Minimizing complexity – The need to reduce complexity is mainly driven by limited ability of most people to hold complex structures and information in their working memories. Reduced complexity is achieved through emphasizing the creation of code that is simple and readable rather than clever.

Why is it important to minimize complexity in a software system?

Complexity Metrics – Fortunately, there have been many methods developed for measuring software complexity. While some may differ slightly from others, most break down software complexity according to the following metrics: : measures how much control flow exists in a program – for example, in RPG, operation codes such as IF, DO, SELECT, etc.

Programs with more conditional logic are more difficult to understand, therefore measuring the level of cyclomatic complexity unveils how much needs to be managed. Using cyclomatic complexity by itself, however, can produce the wrong results. A module can be complex, but have few interactions with outside modules.

A module can also be relatively simple, but be highly coupled with many other modules, which actually increases the overall complexity of the codebase beyond measure. In the first case, complexity metrics will look bad. In the second, the complexity metrics will look good, but the result will be deceptive.

• It is important, therefore, to measure the coupling and cohesion of the modules in the codebase as well in order to get a true system-level, software complexity measure.
• Halstead Volume: measures how much “information” is in the source code and needs to be learned.
• This metric looks at how many variables are used and how often they are used in programs, functions and operation codes.

All of these are additional pieces of information programmers must learn and they all affect data flow. Maintainability index : formulates an overall score of how maintainable a program is. Unlike Cyclomatic Complexity and Halstead Volume, the Maintainability Index is more of an empiric measurement, having been developed over a period of years by consultants working with Hewlett-Packard and its software teams.

1. Greater predictability : knowing the level of complexity of the code being maintained makes it easier to know how much maintenance a program will need
2. : managing software complexity lowers the risk of introducing defects into production.
3. Reduced Costs : being proactive when it comes to keeping software from becoming excessively or unnecessarily complex lowers maintenance costs because an organization can be prepared for what is coming.
4. Extended Value : as illustrated in the CRASH report from past years, excessively complex applications cause issues. Organizations can preserve the value of their software assets and prolong their usefulness by keeping complexity in check.
5. Decision Support : sometimes code can be so complex that it just is not worth saving. With proof of how much it would cost to rewrite, a decision can be made whether it is better to maintain what exists or just rewrite new code.

Fred Brooks, in his landmark paper, No Silver Bullet — Essence and Accidents of Software Engineering, asserts that there are two types of complexity. Essential complexity is the unavoidable complexity required to fulfill the functional requirements. Accidental complexity is the additional complexity introduced by poor design or a lack of complexity management.

Left unchecked, non-essential complexity can get out of hand, leaving behind a poor TCO equation and additional risk to the business. Excess software complexity can negatively affect developers’ ability to manage the interactions between layers and components in an application. It can also make specific modules difficult to enhance and to test.

Every piece of code must be assessed to determine how it will affect the application in terms of robustness and changeability. Software complexity is a major concern among organizations that manage numerous technologies and applications within a multi-tier infrastructure.

What do you mean by complexity in software engineering?

Programming complexity Not to be confused with, Programming complexity (or software complexity ) is a term that includes many properties of a piece of software, all of which affect internal interactions. According to several commentators, there is a distinction between the terms complex and complicated.

1. Complicated implies being difficult to understand but with time and effort, ultimately knowable.
2. Complex, on the other hand, describes the interactions between a number of entities.
3. As the number of entities increases, the number of interactions between them would increase exponentially, and it would get to a point where it would be impossible to know and understand all of them.

Similarly, higher levels of complexity in software increase the risk of unintentionally interfering with interactions and so increases the chance of introducing defects when making changes. In more extreme cases, it can make modifying the software virtually impossible.

What is essential complexity in software?

Essential complexity is a measurement developed by Thomas McCabe to determine how well a program is structured. It measures the number of entry points, termination points, and nondeductible nodes. The closer to 1 this value is, the more well structured the program is.

What are the two main techniques of software engineering principles for reducing problem complexity?

Software engineering principles use two important techniques to reduce problem complexity: abstraction and decomposition. The principle of abstraction (in fig.1.4) implies that a problem can be simplified by omitting irrelevant details.

How does complexity affect the software project?

Why Utilize Software Complexity Metrics? – Fred Brooks, in his landmark paper, “No Silver Bullet — Essence and Accidents of Software Engineering,” asserts that there are two types of complexity. Essential complexity is the unavoidable complexity required to fulfill the functional requirements.

Accidental complexity is the additional complexity introduced by poor design or a lack of complexity management. Left unchecked, non-essential complexity can get out of hand, leaving behind a poor TCO equation and additional risk to the business. Excess software complexity can negatively impact developers’ ability to manage the interactions between layers and components in an application.

It can also make specific modules difficult to enhance and to test. Every piece of code must be assessed to determine how it will affect the application in terms of robustness and changeability. Software complexity is a major concern among organizations that manage numerous technologies and applications within a multi-tier infrastructure.

How does time complexity affect software development?

Linear Time Complexity: O(n) – When time complexity grows in direct proportion to the size of the input, you are facing Linear Time Complexity, or O(n). Algorithms with this time complexity will process the input (n) in “n” number of operations. This means that as the input grows, the algorithm takes proportionally longer to complete. Linear Time Complexity These are the type of situations where you have to look at every item in a list to accomplish a task (e.g. find the maximum or minimum value). Or you can also think about everyday tasks like reading a book or finding a CD (remember them?) in a CD stack: if all data has to be examined, the larger the input size, the higher the number of operations are.

What is software complexity and how can you manage it?

I spoke at LoopConf 2018 on software complexity and how to manage it. This is the companion article that I wrote for it. If you’re just looking for the slides, click here, You can also find a recording of the talk here, As developers, we spend a lot of time writing code.

But we spend even more time maintaining that code. How often do we go back find that that code has become this tangled mess that we almost can’t understand? It’s probably more often than we want to admit! We wonder, “How did this happen? How did this code get so messy?” Well, the most likely culprit is software complexity,

Our code became so complex that it became hard to know what it did. Now, software complexity isn’t a topic that developers are often familiar with when they start coding. We have other things to worry about. We’re trying to learn a new programming language or a new framework.

• We don’t stop and think that software complexity could be making that job harder for us.
• But it is doing precisely that.
• We’re creating code that works, but that’s also hard to maintain and understand.
• That’s why we often come back and ask ourselves, “What was I thinking!? This makes no sense.” That’s why learning about software complexity is important.

It’ll help you increase the quality of your code so that these situations don’t happen as often. And this also has the added benefit of making your code less prone to bugs. (That’s a good thing even if debugging is a great learning tool !) Let’s start by going of software complexity as a concept.

Software complexity is a way to describe a specific set of characteristics of your code. These characteristics all focus on how your code interacts with other pieces of code. The measurement of these characteristics is what determines the complexity of your code. It’s a lot like a software quality grade for your code.

The problem is that there are several ways to measure these characteristics. We’re not going to look at all these different measurements. (It wouldn’t be super useful to do so anyway.) Instead, we’re going to focus on two specific ones: cyclomatic complexity and NPath.

These two measurements are more than enough for you to evaluate the complexity of your code. If we had to pick one metric to use for measuring complexity, it would be cyclomatic complexity, It’s without question the better-known complexity measurement method. In fact, it’s common for developers often use the terms “software complexity” and “cyclomatic complexity” interchangeably.

Cyclomatic complexity measures the number of “linearly independent paths” through a piece of code. A linearly independent path is a fancy way of saying a “unique path where we count loops only once”. But this is still a bit confusing, so let’s look at a small example using this code: function insert_default_value($mixed) return $mixed; } This is a pretty straightforward function.

Why is complexity important in programming?

An algorithm is a specific procedure for solving a well-defined computational problem. The development and analysis of algorithms is fundamental to all aspects of computer science: artificial intelligence, databases, graphics, networking, operating systems, security, and so on.

• Algorithm development is more than just programming.
• It requires an understanding of the alternatives available for solving a computational problem, including the hardware, networking, programming language, and performance constraints that accompany any particular solution.
• It also requires understanding what it means for an algorithm to be “correct” in the sense that it fully and efficiently solves the problem at hand.

An accompanying notion is the design of a particular data structure that enables an algorithm to run efficiently. The importance of data structures stems from the fact that the main memory of a computer (where the data is stored) is linear, consisting of a sequence of memory cells that are serially numbered 0, 1, 2,.

Thus, the simplest data structure is a linear array, in which adjacent elements are numbered with consecutive integer ” indexes ” and an element’s value is accessed by its unique index. An array can be used, for example, to store a list of names, and efficient methods are needed to efficiently search for and retrieve a particular name from the array.

For example, sorting the list into alphabetical order permits a so-called binary search technique to be used, in which the remainder of the list to be searched at each step is cut in half. This search technique is similar to searching a telephone book for a particular name.

Knowing that the book is in alphabetical order allows one to turn quickly to a page that is close to the page containing the desired name. Many algorithms have been developed for sorting and searching lists of data efficiently. Although data items are stored consecutively in memory, they may be linked together by pointers (essentially, memory addresses stored with an item to indicate where the next item or items in the structure are found) so that the data can be organized in ways similar to those in which they will be accessed.

The simplest such structure is called the linked list, in which noncontiguously stored items may be accessed in a pre-specified order by following the pointers from one item in the list to the next. The list may be circular, with the last item pointing to the first, or each element may have pointers in both directions to form a doubly linked list.

Algorithms have been developed for efficiently manipulating such lists by searching for, inserting, and removing items. Pointers also provide the ability to implement more complex data structures. A graph, for example, is a set of nodes (items) and links (known as edges) that connect pairs of items. Such a graph might represent a set of cities and the highways joining them, the layout of circuit elements and connecting wires on a memory chip, or the configuration of persons interacting via a social network,

Typical graph algorithms include graph traversal strategies, such as how to follow the links from node to node (perhaps searching for a node with a particular property) in a way that each node is visited only once. A related problem is the determination of the shortest path between two given nodes on an arbitrary graph.

( See graph theory,) A problem of practical interest in network algorithms, for instance, is to determine how many “broken” links can be tolerated before communications begin to fail. Similarly, in very-large-scale integration ( VLSI ) chip design it is important to know whether the graph representing a circuit is planar, that is, whether it can be drawn in two dimensions without any links crossing (wires touching).

The (computational) complexity of an algorithm is a measure of the amount of computing resources (time and space) that a particular algorithm consumes when it runs. Computer scientists use mathematical measures of complexity that allow them to predict, before writing the code, how fast an algorithm will run and how much memory it will require.

1. Such predictions are important guides for programmers implementing and selecting algorithms for real-world applications.
2. Computational complexity is a continuum, in that some algorithms require linear time (that is, the time required increases directly with the number of items or nodes in the list, graph, or network being processed), whereas others require quadratic or even exponential time to complete (that is, the time required increases with the number of items squared or with the exponential of that number).

At the far end of this continuum lie the murky seas of intractable problems—those whose solutions cannot be efficiently implemented, For these problems, computer scientists seek to find heuristic algorithms that can almost solve the problem and run in a reasonable amount of time.

Further away still are those algorithmic problems that can be stated but are not solvable; that is, one can prove that no program can be written to solve the problem. A classic example of an unsolvable algorithmic problem is the halting problem, which states that no program can be written that can predict whether or not any other program halts after a finite number of steps.

The unsolvability of the halting problem has immediate practical bearing on software development. For instance, it would be frivolous to try to develop a software tool that predicts whether another program being developed has an infinite loop in it (although having such a tool would be immensely beneficial).

Why complex system is important?

THE WHOLE IS MORE THAN THE SUM OF ITS PARTS. – Aristotle – The new Science of Complex Systems is providing radical new ways of understanding the physical, biological, ecological, and social universe. The economic regions that lead this science and its engineering will dominate the twenty first century by their wealth and influence.

1. In all domains, complex systems are studied through increasingly large quantities of data, stimulating revolutionary scientific breakthroughs.
2. Also, many new and fundamental theoretical questions occur across the domains of physical and human science, making it essential to develop the new Science of Complex Systems in an interdisciplinary way.

This new science cuts across traditional scientific boundaries, creating new and shorter paths between scientists and accelerating the flow of scientific knowledge. Complex systems science bridges the natural and social sciences, enriching both, and reduces the gap between science, engineering, and policy.

It will also help reduce the gap between pure and applied science, establishing new foundations for the design, management and control of systems with levels of complexity exceeding the capacity of current approaches. Funding this fundamental scientific research will be popular because its applications will impact on everyone’s life in many obvious ways including medicine, health, welfare, food, environment, transportation, web services.

Thus Complex Systems Science will enhance long-term harmony between science and societal needs.

What is the best way to avoid software complexity?

Wrapping Up – The state-of-the-art metric to evaluate software complexity is the Cyclomatic Complexity. This metric defines software complexity as the number of connections in your control graph. There are multiple tools available to calculate software complexity.

What is complexity explain with example?

In information processing, complexity is a measure of the total number of properties transmitted by an object and detected by an observer. Such a collection of properties is often referred to as a state. In physical systems, complexity is a measure of the probability of the state vector of the system.

What is the process of reducing complexity by focusing on the main idea?

Abstraction (process): The process of reducing complexity by focusing on the main idea. By hiding details irrelevant to the question at hand and bringing together related and useful details, abstraction reduces complexity and allows one to focus on the problem.

Is an important design concept that reduces the complexity?

Modular Design – Modular design reduces the design complexity and results in easier and faster implementation by allowing parallel development of various parts of a system. We discuss a different section of modular design in detail in this section: 1.

1. Functional Independence: Functional independence is achieved by developing functions that perform only one kind of task and do not excessively interact with other modules.
2. Independence is important because it makes implementation more accessible and faster.
3. The independent modules are easier to maintain, test, and reduce error propagation and can be reused in other programs as well.

Thus, functional independence is a good design feature which ensures software quality. It is measured using two criteria:

• Cohesion: It measures the relative function strength of a module.
• Coupling: It measures the relative interdependence among modules.

2. Information hiding: The fundamental of Information hiding suggests that modules can be characterized by the design decisions that protect from the others, i.e., In other words, modules should be specified that data include within a module is inaccessible to other modules that do not need for such information.

How does the complexity of a software system affect the maintenance task?

The Cost Implications of Software Complexity – In 2013, $542 billion was spent on software with $132.2 billion of that being on custom-built software alone, and, considerable attention has been devoted to controlling software costs. Historically, this has been achieved by focusing on tools and techniques designed to make software development as a rapid and inexpensive as possible.

1. This focus is however shifting from the development phase of a software lifecycle to the maintenance phase because for every $1 spent on development, $3 is spent on the maintenance and enhancements,
2. Software complexity has been widely regarded as a major contributor to software maintenance costs because increased complexity means that maintenance and enhancement projects will take longer, cost more, and result in more errors.

Sajeel Chaudhry, consultant at Brickendon says: “Developing with an aim to reduce complexity will lead to a longer development phase, but this will be more than compensated for by the huge savings during the maintenance phase by reducing labour, improving lead times for bug fixes, enhancements and critical changes.” What Factors Need to Be Considered?

Why does increased complexity lead to program errors?

Complexity is a measure of understandability, and lack of understandability leads to errors. A system that is more complex may be harder to specify, harder to design, harder to implement, harder to verify, harder to operate, risky to change, and/or harder to predict its behavior.

Does the complexity of a system affect its security?

Complexity: How to Combat the No.1 Cause of Security Breaches Complex systems are hard to secure. As computing environments’ complexity grows, they become less secure and more vulnerable over time. In this article, I will demonstrate how security is tied to complexity, why increasing complexity of cloud computing environments is inevitable, and the pitfalls of common coping strategies.

First, let’s explore why complexity growth is inevitable. Here’s a hint for the impatient: It’s all about scale. Scaling the World’s Computing To better understand the challenges of scaling the world’s compute systems, we must remember that computing is a collaboration of machines (hardware), applications (software), and humans (peopleware), all of which increase,

Let’s start with hardware. Modern computing environments are big — and constantly getting bigger. Organizations with even a small number of employees often command thousands-of-server fleets that come in a variety of form factors — the cloud, on-premises data centers, managed hosting, smart devices, self-driving vehicles, and so on.

• What drives complexity even further is that cloud environments are elastic; as managing hardware becomes more complicated, so does security.
• How about scaling software? As the tech stack grows, so does the list of technologies that must be configured in a typical cloud computing environment before a cloud-native application is deployed.

And here’s the scary fact: Every software layer comes with its own implementation of encrypted connectivity, client authentication, authorization, and audit, putting pressure on DevOps teams to properly set up these pillars of secure remote access. And, finally, “peopleware” comes with its own scaling pains.

• As companies embrace, the idea of controlling employees’ computers or relying on a network perimeter becomes less feasible.
• Moreover, as the intensifies, companies are forced to operate without having sufficient security expertise on their teams.
• But there’s no turning back.
• Hardware, software, and peopleware complexity will continue to grow, ultimately making computing environments more vulnerable.

Common Coping Strategies How do organizations currently address the resulting security challenges? Unfortunately, most are unable to secure every single technology layer. Some of the most common coping strategies include:

Reliance on the perimeter: This popular strategy of reducing operational overhead is based on securing only the network boundary using solutions like VPNs. The downside is that once the perimeter is breached, attackers can move laterally, increasing the “blast radius” of a breach.

Use of shared credentials: This allows organizations to grow their engineering teams without too much overhead because the secure access is based on shared aliases and uses secure vaults to store shared credentials. However, these credentials need to be managed; they can be stolen or accessed by former employees. Case in point: In a, 83% of respondents said they cannot guarantee that ex-employees can no longer access their infrastructure.

Good ol’ bureaucracy: When nothing else works, implementing manual processes serves as another method to cope with complexity. Not surprisingly, this can negatively affect engineering productivity and drive employees to quit, not to mention invite the creation of personal backdoors into employer infrastructure.

None of these strategies provides sufficient levels of detail for audit purposes. For example, it becomes impossible to tell who dropped a SQL table if the access was performed via a VPN by a user named “dba.” Based on the increasing frequency of reported cyber incidents, it’s clear these approaches are struggling to minimize the operational overhead of infrastructure.

Zero Trust The cybersecurity community is aware of the problem. And the industry prescription for these problems has become, Zero trust is not a true solution, but an architectural pattern. It postulates that every computing resource must distrust all clients equally, whether on the internal or external network.

Essentially, zero trust declares perimeter-based, network-centric approaches to security as obsolete, and requires every server be configured as if exposed to the Internet. Organizations built on cloud-native environments are moving toward identity-based access.

• In this setting, every employee must authenticate into a computing resource as themselves.
• When combined with a zero-trust principle, the “blast radius” of a compromised account is minimized to a single user and resource.
• The scaling of hardware, software, and people has created an ever-growing complexity problem, making computing environments less secure.

To combat this, the industry must prioritize the consolidation of all remote access protocols under a single-solution umbrella, so that identity-based authentication can negate the need for perimeter-based, network-centric access solutions. If we execute on these initiatives swiftly enough, government involvement may not be necessary.

What are the biggest challenges facing software developers?

Bold new challenges – This is all a long way of saying there has perhaps never been more on developers’ plates. Two developer respondents summed it up well: “We have a development capacity challenge, a recruiting challenge and a knowledge-sharing challenge.” “For me, these are the eight biggest challenges we are facing as software developers: 1) Keeping pace with innovation.2) Cultural change.3) Customer experience.4) Data privacy.5) Cybersecurity.6) AI and automation.7) Data literacy.8) Cross-platform functionality.” What do you see as the biggest challenges facing developers? Let us know in the comments field below.

Why is complexity important in programming?

An algorithm is a specific procedure for solving a well-defined computational problem. The development and analysis of algorithms is fundamental to all aspects of computer science: artificial intelligence, databases, graphics, networking, operating systems, security, and so on.

Algorithm development is more than just programming. It requires an understanding of the alternatives available for solving a computational problem, including the hardware, networking, programming language, and performance constraints that accompany any particular solution. It also requires understanding what it means for an algorithm to be “correct” in the sense that it fully and efficiently solves the problem at hand.

An accompanying notion is the design of a particular data structure that enables an algorithm to run efficiently. The importance of data structures stems from the fact that the main memory of a computer (where the data is stored) is linear, consisting of a sequence of memory cells that are serially numbered 0, 1, 2,.

1. Thus, the simplest data structure is a linear array, in which adjacent elements are numbered with consecutive integer ” indexes ” and an element’s value is accessed by its unique index.
2. An array can be used, for example, to store a list of names, and efficient methods are needed to efficiently search for and retrieve a particular name from the array.

For example, sorting the list into alphabetical order permits a so-called binary search technique to be used, in which the remainder of the list to be searched at each step is cut in half. This search technique is similar to searching a telephone book for a particular name.

• Nowing that the book is in alphabetical order allows one to turn quickly to a page that is close to the page containing the desired name.
• Many algorithms have been developed for sorting and searching lists of data efficiently.
• Although data items are stored consecutively in memory, they may be linked together by pointers (essentially, memory addresses stored with an item to indicate where the next item or items in the structure are found) so that the data can be organized in ways similar to those in which they will be accessed.

The simplest such structure is called the linked list, in which noncontiguously stored items may be accessed in a pre-specified order by following the pointers from one item in the list to the next. The list may be circular, with the last item pointing to the first, or each element may have pointers in both directions to form a doubly linked list.

Algorithms have been developed for efficiently manipulating such lists by searching for, inserting, and removing items. Pointers also provide the ability to implement more complex data structures. A graph, for example, is a set of nodes (items) and links (known as edges) that connect pairs of items. Such a graph might represent a set of cities and the highways joining them, the layout of circuit elements and connecting wires on a memory chip, or the configuration of persons interacting via a social network,

Typical graph algorithms include graph traversal strategies, such as how to follow the links from node to node (perhaps searching for a node with a particular property) in a way that each node is visited only once. A related problem is the determination of the shortest path between two given nodes on an arbitrary graph.

• See graph theory,) A problem of practical interest in network algorithms, for instance, is to determine how many “broken” links can be tolerated before communications begin to fail.
• Similarly, in very-large-scale integration ( VLSI ) chip design it is important to know whether the graph representing a circuit is planar, that is, whether it can be drawn in two dimensions without any links crossing (wires touching).

The (computational) complexity of an algorithm is a measure of the amount of computing resources (time and space) that a particular algorithm consumes when it runs. Computer scientists use mathematical measures of complexity that allow them to predict, before writing the code, how fast an algorithm will run and how much memory it will require.

Such predictions are important guides for programmers implementing and selecting algorithms for real-world applications. Computational complexity is a continuum, in that some algorithms require linear time (that is, the time required increases directly with the number of items or nodes in the list, graph, or network being processed), whereas others require quadratic or even exponential time to complete (that is, the time required increases with the number of items squared or with the exponential of that number).

At the far end of this continuum lie the murky seas of intractable problems—those whose solutions cannot be efficiently implemented, For these problems, computer scientists seek to find heuristic algorithms that can almost solve the problem and run in a reasonable amount of time.

• Further away still are those algorithmic problems that can be stated but are not solvable; that is, one can prove that no program can be written to solve the problem.
• A classic example of an unsolvable algorithmic problem is the halting problem, which states that no program can be written that can predict whether or not any other program halts after a finite number of steps.

The unsolvability of the halting problem has immediate practical bearing on software development. For instance, it would be frivolous to try to develop a software tool that predicts whether another program being developed has an infinite loop in it (although having such a tool would be immensely beneficial).

How does the complexity of a software system affect the maintenance task?

The Cost Implications of Software Complexity – In 2013, $542 billion was spent on software with $132.2 billion of that being on custom-built software alone, and, considerable attention has been devoted to controlling software costs. Historically, this has been achieved by focusing on tools and techniques designed to make software development as a rapid and inexpensive as possible.

1. This focus is however shifting from the development phase of a software lifecycle to the maintenance phase because for every $1 spent on development, $3 is spent on the maintenance and enhancements,
2. Software complexity has been widely regarded as a major contributor to software maintenance costs because increased complexity means that maintenance and enhancement projects will take longer, cost more, and result in more errors.

Sajeel Chaudhry, consultant at Brickendon says: “Developing with an aim to reduce complexity will lead to a longer development phase, but this will be more than compensated for by the huge savings during the maintenance phase by reducing labour, improving lead times for bug fixes, enhancements and critical changes.” What Factors Need to Be Considered?

Why complex system is important?

THE WHOLE IS MORE THAN THE SUM OF ITS PARTS. – Aristotle – The new Science of Complex Systems is providing radical new ways of understanding the physical, biological, ecological, and social universe. The economic regions that lead this science and its engineering will dominate the twenty first century by their wealth and influence.

In all domains, complex systems are studied through increasingly large quantities of data, stimulating revolutionary scientific breakthroughs. Also, many new and fundamental theoretical questions occur across the domains of physical and human science, making it essential to develop the new Science of Complex Systems in an interdisciplinary way.

This new science cuts across traditional scientific boundaries, creating new and shorter paths between scientists and accelerating the flow of scientific knowledge. Complex systems science bridges the natural and social sciences, enriching both, and reduces the gap between science, engineering, and policy.

It will also help reduce the gap between pure and applied science, establishing new foundations for the design, management and control of systems with levels of complexity exceeding the capacity of current approaches. Funding this fundamental scientific research will be popular because its applications will impact on everyone’s life in many obvious ways including medicine, health, welfare, food, environment, transportation, web services.

Thus Complex Systems Science will enhance long-term harmony between science and societal needs.