The theory of intelligent design is a hypothesis that can be positively tested. Intelligent design begins with observations of how intelligent agents act when designing things. By observing human intelligent agents, there is actually quite a bit we can learn know and understand about the actions of intelligent designers. Here are some observations:
Ways Designers Act When Designing (Observations)
- Intelligent agents think with an “end goal” in mind, allowing them to solve complex problems by taking many parts and arranging them in intricate patterns that perform a specific function (e.g. complex and specified information):”Agents can arrange matter with distant goals in mind. In their use of language, they routinely ‘find’ highly isolated and improbable functional sequences amid vast spaces of combinatorial possibilities.” (Meyer, 2004 a) “[W]e have repeated experience of rational and conscious agents-in particular ourselves-generating or causing increases in complex specified information, both in the form of sequence-specific lines of code and in the form of hierarchically arranged systems of parts. … Our experience-based knowledge of information-flow confirms that systems with large amounts of specified complexity (especially codes and languages) invariably originate from an intelligent source from a mind or personal agent.” (Meyer, 2004 b))
- Intelligent agents can rapidly infuse large amounts of information into systems: “Intelligent design provides a sufficient causal explanation for the origin of large amounts of information, since we have considerable experience of intelligent agents generating informational configurations of matter.” (Meyer, 2003.) “We know from experience that intelligent agents often conceive of plans prior to the material instantiation of the systems that conform to the plans–that is, the intelligent design of a blueprint often precedes the assembly of parts in accord with a blueprint or preconceived design plan.” (Meyer, 2003.)
- Intelligent agents re-use functional components that work over and over in different systems (e.g., wheels for cars and airplanes):”An intelligent cause may reuse or redeploy the same module in different systems, without there necessarily being any material or physical connection between those systems. Even more simply, intelligent causes can generate identical patterns independently.” (Nelson and Wells, 2003.)
- Intelligent agents typically create functional things (although we may sometimes think something is functionless, not realizing its true function):”Since non-coding regions do not produce proteins, Darwinian biologists have been dismissing them for decades as random evolutionary noise or ‘junk DNA.’ From an ID perspective, however, it is extremely unlikely that an organism would expend its resources on preserving and transmitting so much ‘junk.'” (Wells, 2004.)
So by observing human intelligent agents, there is a lot we can know and understand about intelligent designers. These observations can then be converted into hypotheses and predictions about what we should find if an object was designed. This makes intelligent design a scientific theory capable of generating testable predictions, as seen in Table 2 below:
Predictions of Design (Hypothesis)
- Natural structures will be found that contain many parts arranged in intricate patterns that perform a specific function (e.g. complex and specified information).
- Forms containing large amounts of novel information will appear in the fossil record suddenly and without similar precursors.
- Convergence will occur routinely. That is, genes and other functional parts will be re-used in different and unrelated organisms.
- Much so-called “junk DNA” will turn out to perform valuable functions.
We can then empirically know and understand the actions of intelligent agents, and make testable predictions about what we should find if intelligent causation was at work. The predictions of ID can be put to the test, as discussed in Table 3:
Examining the Evidence (Experiment and Conclusion)
- Language-based codes can be revealed by seeking to understand the workings of genetics and inheritance. High levels of specified complexity and irreducibly complexity are detected in biological systems through theoretical analysis, computer simulations and calculations (Behe & Snoke, 2004; Dembski 1998b; Axe et al. 2008; Axe, 2010a; Axe, 2010b; Dembski and Marks 2009a; Dembski and Marks 2009b; Ewert et al. 2009; Ewert et al. 2010; Chiu et al. 2002; Durston et al. 2007; Abel and Trevors, 2006; Voie 2006), “reverse engineering” (e.g. knockout experiments) (Minnich and Meyer, 2004; McIntosh 2009a; McIntosh 2009b) or mutational sensitivity tests (Axe, 2000; Axe, 2004; Gauger et al. 2010).
- The fossil record shows that species often appear abruptly without similar precursors. (Meyer, 2004; Lonnig, 2004; McIntosh 2009b)
- Similar parts are commonly found in widely different organisms. Many genes and functional parts not distributed in a manner predicted by ancestry, and are often found in clearly unrelated organisms. (Davison, 2005; Nelson & Wells, 2003; Lönnig, 2004; Sherman 2007)
- There have been numerous discoveries of functionality for “junk-DNA.” Examples include recently discovered surprised functionality in some pseudogenes, microRNAs, introns, LINE and ALU elements. (Sternberg, 2002, Sternberg and Shapiro, 2005; McIntosh, 2009a)
Below are about a dozen or so examples of areas where ID is helping science to generate new scientific knowledge and open up new avenues of research. Each example includes citations to mainstream scientific articles and publications by ID proponents that discuss this research.
Avenues of Research
- ID directs research which has detected high levels of complex and specified information in biology in the form of fine-tuning of protein sequences. This has practical implications not just for explaining biological origins but also for engineering enzymes and anticipating / fighting the future evolution of diseases. (See Axe, 2004; Axe, 2000; Axe, 2010 ba)
- ID predicts that scientists will find instances of fine-tuning of the laws and constants of physics to allow for life, leading to a variety of fine-tuning arguments, including the Galactic Habitable Zone. This has huge implications for proper cosmological models of the universe, hints at proper avenues for successful “theories of everything” which must accommodate fine-tuning, and other implications for theoretical physics. (See Gonzalez 2001; Halsmer, 2009.)
- ID has helped scientists to understand intelligence as a scientifically studyable cause of biological complexity, and to understand the types of information it generates. (See Meyer, 2004b; Dembski, 1998b; McIntosh, 2009a.)
- ID has led to both experimental and theoretical research into how limitations on the ability of Darwinian evolution to evolve traits that require multiple mutations to function. This of course has practical implications for fighting problems like antibiotic resistance or engineering bacteria. (See Behe & Snoke, 2004; Gauger et al. 2010).
- ID implies that there are limits to the information-generative powers of Darwinian searches, leading to the finding that the search abilities of Darwinian processes are limited, which has practical implications for the viability of using genetic algorithms to solve problems. Critics sometimes cite the evolution of anti-biotic resistance, antiviral drug resistance, and insecticide resistance as his prime examples of the utility of Darwinian evolution. Ironically, one of the primary the ways that scientists combat such forms of resistance is based upon the premise that there are LIMITS to the amount that organisms can evolve. If biological realities like limits to evolution did not exist, it would be pointless for medical doctors to try to combat antibiotic resistance or antiviral drug resistance, because evolution could always produce an adaptation such that the target organism would become resistant without incurring a fitness cost. So ID’s predictions about the existence of limits to evolution is what helps combat antibiotic, antiviral and pesticide resistance–not knowledge of Darwinian evolution. (See: Dembski and Marks 2009a; Dembski and Marks, 2009b; Ewert et al. 2009; Ewert et al. 2010; Axe et al. 2008.; Axe 2010a; Axe 2010b; Meyer 2004b; McIntosh 2009a; and many others.)
- ID thinking has helped scientists properly measure functional biological information, leading to concepts like complex and specified information or functional sequence complexity. This allows us to better quantify complexity and understand what features are, or are not, within the reach of Darwinian evolution. (See, for example, Meyer, 2004b; Durston et al. 2007; Chiu and Thomas 2002.)
- ID has caused scientists to investigate computer-like properties of DNA and the genome in the hopes of better understanding genetics and the origin of biological systems. (See Sternberg, 2008; Voie, 2006; Abel & Trevors, 2006.)
- ID serves as a paradigm for biology which helps scientists reverse engineer molecular machines like the bacterial flagellum to understand their function like machines, and to understand how the machine-like properties of life allow biological systems to function. (See for example Minnich and Meyer, 2004); McIntosh, 2009a.)
- ID causes scientists to view cellular components as “designed structures rather than accidental by-products of neo-Darwinian evolution,” allowing scientists to propose testable hypotheses about causes of cancer. (See Wells, 2005.)
- ID leads to the view of life as being front-loaded with information such that it is designed to evolve, expecting (and now finding!) previously unanticipated “out of place” genes in various taxa. (See, for example, Sherman, 2007; de Roos, 2005; de Roos, 2007; de Roos, 2006.)
- ID explains the cause of the widespread feature of extreme degrees of “convergent evolution,” including convergent genetic evolution. (See Lönnig, 2004; Nelson, & Wells, 2003; Davison, 2005.)
- ID explains causes of explosions of biodiversity (as well as mass extinction) in the history of life. (See Lönnig, 2004; Meyer, 2004b; Meyer et al., 2003.)
- ID has quite naturally directed scientists to predict function for junk-DNA, leading to various types of research seeking function for non-coding “junk”-DNA, allowing us to understand development and cellular biology. (See Wells, 2004; McIntosh, 2009a); Seaman and Sanford, 2009.)
Many of their peer-reviewed scientific publications are cited among the references below.
Douglas D. Axe, “Extreme Functional Sensitivity to Conservative Amino Acid Changes on Enzyme Exteriors,” Journal of Molecular Biology, Vol. 301:585-595 (2000).
Douglas D. Axe, “Estimating the Prevalence of Protein Sequences Adopting Functional Enzyme Folds,” Journal of Molecular Biology, 1-21 (2004).
Douglas D. Axe, Brendan W. Dixon, Philip Lu, “Stylus: A System for Evolutionary Experimentation Based on a Protein/Proteome Model with Non-Arbitrary Functional Constraints,” PLoS One, Vol. 3(6):e2246 (June 2008).
a. Douglas D. Axe, “The Case Against a Darwinian Origin of Protein Folds,” Bio-Complexity, Vol. 2010).
b. Douglas D. Axe, “The Limits of Complex Adaptation: An Analysis Based on a Simple Model of Structured Bacterial Populations,” BIO-Complexity, Vol. 2010(4):1-10.
Michael J. Behe & David W. Snoke, “Simulating Evolution by Gene Duplication of Protein Features That Require Multiple Amino Acid Residues,” Protein Science, Vol. 13:2651-2664 (2004).
Chiu, David K.Y., and Lui, Thomas W.H., “Integrated Use of Multiple Interdependent Patterns for Biomolecular Sequence Analysis,” International Journal of Fuzzy Systems, Vol. 4(3):766-775 (September, 2002).
John A. Davison, “A Prescribed Evolutionary Hypothesis,” Rivista di Biologia/Biology Forum, Vol. 98: 155-166. (2005).
a. William Dembski, “Intelligent Science and Design,” First Things, Vol. 86:21-27 (October 1998).
b. W.A. Dembski, The Design Inference: Eliminating Chance through Small Probabilities (Cambridge University Press, 1998).
a. William A. Dembski and Robert J. Marks II, “Conservation of Information in Search: Measuring the Cost of Success,” IEEE Transactions on Systems, Man and Cybernetics A, Systems & Humans, Vol. 39 (5):1051-1061 (September, 2009).
b. William A. Dembski, and Robert J. Marks II, “Bernoulli’s Principle of Insufficient Reason and Conservation of Information in Computer Search,” Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics San Antonio, TX, USA, 2647-2652 (October 2009).
Kirk K. Durston, David K. Y. Chiu, David L. Abel, Jack T. Trevors, “Measuring the functional sequence complexity of proteins,” Theoretical Biology and Medical Modelling, Vol. 4:47 (2007).
Winston Ewert, William A. Dembski, and Robert J. Marks II, “Evolutionary Synthesis of Nand Logic: Dissecting a Digital Organism,” Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics San Antonio, TX, USA, 3047-3053 (October 2009).
Winston Ewert, George Montanez, William A. Dembski, Robert J. Marks II, “Efficient Per Query Information Extraction from a Hamming Oracle,” Proceedings of the the 42nd Meeting of the Southeastern Symposium on System Theory, IEEE, University of Texas at Tyler, March 7-9, 2010, pp.290-297.
Ann K Gauger, Stephanie Ebnet, Pamela F Fahey, Ralph Seelke, “Reductive Evolution Can Prevent Populations from Taking Simple Adaptive Paths to High Fitness,” BIO-Complexity, Vol. 2010.
Guillermo Gonzalez et al., “Refuges for Life in a Hostile Universe,” Scientific American (October, 2001).
D. Halsmer et al., “The Coherence of an Engineered World,” International Journal of Design & Nature and Ecodynamics , Vol. 4 (1):47-65 (2009).
Wolf-Ekkehard Lonnig, “Dynamic genomes, morphological stasis, and the origin of irreducible complexity,” in Dynamical Genetics pp. 101-119 (Valerio Parisi, Valeria De Fonzo, and Filippo Aluffi-Pentini eds., 2004).
Casey Luskin, “Human Origins and Intelligent Design,” Progress in Complexity and Design, (Vol 4.1, November, 2005).
Stephen C. Meyer, Marcus Ross, Paul Nelson & Paul Chien, “The Cambrian Explosion: Biology’s Big Bang,” in Darwinism, Design, and Public Education (John A. Campbell and Stephen C. Meyer eds., Michigan State University Press, 2003).
a. Stephen C. Meyer, “The Cambrian Information Explosion,” in Debating Design (edited by Michael Ruse and William Dembski; Cambridge University Press 2004).
b. Stephen C. Meyer, “The origin of biological information and the higher taxonomic categories,” Proceedings of the Biological Society of Washington, Vol. 117(2):213-239 (2004).
a. A.C. McIntosh, “Information and Entropy — Top-Down or Bottom-Up Development in Living Systems?,” International Journal of Design & Nature and Ecodynamics, Vol. 4(4):351-385 (2009).
b. A.C. McIntosh, “Evidence of Design in Bird Feathers and Avian Respiration,” International Journal of Design & Nature and Ecodynamics, Vol. 4(2): 154-169 (2009).
Scott A. Minnich & Stephen C. Meyer, “Genetic analysis of coordinate flagellar and type III regulatory circuits in pathogenic bacteria,” in Proceedings of the Second International Conference on Design & Nature, Rhodes Greece (M.W. Collins & C.A. Brebbia eds., 2004).
Paul Nelson and Jonathan Wells, “Homology in Biology,” in Darwinism, Design, and Public Education, (Michigan State University Press, 2003).
Albert D. G. de Roos, “Origins of introns based on the definition of exon modules and their conserved interfaces,” Bioinformatics, Vol. 21(1):2-9 (2005).
Albert D. G. de Roos, “Conserved intron positions in ancient protein modules,” Biology Direct, Vol. 2:7 (2007).
Albert D. G. de Roos, “The Origin of the Eukaryotic Cell Based on Conservation of Existing Interfaces,” Artificial Life, Vol. 12:513-523 (2006).
Josiah D. Seaman and John C. Sanford, “Skittle: A 2-Dimensional Genome Visualization Tool,” BMC Informatics, Vol. 10:451 (2009).
Michael Sherman, “Universal Genome in the Origin of Metazoa: Thoughts About Evolution,” Cell Cycle, Vol. 6(15):1873-1877 (August 1, 2007).
Richard Sternberg and James A. Shapiro, “How Repeated Retroelements format genome function,” Cytogenetic and Genome Research, Vol. 110: 108-116 (2005).
Richard v. Sternberg, “On the Roles of Repetitive DNA Elements in the Context of a Unified Genomic- Epigenetic System,” Annals of the New York Academy of Sciences, Vol. 981: 154-188 (2002).
Richard v. Sternberg, “DNA Codes and Information: Formal Structures and Relational Causes,” Acta Biotheoretica, Vol. 56(3):205-232 (September, 2008).
J.T. Trevors and D.L. Abel, “Chance and necessity do not explain the origin of life,” Cell Biology International, Vol. 28: 729-739 (2004).
J. T. Trevors and D. Abel, “Self-organization vs. self-ordering events in life-origin models,” Physics of Life Reviews, Vol. 3: 211–228 (2006).
Oyvind Albert Voie, “Biological function and the genetic code are interdependent,” Chaos, Solitons and Fractals, Vol. 28:1000–1004 (2006).
Jonathan Wells, “Using Intelligent Design Theory to Guide Scientific Research” Progress in Complexity, Information, and Design (Vol. 3.1.2, November 2004).
Jonathan Wells, “Do Centrioles Generate a Polar Ejection Force?,” Rivista di Biologia / Biology Forum, Vol. 98:71-96 (2005).