Epistemology Of Simulation Research Articles

Correction to: Polycratic hierarchies and networks: what simulation-modeling at the LHC can teach us about the epistemology of simulation

Correction to: Polycratic hierarchies and networks: what simulation-modeling at the LHC can teach us about the epistemology of simulation

Software engineering standards for epidemiological models.

There are many tangled normative and technical questions involved in evaluating the quality of software used in epidemiological simulations. In this paper we answer some of these questions and offer practical guidance to practitioners, funders, scientific journals, and consumers of epidemiological research. The heart of our paper is a case study of the Imperial College London (ICL) covid-19 simulator, set in the context of recent work in epistemology of simulation and philosophy of epidemiology.

Polycratic hierarchies and networks: what simulation-modeling at the LHC can teach us about the epistemology of simulation

Large scale experiments at CERN’s Large Hadron Collider (LHC) rely heavily on computer simulations (CSs), a fact that has recently caught philosophers’ attention. CSs obviously require appropriate modeling, and it is a common assumption among philosophers that the relevant models can be ordered into hierarchical structures. Focusing on LHC’s ATLAS experiment, we will establish three central results here: (a) with some distinct modifications, individual components of ATLAS’ overall simulation infrastructure can be ordered into hierarchical structures. Hence, to a good degree of approximation, hierarchical accounts remain valid at least as descriptive accounts of initial modeling steps. (b) In order to perform the epistemic function Winsberg (in Magnani L, Nersessian N, Thagard P (eds) Model-based reasoning in scientific discovery. Kluwer Academic/Plenum Publishers, New York, pp 255–269, 1999) assigns to models in simulation—generate knowledge through a sequence of skillful but non-deductive transformations—ATLAS’ simulation models have to be considered part of a network rather than a hierarchy, in turn making the associated simulation modeling messy rather than motley. Deriving knowledge-claims from this ‘mess’ requires two sources of justification: (i) holistic validation (also Lenhard and Winsberg in Stud Hist Philos Sci Part B Stud Hist Philos Modern Phys 41(3):253–262, 2010; in Carrier M, Nordmann A (eds) Science in the context of application. Springer, Berlin, pp 115–130, 2011), and (ii) model coherence. As it turns out, (c) the degree of model coherence sets HEP apart from other messy, simulation-intensive disciplines such as climate science, and the reasons for this are to be sought in the historical, empirical and theoretical foundations of the respective discipline.

Why computer simulations are not inferences, and in what sense they are experiments

The question of where, between theory and experiment, computer simulations (CSs) locate on the methodological map is one of the central questions in the epistemology of simulation (cf. Saam Journal for General Philosophy of Science, 48, 293–309, 2017). The two extremes on the map have them either be a kind of experiment in their own right (e.g. Barberousse et al. Synthese, 169, 557–574, 2009; Morgan 2002, 2003, Journal of Economic Methodology, 12(2), 317–329, 2005; Morrison Philosophical Studies, 143, 33–57, 2009; Morrison 2015; Massimi and Bhimji Studies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics, 51, 71–81, 2015; Parker Synthese, 169, 483–496, 2009) or just an argument executed with the aid of a computer (e.g. Beisbart European Journal for Philosophy of Science, 2, 395–434, 2012; Beisbart and Norton International Studies in the Philosophy of Science, 26, 403–422, 2012). There exist multiple versions of the first kind of position, whereas the latter is rather unified. I will argue that, while many claims about the ‘experimental’ status of CSs seem unjustified, there is a variant of the first position that seems preferable. In particular I will argue that while CSs respect the logic of (deductively valid) arguments, they neither agree with their pragmatics nor their epistemology. I will then lay out in what sense CSs can fruitfully be seen as experiments, and what features set them apart from traditional experiments nonetheless. I conclude that they should be seen as surrogate experiments, i.e. experiments executed consciously on the wrong kind of system, but with an exploitable connection to the system of interest. Finally, I contrast my view with that of Beisbart (European Journal for Philosophy of Science, 8, 171–204, 2018), according to which CSs are surrogates for experiments, arguing that this introduces an arbitrary split between CSs and other kinds of simulations.

Out-of-equilibrium traverse dynamics the time-to-build tradition

An attempt is made, in this paper, to place the fundamental contribution of Amendola and Gaffard on Out-of-Equilibrium Traverse Dynamics in the broad tradition of time-to-build business cycle theory. Since the Amendola-Gaffard approach places primary importance on the epistemology of simulation, i.e., numerical experiments, a further attempt is made to place this innovative stance in the framework of computable economics. The result is formal, non-equilibrium, non-stochastic dynamics that is theoretically amenable to simulational induction.

Out-of-equilibrium traverse dynamics the time-to-build tradition

An attempt is made, in this paper, to place the fundamental contribution of Amendola and Gaffard on Out-of-Equilibrium Traverse Dynamics in the broad tradition of time-to-build business cycle theory. Since the Amendola-Gaffard approach places primary importance on the epistemology of simulation, i.e., numerical experiments, a further attempt is made to place this innovative stance in the framework of computable economics. The result is formal, non-equilibrium, non-stochastic dynamics that is theoretically amenable to simulational induction.

The Philosophy and Epistemology of Simulation: A Review

The philosophical literature on simulations has increased dramatically during the past 40 years. Many of its main topics are epistemological. For example, philosophers consider how the results of simulations help explain natural phenomena. This essay’s review treats mainly simulations in the social sciences. It considers the nature of simulations, the varieties of simulation, and uses of simulations for representation, prediction, explanation, and policy decisions. Being oriented toward philosophy of science, it compares simulations to models and experiments and considers whether simulations raise new methodological issues.The essay concludes that several features of simulations set them apart from models and experiments and make them novel scientific tools, whose powers and limits are not yet well understood.