineering to make sure your Point of Sale system is reliable. ## Behind the scenes: Redpanda Cloud's response to the GCP outage DevFeed: [Behind the scenes: Redpanda Cloud's response to the GCP outage]() Original publisher: [Read original article]() Author: Camilo Aguilar Published: 2025-06-20T00:00:00Z Content type: article Language: en Sources: [Redpanda]() Topics: [Google Cloud Platform (GCP)](), [incident](), [Complex Systems](), [Monitoring](), [observability](), [Cloud](), [Self-hosted](), [dashboards]() Tags: [2025](), [availability](), [cloud-outage-incident-response](), [cloud-platform-safety-practices](), [cloud-service-outage-management](), [cloud-service-reliability-measures](), [complex-systems](), [gcp](), [gcp-automated-quota-update-issue](), [gcp-outage-impact-on-customers](), [gcp-outage-lessons-learned](), [gcp-outage-response](), [google-cloud-platform-outage-2025](), [handling-cloud-platform-outages](), [incident-response](), [monitoring](), [monitoring-cloud-services-during-outage](), [observability](), [outage](), [production](), [redpanda-cloud-gcp-outage](), [self-hosting](), [thought-leadership]() ### AI overview Redpanda Cloud describes how its GCP clusters remained stable during the June 12, 2025 global GCP outage. The article explains the role of cell-based architecture, degraded monitoring, and reliability practices in the company's response. ### Source excerpt On June 12, GCP went down. Here's how we responded at Redpanda Cloud and what it taught us about safety and reliability. ## The flight plan that brought UK airspace to its knees DevFeed: [The flight plan that brought UK airspace to its knees]() Original publisher: [Read original article]() Author: Chris Evans Published: 2024-12-05T09:44:12Z Content type: article Language: en Sources: [The incident.io Blog]() Topics: [Flight](), [bug](), [Complex Systems](), [Incident response](), [Resilience](), [systems]() Tags: [bug](), [complex-systems](), [incident](), [incident-channel](), [incident-management](), [incident-response](), [operations](), [outage](), [post-mortem](), [resilience](), [slack-incident](), [software](), [systems](), [tech]() ### AI overview The article examines the August 28, 2023 failure of UK air traffic control systems after a valid flight plan containing duplicate airport codes triggered an edge case in processing software. The primary system and its backup shut down as a safety measure, causing six hours of downtime, major delays, stranded passengers, and manual processing. It explains the roles of EUROCONTROL and NATS, different flight plan formats, and the FPRSA-R subsystem that converts ADEXP messages into NAS messages. ### Source excerpt On August 28, 2023, a software bug in the UK air traffic control system caused six hours of chaos, reducing air traffic capacity and forcing manual operations. It's a great story of failure, resilience and communications in complex systems. ## Why are we so afraid of code as a commodity? DevFeed: [Why are we so afraid of code as a commodity?]() Original publisher: [Read original article]() Author: Kris Rasmussen Published: 2024-06-26T00:00:00Z Content type: article Language: en Sources: [Figma Blog]() Topics: [Artificial Intelligence](), [Generative AI](), [Figma](), [Code](), [Programming](), [React](), [TypeScript](), [Kotlin](), [Jetpack](), [Complex Systems]() Tags: [ai](), [code](), [figma](), [frameworks](), [generative-ai](), [jetpack](), [kotlin](), [programming](), [react](), [software](), [systems](), [typescript]() ### AI overview The article argues that AI-assisted development may automate code translation and parts of the design-to-code workflow, but engineering remains more than producing code. Engineers continue to add value by identifying the right problems, reasoning from first principles, designing abstractions, and creating elegant, maintainable systems that address user needs. Framework-specific expertise may decline in relative value, while broader engineering judgment remains difficult to commoditize. ### Source excerpt Instead of asking how AI will automate our jobs, we should be focusing on what we, as engineers, uniquely do well. What problems can AI currently solve, and where is the whitespace to go beyond that? ## The two kinds of failure testing DevFeed: [The two kinds of failure testing]() Original publisher: [Read original article]() Author: Sam Rossoff Published: 2024-02-21T00:00:00Z Content type: article Language: en Sources: [Gremlin Blog]() Topics: [Chaos Engineering](), [Resilience](), [Availability](), [Complex Systems](), [systems](), [CI/CD](), [telemetry]() Tags: [availability](), [ci-cd](), [complex-systems](), [engineering](), [metrics](), [on-call](), [resilience](), [scale](), [software](), [systems](), [testing](), [validation]() ### AI overview The article distinguishes two common uses of fault injection: exploratory testing, which reveals unknown failure modes and system responses, and validation testing, which regularly verifies known resilience properties across systems. It explains how these approaches support resilience improvements, operational readiness, availability programs, observability, and CI/CD processes. ### Source excerpt Learn more about exploratory testing and validation testing, the two most common uses of Fault Injection. ## Uncovering hidden reliability risks in complex systems DevFeed: [Uncovering hidden reliability risks in complex systems]() Original publisher: [Read original article]() Author: Andre Newman Published: 2024-02-15T00:00:00Z Content type: article Language: en Sources: [Gremlin Blog]() Topics: [Complex Systems](), [Availability](), [Cloud](), [incident](), [Kubernetes](), [Deployment](), [hosting]() Tags: [availability](), [cloud](), [complex-systems](), [compliance](), [customers](), [deployment](), [deployment-guides](), [documentation](), [gremlin](), [guides](), [hosting](), [incident](), [kubernetes](), [outage](), [product](), [report]() ### AI overview This article explains how Gremlin's Detected Risks capability and Team Risk Report help engineering teams identify hidden reliability risks before they cause outages. It focuses on issues such as single-availability-zone deployments, misconfigurations, bad defaults, and reliability anti-patterns, with particular emphasis on Kubernetes environments. ### Source excerpt Learn how Gremlin automatically detects reliability risks in your environment. Review risks and implement fixes before your customers ever notice any issues. ## Synchronizing mental models DevFeed: [Synchronizing mental models]() Original publisher: [Read original article]() Author: Chris Evans Published: 2023-06-30T11:41:00Z Content type: article Language: en Sources: [The incident.io Blog]() Topics: [incident](), [systems](), [Complex Systems]() Tags: [cognitive-load](), [communication](), [efficiency](), [incident](), [incident-channel](), [incident-management](), [incident-response](), [mental-models](), [operational](), [outage](), [post-mortem](), [slack-incident](), [systems](), [workflows]() ### AI overview The article explains how differing mental models can hinder incident response by slowing decision-making, increasing stress, and reducing operational efficiency. It presents Catalog as a way to define organizational and technical-system relationships so responders can work from a shared operational understanding. ### Source excerpt When everyone has their own mental model, it can hinder our ability to respond to incidents. Catalog creates a shared operational map, enabling faster decision-making, automated workflows, and an overall streamlined response process. ## Workflows as Actors: Is it really possible? DevFeed: [Workflows as Actors: Is it really possible?]() Original publisher: [Read original article]() Author: Fitz Published: 2023-06-27T22:00:00Z Content type: tutorial Language: en Sources: [Temporal Blog]() Topics: [Actor](), [systems](), [Object-oriented programming (OOP)](), [Complex Systems]() Tags: [actor](), [blog-post](), [concurrent](), [design-patterns](), [object-oriented](), [parallel](), [temporal-concepts](), [workflows]() ### AI overview This article introduces the Actor Model and compares actors with objects, focusing on message passing, state, actor creation, and encapsulation. It then explains how Temporal Workflows can be built to behave like actors. ### Source excerpt An overview on how to use Temporal Workflows in the form of an Actor Model ## Creating High-Purpose Environments (High-Purpose Environments, Part 1) DevFeed: [Creating High-Purpose Environments (High-Purpose Environments, Part 1)]() Original publisher: [Read original article]() Author: tobi@techunicorn.builders (Tobias Mende) Published: 2023-05-06T05:00:00Z Content type: article Language: en Sources: [Tobias Mende]() Topics: [Development](), [Software Engineering](), [Agile](), [Complex Systems]() Tags: [agile](), [article](), [complex-systems](), [creativity](), [culture](), [culture-high-purpose-environments-engineering-excellence-organizational-design](), [software-engineering](), [teams]() ### AI overview This first article in a six-part series applies the idea of high-purpose environments to software engineering and product development. It explains how vision, values, guiding principles, and mission can connect day-to-day work with a future ideal, while emphasizing that these signals must influence organizational behavior rather than merely exist as statements. ### Source excerpt Creating High-Purpose Environments (High-Purpose Environments, Part 1) High-purpose environments are cultural spaces that inspire, motivate, and empower individuals to work toward a common goal. A strong sense of purpose, clear values, and a commitment to continuous growth and improvement characterize these environments. ## How Developer Experience Changes When Software Complexity Reaches a Tipping Point DevFeed: [How Developer Experience Changes When Software Complexity Reaches a Tipping Point]() Original publisher: [Read original article]() Author: Dominik Tornow Published: 2023-01-10T17:30:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [Developer experience](), [Software Engineering](), [Complex Systems](), [Django]() Tags: [complexity](), [developer-experience](), [django](), [how-to](), [software-engineering](), [tests]() ### AI overview The article examines how developer experience can scale linearly as a software application grows, then change sharply when additional requirements introduce significantly more complexity. It uses a Django web application to illustrate the contrast and identifies factors such as multistep processes spanning database transactions or services. ### Source excerpt Many complex systems exhibit a phenomenon known as a tipping point. Learn more about tipping points, developing a linear developer experience, and how to adapt. ## Grantee Roundup: September 2021 DevFeed: [Grantee Roundup: September 2021]() Original publisher: [Read original article]() Author: Ethereum Foundation Ecosystem Support Program Published: 2021-10-22T00:00:00Z Content type: article Language: en Sources: [Ethereum Foundation Blog]() Topics: [Ethereum](), [Protocol (disambiguation)](), [Complex Systems](), [Simulation](), [Framework](), [Open Source](), [Learning]() Tags: [complex-systems](), [courses](), [ecosystem-support-program](), [ethereum](), [framework](), [open-source](), [protocol](), [simulation]() ### AI overview This Ethereum Foundation grantee roundup describes progress on SSZ.dev, a resource site for Ethereum's Simple Serialize format, and introduces CadCAD Edu resources for learning to model and simulate complex systems. ### Source excerpt It's always fun to hear about new grants as they're awarded, but what happens after the announcement? In this series, we'll check in on a couple of projects that are well underway - or already at the finish line. Read on to learn about some recent milestones and achievements by... ## HASH: a free, online platform for modeling the world DevFeed: [HASH: a free, online platform for modeling the world]() Original publisher: [Read original article]() Author: Joel Spolsky Published: 2020-06-18T14:12:25Z Content type: article Language: en Sources: [Joel Spolsky]() Topics: [Simulation](), [Complex Systems](), [JavaScript](), [Code]() Tags: [complex](), [free](), [hash](), [javascript](), [news](), [platform](), [simulation](), [systems]() ### AI overview The article introduces HASH, a free online platform for modeling complex systems through JavaScript-based simulations. It explains how users can model individual behaviors, adjust parameters and rules, and use simulations to better understand complex problems. ### Source excerpt Sometimes simulating complex systems is the best way to understand them. Read more "HASH: a free, online platform for modeling the world" ## Introducing Exploranda, a set of tools for exploring complex systems. DevFeed: [Introducing Exploranda, a set of tools for exploring complex systems.]() Original publisher: [Read original article]() Author: Raphael Luckom Published: 2018-01-25T18:02:35Z Content type: article Language: en Sources: [Localytics]() Topics: [Complex Systems](), [data](), [Tool](), [Monitoring](), [Graphs]() Tags: [ci](), [complex-systems](), [data](), [exploration](), [graphs](), [monitoring](), [systems](), [tools]() ### AI overview The article introduces Exploranda, a tool for exploring complex systems and supporting the early stages of data-driven decision-making. It describes how open-ended investigation differs from established tools such as monitoring, CI, and CD, especially when problems or causes are not obvious. ### Source excerpt At Localytics, data is central to our mission. One of our core principles is to "lead with data." When I envision that, I often think of an analyst using graphs to make a case for a business decision, or a principal engineer using benchmarks to advocate for a ## Two Perspectives on the End-to-End Principle DevFeed: [Two Perspectives on the End-to-End Principle]() Original publisher: [Read original article]() Author: Nelson Elhage Published: 2017-06-11T20:42:01Z Content type: opinion Language: en Sources: [Nelson Elhage]() Topics: [systems](), [Architecture & Design](), [Complex Systems](), [Software Engineering]() Tags: [argument](), [complex-systems](), [perspectives](), [software-design](), [systems-engineering]() ### AI overview The article reflects on the end-to-end principle as a general systems-design heuristic. It explains that functions such as correctness may be better handled at the ends of a system rather than at every lower-level interface, and questions whether complex systems can be made correct simply by composing correct subsystems. ### Source excerpt Back when I was an undergraduate, as part of a class called "Computer Systems Engineering", we read numerous classic papers of systems design. I enjoyed and learned a great deal from many of these papers, but one that paper that has stuck with me in particular was Saltzer et al's "End-to-End Arguments in Systems Design". The paper is a very general tract on systems design - it does explore several examples of concrete systems or applications, but it ultimately expounds upon the end-to-end principle as a perspective or design heuristic that can apply to virtually any system design. ## Blameless Postmortems - Examining Failure Without Blame DevFeed: [Blameless Postmortems - Examining Failure Without Blame]() Original publisher: [Read original article]() Author: umer Published: 2016-06-20T00:00:00Z Content type: opinion Language: en Sources: [Code Ahoy - Articles]() Topics: [Complex Systems](), [Resilience](), [systems](), [site-reliability-engineering]() Tags: [complex-systems](), [management](), [mistakes](), [organizational](), [people](), [resilience](), [safety](), [team]() ### AI overview This commentary argues that organizations should examine failures in complex systems without blaming individuals. It presents blameless postmortems and a Just Culture as ways to encourage employees to share information, understand organizational causes, improve system resilience, and prevent recurring failures. ### Source excerpt Let's face it: failure is inevitable in complex systems. It cares not for the number of tests you ran, code reviews or your monitoring tools. It just happens. And how is failure usually dealt with? Instead of learning from it to improve the resilience of the system, the traditional view is to assign blame and point fingers at individuals responsible for the failure. It's easier to identify a culprit than to find the real cause. In The Field Guide to Understanding Human Error, author Sidney Dekker refers to this as the "old view" that leads us nowhere: When faced with a human error problem, you may be tempted to ask 'Why didn't they watch out better? How could they not have noticed?'. You think you can solve your human error problem by telling people to be more careful, by reprimanding the miscreants, by issuing a new rule or procedure. These are all expressions of 'The Bad Apple Theory', where you believe your system is basically safe if it were not for those few unreliable people in it. This old view of human error is increasingly outdated and will lead you nowhere. The new view, in contrast, understands that a human error problem is actually an organizational problem. When employees are blamed and shamed by their superiors, who have the the power of hindsight on their side, few things happen: Employees become defensive and lose motivation. The overall team sociology and culture suffers. Employees start hiding mistakes. The team and the company doesn't learn any lessons and nothing is done to prevent failures from happening again. No one actually takes the responsibility and everybody blames each other. So how should companies handle mistakes? When failure occurs, the role of the management should be to figure out what happened so they can improve something to prevent it from happening again. But the management doesn't have a crystal ball that can give out all the details. They have to rely on their employees for this information. In order for employees to come for ## The Operations Gradient: Improving Safety in Complex Systems DevFeed: [The Operations Gradient: Improving Safety in Complex Systems]() Original publisher: [Read original article]() Author: Marc Brooker Published: 2014-06-29T00:00:00Z Content type: opinion Language: en Sources: [Marc Brooker's Blog](), [Marc Brooker's Blog]() Topics: [Complex Systems](), [systems](), [Availability](), [monitor]() Tags: [availability](), [complex-systems](), [cost](), [engineering](), [management](), [monitor](), [operations](), [safety](), [systems]() ### AI overview The article discusses Richard Cook's lecture on Rasmussen's model of system safety. It explains how economic and engineering pressures can move complex systems toward safety boundaries, while defence-in-depth protections may gradually degrade. It recommends conscious attention to safety, availability, durability, error margins, perceived performance boundaries, and monitoring. ### Source excerpt The Operations Gradient: Improving Safety in Complex Systems Can we improve the safety of complex systems by listening to operators more? This week, I watched an excellent lecture by Richard Cook. He goes in some detail about why failures happen, through the lens of Rasmussen's model of system safety. If you build or maintain any kind of complex system, don't miss this lecture. What is surprising is not that there are so many accidents, it's that there are so few. The model that takes up most of the lecture is best expressed in Rasmussen's Risk Management in a Dynamic Society: A Modelling Problem, a classic paper that deserves more attention among engineers. The core of the insight of the model from Rasmussen's paper comes from Figure 3: Rasmussen describes the process of developing systems as an adaptive search within a boundary defined by a set of economic constraints (it's not economically viable to run the system beyond this boundary), engineering effort constraints (there are not enough actors to push the system beyond this boundary), and safety constraints (the system has failed beyond this boundary). The traditional balance between engineering effort and economic return plays out in pushing the operating point of the system away from two of these boundaries. From the paper: During the adaptive search the actors have ample opportunity to identify an effort gradient and management will normally supply an effective cost gradient. The combination of optimizing for these two gradients tends to push the operating point towards the safety boundary (or boundary of acceptable performance). A conscious push for safety (or availability, durability and other safety-related properties) forces the operating point away from this boundary. One danger of this is that the position of the safety boundary is not always obvious, and it's also not a single clean line. From the paper: in systems designed according to a defence-in-depth strategy, the defenses are likely to degenerat ## Simple Rules, Complex Systems and Software Development DevFeed: [Simple Rules, Complex Systems and Software Development]() Original publisher: [Read original article]() Published: 2009-03-23T15:41:57Z Content type: opinion Language: en Sources: [Blog by Michael Dubakov]() Topics: [Complex Systems](), [systems](), [Development](), [Agile](), [Cellular automaton](), [Simulation]() Tags: [agile](), [communication](), [complex-systems](), [development](), [development-process](), [logic](), [rules](), [simulation](), [systems]() ### AI overview The article argues that simple rules can produce complex behavior, using ant colonies, bird flocks, and Conway's Game of Life as examples. It applies this idea to software development, arguing that simple processes such as Agile and Scrum can support learning, feedback, communication, and cooperation, while excessive process complexity can produce rigid behavior. ### Source excerpt Many complex systems are based on simple rules. A set of several simple rules leads to complex, intelligent behavior. While a set of complex rules often leads to a dumb and primitive behavior. There are many examples. Ants Colony How ants search for food? They do not have cell phones, cars and mini-markets near the nest. They should have something simpler to communicate. Here is how ants work: Travel randomly in search for food. Take a piece of food and head straight back to the nest. On the way back to the nest lay down an odor trail. Notify nestmates of the discovered food encouraging them to leave the nest. These newly recruited ants will follow the odor trail directly to the food source. In their turn, each ant will reinforce the odor trail until the food is gone. Sounds simple? Take a look at this very nice ants colony model. Drop some food and enjoy the action. Birds Flocks Birds flocks are beautiful. You may think that the movement gets orchestrated by one savvy bird. But this is not the case. A bird glock is guided by three simple principles (every decent bird knows them): Separation: steer to avoid stumbling upon local flockmates. Alignment: steer towards the average heading of local flockmates. Cohesion: steer to move towards the average position of local flockmates. Simple? Yes, it is. Look at the picture on the right. It's just amazing! Game of Life Game of Life was invented in 1970 by John Conway. It is a cellular automaton and simulates the birth, death, etc., of organisms based on certain rules: Each cell with one or no neighbors dies, as if of loneliness. Each cell with four or more neighbors dies, as if of overpopulation. Each cell with two or three neighbors survives. Each empty cell with three neighbors becomes populated. Simple rules. But these rules lead to fantastic diversity of the forms. Different types of the forms have been discovered e.g. still objects, oscillators, gliders, spaceships, etc. It is impossible to predict the state of a syste ## Software Development as a Complex Adaptive System DevFeed: [Software Development as a Complex Adaptive System]() Original publisher: [Read original article]() Published: 2008-11-24T15:41:57Z Content type: opinion Language: en Sources: [Blog by Michael Dubakov]() Topics: [Complex Systems](), [content addressed store](), [Development](), [Software Engineering]() Tags: [complex-systems](), [complexity](), [development-process](), [software-development]() ### AI overview The article explains software development through the lens of Complex Adaptive Systems (CAS). It introduces CAS as adaptive, decentralized networks of interacting agents and relates their properties--such as self-organization, feedback, information exchange, cooperation, and adaptation--to software development. ### Source excerpt Complexity science is very young. Cybernetics, ecology, sociology, meteorology all study complexity. In general, a complex system consists of interacting components. The result of this interaction can't be predicted by observing an individual component. For example, human brain consists of neurons. The brain has 'memory', while each neuron doesn't. Ants' colony behavior can't be predicted from the behavior of an individual ant. And it is impossible to understand software development process looking at how just one developer works. We are especially interested in a particular case of complex systems called Complex Adaptive Systems (CAS). The main difference is that CAS may learn and change (adapt) over time based on previous experience. CAS remember the history, and that is the main difference from chaotic systems. There is no common definition of Complex Adaptive System (CAS). One of the most popular definition was offered by John H. Holland A Complex Adaptive System (CAS) is a dynamic network of many agents (which may represent cells, species, individuals, firms, nations) acting in parallel, constantly acting and reacting to what the other agents are doing. The control of a CAS tends to be highly dispersed and decentralized. If there is to be any coherent behavior in the system, it has to arise from competition and cooperation among the agents themselves. The overall behavior of the system is the result of a huge number of decisions made every moment by many individual agents. CAS has quite many common properties. However, hardly a system should have all of them to be CAS. Moreover, researches define different sets of CAS properties. Let's try to review the most common properties and see how software development process can be described in terms of those properties. CAS Property Software Development interpretation Agent System component People in development team and others involved (product owners, stakeholders). Feedback Each agent in CAS reacts to information fl [Next page]()