The discussion revolves around the definitions of "open" and "closed" systems as presented by Hilary Putnam in his book "Representations and Reality." Participants explore the implications of these definitions in the context of finite automata and computational theory, with references to philosophical debates surrounding these concepts.
Participants generally express uncertainty and differing interpretations regarding the definitions and implications of open and closed systems. Multiple competing views remain, particularly concerning the philosophical ramifications of Putnam's claims.
Some limitations in the discussion include missing assumptions about the definitions of open and closed systems, and the dependence on interpretations of Putnam's arguments. The discussion also highlights unresolved mathematical steps in relating finite automata to physical systems.
How would you define an "open system" in this context? How would you define a closed system?every ordinary open system realizes every abstract finite automaton
The latter principle holds that every ordinary system is in different maximal states at different times. (A "maximal" state is a total state of the system, specifying the system's physical makeup in perfect detail). Putnam argues for this on the basis that every such system is exposed to electromagnetic and gravitational radiation from a natural clock. I will accept the principle in the discussion that follows. Even if it does not hold across the board (arguably, signals from a number of sources might cancel each other's effects, leading to a cycle in behavior), the more limited result that every noncyclic system implements every finite-state automaton would still be a strong one.
instead of seeking to justify Putnam's original claim that, "every open system implements every finite state automaton", (FSA), and hence that psychological states of the brain cannot be functional states of a computer, I will seek to establish the weaker result that, over a finite time window every open system implements the trace of a particular FSA Q, as it executes with known input (x). That this result leads to panpsychism is clear as, equating Q(x) to a specified program that is claimed to instantiate phenomenal states as it executes, and following Putnam's procedure, identical computational (and ex-hypothesi phenomenal) states can be found in every open physical system.
Before moving on to look at cognition and how this view of computation is related to it, it is important to dispose of the argument, put forward by Putnam (1988), to the effect that computation is all pervasive. According to Putnam’s proof (in the Appendix of his Reality and Representation), any open system, for example, a rock, can compute any function. If true, this would render void the computationalist’s claim that cognition is simply a particular class of computation, since everything, even a rock, would be capable of cognition!
The essence of Putnam’s argument is as follows: Every ordinary open system will be in different maximal states, say s1, s2, … sn, at each of a sequence of times, t1, t2, … tn. If the transition table for a given finite state automaton (FSA) calls for it to go through a particular sequence of formal states, then it is always possible to map this sequence onto the physical state sequence. For instance, if the FSA is to go through the sequence ABABA, then it is only necessary to map A onto the physical state s1 s3 s5, and B onto s2 s4. In this way any FSA can be implemented by any physical system.
Fortunately (for cognitive science), the argument is not as good as it may at first appear. Putnam’s Principle of Non-Cyclical Behaviour hints at the difficulty. His proof relies on the fact that an open system is always in different maximal states at different times. In other words, it is possible to perform this mapping operation only once (and then probably only with the benefit of hindsight!)
loseyourname said:I think he is using the term in the normal thermodynamic sense. If I had to guess at a reason why he specifies the argument to only open systems, it is because a completely closed system can theoretically reach permanent homeostasis and cease to go into changing physical states as time passes. Don't quote me on that, though. Ask a chemistry expert.
loseyourname said:I think he is using the term in the normal thermodynamic sense. If I had to guess at a reason why he specifies the argument to only open systems, it is because a completely closed system can theoretically reach permanent homeostasis and cease to go into changing physical states as time passes. Don't quote me on that, though. Ask a chemistry expert.
Putnam has argued that computational functionalism cannot serve as a foundation for the study of the mind, as every ordinary open physical system implements every finite-state automaton. …
The theory of computation is often thought to underwrite the theory of mind. In cognitive science, it is widely believed that intelligent behavior is enabled by the fact that the mind or the brain implements some abstract automaton: perhaps a Turing machine, a program, an abstract neural network, or a finite-state automaton. The ambitions of artificial intelligence rest on a related claim of computational sufficiency, …
In an appendix to his book Representation and Reality (Putnam 1988, pp. 120-125), Hilary Putnam argues for a conclusion that would destroy these ambitions. Specifically, he claims that every ordinary open system realizes every abstract finite automaton. He puts this forward as a theorem, and offers a detailed proof. If this is right, a simple system such as a rock implements any automaton one might imagine. Together with the thesis of computational sufficiency, this would imply that a rock has a mind, and possesses many properties characteristic of human mentality. If Putnam's result is correct, then, we must either embrace an extreme form of panpsychism or reject the principle on which the hopes of artificial intelligence rest.
The argument seems to rest on the fact that any given open system (such as a rock exposed to the rest of the universe) is in different maximal states at different times (assuming Putnam’s Principle of Non-Cyclic Behaviour) - and Putnam argues for this on the basis that every such system is exposed to electromagnetic and gravitational radiation from a natural clock.Chalmers said:The argument is general. Given any inputless FSA and any noncyclic physical system, we can map physical states of the system over an arbitrarily long period of time to formal states of the FSA by the same method. We associate the initial physical state of the system with an initial state of the FSA and associate subsequent physical states of the system with subsequent states of the FSA, where the FSA state-evolution is determined by its state-transition rules. The implementation mapping is determined by taking the disjunction of associated physical states for each state of the FSA, and mapping that disjunctive state to the FSA state in question. Under this mapping, it is easy to see that the evolution of the physical states precisely mirrors evolution of the FSA states. Therefore every noncyclic physical system implements every (inputless) FSA.
Before moving on to look at cognition and how this view of computation is related to it, it is important to dispose of the argument, put forward by Putnam (1988), to the effect that computation is all pervasive. According to Putnam’s proof (in the Appendix of his Reality and Representation), any open system, for example, a rock, can compute any function. If true, this would render void the computationalist’s claim that cognition is simply a particular class of computation, since everything, even a rock, would be capable of cognition!
The essence of Putnam’s argument is as follows: Every ordinary open system will be in different maximal states, say s1, s2, … sn, at each of a sequence of times, t1, t2, … tn. If the transition table for a given finite state automaton (FSA) calls for it to go through a particular sequence of formal states, then it is always possible to map this sequence onto the physical state sequence. For instance, if the FSA is to go through the sequence ABABA, then it is only necessary to map A onto the physical state s1 s3 s5, and B onto s2 s4. In this way any FSA can be implemented by any physical system.
Fortunately (for cognitive science), the argument is not as good as it may at first appear. Putnam’s Principle of Non-Cyclical Behaviour hints at the difficulty. His proof relies on the fact that an open system is always in different maximal states at different times. In other words, it is possible to perform this mapping operation only once (and then probably only with the benefit of hindsight!)
Ref: http://www.cs.bilkent.edu.tr/~david/papers/computationalism.doc"But Davenport also says he "disposes" of Putnam's thesis. How does he do that?
Q_Goest said:Ref: http://www.cs.bilkent.edu.tr/~david/papers/computationalism.doc"
I wouldn't say that Davenport manages to do this. His paper isn't particularly convincing IMO, and I don't see anyone referencing him. I'll try and get something together on the issue shortly.
His proof relies on the fact that an open system is always in different maximal states at different times. In other words, it is possible to perform this mapping operation only once (and then probably only with the benefit of hindsight!) But this is of no use whatsoever; for computation, as we have seen, is about prediction. Not only is Putnam’s “computer” unable to repeat the computation, ever, but also it can only actually make one “prediction” (answer one trivial question.) The problem is that the system is not really implementing the FSA in its entirety. A true implementation requires that the system reliably traverse different state sequences from different initial conditions in accordance with the FSA’s transition table. In other words, whenever the physical system is placed in state si it should move into state sj, and whenever it is in sk it should move to sl, and so on for every single transition rule. Clearly, this places much stronger constraints on the implementation. Chrisley (1995), Copeland (1996) and Chalmers (1996) all argue this point in more detail. Chalmers also suggests replacing the FSA with a CSA (Combinatorial State Automata), which is like a FSA except that its states are represented by vectors. This combinatorial structure is supposed to place extra constraints on the implementation conditions, making it even more difficult to find an appropriate mapping. While this is true, as Chalmers points out, for every CSA there is a FSA that can simulate it, and which could therefore offer a simpler implementation!
Imagine 2 machines are engaged in the same physical activity and are running the same consciousness program. One of these machines supports counterfactual states, whereas the other doesn't. The computationalist must claim, based on the nontriviality condition, that the machine capable of supporting counterfactual states is conscious and the other isn't. But this contradicts the supervenience thesis: each machine exhibits the same physical activity, but according to computationalism only one is conscious.
Bishop advances a version of Putnam's argument that every ordinary system implements every computation, uses this to argue that computation can't be the basis of consciousness, and then addresses my response to Putnam. I've argued (e.g. in "Does a Rock Implement Every Finite-State Automaton") that Putnam's systems are not true implementations, since they lack appropriate counterfactual sensitivity. Bishop responds that mere counterfactual sensitivity can't make a difference to consciousness: surely it's what actually happens to a system that matters, not what would have happened if things had gone differently. He runs a version of the fading qualia argument, suggesting that we can remove unused state-transitions one-by-one, thus removing counterfactual sensitivity, while (he argues) preserving consciousness.