Teoría del Conflicto

In this section I will attempt to develop an account of the nature of computation. My account is largely in line with what might be described as the received view of computation, a widely held view that has been championed by Fodor

First and foremost computers are symbol manipulators; given symbols as input they produce symbols as output. Thus co m p u tatio n al processes involve the m an ip u latio n or transformation of symbols. This symbol manipulation isn't random but rather rule governed. More specifically, computers generate o utput symbols from input symbols by applying symbol- m anipulating rules (what this means will be explained in due course).

Computational processes are often complex; that is to say that it is frequently the case that computers generate output from input not in one single symbol manipulating move but rather by executing a whole series of substeps each of which involve applying a rule to a symbol. However, to express the point in these terms fails to

relation between belief-talk and psychological process-talk will be any more direct. (1978, p. 107).

Even shorn of the implicit instrumentalism, such a view goes some way towards indicating how the manipulation of internal representations at the subpersonal level could play a role in belief fixation without beliefs being relations to internal representations.

Another alternative to Fodor's RTM identifies beliefs with whole collections of internal representational states. Horgan and Woodward (1985) portray Minsky's (1981) "Society of Mind" view in such terms. According to Minsky, they write, "the role of a belief (say) is typically played by a vast, highly gerrymandered, conglomeration of C[ognitive] S[cience]-events' (p. 140). In short then, scientific psychologists are not forced to endorse Fodor's RTM and there is no reason to suppose that they generally do.

See 'Methodological Solipsism Considered as a Research Strategy in Cognitive Psychology' where Fodor makes his most explicit statement of his understanding of the nature of computation.

distinguish between two distinct ways in which a computational process can be complex. The first type of complexity is the analogue of the complexity of the task of baking a cake when executed by a single i n d i v i d u a l . ^ 4 The process of baking a cake generates a baked cake as output from a collection of ingredients as input. This process cannot be executed by an individual in one single move; rather she has to make a whole series of simpler moves in a certain order. Examples of such substeps would be those of breaking an egg into a bowl, mixing together some eggs with a quantity of flour, butter, and sugar, pouring a mixture of eggs, sugar, butter, and flour into a lined cake tin, and so on. A cook bakes a cake by performing each of these substeps in a certain order. The point to note is that the system that performs the complex task, namely the cook, is the very system that performs the simpler sub-tasks. A computational process that is complex in this first respect is one that is executed by a system by means of executing a whole series of simpler com putational processes or operations in a certain order. In such a case the system that performs the substeps is the very system that executes the complex process and it does the latter by doing the former.

The second type of complexity is the analogue of the process of cooking meals as performed by a restaurant kitchen. A restaurant kitchen produces from an input of ingredients and representations of customer orders an output of meals which correspond to what the customers have ordered. In executing this process a whole series of simpler processes or operations are executed, but there is a respect in which it is misleading to say that the kitchen as a whole performs these simpler operations. Rather, it is subsystems of the kitchen that perform them. Restaurant kitchens have a hierarchical structure; that is, they can be analysed into subsystems each of which performs a distinct task (or series of tasks) the performance of which contributes to the higher level performance of the complete kitchen. These subsystems doing what they do, and interacting with one another in the process, engender the complex behaviour of the whole system to which they belong. In other words, the kitchen

Examples from the world of food production are frequently used by philosophers and psychologists when explicating the notion of computation. See, for example, Cummins (1983) and Haugeland (1985). My love of food-preparation and consumption stands in the way of the generation of more original examples.

executes its complex process by having components that execute simpler processes and interact with one another in so doing. These subsystems in turn analyse into simpler subsystems - that execute simpler tasks and interact with one another in so doing - that analyse into simpler subsystems until eventually one comes to the basic components of the system. These basic components execute tasks that are complex, if complex at all, only in the first respect described above. The basic components of restaurant kitchens are typically individual humans that do such things as chop vegetables, stir sauces, carve joints of meat, arrange items of cooked food on plates, and so on. At any level in this hierarchy of systems there are operations simple relative to that level, simple in the respect that if they are perform ed by perform ing simpler operations those operations are perform ed by subsystems lower dow n in the hierarchy.

A computational process is complex in the second respect when it is executed by a computer with a hierarchical structure, so that it executes the process in question by having components that execute simpler tasks, and in so doing interacts with other components in that level of the hierarchy. In the case of a computer, the processes at all levels will be symbol-manipulating ones.^^

Sometimes computational processes that are complex in the first respect occur in a computer with a hierarchical structure. This will be the case if at any level in the hierarchy there exists a subsystem that performs its task by executing symbol-manipulating substeps.

The concept of a program is intimately bound up with that of computation. What is a computer program? A program is a description of the means by which a computer executes a particular computational process. A program typically takes the form of a list of lines or a flowchart each line or box of which corresponds to a sub­ step taken in executing the complex process in question, and thus to a symbol-manipulating capacity. The program as a whole will not

The hierarchical nature of (many) computers is described by Fodor (1968), Demiett (1978), Lycan (1982) and Cummins (1983). All these writers emphasise this characteristic of computers in order to make a point about the explanation of computer capacities, the point being that explaining such capacities involves analysing the system under study into subsystems with simpler capacities the execution of which engender the execution of the target capacity.

merely specify the steps taken but also the order in which they are made. Each of the substeps will of course be a computational operation, that is, a symbol-manipulating move made by applying a rule to a symbol to generate from it another symbol. Thus each line (or box) of a program will correspond to a symbol-manipulating rule applied by the computer (or one of its subsystems) in executing a complex computational process. A program will rarely tell the whole story as to how a computer performs a given complex process, for typically each line of the program will correspond to an operation that is not absolutely simple but rather complex in at least one of the respects described above.

Programs are thus rather like recipes in cookbooks, for such recipes specify the operations to be made or the steps to be taken (and the order of execution) in executing the process of producing a certain dish. Each line of the recipe will specify a culinary operation which is not absolutely simple but simple only relative to the language of that cookery book; executing that operation may well involve executing a whole series of simpler operations in a certain order. Similarly, computer programs are typically written in a programming language (for example LISP or FORTRAN) and each line of a program will specify a computational operation simple only relative to that language. Thus in executing an operation specified by a single line of a LISP program a computer (or one of its subsystems) might have to execute a whole series of simpler computational operations.

This way of viewing computer programs^^ does not require a program to be explicitly represented in a computer that runs it. To run a program just is to execute a computational process by executing the steps specified by the program in that order. To do this no more requires the program to be explicitly represented in the computer than baking a cake in the manner described by a recipe requires the cook to consult some explicit representation of the recipe. In other words programs describe how computers perform computational processes, rather than specify the representations that are causally implicated whenever the process is executed. To say this is not to deny that programs are ever explicitly represented in computers. On the contrary, they often are, for programming a von Neuman machine typically involves bringing about changes in its memory so

that it comes to contain representations that correspond to the lines of the program, representations that will be causally implicated in the machine's sym bol-manipulating activity whenever it runs the program or executes the process that the program is a program of. But even computers that explicitly represent some of the programs they run cannot explicitly represent all of the programs that they run on pain of infinite regress. This is because a computer can only run an explicitly represented program if it "knows-how" to respond to those

representations. If how it is to respond to such explicit

representations has to be explicitly represented, then in turn how it is to respond to that explicitly represented program will need to be explicitly represented, and so on ad infinitum. To bring an end to the regress w hat is needed is w hat D ennett (1983) calls tacit representation. Tacit representation is 'know-how' that is 'built into the system in some fashion that does not require it to be (explicitly) represented in the system' (p. 218). If a computer didn't have such know-how then it w ouldn't be able to respond to any explicitly represented programs. For a computer to have such know-how is for it to have certain computational capacities hard wired into it.

Computers are sensitive only to the formal properties of the symbols that they m anipulate, being blind to such semantic properties as meaning, content, truth value, reference, and the like. Fodor expresses the point in the following terms:

computational processes are both symbolic and formal. They are

symbolic because they are defined over representations, and they are formal because they apply to representations in virtue of

(roughly) the syntax of the representations. . . . What makes

syntactic operations a species of formal operations is that being

syntactic is a way of not being semantic. (1980, 227)

Thus the rules that computers apply to representations are formal rules. Two computers, or computational processes, will belong to the same computational type if and only if they produce the same formally individuated symbolic output from the same formally individuated symbolic input, and do it by applying the same formal rules. Two computers or computational processes might meet this requirement despite the fact that the symbols that they manipulate

are divergent in their semantic properties. But this would not stand in the way of their computational equivalence. Alternatively, two computers, or computational processes, could produce semantically identical yet formally divergent output from semantically identical yet formally divergent input. In such a case, despite the semantic equivalence, the systems or processes would belong to different computational types.

How are we to understand this term "formal"? Syntactic properties are a species of formal property, but not all formal properties are syntactic. In this context the term "formal" can be replaced with "syntactic", a substitution that aids clarity given the familiarity of the latter notion. This substitution can be made because most scientific psychologists take the brain to be a type of computer that (like a von Neuman machine) is sensitive to syntactic properties, and they therefore understand the com putational operations that underlie cognition to be syntactic operations. Thus for our purposes, computation can be understood as the process of m anipulating syntactically individuated symbols by means of the application of syntactic rules.

What does it mean to say that computers apply syntactic rules to symbols? A syntactic rule, or a representation of a syntactic rule, defines or specifies a function the arguments and values of which are syntactically individuated symbols. Suppose a syntactic rule R defines a function F. A computational system or process C generates its output from its input by applying R to its input if and only if its input-output behaviour satisfies F: in other words, if and only if it takes arguments of F as input and whenever it takes such an argument as input, it produces as output the value of F for that argument. This characterisation of what it is to apply a syntactic rule indicates that a system or process can manipulate symbols by applying such rules w ithout having either to represent them explicitly or to understand them.

It is because of the fact that computers manipulate syntactically individuated symbols by applying syntactic rules to them that they are described as syntactic e n g i n e s . B u t what about this term "syntax"? What are syntactic properties? For a symbol to have syntactic properties it must belong to a language. In this connection

the term "language" is best thought of as referring to formal languages rather than natural languages. Formal languages are defined w ithout reference to meaning or any other semantic property. A formal language consists of a set of sentences of well formed formulae (wff s for short), typically denumerably many such sentences. Defining a formal language involves specifying the basic or atomic symbols of the language (the words of the language), making clear to which syntactic category each such symbol belongs (the categories being: predicate symbol, constant, quantifier, logical connective, and so on) and a set of formation rules that determine which combination of words of the language are w ff s. Symbols can be combined in accord with the rules to create complex expressions of the language. Such sentences can similarly be combined in accord with the rules to create further, more complex, sentences. Given that one can continue combining sentences in this manner to create new ones, a formal language with only a small number of words and formation rules will comprise denumerably many distinct sentences or wffs.

Describing the syntactic structure or the syntactic properties of a complex wff (that is, one made from combining simpler wffs) is a m atter of specifying the simpler w ffs that comprise it and the m anner in which they are combined. Similarly, describing the syntactic structure of a simple wff (that is, one that cannot be analysed into simpler w ffs) involves specifying the words that are its constituents and the manner in which they are combined. Thus specifying the syntactic properties of a wff involves analysing it into simpler components and describing how those components are combined. Ultimately these components will be w ords of the language.

Thus the notion of syntax is inextricably bound up with that of a language; you don't have syntactic properties where you don't have language. Moreover, syntactic properties are inextricably bound up with semantic properties despite the frequently highlighted contrast between them. This is so for several reasons. Firstly, it is part of the essence of symbols that they are meaningful items, so something has syntactic properties only if it has semantic properties. Secondly, a property of a symbol is a syntactic property only if it has a bearing on the symbol's meaning. As Dennett puts it, 'what makes a feature

syntactic is its capacity to make a semantic difference' (1982, p. 141). Thus the colour or size of a printed word is not a syntactic feature of it. Thirdly, to assign a symbol to a syntactic category is to imply something about its meaning. For example, to say that a particular symbol is a two-placed predicate-symbol is to say that it expresses a relational property that, when instantiated, holds between two individuals. (Bermudez, 1995).

The close relationship between semantic and syntactic properties might appear to put pressure on the claim that computers are sensitive only to syntactic properties by suggesting an argument such as the following. For any computer there will be a systematic relationship between the semantic properties of the symbols that it m anipulates and the syntactic properties of those symbols; for example, semantic differences between symbols will be reflected in syntactic differences between them. Consequently, corresponding to any counterfactual-supporting syntactic generalisation true of a com puter there will be a counterfactual supporting semantic generalisation. What this entails is that how a computer processes its input will depend upon the semantic properties of that input in the

respect that: (i) ceteris paribus, to have been processed differently the

input would have to have been semantically different (for only then

would it have been syntactically different); and (ii) ceteris paribus,

had the input been semantically different it would have been processed differently (as it would have been syntactically different). All this makes it difficult to avoid the conclusion that if computers are sensitive to syntactic properties then they are just as sensitive to semantic properties.^^

One reply to this argument is that it supplies the ammunition for its own downfall by implying that computers are sensitive to semantic properties in virtue of their sensitivity to syntactic properties (and not vice versa). This dependency relationship suggests that, first and foremost, computers are syntactic engines. However, replying in this