Modelling Scientific Discovery

Traditionally, the Philosophy of Science has examined the nature of scientific discovery. In recent years, Cognitive Science has gathered together work in Artificial Intelligence (AI) and Cognitive Psychology that attempts to understand scientific discovery. However, at present, there is no generally accepted account of scientific discovery in any of these disciplines. This thesis aims further to explore the nature of scientific discovery from an AI perspective, but does so within a clearly defined Framework, designed to structure cognitive science research on scientific discovery. The framework proposes a minimum set of components as a guide to the construction of acceptable accounts of scientific discovery. The focal concept is the Research Programme; a body of research that investigates a delimited set of phenomena using a Theoretical component and an Experimental component. The framework posits: three types of theoretical knowledge; three levels of experiments; inferences to apply and generate new theoretical & experimental knowledge; criteria for assessing the acceptability of theories & the reliability of experiments; and multiple levels of communication between the components. Previous computer models and empirical studies of scientific discovery are reviewed. They tend not to offer complete accounts of scientific discovery, as defined by the framework. In particular, many completely ignore the crucial role of experiments. The STERN computational model of scientific discovery is introduced. It instantiates all the components of the Framework. STERN currently models discoveries made by Galileo in the domain of naturally accelerated terrestrial motion, although it may be applied more generally. STERN has four main strategies that are used to make discoveries: (i) confirming existing hypotheses; (ii) generalizing experimental results to form new hypotheses; (iii) generating new hypotheses from known hypotheses; and (iv) generating new experiments. STERN is more complete than previous computational models. As such it allows novel heuristics at the level of research programmes to be investigated and high level abilities to emerge from its complexity

Lost in modelling and simulation?

Over the past few decades, physiologically-based pharmacokinetic modelling (PBPK) has been anticipated to be a powerful tool to improve the productivity of drug discovery and development. However, recently, multiple systematic evaluation studies independently suggested that the predictive power of current oral absorption (OA) PBPK models needs significant improvement. There is some disagreement between the industry and regulators about the credibility of OA PBPK modelling. Recently, the editorial board of AMDET&DMPK has announced the policy for the articles related to PBPK modelling (Modelling and simulation ethics). In this feature article, the background of this policy is explained: (1) Requirements for scientific writing of PBPK modelling, (2) Scientific literacy for PBPK modelling, and (3) Middle-out approaches. PBPK models are a useful tool if used correctly. This article will hopefully help advance the science of OA PBPK models.

The Science of Galaxy Formation

Our knowledge of the Universe remains discovery-led: in the absence of adequate physics-based theory, interpretation of new results requires a scientific methodology. Commonly, scientific progress in astrophysics is motivated by the empirical success of the "Copernican Principle", that the simplest and most objective analysis of observation leads to progress. A complementary approach tests the prediction of models against observation. In practise, astrophysics has few real theories, and has little control over what we can observe. Compromise is unavoidable. Advances in understanding complex non-linear situations, such as galaxy formation, require that models attempt to isolate key physical properties, rather than trying to reproduce complexity. A specific example is discussed, where substantial progress in fundamental physics could be made with an ambitious approach to modelling: simulating the spectrum of perturbations on small scales.Comment: paper at IAU256, The Galaxy Disk in Cosmological Context, Copenhagen, 2008 eds J. Andersen, J. Bland-Hawthorn & B. Nordstro

Scientific Discovery Through Fictionally Modelling Reality

How do scientific models represent in a way that enables us to discover new truths about reality and draw inferences about it? Contemporary accounts of scientific discovery answer this question by focusing on the cognitive mechanisms involved in the generation of new ideas and concepts in terms of a special sort of reasoning—or model-based reasoning—involving imagery. Alternatively, I argue that answering this question requires that we recognise the crucial role of the propositional imagination in the construction and development of models for the purpose of generating hypotheses that are plausible can- didates for truth. I propose simple fictionalism as a new account of models as Waltonian games of make-believe and suggest that models can lead to genuine scientific discovery when they are used as representations that denote real world phenomena and generate two main kinds of theoretical hypotheses, model-world comparisons and direct attributions

Research exercise: Solving Crime Using Mathematics

Mathematics is used in almost every area of life. With the development of modern computers, mathematical modelling and numerical simulation is new synergy in scientific discovery. In this work nonlinear equations are solved in order to determine the time of death to solve a crime. The equations are solved with few methods and we compare the accuracy of methods.https://ecommons.udayton.edu/stander_posters/1554/thumbnail.jp

Automated modelling of lakes from data and expert knowledge: evaluation of applications

Ecological models of lakes are useful tools for a better understanding of the ecosystem behaviour, lake management, policy making, as well as testing and accepting engineering solutions. Setting such model is a difficult task due to the complexity of these ecosystems. Therefore it is reasonable to use as many approaches as possible to construct a reliable model of the observed domain. In this paper the evaluation of an automated modelling method, called Lagramge, that combines the two basic approaches, i.e. data-driven (inductive) approach and knowledge-driven (deductive) approach, is given. The method supports the introduction of domain knowledge in the procedure of equation discovery from measured data, where the domain modelling knowledge is introduced in a form of modelling knowledge library. Four applications of the method, i.e. Lake Glumsø, Lake Bled, Lake Kasumigaura, and Greifensee, comprise different modelling tasks for Lagramge, each of them resulting in a specific model of the observed domains. The models are evaluated in terms of their descriptive power and their performance (goodness of fit to the measurements). Although faced with some constraints, the method can be successfully used in complex domains. It can be used successfully for model discovery as well as for other scientific discoveries, such as identifying dynamic patterns in the observed system, i.e. dynamic structure of the ecosystem