Online research in philosophy

This paper starts by looking at the coincidence of surprising behavior on the nanolevel in both matter and simulation. It uses this coincidence to argue that the simulation approach opens up a pragmatic mode of understanding oriented toward design rules and based on a new instrumental access to complex models. Calculations, and their variation by means of explorative numerical experimentation and visualization, can give a feeling for a model's behavior and the ability to control phenomena, even if the model itself remains epistemically opaque. Thus, the investigation of simulation in nanoscience provides a good example of how science is adapting to a new instrument: computer simulation.

This paper examines the relationship between simulation and experiment. Many discussions of simulation, and indeed the term "numerical experiments," invoke a strong metaphor of experimentation. On the other hand, many simulations begin as attempts to apply scientific theories. This has lead many to characterize simulation as lying between theory and experiment. The aim of the paper is to try to reconcile these two points of viewto understand what methodological and epistemological features simulation has in common with experimentation, while at the same time keeping a keen eye on simulation's ancestry as a form of scientific theorizing. In so doing, it seeks to apply some of the insights of recent work on the philosophy of experiment to an aspect of theorizing that is of growing philosophical interest: the construction of local models.

Computer simulation is shown to be philosophically interesting because it introduces a qualitatively new methodology for theory construction in science different from the conventional two components of "theory" and "experiment and/or observation". This component is "experimentation with theoretical models." Two examples from the physical sciences are presented for the purpose of demonstration but it is claimed that the biological and social sciences permit similar theoretical model experiments. Furthermore, computer simulation permits theoretical models for the evolution of physical systems which use cellular automata rather than differential equations as their syntax. The great advantages of the former are indicated.

Computer simulation has become important in ecological modeling, but there have been few assessments on how complex simulation models differ from more traditional analytic models. In Part I of this paper, I review the challenges faced in complex ecological modeling and how models have been used to gain theoretical purchase for understanding natural systems. I compare the use of traditional analytic simulation models and point how that the two methods require different kinds of practical engagement. I examine a case study of three models from the insect resistance literature in transgenic crops to illustrate and explore differences in analytic and computer simulation models. I argue that analyzing simulation models has been often inappropriately managed with expectations derived from handling analytic models. In Part II, I look at simulation as a hermeneutic practice. I argue that simulation models are a practice or techné. I the explore five aspects of philosophical hermeneutics that may be useful in complex ecological simulation: (1) an openness to multiple perspectives allowing multiple levels of scientific pluralism, (2) the hermeneutic circle, a back and forth in active communication among both modelers and ecologists; (3) the recognition of human factors and the nature of human practices as such, including recognizing the role of judgments and choices in the modeling enterprise; (4) the importance of play in modeling; (5) the non-closed nature of hermeneutic engagement, continued dialogue, and recognizing the situatedness, incompleteness, and tentative nature of simulation models.

Starting from some remarks about the use of models in psychology, Human Information Processing (henceforth called H.I.P.) models which sometimes use computer simulation will be examined. An attempt to show that simulation in psychology does not necessarily imply an H.I.P. approach is then made.

After showing how Deborah Mayo’s error-statistical philosophy of science might be applied to address important questions about the evidential status of computer simulation results, I argue that an error-statistical perspective offers an interesting new way of thinking about computer simulation models and has the potential to significantly improve the practice of simulation model evaluation. Though intended primarily as a contribution to the epistemology of simulation, the analysis also serves to fill in details of Mayo’s epistemology of experiment.

The personal computer has become the primary research tool in many scientific and engineering disciplines. The role of the computer has been extended to be an experimental and modelling tool both for convenience and sometimes necessity. In this paper some of the relationships between real models and virtual models, i.e. models that exist only as programs and data structures, areexplored. It is argued that the shift from experimenting with real objects to experimentation with computer models and simulations may also require a new approach for evaluating scientific theories derived from these models. Accepting the additional sets of assumptions that are associated with computer models and simulations requires ‘leaps of faith’, which we may not want to make in order to preserve scientific rigor. There are problems in providing acceptable arguments and explanations as to why a particular computer model or simulation should be judged scientifically sound, plausible, or even probable. These problems not only emerge from models that are particularly complex, but also in models that suffer from being too simplistic.

A number of recent discussions comparing computer simulation and traditional experimentation have focused on the significance of “materiality.” I challenge several claims emerging from this work and suggest that computer simulation studies are material experiments in a straightforward sense. After discussing some of the implications of this material status for the epistemology of computer simulation, I consider the extent to which materiality (in a particular sense) is important when it comes to making justified inferences about target systems on the basis of experimental results.

There has been relatively little effort to provide a systematic overview of different forms of exploratory experimentation (EE). The present paper examines the growing subdiscipline of nanotoxicology and suggests that it illustrates at least four ways that researchers can engage in EE: searching for regularities; developing new techniques, simulation models, and instrumentation; collecting and analyzing large swaths of data using new experimental strategies (e.g., computer-based simulation and “high-throughput” instrumentation); and structuring an entire disciplinary field around exploratory research agendas. In order to distinguish these and other activities more effectively, the paper proposes a taxonomy that includes three dimensions along which types of EE vary: (1) the aim of the experimental activity, (2) the role of theory in the activity, and (3) the methods or strategies employed for varying experimental parameters.

Morrison points out many similarities between the roles of simulation models and other sorts of models in science. On the basis of these similarities she claims that running a simulation is epistemologically on a par with doing a traditional experiment and that the output of a simulation therefore counts as a measurement. I agree with her premises but reject the inference. The epistemological payoff of a traditional experiment is greater (or less) confidence in the fit between a model and a target system. The source of this payoff is the existence of a causal interaction with the target system. A computer experiment, which does not go beyond the simulation system itself, lacks any such interaction. So computer experiments cannot confer any additional confidence in the fit (or lack thereof) between the simulation model and the target system.