The Design Inference

The design inference uncovers intelligent causes by isolating their key trademark: specified events of small probability. Just about anything that happens is highly improbable, but when a highly improbable event is also specified (i.e. conforms to an independently given pattern) undirected natural causes lose their explanatory power. Design inferences can be found in a range of scientific pursuits from forensic science to research into the origins of life to the search for extraterrestrial intelligence. This challenging and provocative book shows how incomplete undirected causes are for science and breathes new life into classical design arguments. It will be read with particular interest by philosophers of science and religion, other philosophers concerned with epistemology and logic, probability and complexity theorists, and statisticians.

• Provides a solution to the long-standing problem of how to eliminate chance through small probabilities • Breathes new life into classical design arguments, offering a challenging new method for detecting intelligent causes • Of interest to a wide range of philosophers concerned with science, religion, epistemology, and logic, as well as probability and complexity theorists, and statisticians

Preface; Acknowledgments; 1. Introduction; 1.1. Historical overview; 1.2. The man with the golden arm; 1.3. Intellectual property protection; 1.4. Forensic science and detection; 1.5. Data falsification; 1.6. Cryptography (and SETI); 1.7. Randomness; 2. Overview of the design inference; 2.1. The explanatory filter; 2.2. The logic of the Inference; 2.3. Case study - the creation-evolution controversy; 2.4. From design to agency; 3. Probability theory; 3.1. The probability of an event; 3.2. Events; 3.3. Background information; 3.4. Likelihood; 3.5. The best available estimate; 3.6. Axiomatization of probability; 4. Complexity theory; 4.1. The complexity of a problem; 4.2. Problems and resources; 4.3. Difficulty and its estimation; 4.4. Axiomatization of complexity; 4.5. Calibration through complexity bounds; 4.6. Information measures; 4.7. RMS measures; 4.8. Technical supplement on RMS Measures; 5. Specification; 5.1. Patterns; 5.2. The requisite precondition; 5.3. Detachability; 5.4. Specification defined; 5.5. Pyramids and presidents; 5.6. Information tucked within information; 5.7. Prediction; 5.8. Increasing the power of a complexity measure; 5.9. Caputo revisited; 5.10. Randomness revisited; 6. Small probability; 6.1. Probabilistic resources; 6.2. The generic chance elimination argument; 6.3. The Magic Number 1/2; 6.4. Statistical significance testing; 6.5. Local and universal small probabilities; 6.6. The inflationary fallacy; 6.7. The Law of Small Probability; 7. Epilogue; Notes; References.