Sistemas BZP-BPN/NPT

Computer simulations have been routinely compared with laboratory experimenta- tion, as in many cases they are used in similar ways and for similar purposes.1_Ex-

perimentation is typically conceived as a multidimensional activity stemming not only from the complexity surrounding the empirical phenomena under study, but also from the practice of experimentation in itself, which is intricate in design and complex in structure. That is why, when philosophers talk about experiments, they are referring to a host of interwoven topics, methodologies, and ways of practicing science. Experiments, for instance, are used for observing processes, detecting new entities, measuring variables, and even in some cases for ‘testing’ the validity of a theory. The questions that guide this section are, then, to what extent can we say that computer simulations are epistemically close – or even superior – to experimenta- tion? and what set of characteristics do make them two distinctive – or similar – practices? Let us begin by gaining a better grasp of what experimentation is.

The idea of experimenting with Nature can be tracked back to the early times of civilization. Aristotle recorded his observation of the embryology of the chick in his Historia Animalium (Aristotle 1965), facilitating our early understanding of chicken and human development. In fact, Aristotle’s studies correctly deduced the role of the placenta and the umbilical cord in humans. Although his methodology is flawed in several ways, it nonetheless resembles much of the modern scientific method: observation, measurement, and documenting every stage of growth – three aspects of scientific practice still in use until today.2_{Now, despite its undeniable centrality}

in our modern understanding of the empirical world, laboratory experimentation has not always received the appreciation it deserves.

It was not until the arrival of logical empiricism in the 1920s and 1930s that ex- periments began to receive some attention in the general philosophy of science. To the logical empiricist, however, experimentation represented not so much a philo- sophical problem in itself, as a subsidiary methodology for understanding theory. In fact, the most important use of experiments was for the confirmation and refutation of a theory, the most pervasive philosophical issue at the time.

A few decades later, logical empiricism began to experience a number of ob- jections and attacks from different flanks. One particular objection played a funda- mental role in their demise, which later became known as the underdetermination of theory by evidence. At its heart, this objection states that the evidence collected from experimentation might be insufficient for the confirmation or refutation of a given theory at a given time. Logical empiricists, then, had no option but to embrace experimentation as a genuine part of scientific and philosophical inquiry.

1_{Allow me to make one terminological clarification and one delimitation of topic. The clarification}

is that I use interchangeably and without further discussion the notion of laboratory experimen- tation and experiment. The subtleties of the distinction are of no interest for our purposes. As for the delimitation, I leave out of consideration field experiments as they typically require a different philosophical approach.

3.1 Laboratory experimentation and computer simulations 79

Robert Ackerman and Deborah Mayo, two major names in the philosophy of experimentation, refer to the era where experimentation is at the center of philo- sophical inquiry as new experimentalism.3 _{New experimentalism, as presented,}

supplements the traditional, theory-based view of logical empiricism with a more experimental-based view of scientific practice.

Although the advocates of the new experimentalism are interested in different kinds of problems emerging from experiments and their practices, they all share the assertion that scientific experimentation is at the heart of much of our understanding of the empirical world. The philosopher of experimentation Marcel Weber proposes five general trends that characterize new experimentalism. First, experimentation is explorative, that is, it aims at discovering new phenomena and empirical regularities. Second, new experimentalists reject the view that observation and experimentation is guided by theory. They maintain that in an important number of cases, theory-free experiments are possible and do occur in scientific practice. Third, new experimen- talism has given new life to the distinction between observation and experimenta- tion. Four, advocates of new experimentalism have challenged the positivist idea that theories somehow relate to nature on the basis of experimental results. And fifth, it has been stressed that more attention must be given to experimental prac- tice in order to answer questions concerning scientific inference and theory testing (Weber 2005).

The shift from a traditional ‘top-down’ schemata (i.e., from theory to the em- pirical world) into a ‘bottom-up’ conceptualization is the distinctive mark of new experimentalism. Even notions like natural phenomenon went through some trans- formations. Under the new experimentalist point of view, a phenomenon can be from the directly observable cars crashing in front of our houses, to the invisible microbes, astronomical events, and quantum world.

Researchers give the label ‘experimentation’ to a wide range of activities. Aris- totle’s observation of the chick is perhaps the most straightforward use of the term. There is another broader use of the term that involves intervention or manipulation of nature. The idea is very simple and appealing: scientists manipulate an experi- mental set-up as if they were manipulating the empirical phenomenon itself. What- ever the epistemic gain from the former is, it can be extrapolated to the latter. Under this notion several activities can be identified. One such is discovering new entities, a highly valuable occupation in scientific research. For instance, Wilhelm R¨ontgen’s discovery of the X-Rays is a good example of discovering a new type or radiation by manipulating nature.

Another example of manipulating nature are some types of measuring quantities. For instance, measuring the speed of light in the middle 1800s required a beam of light to reflect onto a mirror a few kilometers away. The experiment was set up

3_{The work of Ackerman can be found in (Ackermann 1989), and of Mayo in (Mayo 1994). Let}

the reader be advised that I am skipping several years of good philosophy of experimentation. Of particular interest is Norwood R. Hanson, a philosopher of science and fierce opponent of the logical empiricism, who made fundamental contributions to the transition from experiments as a subsidiary methodology of theory, to experiments as units of study in their own right. For references, see (Hanson 1958).

in such a way that the beam would have to pass through the gaps between teeth of a rapidly rotating wheel. The speed of the wheel, then, was increased until the returning light passed through the next gap and could be seen. A very clever solution used by Hippolyte Fizeau to improve the accuracy of past measurements.

Understanding experiments and experimental practice this way raises questions about the relation between experiments and computer simulations. Are they epis- temically on a par? or does the capacity to manipulate the real-world give experi- ments an epistemic advantage over computer simulations? Perhaps the most cele- brated criteria for analyzing computer simulations and laboratory experiments is the so-called materiality argument. At its basis, the materiality argument says that, in genuine experiments the same material causes are at work in the experimental set- up as well as in the target system; in computer simulations, on the contrary, there is a formal correspondence between the simulation model and the target system.

Thus understood, the materiality argument offers different forms of ontological and epistemological commitments. One such a form takes experiments to be made of the same material causes as the target system, while computer simulations only share a formal correspondence with such target system. Under this interpretation, inferences about the target system are more justified in an experiment than in a com- puter simulation.4_{Alternatively, an argument can be advanced where experiments}

are similar to computer simulations, and therefore inferences by both are equally justified.

In the following, I partly reproduce an article of mine published in 2013 where I discuss in detail different ways to understand the materiality argument and its impact in the epistemological evaluation of computer simulations. This article provides, I hope, a similar level of technical and philosophical detail as the book. Let me finally say that much more philosophical work on the relation between computer simula- tions and experimentation has been published after this article. Examples are the excellent work of Emily Parke (Parke 2014), Michela Massimi and Wahid Bhimji (Massimi and Bhimji 2015), and more recently Claus Beisbart (Beisbart 2017).