Blog all dog-eared unpages: Philosophy & Simulation: The Emergence of Synthetic Reason by Manuel DeLanda

Philosophy & Simulation: The Emergence of Synthetic Reason by Manuel DeLanda

This was incredibly hard-work as a read for this bear of little brain, but worth it. Very rewarding and definitely in resonance with earlier non-fiction reads this year (The Information, What Technology Wants, The Nature of Technology)

I’ve put the things that really gave me pause in bold below.

an unmanifested tendency and an unexercised capacity are not just possible but define a concrete space of possibilities with a definite structure.

a mathematical model can capture the behavior of a material process because the space of possible solutions overlaps the possibility space associated with the material process.

Gliders and other spaceships provide the clearest example of emergence in cellular automata: while the automata themselves remain fixed in their cells a coherent pattern of states moving across them is clearly a new entity that is easily distinguishable from them.

This is an important capacity of simulations not shared by mathematical equations: the ability to stage a process and track it as it unfolds.

In other words, each run of a simulation is like an experiment conducted in a laboratory except that it uses numbers and formal operators as its raw materials. For these and other reasons computer simulations may be thought as occupying an intermediate position between that of formal theory and laboratory experiment.

Let’s summarize what has been said so far. The problem of the emergence of living creatures in an inorganic world has a well-defined causal structure.

The results of the metadynamic simulations that have actually been performed show that the spontaneous emergence of a proto-metabolism is indeed a likely outcome, one that could have occurred in prebiotic conditions.

Because recursive function languages have the computational capacity of the most sophisticated automata, and because of the random character of the collisions, this artificial chemistry is referred to as a Turing gas.

An evolving population may, for example, be trapped in a local optimum if the path to a singularity with greater fitness passes through points of much lesser fitness.

Roughly, the earliest bacteria appeared on this planet three and a half billion years ago scavenging the products of non-biological chemical processes; a billion years later they evolved the capacity to tap into the solar gradient, producing oxygen as a toxic byproduct; and one billion years after that they evolved the capacity to use oxygen to greatly increase the efficiency of energy and material consumption. By contrast, the great diversity of multicellular organisms that populate the planet today was generated in about six hundred million years.

The distribution of singularities (fitness optima) in this space defines the complexity of the survival problem that has to be solved: a space with a single global optimum surrounded by areas of minimum fitness is a tough problem (a needle in a haystack) while one with many local optima grouped together defines a relatively easy problem.

from the beginning of life the internal models mediating the interaction between a primitive sensory system and a motor apparatus evolved in relation to what was directly relevant or significant to living beings.

with the availability of neurons the capacity to distinguish the relevant from the irrelevant, the ability to foreground only the opportunities and risks pushing everything else into an undifferentiated background, was vastly increased.

Finally, unlike the conventional link between a symbol and what the symbol stands for, distributed representations are connected to the world in a non-arbitrary way because the process through which they emerge is a direct accommodation or adaptation to the demands of an external reality.

This simulation provides a powerful insight into how an objective category can be captured without using any linguistic resources. The secret is the mapping of relations of similarity into relations of proximity in the possibility space of activation patterns of the hidden layer.

Both manual skills and the complex procedures to which they gave rise are certainly older than spoken language suggesting that the hand may have taught the mouth to speak, that is, that ordered series of manual operations may have formed the background against which ordered series of vocalizations first emerged.

When humans first began to shape flows of air with their tongues and palates the acoustic matter they created introduced yet another layer of complexity into the world.

Says(Tradition, Causes(Full Moon, Low Tide)) Says(My Teacher, Causes(Full Moon, Low Tide))

A mechanism to transform habit into convention is an important component of theories of non-biological linguistic evolution at the level of both syntax and semantics.

a concentration of the capacity to command justified by a religious tradition linking elite members to supernatural forces or, in some cases, justified by the successful practical reasoning of specialized bureaucracies.

Needless to say, the pyramid’s internal mechanism did not allow it to actually transmute a king into a god but it nevertheless functioned like a machine for the production of legitimacy.

social simulations as enacted thought experiments can greatly contribute to develop insight into the workings of the most complex emergent wholes on this planet.

abandon the idea of “society as a whole” and replace it with a set of more concrete entities (communities, organizations, cities) that lend themselves to partial modeling in a way that vague totalities do not.

4 thoughts on “Blog all dog-eared unpages: Philosophy & Simulation: The Emergence of Synthetic Reason by Manuel DeLanda

  1. […] had read this piece about Case before and the talk contained not much new and DeLanda who I had got pointed to by Matt Jones very recently took his time to introduce us to genetic algorithms as form finding functions. A […]

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