On discovering scientific laws

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Behind Civilisation (second edition)

Darwin’s evolution theory revolutionises our understanding of the biological world. In parallel, this book is a critical breakthrough in scientific philosophy. It revolutionises our understanding of nature, particularly on civilisation, through discovering a fundamental mechanism which not only governs lifeless objects but intelligence-driven civilisation as well. This discovery provides an alternative to challenge the most fundamental issue – “the Theory of Everything”. As this mechanism is the most fundamental level, it is the foundation of similarity, including the amazing similarities between a human body and a society. Upon this theoretical basis, following the rules of this mechanism and using a human body as a model, civilisation is systematically investigated. As such, technological development and its consequential social development are understood from the most fundamental level. As a result, the human future have been successfully predicted and many historical puzzles have been unequivocally solved, such as: why could the ancient Greeks create a brilliant civilisation; why were there so many great thinkers; why did Scientific Revolution and the model of university education originate from Europe rather than from other places; why did the Chinese civilisation remain stagnant when the West was rapidly rising after Scientific Revolution. Finally, one of the long-lasting enigmatic question, “what is beauty”, is unequivocally answered once and for all. It is the expression of one of the fundamental laws of physics but it is not symmetry

Toward a Best Predictive System Account of Laws of Nature

This paper argues for a revised Best System Account (BSA) of laws of nature. David Lewis's original BSA has two main elements. On the one hand, there is the Humean base, which is the totality of particular matters of fact that obtain in the history of the universe. On the other hand, there is what I call the "Nomic Formula", which is a particular operation that gets applied to the Humean base in order to output the laws of nature. My revised account focuses on this latter element of the view. Lewis conceives of the Nomic Formula as a balance of simplicity and strength, but I argue that this is a mistake. Instead, I motivate and develop a different proposal for the standards that figure into the Nomic Formula, and I suggest a rationale for why these should be the correct standards. Specifically, I argue that the Nomic Formula should be conceived as a collection of desiderata designed to generate principles that are predictively useful to creatures like us. The resulting view, which I call the "Best Predictive System Account" of laws, is thus able to explain why scientists are interested in discovering the laws, and it also gives rise to laws with the sorts of features that we find in actual scientific practice

Toward a Best Predictive System Account of Laws of Nature

This paper argues for a revised Best System Account (BSA) of laws of nature. David Lewis's original BSA has two main elements. On the one hand, there is the Humean base, which is the totality of particular matters of fact that obtain in the history of the universe. On the other hand, there is what I call the "Nomic Formula", which is a particular operation that gets applied to the Humean base in order to output the laws of nature. My revised account focuses on this latter element of the view. Lewis conceives of the Nomic Formula as a balance of simplicity and strength, but I argue that this is a mistake. Instead, I motivate and develop a different proposal for the standards that figure into the Nomic Formula, and I suggest a rationale for why these should be the correct standards. Specifically, I argue that the Nomic Formula should be conceived as a collection of desiderata designed to generate principles that are predictively useful to creatures like us. The resulting view, which I call the "Best Predictive System Account" of laws, is thus able to explain why scientists are interested in discovering the laws, and it also gives rise to laws with the sorts of features that we find in actual scientific practice

Discovering Causal Relations and Equations from Data

Physics is a field of science that has traditionally used the scientific method to answer questions about why natural phenomena occur and to make testable models that explain the phenomena. Discovering equations, laws and principles that are invariant, robust and causal explanations of the world has been fundamental in physical sciences throughout the centuries. Discoveries emerge from observing the world and, when possible, performing interventional studies in the system under study. With the advent of big data and the use of data-driven methods, causal and equation discovery fields have grown and made progress in computer science, physics, statistics, philosophy, and many applied fields. All these domains are intertwined and can be used to discover causal relations, physical laws, and equations from observational data. This paper reviews the concepts, methods, and relevant works on causal and equation discovery in the broad field of Physics and outlines the most important challenges and promising future lines of research. We also provide a taxonomy for observational causal and equation discovery, point out connections, and showcase a complete set of case studies in Earth and climate sciences, fluid dynamics and mechanics, and the neurosciences. This review demonstrates that discovering fundamental laws and causal relations by observing natural phenomena is being revolutionised with the efficient exploitation of observational data, modern machine learning algorithms and the interaction with domain knowledge. Exciting times are ahead with many challenges and opportunities to improve our understanding of complex systems.Comment: 137 page

Towards an integrated discovery system

Previous research on machine discovery has focused on limited parts of the empirical discovery task. In this paper we describe IDS, an integrated system that addresses both qualitative and quantitative discovery. The program represents its knowledge in terms of qualitative schemas, which it discovers by interacting with a simulated physical environment. Once IDS has formulated a qualitative schema, it uses that schema to design experiments and to constrain the search for quantitative laws. We have carried out preliminary tests in the domain of heat phenomena. In this context the system has discovered both intrinsic properties, such as the melting point of substances, and numeric laws, such as the conservation of mass for objects going through a phase change

A cognitive architecture for learning in reactive environments

Previous research in machine learning has viewed the process of empirical discovery as search through a space of 'theoretical' terms. In this paper, we propose a problem space for empirical discovery, specifying six complementary operators for defining new terms that ease the statement of empirical laws. The six types of terms include: numeric attributes (such as PV/T); intrinsic properties (such as mass); composite objects (such as pairs of colliding balls); classes of objects (such as acids and alkalis); composite relations (such as chemical reactions); and classes of relations (such as combustion/oxidation). We review existing machine discovery systems in light of this framework, examining which parts of the problem space were, covered by these systems. Finally, we outline an integrated discovery system (IDS) we are constructing that includes all six of the operators and which should be able to discover a broad range of empirical laws

A framework for empirical discovery

Previous research in machine learning has viewed the process of empirical discovery as search through a space of 'theoretical' terms. In this paper, we propose a problem space for empirical discovery, specifying six complementary operators for defining new terms that ease the statement of empirical laws. The six types of terms include: numeric attributes (such as PV/T); intrinsic properties (such as mass); composite objects (such as pairs of colliding balls); classes of objects (such as acids and alkalis); composite relations (such as chemical reactions); and classes of relations (such as combustion/oxidation). We review existing machine discovery systems in light of this framework, examining which parts of the problem space were, covered by these systems. Finally, we outline an integrated discovery system (IDS) we are constructing that includes all six of the operators and which should be able to discover a broad range of empirical laws