The epistemic superiority of experiment to simulation

Journal of Artificial Societies and Social Simulation, 2010

Synthese

Computer simulations are an exciting tool that plays important roles in many scientific disciplines. This has attracted the attention of a number of philosophers of science. The main tenor in this literature is that computer simulations not only constitute interesting and powerful new science, but that they also raise a host of new philosophical issues. The protagonists in this debate claim no less than that simulations call into question our philosophical understanding of scientific ontology, the epistemology and semantics of models and theories, and the relation between experimentation and theorising, and submit that simulations demand a fundamentally new philosophy of science in many respects. The aim of this paper is to critically evaluate these claims. Our conclusion will be sober. We argue that these claims are overblown and that simulations, far from demanding a new metaphysics, epistemology, semantics and methodology, raise few if any new philosophical problems. The philosop...

The best sources of scientific knowledge : the simulation experiment or the thought experiment ?

The contribution of knowledge in science is subject to experience. In Pascal's vision, the ancients found them only sketchy, and we will leave them to those who will come after us in a more accomplished state than we have received them, seeing themselves as infinite in the scope of their research. This paper proposes to explore the philosophical tension around the simulation experiment and the thought experiment which both create and explore hypothetical worlds.

In: Anne Reboul (ed.) Mind, Value and Metaphysics: Philosophical Papers Dedicated to Kevin Mulligan. Dordrecht: Springer

Simulation , if used as a way of becoming aware of other people’s mental states, is the joint exercise of imagination and attribution. If A simulates B, then (i) A attributes to B the mental state in which A finds herself at the end of a process in which (ii) A has imagined being in B’s situation. Although necessary, imagination and attribution are not sufficient for simulation: the latter occurs only if (iii) the imagination process grounds or justifies the attribution. Depending on the notion of justification we use to make sense of the idea that an episode of imagining serves as a reason for attributing a mental state, the shape of the debate and the options it offers look very different. Reconfiguring the discussion in this way, we claim, shifts the focus of the simulation vs. theory-theory debate to a question located in epistemology.

2011

Minds and Machines

Arguably, computer simulation has become a third major branch of science, supplementing traditional theoretical and experimental research. Computer simulation has successfully permitted investigation into topics where cognitive, ethical, political, or practical barriers would otherwise prevent traditional experimental inquiry (See Symons and Boschetti 2013, 809). In important and sometimes controversial matters such as nuclear weapons testing, climate science, and studies of the behavior of epidemics, to take just a handful of cases, simulation using computers is often our only viable means of investigation. Because of its increasingly important scientific and policy role, there is an increased focus on how to determine the appropriate level of credence that ought to be assigned to the results of simulations (Boschetti et. al 2012). This is not a concern solely for the scientific and philosophical communities. Understanding the epistemic status of the claims produced using computer simulations presents an increasingly pressing practical challenge for citizens in democratic societies and for commercial decisionmakers (See Vallor 2017). In this paper, we assume that computer simulation has made positive contributions to scientific progress in a variety of fields. Astrophysics, climate science, epidemiology, fluid mechanics, and particle physics are some of the scientific disciplines in which computer simulations have played a crucial role advancing the state of our knowledge (Oreskes et al., 1994; Oreskes, 2004; Norton and Suppe, 2004) What we mean by computer simulation is broadly inclusive. On our view, computer simulation is the scientific or engineering practice of studying the behavior of actual or possible natural or social systems using software implemented in electronic devices. This software often novel ways (Frigg and Reiss, 2009; Humphreys, 2009). 2 Moreover, the way that we respond to these questions is likely to have significant practical implications. In addition to shaping the practice of science and engineering, the way we understand the nature and justification of simulation will affect the priorities of funding agencies, as well as the reception of science by policy-makers. Thus, getting the epistemology of computer simulation right is crucially important. While the questions listed above cannot be answered in detail in the space of a single paper, they all involve the problem of understanding how we are entitled to trust the results of computer simulations. 3 In this paper we argue for a conservative position, namely that a high-level of trust in the results of computer simulations is only appropriate when they are grounded in well-curated data, plausible scientific theory, empirical evidence, and good engineering practice. Though these normative criteria may seem obvious, they are not always followed in practice and they are certainly not emphasized in philosophical discussions. Simulations that are not grounded in these ways, we argue, may turn out to be interesting sources of insight or inspiration, but they should not be accepted as guides in decision-making where potentially harmful or expensive risks are involved. 4 Our general approach to the epistemology of simulation runs counter to two major tendencies in the current philosophical literature. The first attempts to justify our reliance on computer simulation by analogy with our ordinary epistemic practices. Specifically, it is widely held that in ordinary life a condition for the possibility of knowledge is that we must rely on the testimony of other people and on the reliability of our senses. Furthermore, in ordinary experience we must treat our basic cognitive capacities, such as our memory as capable of transmitting information without altering it in epistemically significant ways. Reasoning about the conditions for the possibility of ordinary epistemic judgments is valuable and productive, we do not object to this as an enterprise. However, 2 Philosophical treatments of the epistemology of computer simulations are in an early stage of development and work on this topic so far has been covered well by Eric Winsberg in his Stanford Encyclopedia of Philosophy article (2015). 3 In this paper we give particular attention to the first two questions. Thorough discussions of the two last questions can be found elsewhere (Symons and Horner, 2014; Symons and Alvarado, 2016; Humphreys and Alvarado, 2017) 4 Policy regarding large scale institutional interventions, life-critical systems assessment, pharmaceutical tests and the outcome of otherwise untested medical procedures fall within this category.

Journal of Artificial Societies and Social Simulation, 2010

Zeitschrift für Medien- und Kulturforschung, 2011

Simulation in Industry-Proc. of the 13th …, 2001

The credibility of digital computer simulations has always been a problem. Today, through the debate on verification and validation, it has become a key issue. I will review the existing theses on that question. I will show that, due to the role of epistemological beliefs in science, no general agreement can be found on this matter. Hence, the complexity of the construction of sciences must be acknowledged. I illustrate these claims with a recent historical example. Finally I temperate this diversity by insisting on recent trends in environmental sciences and in industrial sciences.

International Studies in the Philosophy of Science, 2017

It is often said that computer simulations generate new knowledge about the empirical world in the same way experiments do. My aim is to make sense of such a claim. I first show that the similarities between computer simulations and experiments do not allow them to generate new knowledge but invite the simulationist to interact with simulations in an experimental manner. I contend that, nevertheless, computer simulations and experiments yield new knowledge under the same epistemic circumstances, independently of any features they may share.