Complex systems: a review

Engineers have worked on complex systems ever since engineering began. But the sciences of complexity have come in to their own in the last few decades. Hoping to find common threads that weave their disciplines together, researchers from the fields of physics, biology, chemistry, math, computer science, economics, anthropology, linguistics, et al. have banded together to try to develop unifying frameworks for understanding complex systems. This paper reports on successes and failures of these efforts

On Unifiers, Diversifiers, and the Nature of Pattern Recognition

AbstractWe study a dichotomy of scientific styles, unifying and diversifying, as proposed by Freeman J. Dyson. We discuss the extent to which the dichotomy transfers from the natural sciences (where Dyson proposed it) to the field of Pattern Recognition. To address this we must firstly ask what it means to be a “unifier” or “diversifier” in a field, and what are the relative merits of each style of thinking. Secondly, given that Dyson applied this to the sciences, does it also apply in a field known to be a blend of science and engineering? Parallels are drawn to Platonic/Aristotelian views, and to Cartesian/Baconian science, and questions are asked on what drives the Kuhnian paradigm shifts of our field. This article is intended not to marginalise individuals into categories (unifier/diversifier) but instead to demonstrate the utility of philosophical reflection on our field, showing the depth and complexities a seemingly simple idea can unearth

Contrasting Views of Complexity and Their Implications For Network-Centric Infrastructures

There exists a widely recognized need to better understand and manage complex “systems of systems,” ranging from biology, ecology, and medicine to network-centric technologies. This is motivating the search for universal laws of highly evolved systems and driving demand for new mathematics and methods that are consistent, integrative, and predictive. However, the theoretical frameworks available today are not merely fragmented but sometimes contradictory and incompatible. We argue that complexity arises in highly evolved biological and technological systems primarily to provide mechanisms to create robustness. However, this complexity itself can be a source of new fragility, leading to “robust yet fragile” tradeoffs in system design. We focus on the role of robustness and architecture in networked infrastructures, and we highlight recent advances in the theory of distributed control driven by network technologies. This view of complexity in highly organized technological and biological systems is fundamentally different from the dominant perspective in the mainstream sciences, which downplays function, constraints, and tradeoffs, and tends to minimize the role of organization and design

Superhuman science: How artificial intelligence may impact innovation

New product innovation in fields like drug discovery and material science can be characterized as combinatorial search over a vast range of possibilities. Modeling innovation as a costly multi-stage search process, we explore how improvements in Artificial Intelligence (AI) could affect the productivity of the discovery pipeline in allowing improved prioritization of innovations that flow through that pipeline. We show how AI aided prediction can increase the expected value of innovation and can increase or decrease the demand for downstream testing, depending on the type of innovation, and examine how AI can reduce costs associated with well-defined bottlenecks in the discovery pipeline. Finally, we discuss the critical role that policy can play to mitigate potential market failures associated with access to and provision of data as well as the provision of training necessary to more closely approach the socially optimal level of productivity enhancing innovations enabled by this technology

Quantitative Modeling in Cell Biology: What Is It Good for?

Recently, there has been a surge in the number of pioneering studies combining experiments with quantitative modeling to explain both relatively simple modules of molecular machinery of the cell and to achieve system-level understanding of cellular networks. Here we discuss the utility and methods of modeling and review several current models of cell signaling, cytoskeletal self-organization, nuclear transport, and the cell cycle. We discuss successes of and barriers to modeling in cell biology and its future directions, and we argue, using the field of bacterial chemotaxis as an example, that the closer the complete systematic understanding of cell behavior is, the more important modeling becomes and the more experiment and theory merge

Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

What does the free energy principle tell us about the brain?

The free energy principle has been proposed as a unifying account of brain function. It is closely related, and in some cases subsumes, earlier unifying ideas such as Bayesian inference, predictive coding, and active learning. This article clarifies these connections, teasing apart distinctive and shared predictions.Comment: Accepted for publication in Neurons, Behavior, Data Analysis, and Theor

POSaM: a fast, flexible, open-source, inkjet oligonucleotide synthesizer and microarrayer

DNA arrays are valuable tools in molecular biology laboratories. Their rapid acceptance was aided by the release of plans for a pin-spotting microarrayer by researchers at Stanford. Inkjet microarraying is a flexible, complementary technique that allows the synthesis of arrays of any oligonucleotide sequences de novo. We describe here an open-source inkjet arrayer capable of rapidly producing sets of unique 9,800-feature arrays