Lee Smolin explains physical law as evolving via a Darwinian process. Universal Darwinism - The Book. Bayesian Methods and Universal Darwinism. Lest this sound a little too 'New Age' or postmodern please be assured that the researchers featured on this site embody the highest calibre of science. To find a short overview of the researchers whose work is featured on this site, with an emphasis on their scientific credentials and research, please see the list below. About Universal Darwinism Universal Darwinism is the collection of scientific theories which explain design found in the universe as the creation of Darwinian processes.

Susan Blackmore Ok, Susan Blackmore may appear somewhat new-age but let me promise you her understanding of the boundaries and operation of science is unsurpassed. Sue holds a PHD in psychology and spent much of her scientific career attempting to find evidence regarding the existence of psychic powers. In spite of devoting many years to this pursuit she eventually abandoned it, having come to understand the low probability of any confirming evidence showing up that could be considered scientific. Since then her research has been roughly split between consciousness studies and Memetics.

Her efforts on Consciousness, what the philosophers refer to as 'the hard problem", has been informed by her research into Memetics. Memetics is the study of cultural evolution by means of replicating memes and is a theory within the umbrella of Universal Darwinism. Richard Dawkins, who first introduced Memetics in his book The Selfish Gene , which is considered one of the great works of 20th century biology, called Blackmore's book The Meme Machine the best shot Memetics could have in establishing itself as a scientific discipline.

John Campbell That would be me! This site is a result of my lifelong fascination with Darwinian processes and their potential for providing a unified understanding of Science. A perhaps interesting interpretation of this definition is that knowledge occurs within the confines of entropy or ignorance. Let's say some evidence becomes available and the model's entropy or ignorance is reduced to three bits. The effect which evidence has on the model is to increase its knowledge by reducing the scope of its ignorance.

It is unfortunate that both Bayesian and Frequentist interpretations deny the existence of knowledge outside of the human realm because it forbids the application of Bayesian inference to phenomena other than models conceived by humans, it denies that knowledge may be accumulated in natural processes unconnected to human agency and it acts as a barrier in realizing our close relationship to the rest of nature. Thus, even though natural selection is clearly described in terms of the mathematics of Bayesian inference, neither Bayesians such as Jaynes nor frequentists such as Frank can acknowledge this fact due to another hard fact: In both their views this may rule out a Bayesian interpretation.

I believe that the correct way out of this conundrum is to simply acknowledge that in many cases inference is performed by non-human agents as in the case of natural selection and that inference is an algorithm which we share with much of nature. The genome may for instance be understood as an example of a non-human conceived model involving families of competing hypotheses in the form of competing alleles within the population.

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Such models are capable of accumulating evidence-based knowledge in a Bayesian manner. The evidence involved is simply the proportion of traits in ancestral generations which make it into succeeding generations. In other words, we just need to broaden Jaynes' definition of probability to include non-human agency in order to view natural selection in terms of Bayesian inference.

In this view the accumulation of knowledge is a preoccupation we share with the rest of nature. It allows us to view nature as possessing some characteristics, such as surprise and expectations, previously thought by many as unique to humans or at least to animals. Bayesian probability, epistemology and science in general tend to draw a false distinction between the human and non-human realms of nature. In this view the human realm is replete with knowledge and thus, infused with meaning, purpose and goals, and Bayesian inference may be used to describe its knowledge-accumulating attributes.

On the other hand, the non-human realm is viewed as devoid of these attributes and thus Bayesian inference is considered inapplicable. However, if we recognize expanded instances, such as natural selection, in which nature accumulates knowledge then we may also recognize that Bayesian inference, as well as equivalent mathematical forms, provides a suitable mathematical description in both realms. Evolutionary processes, as described by the mathematics of Bayesian inference, are those which accumulate knowledge for a specific purpose, knowledge required for increased fitness, for increased chances of continued existence.

Thus, the mathematics implies purpose, meaning and goals, and provides legitimacy for Daniel Dennett's interpretation of natural selection in those terms Dennett, If we allow an expanded scope for Bayesian inference, we may view Dennett's poetic interpretation of Darwinian processes as having support from its most powerful mathematical formulations.

An important aspect of these mathematics is that they apply not only to natural selection but also to any generalized evolutionary processes where inherited traits change in frequencies between generations. As noted in a cosmological context by Gardner and Conlon Specifically, Price's equation of evolutionary genetics has generalized the concept of selection acting upon any substrate and, in principle, can be used to formalize the selection of universes as readily as the selection of biological organisms.

At the core of Bayesian inference, underlying both the Price equation and the principle of free energy minimization we find an extremely simple mathematical expression: Simply put this equality says that the probabilities assigned to the hypotheses of a probabilistic model are updated by new data or experience according to a ratio, that of the probability of having the experience given that the specific hypothesis is correct to the average probability assigned by the model to having that experience. Those hypotheses supported by the data, those that assign greater than average probability to having the actual experience, will be updated to greater values and those hypotheses not supported by the data will be updated to lesser values.

This simple equation describes the accumulation of evidence-based knowledge concerning fitness. When Bayes' theorem is used to describe an evolutionary process the ratio involved is one of relative fitness, the ratio of the fitness of a specific form of a trait to the average fitness of all forms of that trait. It is thus extremely general in describing any entity able to increase its chances of survival or to increase its adaptiveness. When cast in terms of the principle of free energy minimization some further implications of this simple equation are revealed see above.

In a biological evolutionary context, the Price equation is traditionally understood as the mathematics of evolutionary change. However, the Price equation may be derived from a form of Bayes' theorem Gardner, ; Shalizi, ; Frank, b which means it describes a process of Bayesian inference, a very general form of Bayesian inference which according to Gardner Gardner, applies to any group of entities that undergo transformations in terms of a change in probabilities between generations or iterations.

Even with this great generality it provides a useful model as it partitions evolutionary change in terms of selection and transmission Frank, a. There are numerous examples of these equivalent mathematical forms used in the literature to describe evolutionary change across a wide scope of scientific subject matter, specifically evolutionary change in biology Gardner, ; Frank, b , neuroscience Friston, ; Fernando et al.

It is interesting to speculate on the similarity of these mathematical forms to those which may be used to describe quantum physics. Quantum physics is also based upon probabilistic models which are updated by information received through interactions with other entities in the world. Wojciech Zurek, the founder of the theory of quantum Darwinism Zurek, , notes that the update of quantum states may be understood in terms of ratios acting to update probabilistic models Zurek, A conceptual shift acknowledging that inference is a natural algorithm which may be performed in processes outside of the human brain may go some way to allowing quantum Darwinism to be understood as a process of Bayesian inference conducted at the quantum level Campbell, A vast array of phenomena is subject to evolutionary change and describable by the equivalent mathematical forms discussed here.

These forms interpret evolutionary change as based on the accumulation of evidence-based knowledge. Conversely, many instances of evidence-based knowledge found in nature are describable using this mathematics. We might speculate that all forms of knowledge accumulation found in nature may eventually find accommodation within this paradigm. Certainly, the theorem proved by Cox identifies Bayesian inference as the unique method by which models may be updated with evidence.

Inductive inference is the only process known to us by which essentially new knowledge comes into the world. Of course he was referring to experimental design and considered it unnecessary to specify that he was referring only to human knowledge. Probably he assumed that no other repositories of knowledge exist. The stage may now be set for us to understand his assertion as literally true in its full generality. Ultimately the scope and interpretation of universal Darwinism, the study of phenomena which undergoes evolutionary change, will depend on the mathematical model underlying it.

Those phenomena which are accurately and economically described by the mathematics must be judged to be within the scope of universal Darwinism. Given the great generality and substrate independence of current mathematical models, a unification of a wide range of scientific subject matters within this single paradigm may be possible. The author confirms being the sole contributor of this work and approved it for publication.

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The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. I appreciate discussions with Karl Friston clarifying the role of the free energy minimization principle in evolutionary change. Relative entropy in biological systems.

The interplay of bayesian and frequentist analysis. Quantum Darwinism as a Darwinian process.

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## Universal Darwinism

The New American Library. The Autobiography of Charles Darwin Cambridge University Press , 45— Evolution and the Meanings of Life.

- [] Universal Darwinism as a process of Bayesian inference?
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Selectionist and evolutionary approaches to brain function: The Genetical Theory of Natural Selection. The Design of Experiments, 9th Edn. Selection versus transmission and the levels of selection. How to read the fundamental equations of evolutionary change in terms of information theory. The free energy principle: Life as we know it. Typical laws of heredity.

## About Universal Darwinism

Cosmological natural selection and the purpose of the universe. A framework for the unification of the behavioral sciences. The Replicator Equation as an Inference Dynamic.

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