The Scientific Era
The Age of Enlightenment in Europe, which reached maturity in the 18th century, profoundly impacted the way life is perceived at all levels, from the mundane to the profound. Its indelible effects colour human beliefs and actions to this day. Science liberated minds from centuries of control by religious dictates and ancient philosophies. However, the new age of reason substituted its own conventions, more enlightened than before but still limiting in some respects.
Descartes (1596–1650) and Newton (1642–1727) set the scene. The former advocated rationalism while the latter unearthed a collection of fundamental laws. A flood of discoveries soon followed, injecting a sense of confidence in the power of reason to tackle any situation.
Positivism
Positivism, a term coined by Comte (1798–1857), describes the structure of beliefs associated with the scientific era. It rejects value judgments in all issue areas, including the social sciences, and promotes a focus on observable facts and relationships.
In principle, and contrary to arguments put forward by certain postmodernists, there is nothing wrong with using objective facts and relationships as a basis for decision-making. However, selecting the pertinent facts and identifying relevant relationships can be difficult.
Nonlinear situations exacerbate this difficulty because internal dynamics dictate global patterns and future evolution. In theory, it is possible to determine facts and relationships for all situations, but a nonlinear process requires a vast amount of computing power. A simple game of chess illustrates this point. Even with a small number of elements and rules, simulation demands significant resources. Linear processes may be solved with mental arithmetic or paper and pencil, while nonlinear ones often exceed the capacity of even the most powerful computers.
The Linear Paradigm
The traditional scientific method survived well into the twentieth century. The linear paradigm that it reflects rests on four golden rules:
- Order: Given causes lead to known effects at all times and places.
- Reductionism: The behaviour of a system can be understood by examining the behaviour of its parts. The whole is the sum of the parts, no more and no less.
- Predictability: Once global behaviour is defined, the future course of events can be predicted by applying the appropriate inputs.
- Determinism: Processes flow along orderly and predictable paths that have clear beginnings and rational ends.
Successes
The conventional method proved effective for specific systems, yielding reliable results, including notable achievements such as space travel. Linear systems exhibit gradual behavior without sudden upheavals. A projectile moving under the influence of gravity is a typical example. In general, their behaviour can be represented on a line diagram, hence the reference to linearity.
Reductionist management styles, based on command-and-control methods, proved effective in cases involving linear systems. A process could be divided into smaller units, each managed separately, to achieve desirable outcomes for the whole. Assembly lines in car manufacturing provide a clear example. Detailed planning and strict control, facilitated through a hierarchical structure, were essential for success.
Failures
Success in the natural sciences influenced thinking in politics and economics. Theorists divided issues into smaller segments for modeling, then reassembled them to predict larger outcomes. When applied to companies, economies, or nations, the results were often indifferent or even disastrous. Efforts spanning more than half a century in the field of development produced limited outcomes under this approach.
A Fundamental Shift
For many years, problems with the linear paradigm were addressed by ad-hoc measures that treated isolated difficulties. These efforts rarely addressed the root issues. In recent decades, scholars began to ask fundamental questions about the nature of the systems under study.
Natural scientists eventually concluded that science should embrace both linear and nonlinear phenomena. With this recognition, nonlinear analytical methods gained wider use in the social sciences.
Applying linear methods to nonlinear systems, where internal dynamics significantly shape outcomes, has proven ineffective and sometimes harmful. A change in viewpoint became unavoidable. The search for a new consensus is underway, with complexity theory providing a significant direction.