Save for Anthropologists, few social scientists have been among the participants in the discussions about the appropriate structure of a ‘Universal Darwinism’. Yet evolutionary theorizing about cultural, social, and economic phenomena has a long tradition, going back well before Darwin. And over the past quarter century significant literatures have grown up concerned with the processes of change operating on science, technology, business organization and practice, and economic change more broadly, that are explicitly evolutionary in theoretical orientation. In each of these (...) fields of study, the broad proposition put forth by Darwin that change proceeds through a process involving variation, systematic selection, renewed variation... has proved both persuasive and powerful. On the other hand, the evolutionary processes involved in these areas differ in essential ways from those we now know are operative in the evolution of biological species. The objective of this essay is to highlight those differences, which a ‘Universal Darwinism’ needs to encompass, if it is to be broad enough to be a theory that is applicable to the evolution of human cultures as well as evolution in biology. (shrink)

Do sensory measurements deserve the label of “measurement”? We argue that they do. They fit with an epistemological view of measurement held in current philosophy of science, and they face the same kinds of epistemological challenges as physical measurements do: the problem of coordination and the problem of standardization. These problems are addressed through the process of “epistemic iteration,” for all measurements. We also argue for distinguishing the problem of standardization from the problem of coordination. To exemplify our claims, we (...) draw on olfactory performance tests, especially studies linking olfactory decline to neurodegenerative disorders. (shrink)

My brand of evolutionary economics recognizes, highlights, that modern economies are always in the process of changing, never fully at rest, with much of the energy coming from innovation. This perspective obviously draws a lot from Schumpeter. Continuing innovation, and the creative destruction that innovation engenders, is driving the system. There are winners and losers in the process, but generally the changes can be regarded as progress. The processes through which economic activity and performance evolve has a lot in common (...) with evolution in biology. In particular, at any time the economy is marked by considerable variety, there are selection forces winnowing on that variety, but also continuing emergence of new ways of doing things and often economic actors. But there also are important differences from biological evolution. In particular, both innovation and selection are to a considerable degree purposive activities, often undertaken on the basis of relatively strong knowledge. (shrink)

Technology has a proprietary aspect and a public good aspect. The proprietary aspect makes it profitable for firms to invest in its advance. The public aspect enables the community as a whole to benefit from technological advance. In order for technical advance to proceed rapidly and for the gams to be widely shared, there must be an appropriate balance between the proprietary and public aspects. Recent policy discussions have emphasized the proprietary aspects of technology, calling for a tightening and broadening (...) of intellectual property rights. Universities have been encouraged to be more proprietary. We may be in danger of leaning too far in this direction. (shrink)

Suspicion of “physics envy” surrounds the standard statistical toolbox used in the empirical sciences, from biology to psychology. Mainstream methods in these fields, various lines of criticism point out, often fall short of the basic requirements of measurement. Quantitative scales are applied to variables that can hardly be treated as measurable magnitudes, like preferences or happiness; hypotheses are tested by comparing data with conventional significance thresholds that hardly mention effect sizes. This article discusses what I call “shmeasurement.” To “shmeasure” is (...) to fail to apply quantitative tools to quantitative questions. We “shmeasure” when we try to measure what cannot be measured, or, conversely, when we ask binary questions of continuous measurements. Following the critics of standard statistical tools, it is argued that our statistical toolbox is indeed less concerned with the measurement of magnitudes than we take it to be. This article adds, however, that measurement is not all there is to scientific activity. Most techniques of proof do not resemble measurement as much as voting—a practice that makes frequent use of numbers, figures, or measurements, yet is not chiefly concerned with assessing quantities. Measurement is only one among three functions of the scientific toolbox, the other two being collating observations and deciding which hypotheses to relinquish. I thus make a plea for “shmeasurement”: the mismeasure of things starts to make more sense once we take into account the nonquantitative side of scientific practice. (shrink)

Without doubt, good numbers that characterize sharply and completely the phenomena being studied, and precise explanation of these phenomena that can be expressed mathematically, are tremendous advantages for a field of science. But not all fields of science are lucky enough to be able to achieve these features. And when they are not, nonetheless to force the phenomena studied to be characterized largely with numbers and the causal mechanisms to be described mathematically can court seriously limiting and distorting the field (...) of study. Research by economists on long-run economic development is an example of a field of research where this has happened. But unfortunately the phenomena are widespread throughout science. (shrink)

Any discipline of human knowledge is characterized by three fundamental elements: the complexity of its content, the method used for its elaboration, and the language used for its expression. This article argues that any method for making knowledge is a particular combination of three main components that we can call science, art, and revelation. The right combination depends on the complexity of the slice of reality that we wish to understand in each case. Is there a relationship between the quantity (...) and quality of a particular piece of knowledge and the quantity and quality of its eventual audience? Such a relationship serves, I believe, to avoid certain old misunderstandings. (shrink)

Biological sciences have strived to adopt the conceptual framework of physics and have become increasingly quantitatively oriented, aiming to refute the assertion that biology appears unquantifiable, unpredictable, and messy. But despite all effort, biology is characterized by a paucity of quantitative statements with universal applications. Nonetheless, many biological disciplines—most notably molecular biology—have experienced an ascendancy over the last 50 years. The underlying core concepts and ideas permeate and inform many neighboring disciplines. This surprising success is probably not so much attributable (...) to mathematical and statistical approaches in molecular biology, but rather to the preponderance of qualitative approaches, especially visualization. Visualizations can be afforded by quantitative research, but usually they rely on both, quantitative and qualitative research. I claim the following three features to be responsible for the unceasing zeal for using visualizations: visual representations facilitate reasoning, images can be cognitively processed in a “fast” manner, and abstractions of visual representations prompt conceptual advances. In summary, visualizations have largely contributed to the success of molecular biology by conveying its concepts to other disciplines, and at the same time, eclipsing mere quantitative approaches. However, visualizations also bear the risk of misinterpretation when traversing neighboring disciplines and even more so when pervading nonscientific domains. (shrink)