The limits of overly simplistic theory in textbook economics: the case of child labour

Alexander Krauss outlines why analysing complex phenomena like child labour needs to be done using cross-disciplinary approaches and mixed methods

How nobel-prize breakthroughs in economics emerge and the field's influential empirical methods

What drives groundbreaking research in economics? Nobel-prize-winning work has had an important impact on public policies, but we still do not understand well what drives such breakthroughs. We collect data on all nobel-prize discoveries in economics to address this question. We find that major advances in the field of economics are brought about by methodological innovation: by developing new and improved research methods. We find that developing for example econometrics in 1933, randomised controlled trials in 1948 and new game theory methods in 1950 were essential to opening the new fields of corporate finance, experimental economics and information economics, respectively. We identify the development of new methods as the main mechanism driving new discoveries and research fields. Fostering this general mechanism (generating novel methods) holds the potential to greatly increase the rate at which we make new breakthroughs and fields. We also show that many of the main methods of economics – such as randomised controlled trials, natural experiments, regression discontinuity, instrumental variables and other statistical methods – had been developed and used in other fields like public health, before economists adopted them. This shift towards more powerful empirical methods in the field has important implications on developing new and better methods and adopting them from related fields to make new advances more rapidly

Redefining the scientific method: as the use of sophisticated scientific methods that extend our mind

Scientific, medical, and technological knowledge has transformed our world, but we still poorly understand the nature of scientific methodology. Science textbooks, science dictionaries, and science institutions often state that scientists follow, and should follow, the universal scientific method of testing hypotheses using observation and experimentation. Yet, scientific methodology has not been systematically analyzed using large-scale data and scientific methods themselves as it is viewed as not easily amenable to scientific study. Using data on all major discoveries across science including all Nobel Prize and major non-Nobel Prize discoveries, we can address the question of the extent to which “the scientific method” is actually applied in making science’s groundbreaking research and whether we need to expand this central concept of science. This study reveals that 25% of all discoveries since 1900 did not apply the common scientific method (all three features)—with 6% of discoveries using no observation, 23% using no experimentation, and 17% not testing a hypothesis. Empirical evidence thus challenges the common view of the scientific method. Adhering to it as a guiding principle would constrain us in developing many new scientific ideas and breakthroughs. Instead, assessing all major discoveries, we identify here a general, common feature that the method of science can be reduced to: making all major discoveries has required using sophisticated methods and instruments of science. These include statistical methods, particle accelerators, and X-ray methods. Such methods extend our mind and generally make observing, experimenting, and testing hypotheses in science possible, doing so in new ways and ensure their replicability. This provides a new perspective to the scientific method—embedded in our sophisticated methods and instruments—and suggests that we need to reform and extend the way we view the scientific method and discovery process

Climate change, resource depletion and population growth: the elephant in the room

Following the COP22 talks in Marrakesh, Alexander Krauss and Thomas Kastning, argue that politicians are ignoring the solution with the largest potential to mitigate climate change: slowing population growth

Measures of effectiveness in medical research: reporting both absolute and relative measures

Biomedical research, especially pharmaceutical research, has been criticised for engaging in practices that lead to over-estimations of the effectiveness of medical treatments. A central issue concerns the reporting of absolute and relative measures of medical effectiveness. In this paper we critically examine proposals made by Jacob Stegenga to (a) give priority to the reporting of absolute measures over relative measures, and (b) downgrade the measures of effectiveness (effect sizes) of the treatments tested in clinical trials (Stegenga, 2015a). After exposing significant flaws in a central case study used by Stegenga to bolster his first proposal (a), we go on to argue that neither of these proposals is defensible (a or b). We defend the practice, in line with the New England Journal of Medicine, of reporting both absolute and relative measures whenever feasible

Generalization bias in science

Many scientists routinely generalize from study samples to larger populations. It is commonly assumed that this cognitive process of scientific induction is a voluntary inference in which researchers assess the generalizability of their data and then draw conclusions accordingly. We challenge this view and argue for a novel account. The account describes scientific induction as involving by default a generalization bias that operates automatically and frequently leads researchers to unintentionally generalize their findings without sufficient evidence. The result is unwarranted, overgeneralized conclusions. We support this account of scientific induction by integrating a range of disparate findings from across the cognitive sciences that have until now not been connected to research on the nature of scientific induction. The view that scientific induction involves by default a generalization bias calls for a revision of the current thinking about scientific induction and highlights an overlooked cause of the replication crisis in the sciences. Commonly proposed interventions to tackle scientific overgeneralizations that may feed into this crisis need to be supplemented with cognitive debiasing strategies against generalization bias to most effectively improve science

Recursive Definitions of Monadic Functions

Using standard domain-theoretic fixed-points, we present an approach for defining recursive functions that are formulated in monadic style. The method works both in the simple option monad and the state-exception monad of Isabelle/HOL's imperative programming extension, which results in a convenient definition principle for imperative programs, which were previously hard to define. For such monadic functions, the recursion equation can always be derived without preconditions, even if the function is partial. The construction is easy to automate, and convenient induction principles can be derived automatically.Comment: In Proceedings PAR 2010, arXiv:1012.455

Early-career factors largely determine the future impact of prominent researchers: evidence across eight scientific fields

Abstract Can we help predict the future impact of researchers using early-career factors? We analyze early-career factors of the world’s 100 most prominent researchers across 8 scientific fields and identify four key drivers in researchers’ initial career: working at a top 25 ranked university, publishing a paper in a top 5 ranked journal, publishing most papers in top quartile (high-impact) journals and co-authoring with other prominent researchers in their field. We find that over 95% of prominent researchers across multiple fields had at least one of these four features in the first 5 years of their career. We find that the most prominent scientists who had an early career advantage in terms of citations and h-index are more likely to have had all four features, and that this advantage persists throughout their career after 10, 15 and 20 years. Our findings show that these few early-career factors help predict researchers’ impact later in their careers. Our research thus points to the need to enhance fairness and career mobility among scientists who have not had a jump start early on

Homo methodologicus and the origin of science and civilisation

Few things have impacted our lives as much as science and technology, but how we developed science and civilisation is one of the most challenging questions that has not yet been well explained. Attempting to identify the central driver, leading scientists have highlighted the role of culture, cooperation and geography. They focus thus on broad factors that are important basic preconditions but that we cannot directly influence. To better address the question, this paper integrates evidence from evolutionary biology, cognitive science, methodology, archaeology and anthropology. The paper identifies 9 main preconditions necessary for contemporary science, which include 6 main preconditions for civilisation. Using a kind of quasi-experimental research design we observe that some cultures (experimental groups) met the preconditions while other cultures (control groups) did not. Among the preconditions, we explain how our mind's evolved methodological abilities (to observe, solve problems and experiment) have directly enabled acquiring knowledge about the world and collectively developing increasingly sophisticated methods (such as mathematics and more systematic experimentation) that have enabled science and civilisation. We have driven the major revolutions throughout our history – the palaeolithic technological and agricultural revolutions and later the so-called scientific, industrial and digital revolutions – by using our methodological abilities in new ways and developing new methods and tools, i.e. through methodological revolutions. Viewing our methods as the main mechanism through which we have directly developed scientific and technological knowledge, and thus science and civilisation, provides a new framework for understanding science and the history of science. Viewing humans as homo methodologicus, using an expanding methodological toolbox, provides a nuanced explanation of how we have been directly able to meet our needs, solve problems and develop vast bodies of technological and scientific knowledge. By better understanding the origin and foundations of science, we can better understand their limits and, most importantly, how to push those limits. We can do so especially by addressing the evolved cognitive constraints and biases we face and improving the methods we use