Discovering Complexity

An analysis of two heuristic strategies for the development of mechanistic models, illustrated with historical examples from the life sciences. In Discovering Complexity, William Bechtel and Robert Richardson examine two heuristics that guided the development of mechanistic models in the life sciences: decomposition and localization. Drawing on historical cases from disciplines including cell biology, cognitive neuroscience, and genetics, they identify a number of "choice points" that life scientists confront in developing mechanistic explanations and show how different choices result in divergent explanatory models. Describing decomposition as the attempt to differentiate functional and structural components of a system and localization as the assignment of responsibility for specific functions to specific structures, Bechtel and Richardson examine the usefulness of these heuristics as well as their fallibility—the sometimes false assumption underlying them that nature is significantly decomposable and hierarchically organized. When Discovering Complexity was originally published in 1993, few philosophers of science perceived the centrality of seeking mechanisms to explain phenomena in biology, relying instead on the model of nomological explanation advanced by the logical positivists (a model Bechtel and Richardson found to be utterly inapplicable to the examples from the life sciences in their study). Since then, mechanism and mechanistic explanation have become widely discussed. In a substantive new introduction to this MIT Press edition of their book, Bechtel and Richardson examine both philosophical and scientific developments in research on mechanistic models since 1993.

Dive into Python's advanced possibilities, including algorithm analysis, graphs, scale-free networks, and cellular automata with this in-depth, hands-on guide.

Unexpected discoveries in nonequilibrium physics and nonlinear dynamics are changing our understanding of complex phenomena. Recent research has revealed fundamental new properties of matter in far-from-equilibrium conditions, and the prevalence of instability-where small changes in initial conditions may lead to amplified effects.

Many scholars agree that we are currently living in post-ideological times, and that the role of religion and social ideology has become increasingly limited as a means to organise society. Some even talk of a ‘post-truth’ era, as truth itself has become suspect, and public debate has become infected with terms such as ‘fake news’ and ‘alternative truths.’ In fact, in many scientific areas the notions of truth, objectivity and fact are being questioned, and are often even considered instruments that aim to perpetuate relationships of power of elites. This book aims to take a position in these debates by looking at the often-implicit associations behind truth, objectivity, and fact. By taking a complexity-informed, dialectical approach, a more encompassing understanding of these concepts can be developed, that both respects the formidable achievements of science, while being sensitive to the critique that has been raised, most notably by postmodern thought.

Traditional economic theory studies idealized markets in which prices alone can guide efficient allocation, with no need for central organization. Such models build from Adam Smith’s famous concept of an invisible hand, which guides markets and renders regulation or interference largely unnecessary. Yet for many markets, prices alone are not enough to guide feasible and efficient outcomes, and regulation alone is not enough, either. Consider air traffic control at major airports. While prices could encourage airlines to take off and land at less congested times, prices alone do just part of the job; an air traffic control system is still indispensable to avoid disastrous consequences. With just an air traffic controller, however, limited resources can be wasted or poorly used. What’s needed in this and many other real-world cases is an auction system that can effectively reveal prices while still maintaining enough direct control to ensure that complex constraints are satisfied. In Discovering Prices, Paul Milgrom—the world’s most frequently cited academic expert on auction design—describes how auctions can be used to discover prices and guide efficient resource allocations, even when resources are diverse, constraints are critical, and market-clearing prices may not even exist. Economists have long understood that externalities and market power both necessitate market organization. In this book, Milgrom introduces complex constraints as another reason for market design. Both lively and technical, Milgrom roots his new theories in real-world examples (including the ambitious U.S. incentive auction of radio frequencies, whose design he led) and provides economists with crucial new tools for dealing with the world’s growing complex resource-allocation problems.

Our world is enormously sophisticated and nature's complexity is literally inexhaustible. As a result, projects to describe and explain natural science can never be completed. This volume explores the nature of complexity and considers its bearing on our world and how we manage our affairs within it. Rescher's overall lesson is that the management of our affairs within a socially, technologically, and cognitively complex environment is plagued with vast management problems and risks of mishap. In primitive societies, failure to understand how things work can endanger a family or, at worst, a clan or tribe. In the modern world, man-made catastrophes on the model of Chernobyl can endanger millions, possibly even risking the totality of human life on our planet. Rescher explains "technological escalation" as a sort of arms race against nature in which scientific progress requires more powerful technology for observation and experimentation, and, conversely, scientific progress requires the continual enhancement of technology. The increasing complexity of science and technology (and, in consequence, of social systems) along with problems growing faster than solutions confront us with major management and decision problems. This study is the first of its kind. There have been many specialized studies of complexity in physics and computation theory, but no overall analysis of the phenomenon. Although Rescher offers a sobering outlook, he also believes that complexity entails mixed blessings: our imperfect knowledge provides a rationale for putting forth our best efforts. Rescher urges us to gear the conduct of life's practical affairs to the demands of a complex world. This highly readable and accessible volume will be of interest to those interested in philosophy, the philosophy of science, science policy studies, and future studies.

This is the substantive scholarly work to provide a map of the state of art research in the growing field emerging at the intersection of complexity science and management studies.

This book constitutes the thoroughly refereed post-proceedings of the First International Workshop on Knowledge Discovery and Emergent Complexity in Bioinformatics, KDECB 2006, held in Ghent, Belgium, in May 2006, in connection with the 15th Belgium-Netherlands Conference on Machine Learning. The 12 revised full papers cover various topics in the areas of knowledge discovery and emergent complexity research in bioinformatics.

Agent-based modeling and simulation (ABMS), a way to simulate a large number of choices by individual actors, is one of the most exciting practical developments in business modeling since the invention of relational databases. It represents a new way to understand data and generate information that has never been available before--a way for businesses to view the future and to understand and anticipate the likely effects of their decisions on their markets and industries. It thus promises to have far-reaching effects on the way that businesses in many areas use computers to support practical decision-making.Managing Business Complexity is the first complete business-oriented agent-based modeling and simulation resource. It has three purposes: first, to teach readers how to think about ABMS, that is, about agents and their interactions; second, to teach readers how to explain the features and advantages of ABMS to other people and third, to teach readers how to actually implement ABMS by building agent-based simulations. It is intended to be a complete ABMS resource, accessible to readers who haven't had any previous experience in building agent-based simulations, or any other kinds of models, for that matter. It is also a collection of ABMS business applications resources, all assembled in one place for the first time. In short, Managing Business Complexity addresses who needs ABMS and why, where and when ABMS can be applied to the everyday business problems that surround us, and how specifically to build these powerful agent-based models.