Simulation models and probabilities: a Bayesian defense of the value-free ideal

SIMULATION ◽  
2021 ◽  
pp. 003754972110288
Author(s):  
Alejandro Cassini
Keyword(s):  
Bayesian Methods ◽  
Climate Models ◽  
Social Values ◽  
Simulation Models ◽  
Value Judgments ◽  
Inductive Risk ◽  
Complex Simulation ◽  
Epistemic Opacity ◽  
Value Free Ideal

Some philosophers of science have recently argued that the epistemic assessment of complex simulation models, such as climate models, cannot be free of the influence of social values. In their view, the assignment of probabilities to the different hypotheses or predictions that result from simulations presupposes some methodological decisions that rest on value judgments. In this article, I criticize this claim and put forward a Bayesian response to the arguments from inductive risk according to which the influence of social values on the calculation of probabilities is negligible. I conclude that the epistemic opacity of complex simulations, such as climate models, does not preclude the application of Bayesian methods.

scholarly journals Values and inductive risk in machine learning modelling: the case of binary classification models

2021 ◽  
Vol 11 (4) ◽  
Author(s):  
Koray Karaca
Keyword(s):  
Machine Learning ◽  
Social Values ◽  
Binary Classification ◽  
Social Value ◽  
Classification Models ◽  
Value Judgments ◽  
Philosophical Debate ◽  
Inductive Risk ◽  
The Social ◽  
Error Costs

AbstractI examine the construction and evaluation of machine learning (ML) binary classification models. These models are increasingly used for societal applications such as classifying patients into two categories according to the presence or absence of a certain disease like cancer and heart disease. I argue that the construction of ML (binary) classification models involves an optimisation process aiming at the minimization of the inductive risk associated with the intended uses of these models. I also argue that the construction of these models is underdetermined by the available data, and that this makes it necessary for ML modellers to make social value judgments in determining the error costs (associated with misclassifications) used in ML optimization. I thus suggest that the assessment of the inductive risk with respect to the social values of the intended users is an integral part of the construction and evaluation of ML classification models. I also discuss the implications of this conclusion for the philosophical debate concerning inductive risk.

RTCONTACT: An Efficient Wheel-Rail Contact Algorithm for Real-Time Dynamic Simulations

2012 ◽  
Author(s):  
N. Bosso ◽  
A. Gugliotta ◽  
N. Zampieri
Keyword(s):  
Real Time ◽  
Computational Efficiency ◽  
Simulation Models ◽  
Experimental Tests ◽  
Contact Forces ◽  
Dynamic Simulations ◽  
Time Dynamic ◽  
Contact Algorithm ◽  
Complex Simulation ◽  
Precise Representation

Determination of contact forces exchanged between wheel and rail is one of the most important topics in railway dynamics. Recent studies are oriented to improve the existing contact methods in terms of computational efficiency on one side and on the other side to develop more complex and precise representation of the contact problem. This work shows some new results of the contact code developed at Politecnico di Torino identified as RTCONTACT; this code, which is an improvement of the CONPOL algorithm, is the result of long term activities, early versions were used in conjunction with MBS codes or in Matlab® environment to simulate vehicle behaviour. The code has been improved also using experimental tests performed on a scaled roller-rig. More recently the contact model was improved in order to obtain a higher computational efficiency that is a required for the use inside of a Real Time process. Benefit of a Real Time contact algorithm is the possibility to use complex simulation models in diagnostic or control systems in order to improve their performances. This work shows several comparisons of the RTCONTACT contact code respect commercial codes, standards and benchmark results.

Hider: A Methodology for Early-Stage Exploration of Design Space

1996 ◽  
Author(s):  
Sudhakar Y. Reddy
Keyword(s):  
Design Space ◽  
Optimization Problems ◽  
Early Stage ◽  
Simulation Models ◽  
Design Tool ◽  
System Level ◽  
Complex Simulation ◽  
Machine Learning Approach ◽  
Detailed Simulation ◽  
And Performance

Abstract This paper describes HIDER, a methodology that enables detailed simulation models to be used during the early stages of system design. HIDER uses a machine learning approach to form abstract models from the detailed models. The abstract models are used for multiple-objective optimization to obtain sets of non-dominated designs. The tradeoffs between design and performance attributes in the non-dominated sets are used to interactively refine the design space. A prototype design tool has been developed to assist the designer in easily forming abstract models, flexibly defining optimization problems, and interactively exploring and refining the design space. To demonstrate the practical applicability of this approach, the paper presents results from the application of HIDER to the system-level design of a wheel loader. In this demonstration, complex simulation models for cycle time evaluation and stability analysis are used together for early-stage exploration of design space.

EVIDENCE, VALUES, AND DECISION MAKING

2014 ◽  
Vol 30 (2) ◽  
pp. 233-238 ◽  
Author(s):  
Michael D. Rawlins
Keyword(s):  
Decision Making ◽  
Cost Effectiveness ◽  
Observational Study ◽  
Social Values ◽  
Clinical Excellence ◽  
Health Technology ◽  
Evidence Base ◽  
Social Value ◽  
Value Judgments ◽  
Study Designs

Background: The evidence supporting the use of new, or established, interventions may be derived from either (or both) experimental or observational study designs. Although a rigorous examination of the evidence base for clinical and cost-effectiveness is essential, it is never sufficient, and those undertaking a health technology assessment (HTA) also have to exercise judgments.Methods: The basis for this discussion is largely from the author's experience as chairman of the national Institute for Health and Clinical Excellence (NICE).Results: The judgments necessary for HTA to make are twofold. Scientific judgments relate to the interpretation of the science. Social value judgments are concerned with the ethical principles, preferences, culture, and aspirations of society.Conclusions: How scientific and social value judgments might be most appropriately captured is a challenge for all HTA agencies. Although competent HTA bodies should be able to exercise scientific judgments they have no legitimacy to impose their own social values. These must ultimately be informed by the general public.

scholarly journals What Does Good Science-Based Advice to Politics Look Like?

2021 ◽  
Author(s):  
Martin Carrier
Keyword(s):  
Social Values ◽  
Moral Values ◽  
Policy Advice ◽  
Social Goals ◽  
Scientific Policy ◽  
Good Science ◽  
The Public ◽  
Scientific Policy Advice ◽  
Political Economic ◽  
Value Free Ideal

AbstractI address options for providing scientific policy advice and explore the relation between scientific knowledge and political, economic and moral values. I argue that such nonepistemic values are essential for establishing the significance of questions and the relevance of evidence, while, on the other hand, such social choices are the prerogative of society. This tension can be resolved by recognizing social values and identifying them as separate premises or as commissions while withholding commitment to them, and by elaborating a plurality of policy packages that envisage the implementation of different social goals. There are limits to upholding the value-free ideal in scientific research. But by following the mentioned strategy, science can give useful policy advice by leaving the value-free ideal largely intact. Such scientific restraint avoids the risk of appearing to illegitimately impose values on the public and could make the advice given more trustworthy.

Epistemic Opacity

2019 ◽  
pp. 98-131
Author(s):  
Johannes Lenhard
Keyword(s):  
Mathematical Models ◽  
Simulation Models ◽  
Scientific Understanding ◽  
The Other ◽  
Weak Version ◽  
Simulation Based ◽  
Lower Standard ◽  
Epistemic Standards ◽  
Epistemic Opacity ◽  
Experimental Runs

This chapter shows that—and how—simulation models are epistemically opaque. Nevertheless, it is argued, simulation models can provide a means to control dynamics. Researchers can employ a series of iterated (experimental) runs of the model and can learn to orient themselves within the model—even if the dynamics of the simulation remain (at least partly) opaque. Admittedly, such an acquaintance with the model falls short of the high epistemic standards usually ascribed to mathematical models. This lower standard is still sufficient, however, when the aim is controlled intervention in technological contexts. On the other hand, opacity has to be accepted if the option for control is to remain in any way open. This chapter closes by discussing whether epistemic opacity restricts simulation-based science to a pragmatic—“weak”—version of scientific understanding.

scholarly journals Social values influence the adequacy conditions of scientific theories: beyond inductive risk

2015 ◽  
Vol 45 (3) ◽  
pp. 326-356 ◽  
Author(s):  
Ingo Brigandt
Keyword(s):  
Human Evolution ◽  
Scientific Explanation ◽  
Social Values ◽  
Social Consequences ◽  
Epistemic Values ◽  
Inductive Risk ◽  
The Social ◽  
Philosophical Framework ◽  
Theory Acceptance ◽  
The Impact

The ‘death of evidence’ issue in Canada raises the spectre of politicized science, and thus the question of what role social values may have in science and how this meshes with objectivity and evidence. I first criticize philosophical accounts that have to separate different steps of research to restrict the influence of social and other non-epistemic values. A prominent account that social values may play a role even in the context of theory acceptance is the argument from inductive risk. It maintains that the more severe the social consequences of erroneously accepting a theory would be, the more evidence is needed before the theory may be accepted. However, an implication of this position is that increasing evidence makes the impact of social values converge to zero; and I argue for a stronger role for social values. On this position, social values (together with epistemic values and other empirical considerations) may determine a theory's conditions of adequacy, which among other things can include considerations about what makes a scientific account unbiased and complete. I illustrate this based on recent theories of human evolution and the social behaviour of non-human primates, where some of the social values implicated are feminist values. While many philosophical accounts (both arguments from inductive risk and from underdetermination) conceptualize the relevance of social values in terms of making inferences from evidence, I argue for the need for a broader philosophical framework, which is also motivated by issues pertaining to scientific explanation.

scholarly journals Uncertainty Quantification in Complex Simulation Models Using Ensemble Copula Coupling

2013 ◽  
Vol 28 (4) ◽  
pp. 616-640 ◽  
Author(s):  
Roman Schefzik ◽  
Thordis L. Thorarinsdottir ◽  
Tilmann Gneiting
Keyword(s):  
Uncertainty Quantification ◽  
Simulation Models ◽  
Complex Simulation
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