1. What is Autonomy? John Collier Philosophy, University of KwaZulu-Natal
http//web.ncf.ca/collier

2. Origins of autonomy
Autonomy literally means making ones own laws, or self rule.
It is a form of control, and therefor involves causal or dynamical activity originating in the agent.
Although Kant is most associated with moral and personal autonomy he began his discussion with biological autonomy.
This suggests that he consided it most basic, and I follow him in this.
Cognitive autonomy, personal autonomy, moral autonomy, social autonomy and national autonomy are higher levels, and my conflict with lower levels.

3. Dynamical realism
A central principle I adopt: Anything that is real is dynamical, or can be understood dynamically.
Individuation, cohesion, closure
Empirical access, interactivism
Organized complexity
Emergence
Functionality
Intentionality

4. Autonomy and Functionality
Functionality is a teleological notion, related to some goal.
It is autonomy that sets goals, not individually but in general.
More specific goals are derivative from autonomy by the way they contribute to it.
So functionality must be understood in terms of how it contributes to autonomy (at whatever level).
As mentioned autonomy at various levels can conflict, leading to inconsistent goals.
Functionality may be mistaken (perhaps a necessary characteristic).

5. Functionality and Function
Functions are specific to an organ or distinct element in the overall organization. For example the function of the heart is to pump blood. The function of the Central Bank is to moderate the economy through interest rates and quantative easing.
Many goal oriented actions (contributing to autonomy) are not so localized in individual organs or methods.
Examples are the actions of blood flow through various kinds of blood vessels, and the role of money in determining prices.
Functionality is more basic than functions, and we should prefer it to avoid errors of overspecificity.

6. Weak versus Strong Autonomy
Some devices such as robots (e,g, guided missiles) take information from their surroundings and act more or less independently. Perhaps some primitive biological entities behave this way.
Dubois called this weak autonomy. Internal states (system laws) remain constant or are deductive consequences. This system is basically controlled by its sensors and the environment.
Strong autonomy on Dubois’ approach includes internal representations of the system (internal model). This makes the system at least partial subject to its own rules.
I agree with Dubois, but I also believe that the internal laws are also self-mutable in complex systems, adding another level of complexity.

7. Identity and Individuation
The identity conditions for an object also individuate it from other objects.
Identity is a symmetrical, transitive and reflexive relation.
Thus any identity condition must satisfy these conditions.
I mathematics, for example, a definition must be shown to apply to something that exists and is unique.
In areas of pure thought we can stipulate that the define entity is unique.
However with dynamical objects, the same form may occur many times.

8. Cohesion: The Dividing Glue
Cohesion is the dynamical relation between parts of a thing that make it parts of the same thing.
The closure of this relation defines a unique entity.
The closure relation is symmetrical, transitive and reflexive, satisfying the conditions for identity.

9. Organization
The basic idea of an organized system is that it is interconnected in complex ways, so that there are both local and non-local effects.
A system can be strictly hierarchical:
the higher levels are the sum of the effects of the parts, or
the behaviour of the parts is strictly controlled from above.
Such systems are decomposable and are not complexly organized.
Complex organization can be more flexible:
involves neither summation nor top down control, but shows an interaction of bottom-up effects and top-down effects
Complexly organized systems cannot be decomposed

10. Form (pattern) dominates in biology
In most of the physical sciences constraints are secondary, processes being governed by energy flows.
In biology, however, energy differences are small, and information flows are primary.
Information is a measure of form (or pattern), so changes in pattern are primary in biology.

11. Pattern in biology
Patterns in biology are found (famously) in systematics, but also in developmental theory, evolutionary theory, and more recently in systems biology. Shape is also important in molecular biology (enzymes, immunology, etc.).
Pattern change is a form of dynamics, as it involves a time parameter. However it is not a dynamical theory in the stronger sense of being based in forces and flows, or in causal relations.
I will argue that pattern dynamics alone are inadequate for the study of pattern change.

12. Biological laws are primarily laws about change or constancy of form
In the physical sciences most laws involve energy either directly or indirectly.
Because form dominates in biology, biological laws (such as they are) involve the preservation of form or change of form in regular ways.
This might be why many philosophers have denied the existence of laws in biology. Biological laws are often local to a system type (after K. Waters). This is not unheard of in the physical sciences (e.g., geomorphology), but it is less common, and universal laws are preferred.

13. Autonomy is a special type of organization based cohesion
Cohesion is maintained actively though contributions of component processes to the continued existence of the system directly or through intermediate processes.
To be active requires doing work. Doing work requires non-equilibrium.
In order to have self control, a system must be internally differentiated.
This requires a certain flexibility that systems whose cohesion is based in high energy differentials don’t have.
Thus we can expect autonomous systems do not have energy as their primary concern, but rather organization of their processes so as to divert energy as suitable for their survival.
It would be proper, then, to define autonomous systems, and the degree of autonomy itself, in terms of relative organization rather than in terms of relative energies of interactions.
We should expect autonomous systems to show holistic organization that is somewhat modular and hierarchical, where open aspects of lower level processes are closed at higher levels (top-down constraint, “downward causation”).
Internal organizational closure is greater than the interactive closure. (Centripetal forces > centrifugal forces). 

14. Kinds of systems
Type I systems are typical of basic examples of laws: pendulum, crystal
Type II systems are typical of engineered systems: computer
Type III systems have statistical behaviour: gas, stars in a cluster
Type IV systems cover everything else

15. What makes Type IV systems difficult?
Mathematically difficult or impossible to solve
Mutual interaction of parts (nonlinearity)
Parts interact in aspects, often different for different interactions (partial differentials)
Cannot be broken up into parts (analyzed) that can then be added together (synthesized)
Mutual interaction between the whole and the parts (upward and downward causation)

16. Autonomy requires complex organization
Kant proposed that we need a different notion of causation to explain autonomy. Efficient causation of mechanistic views don’t work, at least in part because they don’t account for goals.
Terry Deacon, goals as an absence, ententionlity: future directed but not explicit, nonetheless a form of teleology called teleonomy
Alicia Juarerro, traditional accounts of action remove the agent. Complex organization accounts put the cause in the agent.
So complex organization is required for autonomy.

17. Function and intention
Function: a trait is functional if and only if it contributes to the autonomy of that which has the trait.
Intentionality is a functional trait.
However not all intentionality contributes to survival.
Hypothesis: a trait is intentional iff it contributes to the purposes of the bearer of the trait.

18. Theory of mind Action generated through internal factors
Intentions open-ended and non-reducible to:
Input-output (behaviorism)
Neural processes (identity theory)
Internal input-output (functionalism)
Some caveats on non-reducibility: reductive explanation, locally accurate models
Intentions (meanings) can match complex, non-mechanistic process in the world.
Both internally and externally originated creativity are possible.

19. Autonomy permits novel experiences
In stable networks, the Onsager reciprocity relations hold (Rashevsky) and the system can be treated with near to equilibrium methods.
However, when new nodes are formed, the dynamics peculiar to self- organizing far from equilibrium systemss become indispensable.
This happens in growing systems, especially in evolution and development.
In such cases a strong force will tend to organize the system so ass to minimize he entropy production in the generalized direction of the force.
This will cascade in orthoganal directions until the force dissipates. The system is now accommodated to such forces and can adapt to el with them.