On Two Competing Paradigms

We briefly compare two different systems of axioms: the evolutionist (emergentist) paradigm and the one based on intelligent design (ID) detection. The list below reveals the weaknesses of evolutionism as a philosophical basis of life sciences. The paradigm based on the theory of intelligent design detection, in contrast, appears to be more powerful and more generic. The evolutionary mechanisms play here a secondary role and are quite limited, which reflects reality better. From the science philosophical point, the fact that the problem of apparent design in biosystems is adequately addressed is a definite strength of ID. Evolutionism, on the contrary, claims that design in biosystems, which is obvious to an unbiased observer, is but an illusion. Since evolutionism does not include choice contingent causality in the set of legitimate causal factors, its axiomatics cannot stand the challenges of information theory and cybernetics. In contrast, the inclusion of choice as a causal factor—as is done in ID—allows us to fully appreciate the commonality between complex artificial systems of information processing and living organisms in that they share cybernetic control, formalism and semiosis.

• Apologies for the long list. Unfortunately, tables here at blogspot.co.uk are not parsed correctly by Internet Explorer.

1. Causal factors used in scientific analysis
1. Evolutionism: Chance contingency, necessity. The legitimacy of choice is denied.
2. Intelligent Design Detection: Choice contingency, chance contingency, necessity.
2. Infinite regress
1. Yes.
2. No (on condition that intelligence is assumed to transcend matter).
3. The problem of initial conditions
1. Needs to be solved. The probability of randomly hitting a functional target zone in a vast configuration space is below the plausibility threshold on the gamut of terrestrial interactions.
2. Solved.
4. Design of biosystems
1. Claimed an illusion.
2. Acknowledged in principle.
5. Teleology and goal setting
1. Local. The existence of a global goal, foresight or planning is denied on all levels be it in the universe or in human life.
2. Local and global. Hierarchies of goals. The goal of an object is thought extraneous to it in relation to other objects.
6. Forecasting
1. Problematic and only retrospective.
2. Possible.
7. Origin of control, formalism and hierarchy in complex systems
1. Spontaneous. No empirical data exists to support the claim. Statistical implausibility of spontaneous generation thereof is illustrated by the infinite monkey theorem.
2. Purposive intelligent generation supported by massive observation (complex artificial systems and bioengineering).
8. Primacy of constraints vs. rules
1. Constraints are primal to rules.
2. Rules are primal to constraints.
9. Spontaneous generation of regular configurations (self-ordering) in open thermodynamic systems
1. Accepted.
2. Accepted.
10. Possibility of detection of intelligent agency in the origins of biosystems
1. Denied.
2. Accepted.
11. Adaptational and preadaptational mechanisms and the possibility of their change
1. Accepted. The changes are postulated unguided.
2. Accepted. The possibility and the limits of change are determined by the uploaded meta-rules.
12. Image of the biosphere in the configuration space
1. A continent of functionality. Plasticity of biosystems [Darwin 1857].
2. An archipelago: islands of function in the ocean of chaos. Adaptational movement is limited to occur within particular islands.
13. Geometric image
1. A single phylogenetic tree (classical Darwinism) or a network (considering epigenetic phenomena).
2. A phylogenetic forest (short trees) or a set of low-cycle graphs (considering epigenetic phenomena).
14. Common descent
1. Postulated (sometimes the possibility of several common ancestors is acknowledged ). Genome homology is used as proof/
2. Accepted in principle. Genome homology can alternatively be viewed as a result of common design (by analogy to software design process or to text writing).
15. The strong anthropic principle
1. An illusion. The problem of fine-tuning the universal constants for compatibility with life is claimed non-existent.
2. The necessity to solve the problem is acknowledged. Methodological means to solve it are provided.
16. The problem of defining and studying the phenomenon of intelligence
1. Reduced to the basic physico-chemical interactions (physicality).
2. Addressed in its own right without reduction to physicality. Information semantics cannot and is not reduced to semiotics or physicality. The meaning of a message cannot be reduced to the mere physics of the information channel.
17. The problems of psychology and human psychics
1. Induced by the basic physico-chemical factors. Feelings, cognitive and rational activities are ultimately reduced to the four basic physical interactions.
2. Include the physico-chemical aspect but cannot be exhausted by it. Solved in their own right in view of their irreducibility to physicalty. The hierarchy of complexity of reality is acknowledged. The succession non-living matter-> life-> intelligence-> consciousness is analogous to the hierarchy classes of decision problems (see here).
18. Free will
1. An illusion. In essence, it is denied since all is determined by chance and necessity and, ultimately, by the four basic physical interactions.
2. Acknowledged since legitimate causality includes choice contingency.
19. Creativity
1. Practically non-existent since it is substantially limited by the probabilistic resources available in a given system while creative causality is entirely represented by chance alone. Necessity of selection acts passively and therefore cannot be a factor of creativity.
2. Possible in the full sense by means of impacting functionally specified information to systems via programming the configurable switch bridge [Abel 2011].
20. Origin of life
1. Abiogenesis: spontaneous generation of protobiological structures from inorganic compounds.
2. Purposive generation of initial genomes, tuning the homeostatic state, replication, reaction to stimuli, etc. Uploading the protocols of genetic instruction interpretation as well as meta-rules controlling the limits of adaptive change in those protocols.
21. Isolatedness and extreme rarity of functional protein domains
1. The model to explain functional domain generation includes mutations, drift and selection. Does not satisfy the statistic plausibility criterion. Extreme rarity and deep isolation of biofunction in the configuration space in practice rules out incremental solution generation by blind unguided search on the gamut of terrestrial physico-chemical interactions. This problem is inherently related to the problem of initial conditions (an unacceptably low probability of hitting the target zones of parameters compatible with life).
2. Purposive intelligent generation of functional domains. Its proof of concept is presented by bioengineering.
22. The role of mutation, recombination, selection and drift
1. Primary in explaining all observed biodiversity.
2. Secondary, adaptational within the given higher taxa.
23. The universal plausibility criterion [Abel 2009]
1. Not satisfied: the probability of spontaneous generation of genetic instructions is orders of magnitude below the plausibility threshold on the gamut of terrestrial physico-chemical interactions; the time necessary to accumulate the observed amounts of specified functional information is orders of magnitude greater than the lifespan of the universe.
2. Satisfied. Intelligent choice of initial conditions and rules for the functioning of biosystems in analogy to the known complex artificial systems of information processing.
24. The presence of functionally specific information in biosystems
1. Accepted. Nucleotide sequences code bio-function.
2. Accepted. The amount of functionally specific information associated with a given function is proportional to the ratio of the number of sequences coding for that function to the max possible number of sequences [Durston et al. 2007].
25. The source of functional specific information in biosystems
1. Spontaneous and law-like causal factors: mutation, recombination, drift, selection. Unsupported empirically.
2. Choice contingent causality. Strongly empirically supported: apart from biosystems, only complex artefacts such as languages and information processing systems exhibit functional specific information.
26. Commonality of complex artificial systems and biosystems
1. Denied.
2. Determined based on analyses of available observations. Both complex artefacts and biosystems  are systems of semiotic information processing. The functioning of such systems assumes a priori uploading a common alphabet, rules of syntax and semantics for future information exchange between system components. Large amounts of functionally specific information is only observed in complex artificial systems and in living organisms.
27. Possibility of spontaneous generation of intelligence
1. Assumed as the only option. Unwarranted empirically.
2. Denied based on vast empirical evidence and on the analysis of functionally specified information capable of being spontaneously generated on the gamut of probabilistic resources available in a given system.

References

1. David L. Abel (2009),  The Universal Plausibility Metric (UPM) & Principle (UPP). Theoretical Biology and Medical Modelling, 6:27.
2. David Abel (2011), The First Gene. 
3. Durston, K.K., D.K.Y. Chiu, D.L. Abel and J.T. Trevors (2007)"Measuring the functional sequence complexity of proteins", Theoretical Biology and Medical Modelling 4:47. [doi:10.1186/1742-4682-4-47]
4. Charles Darwin (1857), On the Origin of Species.
5. UncommonDescent.com
6. Wikipedia.