Systems of logico-probabilistic reasoning characterize inference from conditional assertions that express high conditional probabilities. In this paper we investigate four prominent LP systems, the systems _O, P_, _Z_, and _QC_. These systems differ in the number of inferences they licence _. LP systems that license more inferences enjoy the possible reward of deriving more true and informative conclusions, but with this possible reward comes the risk of drawing more false or uninformative conclusions. In the first part of the paper, we (...) present the four systems and extend each of them by theorems that allow one to compute almost-tight lower-probability-bounds for the conclusion of an inference, given lower-probability-bounds for its premises. In the second part of the paper, we investigate by means of computer simulations which of the four systems provides the best balance of reward versus risk. Our results suggest that system _Z_ offers the best balance. (shrink)

At least since Kuhn’s Structure, philosophers have studied the influence of social factors in science’s pursuit of truth and knowledge. More recently, formal models and computer simulations have allowed philosophers of science and social epistemologists to dig deeper into the detailed dynamics of scientific research and experimentation, and to develop very seemingly realistic models of the social organization of science. These models purport to be predictive of the optimal allocations of factors, such as diversity of methods used in science, (...) size of groups, and communication channels among researchers. In this paper we argue that the current research faces an empirical challenge. The challenge is to connect simulation models with data. We present possible scenarios about how the challenge may unfold. (shrink)

The philosophy of science community mourns the loss of Margaret Catherine Morrison, who passed away on January 9, 2021, after a long battle with cancer. Margie, as she was known to all who knew her, was highly regarded for her influential contributions to the philosophy of science, particularly her studies of the role of models and simulations in the natural and social sciences. These contributions made her a world-leading philosopher of science, instrumental in shifting philosophers' attention from the structure (...) of scientific theories to the practice of science. Her sophisticated studies of the function of models in scientific practice drew on detailed knowledge of the theories and experiments of physics as well as the history of physics. In emphasizing the autonomy of scientific models and their interventional character, her insights had some affinity with Cartwright's and Hacking’s views on phenomenological laws, entity realism, the instrumentalist interpretation of scientific theories, and the disunity of science. But Morrison’s approach was distinguished by the conviction that the existence of unobservable entities cannot be defended independently of the theories that support their evidence, and that scientific practice cannot be adequately understood without examining the reasons for theory unification. (shrink)

Computer simulations are often claimed to be opaque and thus to lack transparency. But what exactly is the opacity of simulations? This paper aims to answer that question by proposing an explication of opacity. Such an explication is needed, I argue, because the pioneering definition of opacity by P. Humphreys and a recent elaboration by Durán and Formanek are too narrow. While it is true that simulations are opaque in that they include too many computations and thus (...) cannot be checked by hand, this doesn’t exhaust what we might want to call the opacity of simulations. I thus make a fresh start with the natural idea that the opacity of a method is its disposition to resist knowledge and understanding. I draw on recent work on understanding and elaborate the idea by a systematic investigation into what type of knowledge and what type of understanding are required if opacity is to be avoided and why the required sort of understanding, in particular, is difficult to achieve. My proposal is that a method is opaque to the degree that it’s difficult for humans to know and to understand why its outcomes arise. This proposal allows for a comparison between different methods regarding opacity. It further refers to a kind of epistemic access that is important in scientific work with simulations. (shrink)

The minimal perimeter enclosing N planar regions, each being simply connected and of the same area, is an open problem, solved only for a few values of N . The problems of how to construct the configuration with the smallest possible perimeter E and how to estimate the value of E are considered. Defect-free configurations are classified and we start with the naïve approximation that the configuration is close to a circular portion of a honeycomb lattice. Numerical simulations and (...) analysis that show excellent agreement to within one free parameter are presented; this significantly extends the range of values of N for which good candidates for the minimal perimeter have been found. We provide some intuitive insight into this problem in the hope that it will help the improvement in future numerical simulations and the derivation of exact results. (shrink)

The claim defended in the paper is that the mechanistic account of explanation can easily embrace idealization in big-scale brain simulations, and that only causally relevant detail should be present in explanatory models. The claim is illustrated with two methodologically different models: Blue Brain, used for particular simulations of the cortical column in hybrid models, and Eliasmith’s SPAUN model that is both biologically realistic and able to explain eight different tasks. By drawing on the mechanistic theory of computational (...) explanation, I argue that large-scale simulations require that the explanandum phenomenon is identified; otherwise, the explanatory value of such explanations is difficult to establish, and testing the model empirically by comparing its behavior with the explanandum remains practically impossible. The completeness of the explanation, and hence of the explanatory value of the explanatory model, is to be assessed vis-à-vis the explanandum phenomenon, which is not to be conflated with raw observational data and may be idealized. I argue that idealizations, which include building models of a single phenomenon displayed by multi-functional mechanisms, lumping together multiple factors in a single causal variable, simplifying the causal structure of the mechanisms, and multi-model integration, are indispensable for complex systems such as brains; otherwise, the model may be as complex as the explanandum phenomenon, which would make it prone to so-called Bonini paradox. I conclude by enumerating dimensions of empirical validation of explanatory models according to new mechanism, which are given in a form of a “checklist” for a modeler. (shrink)

An in-situ transmission electron microscopy straining technique has been used to investigate the dynamics of dislocation-defect interactions in ion-irradiated copper and the subsequent formation of defect-free channels. Defect removal frequently required interaction with multiple dislocations, although screw dislocations were more efficient at annihilating defects than edge dislocations were. The defect pinning strength was determined from the dislocation curvature prior to breakaway and exhibited values ranging from 15 to 175 MPa. Pre-existing dislocations percolated through the defect field but did not show (...) long-range motion, indicating that they are not responsible for creating the defect-free channels and have a limited contribution to the total plasticity. Defect-free channels were associated with the movement of many dislocations, which originated from grain boundaries or regions of high stress concentration such as at a crack tip. These experimental results are compared with atomistic simulations of the interaction of partial dislocations with defects in copper and a dispersed-barrier-hardening crystal plasticity model to correlate the observations to bulk mechanical properties. (shrink)

Massively multiplayer online games are not merely electronic communication systems based on computational databases, but also include artificial intelligence that possesses complex, dynamic structure. Each visible action taken by a component of the multi-agent system appears simple, but is supported by vastly more sophisticated invisible processes. A rough outline of the typical hierarchy has four levels: interaction between two individuals, each either human or artificial, conflict between teams of agents who cooperate with fellow team members, enduring social-cultural groups that seek (...) to accomplish shared goals, and large-scale cultural traditions, often separated into virtual geographic regions. In many MMOs, both magic and religion are represented, in ways that harmonize with a social-scientific theory that defines them in terms of specific versus general psychological compensators. This article draws empirical examples from five diverse MMOs: Dark Age of Camelot, Dungeons and Dragons Online, World of Warcraft, A Tale in the Desert and Gods and Heroes. (shrink)

Explanation is commonly considered one of the central goals of science. Although computer simulations have become an important tool in many scientific areas, various philosophical concerns indicate that their explanatory power requires further scrutiny. We examine a case study in which atomistic simulations have been used to examine the factors responsible for the transport selectivity of certain channel proteins located at cell membranes. By elucidating how precisely atomistic simulations helped scientists draw inferences about the molecular system under (...) investigation, we respond to some concerns regarding their explanatory power. We argue that atomistic simulations can be tools for managing molecular complexity and for systematically assessing how the occurrence of the explanandum is sensitive to a range of factors. (shrink)

Using four examples of models and computer simulations from the history of psychology, I discuss some of the methodological aspects involved in their construction and use, and I illustrate how the existence of a model can demonstrate the viability of a hypothesis that had previously been deemed impossible on a priori grounds. This shows a new way in which scientists can learn from models that extends the analysis of Morgan (1999), who has identified the construction and manipulation of models (...) as those phases in which learning from models takes place. (shrink)

Robots, as well as software agents, can be of use in biology as implementations of a theory rather than as simulations of specific real world target systems. Such implementations generate hypotheses rather than representing them. Their behavior is not predicted, but rather observed, and is not expected to duplicate that of a target system. Scientific knowledge is gained through the testing of generated hypotheses.

Although we are beginning to understand the neurocognitive processes that underlie the emergence of dreaming, what accounts for the bizarre phenomenology of dreams remains debated. I address this question by comparing dreaming with waking mind wandering and challenging previous accounts that utilize bizarreness to mark a sharp divide between conscious experiences in waking and sleeping. Instead, I propose that bizarreness is a common, non-deficient feature of spontaneous offline simulations occurring across the sleep-wake cycle and can be tied to the (...) specific characteristics of spontaneous thought as being dynamic, unconstrained, (hyper)associative and highly variable in content. Rather than misrepresenting waking reality, bizarreness can be employed to investigate the very building blocks of spontaneous cognition. The absence of bizarreness in thought processes is imposed by automatic and deliberate cognitive constraints. By contrast, thought and memory processes operating on their own without such constraints are inherently marked by different degrees and types of bizarreness. (shrink)

Simulation techniques, especially those implemented on a computer, are frequently employed in natural as well as in social sciences with considerable success. There is mounting evidence that the "model-building era" (J. Niehans) that dominated the theoretical activities of the sciences for a long time is about to be succeeded or at least lastingly supplemented by the "simulation era". But what exactly are models? What is a simulation and what is the difference and the relation between a model and a simulation? (...) These are some of the questions addressed in this article. I maintain that the most significant feature of a simulation is that it allows scientists to imitate one process by another process. "Process" here refers solely to a temporal sequence of states of a system. Given the observation that processes are dealt with by all sorts of scientists, it is apparent that simulations prove to be a powerful interdisciplinarily acknowledged tool. Accordingly, simulations are best suited to investigate the various research strategies in different sciences more carefully. To this end, I focus on the function of simulations in the research process. Finally, a somewhat detailed case-study from nuclear physics is presented which, in my view, illustrates elements of a typical simulation in physics. (shrink)

Various geoengineering technologies that would deliberately alter the climate system have been proposed as a way to alleviate risks of global warming. Technologies that would shield incoming sunlight to cool the planet, so called solar radiation management, are particularly controversial. Considering insights from social studies of simulation modeling and research on expectations in science and technology, I argue that climate modeling has a central role in producing visions of SRM. I draw upon an empirical analysis of scientific research on SRM (...) to examine how a creative play with technological ideas becomes possible through climate modeling. This enables scientists to project and study environmental impacts of speculative SRM methods in virtual experiments and to develop and refine ideas for adjusting sunlight. Hence, while climate models are used to improve scientific understandings of climate system behavior and to anticipate possible environmental impacts of SRM, they also become inventive tools, allowing scientists to envision novel ways of climate control and optimization. Given the importance of simulation studies to knowledge production on SRM, I critically reflect on the challenges that arise when visions about an engineered climate future are first and foremost produced in climate simulations. (shrink)

How veridical is perception? Rather than representing objects as they actually exist in the world, might perception instead represent objects only in terms of the utility they offer to an observer? Previous work employed evolutionary modeling to show that under certain assumptions, natural selection favors such “strict‐interface” perceptual systems. This view has fueled considerable debate, but we think that discussions so far have failed to consider the implications of two critical aspects of perception. First, while existing models have explored single (...) utility functions, perception will often serve multiple largely independent goals. (Sometimes when looking at a stick you want to know how appropriate it would be as kindling for a campfire, and other times you want to know how appropriate it would be as a weapon for self‐defense.) Second, perception often operates in an inflexible, automatic manner—proving “impenetrable” to shifting higher‐level goals. (When your goal shifts from “burning” to “fighting,” your visual experience does not dramatically transform.) These two points have important implications for the veridicality of perception. In particular, as the need for flexible goals increases, inflexible perceptual systems must become more veridical. We support this position by providing evidence from evolutionary simulations that as the number of independent utility functions increases, the distinction between “interface” and “veridical” perceptual systems dissolves. Although natural selection evaluates perceptual systems only on their fitness, the most fit perceptual systems may nevertheless represent the world as it is. (shrink)

The advancement of computing technology makes it possible to build extremely accurate digital reconstructions of brain circuits. Are such unprecedented levels of biological accuracy essential for brain simulations to play the roles they are expected to play in neuroscientific research? The main goal of this paper is to clarify this question by distinguishing between various roles played by large-scale simulations in contemporary neuroscience, and by reflecting about what makes a simulation biologically accurate. It is argued that large-scale (...) class='Hi'>simulations may play model-oriented and prediction-oriented roles in brain research, and that the concept of biological accuracy can be interpreted as related to the plausibility of the theoretical model implemented in the simulation system, to the accuracy of the computer implementation, and to the level of details of the implemented model. Building on these observations and distinctions, it is argued that biological accuracy is not essential for a computer simulation to play the epistemic roles it is expected to play in brain research. (shrink)

In this article, I focus on the role of computer simulations as exploratory strategies. I begin by establishing the non-theory-driven nature of simulations. This refers to their ability to characterize phenomena without relying on a predefined conceptual framework that is provided by an implemented mathematical model. Drawing on Steinle’s notion of exploratory experimentation and Gelfert’s work on exploratory models, I present three exploratory strategies for computer simulations: (1) starting points and continuation of scientific inquiry, (2) varying the (...) parameters, and (3) scientific prototyping. (shrink)

The pervasive use of computer simulations in the sciences brings novel epistemological issues discussed in the philosophy of science literature since about a decade. Evolutionary biology strongly relies on such simulations, and in relation to it there exists a research program (Artificial Life) that mainly studies simulations themselves. This paper addresses the specificity of computer simulations in evolutionary biology, in the context (described in Sect. 1) of a set of questions about their scope as explanations, the (...) nature of validation processes and the relation between simulations and true experiments or mathematical models. After making distinctions, especially between a weak use where simulations test hypotheses about the world, and a strong use where they allow one to explore sets of evolutionary dynamics not necessarily extant in our world, I argue in Sect. 2 that (weak) simulations are likely to represent in virtue of the fact that they instantiate specific features of causal processes that may be isomorphic to features of some causal processes in the world, though the latter are always intertwined with a myriad of different processes and hence unlikely to be directly manipulated and studied. I therefore argue that these simulations are merely able to provide candidate explanations for real patterns. Section 3 ends up by placing strong and weak simulations in Levins’ triangle, that conceives of simulations as devices trying to fulfil one or two among three incompatible epistemic values (precision, realism, genericity). (shrink)

Behavioral evidence for the COVIS dual‐process model of category learning has been widely reported in over a hundred publications (Ashby & Valentin, ). It is generally accepted that the validity of such evidence depends on the accurate identification of individual participants' categorization strategies, a task that usually falls to Decision Bound analysis (Maddox & Ashby, ). Here, we examine the accuracy of this analysis in a series of model‐recovery simulations. In Simulation 1, over a third of simulated participants using (...) an Explicit (conjunctive) strategy were misidentified as using a Procedural strategy. In Simulation 2, nearly all simulated participants using a Procedural strategy were misidentified as using an Explicit strategy. In Simulation 3, we re‐examined a recently reported COVIS‐supporting dissociation (Smith et al., ) and found that these misidentification errors permit an alternative, single‐process, explanation of the results. Implications for due process in the future evaluation of dual‐process theories, including recommendations for future practice, are discussed. (shrink)

This paper reports on a preliminary investigation of the pedagogical uses and possibilities of interactive ethics audit simulations. We want to foster experience-based learning in business ethics and examine how simulated social audits of corporations can be useful supplements to traditional textbook-oriented pedagogy. We argue that social audit simulations may offer many benefits for business ethics instruction, especially when it comes to developing ethical literacy for institutionally complex and morally complicated multi-stakeholder scenarios. We conclude that ethics education based (...) on broadly accepted standards of social accountability (SA8000, GRI, ISO 26000, SOX 404) should be advanced through audit-simulation technology. Thus certain decision-making problems, which students might encounter in the real world, can be studied in the classroom. Our research contributes to the field in several ways. It describes a new avenue for self-directed learning that encourages critical thinking about ethical issues and complex stakeholder scenarios in an enjoyable, interesting and safe way. Moreover, it emphasizes “soft” skill development, and empowers students to advance their skills through exploration, practice, and learning from their mistakes. By advancing the pedagogical toolkit of educators, this new interactive technology can improve awareness of how codes of business ethics should work in the real world and inspire further scholarly debate and research. (shrink)

Whereas experiments and computer simulations seem very different at first view because the former, but not the latter, involve interactions with material properties, we argue that this difference is not so important with respect to validation, as far as epistemologyEpistemology is concerned. Major differences remain nevertheless from the methodological point of view. We present and defend this distinction between epistemology and methodology. We illustrate this distinction and related claims by comparing how experiments and simulations are validated in evolutionary (...) studies, a domain in which both experiments in the lab and computer simulations are relatively new but mutually reinforcing. (shrink)

This chapter elaborates and develops the thesis originally put forward by Mary Morgan (2005) that some mathematical models may surprise us, but that none of them can completely confound us, i.e. let us unable to produce an ex post theoretical understanding of the outcome of the model calculations. This chapter intends to object and demonstrate that what is certainly true of classical mathematical models is however not true of pluri-formalized simulations with multiple axiomatic bases. This chapter thus proposes to (...) show that - and why - some of these computational simulations that are now booming in the sciences not only surprise us but also confound us. To do so, it shows too that it is needed to elaborate and articulate with some new precision the concept of weak emergence initially due, for its part, to Mark A. Bedau (1997). (shrink)