The Simulation Argument and Research Potential

In 2002, Nick Bostrom proposed his version of the simulation argument. Anders Hammarström concluded with the following in his MA thesis:

The conclusion reached in this paper is that the argument is, at our current stage of technological development, in principle irrefutable. It all depends on whether or not consciousness can emerge from advanced computer simulations of the human brain, and the answer to this question is, unfortunately, out of our current reach.

David Chalmers in an entry on his blog makes the following (among other interesting) comments:

As for intelligent design, I'm on the record as saying that I can't rule out the hypothesis that we're living in a computer simulation, so I suppose that it follows that I can't rule out the hypothesis that our world is designed.

The simulation argument could thus provide a starting point to look for evidence that our universe might be as a result of mind by focusing on the simulation argument and looking for evidence that might support the simulation hypothesis.

What kind of simulation?

Memetic algorithms and pre-existing realities. An example.

Memetic Algorithms (MAs) are search techniques used to solve problems by mimicking molecular processes of evolution including selection, recombination, mutation and inheritance.

A few important aspects of MAs (Figure 1):
1. The fitness landscape needs to be finite.
2. The search space of the MA is limited to the fitness landscape.
3. There is at least one solution in the fitness landscape (Figure 2).
4. A fitness function determines the relationship between the fitness of the genotype (or phenotype) and the fitness landscape.
5. Selection is based on fitness.

Figure 1: A) Basic lay out of memetic algorithms. A population of individuals is randomly seeded with regard to fitness (initialized). The individuals are randomly mutated and their fitness is measured. Individuals with optimal fitness are further mutated until convergence of a local optima is reached. The process is carried out for the entire initialized population. The global optima is selected from the various local optima. B) Fitness landscape with local optima (A, B and D) and a global optima (C). In a memetic algorithm, the initial population of individual are randomly seeded and can be viewed as any of the arrows indicated in the figure.

Autodock (a molecular docking program) employs a MA in order to try and predict the orientation of a ligand within a protein receptor. A docking run with Autodock can be characterized by the following:

1. Finite fitness landscape: The physical properties of the protein receptor (E.g. electrostatic properties, Van der Waals interactions, desolvation energies etc.). This can be characterized as the pre-existing fitness landscape.
2. Search space: Confined to the protein receptor.
3. At least one solution: The original crystallographic pose.
4. Fitness function: Estimated Free Energy of Binding pose. This is determined through a combination of various interactions including Van der Waals-, electrostatic-, desolvation-, hydrogen bond- and torsional free energy.
5. Selection (guiding function): Selection is based on fitness, i.e. The Estimated Free Energy of Binding pose.

Using Autodock as an example, a docking simulation of Colchicine was run 4 times by docking Colchicine into the tubulin receptor. Each time the ligand is docked, 30 populations with 250 individuals (ligands) are randomly placed within the receptor and the position of each ligand is randomly "mutated" after which the Estimated Free Energy of the pose is measured. The position of each ligand is "mutated" until a local optima of the Estimated Free Energy of a ligand is reached. The local optima of each of the four docking runs were measured (results here) and in all four runs, the convergence of the global optima (in each run) corresponded reasonably well to the crystallographic pose (RMSD<1.8). Two conclusions can be reached thus far:

1. The software can predict the best pose (biologically relevant) of a ligand in a protein with reasonable success.

2. Separate runs converge on similar local optima after random variation and selection in a pre-existing finite fitness landscape.

Are there parallels between the above mentioned docking simulation and the evolution of life?

Compare MAs to our current understanding of some aspects of the universe.

1. Finite fitness landscape: From Einstein's equation, E=mc^2, all matter was ultimately created out of energy, and is theoretically reducible to energy, and thus energy can be understood to be the ultimate foundation of all matter in this universe. Recent Quantum Teleportation experiments have shown that it is possible to accept that energy supervenes on information (informationalism). What are some of the aspects of information which energy supervenes on? Mass, spin and charge? What about elemental proto-experiences (PEs) as phenomenal aspects that are properties of elementary particle (superimposed) described in this paper (another paper)?. Thus, the finite fitness landscape of the universe can be viewed to be the elemental physical properties; mass, spin, charge and PEs. Granted that PEs are currently a philosophical construct and not testable entities. Whether they are scientifically testable remains to be seen.
2. Search space: Confined to this universe, and since the beginning of life on earth, confined to earth.
3. At least one solution: Consciousness is at least one solution. Us.
4. Fitness function: Standard evolutionary theory describes fitness as the capability of an individual and/or a population of a certain genotype to reproduce (self-replicate). Is this fitness function solely confined to self-replication prowess? Intelligence and agency also seem to play a role in organisms that do not self-replicate in high numbers (e.g. elephants [low] vs bacteria [high]). Self-replication entities do not necessarily result in intelligent self-replicating entities, and intelligent self-replicating entities do not necessarily result in intelligent self-replicating agents. In order to differentiate between intelligence and agency, intelligence can be viewed as an ability to process information (e.g. genetics, proteomics, metabolomics) and agency can be viewed as the ability to willfully manipulate information. Can self-replication, intelligence and agency all be part of the fitness function?
5. Selection (guiding function): Natural selection and the ability of an organism to survive. Self-replication, intelligence and agency would thus play a part.

What about the evidence?

A. The Memetic Algorithms of life.
1) A reasonably optimal genetic code that seems to be optimized for evolution and random searches. It also maintains its own functional integrity.
2) Quality control systems are in place. These include DNA repair, protein folding and programmed cell death (also in unicellular organisms). Some of these systems are so efficient that they remove even functional mutated proteins from the population of proteins generated in the genome. Thus this serve to constrain evolution, preventing certain functional proteins from entering a population.
3) Variation inducers. These include cytosine deaminases, Low vs High fidelity polymerases, gene conversion and homologous recombination. The immune system harnesses the properties of the genetic code for antibody diversification. Protein folding is an exquisitely controlled process, but cells can tweak this process under periods of stress to introduce variation. For example:
Misfolded Proteins Accelarate Yeast Evolution

ScienceDaily (Nov. 24, 2008) — Under stress, yeast cells can unleash a remarkable mechanism based on protein-misfolding that gives them new characteristics without requiring genetic mutations.

The article continues to comment that this mechanism serves as a mechanism tailored for evolution? Irrespective of its origins, somehow, life utilizes evolution FOR evolution.

B. Convergence
As seen in the docking simulation, the solutions all converge on relatively similar local optima. Examples of molecular and structural convergence are abundant in nature. Also, abiogenesis spectacularly converged into a reasonably optimized genetic code (with a few derivatives) and life's memetic algorithms. Convergence in virtual simulations and in nature thus serve to point to similarities between them.

C. Quantum Physics and Consciousness
The Penrose-Hameroff orchestrated objective reduction (orch. OR) model provides a basis to connect consciousness with quantum mechanics. Philosophical models compatible with this view include Type-F monism (panprotopsychism) (Article) or Type-D Dualism. This view is also not incompatible with the Metaphysical Hypothesis discussed here (p10), and fits in nicely with the view as that information is an irreducible property of nature. Also, the Cambrian explosion and the emergence of consciousness (and agency?) can theoretically be connected by viewing the ability of an organism to quantum mechanically interact with the irreducible information of the universe as an adaptive advantage.

D) The Biased Nature of Evolution.
After running the docking simulations, the software seemed to have been biased towards a few local optima. Compare the developmental program to evolution. An interesting article that shows the parallels between evolution and development.

For development:
Primordial germ cells (PGC) are prevented from entering the somatic program and are demethylated (genome-wide erasure of existing epigenetic modifications). Then the gametes are imprinted (targeted DNA methylation) during gametogenesis, only to be demethylated again after fertilization. Then during development, DNA is methylated again, causing totipotential cells to become pluripotent. X-inactivation and reactivation (of the paternal gamete I think) also occurs. The whole process is governed by the genetic (and epigenetic?) program. During the unfolding of this somatic program, random variation and selection occur, ultimately leading to just a few endpoints, every time it is successful. The process is constrained (few end points) as a result of pre-existing information that is set up during the initiation of the process. All this is controlled by information in the genome.

For evolution:
There also seems to be only a few endpoints (small subset, limited variation) out of all the possible endpoints.
In the article:
An End to Endless Forms: Epistasis, Phenotype Distribution Bias, and Nonuniform Evolution
It is argued to be as a result of genetic instructions dating earlier in evolutionary time. (Preadaptations)

As in the case of the evolution of eyes, as soon as these sets of genes were formed (E.g. Pax genes), (through whatever mechanism), evolution seemed to have been biased to a few end points, and these few endpoints arose 40-60 times, independently, as a result of pre-existing (preadaptations) information in the case of eyes. To make it even more intriguing is the notion that the whole process is facilitated and under intrinsic control. Why? What other "biased" end points can there be? Nervous systems, smell, hearing? And why would evolution be biased, as in development, to only reach a few end points over and over?

To conclude, some evidence seem to point to parallels between our own designed simulated docking runs and the evolution of life. Humans seem to be teleological agents by nature. We plan, we create, we intentionally manipulate nature as a means to an end. Science by its very nature also seem teleological as scientists plan and execute experiments in order to gain an understanding of the universe and ourselves with the assumption and faith that we will be able to understand it. What to do?

Research Potential?

1) What other mechanisms exist that could bias evolution? Riboswitches? Biomelucular machines? Preadaptations?
2) Simulate a Front-loaded state and see what happens.
3) Can quantum mechanics and biological structures (e.g. tubulin) explain consciousness? What about elemental proto-experiences? Is it testable?
4) Why does the scientific endeavour seem to be biased toward teleological explanations for our existence, and why would one want to force non-teleological explanations?