Descriptive Complexity, Computational Tractability, and the Logical and Cognitive Foundations of Mathematics

In computational complexity theory, decision problems are divided into complexity classes based on the amount of computational resources it takes for algorithms to solve them. In theoretical computer science, it is commonly accepted that only functions for solving problems in the complexity class P, solvable by a deterministic Turing machine in polynomial time, are considered to be tractable, i.e., efficiently computable (Papadimitriou 1994; Arora and Barak 2007). In cognitive science and philosophy, this tractability result has been used (see, e.g., Frixione 2001; Gigerenzer et al. 2008) to argue that only functions in P can feasibly work as computational models of human cognitive capacities. This is called the P-cognition thesis in the literature (van Rooij 2008).

One interesting area of computational complexity theory is descriptive complexity, which connects the expressive strength of systems of logic with the computational complexity classes. One of the most important results of descriptive complexity is that while first-order systems (linearly ordered, with a least fixed-point operator) are connected with the complexity class P, richer logical systems yield complexity classes beyond what is considered tractable (Immerman 1999). In particular, given the generally accepted conjecture that the complexity class NP, the class of decision problems solvable by a non-deterministic Turing machine in polynomial time, is strictly greater than P, it follows that second-order systems of logic are considered to be computationally intractable. Hence, mirroring the above-mentioned reasoning in cognitive science, the proponents of the P-cognition thesis could argue that second-order logic is also unfit to model human cognitive capacities.

Throughout this paper, I am not targeting a particular theory proposed in cognitive science or philosophy. I acknowledge that many researchers would not advocate a drastic use of computational complexity measures as limits to computational modelling in the cognitive sciences. Furthermore, to the best of my knowledge, descriptive complexity is not discussed in the current literature in cognitive science when it comes to mathematical cognition. Nevertheless, I believe that there are three reasons why the present topic is important to tackle. First, as we will see, computational complexity measures are an important part of the discussion on the theory of computational modelling, both among cognitive scientists and philosophers. While there have been important critical assessments of how computational complexity should be applied in cognitive science (e.g., van Rooij 2008; Isaac et al. 2014; Szymanik 2016; Szymanik and Verbrugge 2018; Van Rooij et al. 2019; Pantsar 2019b; Fabry & Pantsar 2019), limitations like the above P-cognition thesis are still too often misunderstood and given excessive importance. Second, these problems can extend to applications of descriptive complexity through the connections between computational complexity measures and the complexity of logical systems. Therefore, part of the purpose of this paper is to pre-empt potential confusions regarding descriptive complexity and the computational modelling of cognitive capacities, thus providing a feasible way in which computational complexity, and descriptive complexity in particular, can be applied in cognitive science and philosophy.

Third, I believe that mathematics and mathematical problem solving provide a clearly specified field of study for discussing computational complexity measures in the context of cognitive modelling, and this extends also to descriptive complexity. The mathematical consequences of adopting different logical systems as the foundation for mathematics is a much-researched topic. However, while perhaps implicitly present, these considerations have not been explicitly linked to the potential limits of human cognitive capacities. I will show that by connecting descriptive complexity measures to considerations on the modelling of cognitive processes, we can get a fruitful platform for discussing the relationships between logical and cognitive foundations of mathematics. In particular, I will show how drawing careless connections between the two can lead us astray.

I will begin in Sect. 2 by presenting computational complexity measures and how they have been used to argue for tractability principles when it comes to cognitive modelling. In Sect. 3, I will present the field of descriptive complexity and how it is connected to the computational complexity measures and other notions of complexity. Sect. 4 then presents a fundamental tension of logical systems as the foundations of mathematics: since many important results in mathematics require second-order logic, either we must commit to computationally intractable logic as the foundation of mathematics, or else we must use a system of logic that is too weak to express crucial mathematical concepts and/or prove theorems concerning them. In Sect. 5, I will then propose a solution to this tension by analysing the way the computational complexity measures and descriptive complexity should be understood in the framework of human mathematical problem solving. I will then compare my account to two prominent alternatives in the literature, provided by van Rooij (2008) and Szymanik (2016). I will conclude that with well-considered specifications with regard to relevant input sizes and problem solving algorithms, tractability principles like the P-cognition thesis can be given their proper place as potentially useful guidelines, but not as anything resembling strict limits.

In theoretical computer science, the complexity of decision problems is a fundamental question and it is standardly studied in the theoretical framework of Turing machines (Turing 1936).¹ Turing presented his model of a universal machine in order to study computation theoretically. Shortly put, a Turing machine functions by reading and writing symbols on a tape one at a time. The Turing machine is always in some inner state and based on the input symbol and the state, the machine has instructions to read and write symbols on the tape, move the tape, and change to a new inner state (or remain in the same state). The set of these instructions is called an algorithm. Since the Turing machine is a theoretical construct, no limits are made to the size of the tape or the number of operations that the machine can carry out.

According to the generally accepted Church-Turing thesis, if there is a mechanical procedure for solving a problem, then there is a Turing machine that can solve it (Turing 1936; Church 1936). This way, Turing machine has become the standard framework for studying computational complexity, but also for studying the problem-solving potential of algorithmic, mechanical procedures in general (see, e.g., Arora and Barak 2007). Consequently, in the study of complexity in theoretical computer science, researchers are not interested in the computing capacities of particular computers. Instead, they want to study the inherent complexities of different tasks free from the limitations of physical computers. Under this approach, the complexity of a mathematical problem can be characterised by the complexity of a minimally complex Turing machine (i.e., an algorithm run by a Turing machine that takes minimal resources) that solves the problem. Such an algorithm is called optimal.

Optimality of algorithms, however, is not a straight-forward matter. One important question concerns what the resource is that the algorithm should be optimal over. There are two common answers to that: time and space. Since the Turing machine is a theoretical construct, time and space are measured as functions of the size of the input, rather than any physical measure. But since there are no physical limits to the Turing machine, there are also no limits to the size of the input. That is one reason why the complexity measures based on the Turing machines are asymptotic: they characterise the complexity of algorithms as the input sizes grow without limit.²

The great advantage of the asymptotic complexity measures is that they can be used to divide problems into complexity classes. The plethora of different complexity classes and the relationships between them is an active topic of research and it is not possible to go extensively into the details here (for more, see Papadimitriou 1994; Arora and Barak 2007).³ But taking time as the relevant measure, we can use two complexity classes to show how complexity classes work and what kind of relevance they can have for philosophy. One of the most important complexity classes is called P (or PTIME) and it is defined as the class of decision problems that can be solved by a deterministic Turing machine in polynomial time. An algorithm (i.e., a Turing machine) is said to run for polynomial time if its running time has an upper bound of a polynomial function of the size of the input for the algorithm. This means that if the size of the input is n, the running time has an upper bound of some function \(n^{k}\) for some constant k. Another complexity class relevant here is called EXP (or EXPTIME) and it is the class of decision problems that are solvable by a deterministic Turing machine in exponential time. An algorithm runs for exponential time if its running time has a lower bound of some exponential function of the size of the input, i.e., for input size of n, the running time has a lower bound of some function \(2^{p(n)}\) where p(n) is some polynomial function of n.

Here I have wanted to present P and EXP for two reasons. First, following the so-called Cobham’s thesis (or Cobham-Edmonds thesis), P is standardly accepted as the class of problems that can be feasibly solved by a computer (Cobham 1964; Edmonds 1965). Second, it has been proven that EXP is strictly greater than P. Algorithms for solving problems in P are called efficient or tractable (Garey and Johnson 1979). Algorithms for solving problems in EXP (that are not in P), on the other hand, are considered to be inefficient or intractable (ibid.). A simple example of an efficient algorithm is the standard schoolbook algorithm for multiplying integers, which takes roughly \(n^{2}\) steps of computation for two n-digit integers. The standard algorithm for factoring integers into primes, on the other hand, is a good example of an inefficient algorithm. An n-digit integer takes about \(2^{n^{1/3}}\)computational steps (Pomerance 1996).⁴

While it is proven that EXP is strictly greater than P, perhaps the most important inclusion relation between the complexity classes is still a matter of conjecture. The complexity class NP is defined as the class of decision problems that can be solved by a non-deterministic Turing machine in polynomial time.⁵ It is easy to see that P is a subclass of NP, but it has proven to be difficult to show whether it is a proper subclass of it, i.e., whether \(\mathbf {P\ne NP}.\) Although this is perhaps the most famous unsolved problem in theoretical computer science, it is a generally accepted conjecture that the class NP is strictly greater than P. This is important for the present context since because the conjecture \(\mathbf {P\ne NP}\) is generally accepted, the complexity class NP is de facto the lowest complexity class of problems which are thought to be computationally intractable.

For computer science, the distinction between tractable and intractable algorithms is central, but what is its relevance for philosophy and the cognitive sciences? It turns out that this distinction can be potentially useful for characterizing cognitive complexity, i.e., the complexity of human cognitive tasks and processes. In computational modelling in the cognitive sciences, the focus is often on what Marr (1982) calls the computational level of explanation. This means that rather than focusing on the actual algorithms used in performing a cognitive task (the algorithmic level in Marr’s terminology), or the neuronal activity (Marr’s implementational level), the focus is on identifying a mathematical function that can model a cognitive process (Marr 1982; Pantsar 2019b). This is based on the understanding of cognitive tasks as employing cognitive capacities to transfer input states (e.g., perceptions) into output states (e.g., decisions), thus yielding a function that models the cognitive task as input-output mappings (Cummins 2000). In the computational-level approach, it is then possible to study these functions in terms of their computational complexity.⁶ This computational-level framework has been highly influential for studying cognitive capacities (see, e.g., Newell 1982; Pylyshyn 1984; Horgan and Tienson 1996). As explained by Frixione:

The aim of a computational theory is to single out a function that models the cognitive phenomenon to be studied. Within the framework of a computational approach, such a function must be effectively computable. However, at the level of the computational theory, no assumption is made about the nature of the algorithms and their implementation. (Frixione 2001, 381)

This way, many researchers accept that computationally intractable functions cannot accurately model human cognitive tasks. This has become known as the tractable cognition thesis in the literature (van Rooij 2008; Isaac et al. 2014). According to the tractable cognition thesis, when we are looking for functions that can potentially model cognitive capacities, we should limit ourselves to those functions that can be computed by tractable algorithms. Standardly, based on Cobham’s thesis, this has been understood as the P-cognition thesis, stating that we should limit our considerations to functions associated with problems in the complexity class P (Arora and Barak 2007; van Rooij 2008; Van Rooij et al. 2019). Many cognitive scientists have accepted the P-cognition thesis as a fundamental rule of computational-level explanations. For example, according to Gigerenzer and colleagues:

The computations postulated by a model of cognition need to be tractable in the real world in which people live, not only in the small world of an experiment with only a few cues. This eliminates NP-hard models that lead to computational explosion (...) (Gigerenzer et al. 2008, 236; quoted in Van Rooij et al. 2019, 17).

Thus the approach of Gigerenzer and others draws a direct link between computational complexity and cognitive complexity. In the case of mathematical problem solving, this implies that mathematical cognitive processes should be computationally modelled by functions whose values can be computed by algorithms for solving decision problems in the complexity class P.⁷

With Regina E. Fabry, we have argued that such a direct connection between computational and cognitive complexity is potentially problematic since the computational complexity approach does not transfer in a straight-forward manner into studying the complexity of human cognitive processes (Pantsar 2019b; Fabry & Pantsar 2019). One of the reasons for this is that human mathematical problem solving often includes visual and heuristic reasoning (e.g., the use of diagrams and spatial manipulation of symbols (Fabry & Pantsar 2019)) that is not usually included in the algorithmic approach to problem solving used in theoretical computer science (Pantsar 2019b). Thus it is important to realize that human mathematical problem solving has its own particular characteristics that do not always correspond to the computational complexity approach. In this paper, I want to extend the critical evaluation of applying results from computational complexity theory to cognitive modelling to one important subfield of computational complexity, which has not been extensively studied in the philosophical or cognitive scientific literature so far. This subfield is the complexity of systems of logic.

The computational complexity measures such as P, NP and EXP are standardly used for classifying decision problems, but they have also turned out to have important connections with languages of logic. This field of study is called descriptive complexity (Immerman 1999). Typically in computational complexity theory, we ask about the complexity of the task of checking if a certain input has a certain property. In descriptive complexity, we ask how complex it is to express that property in a formal language (Immerman 1995). This way, rather than measuring complexity in terms of time or space requirements for solving decision problems, as in standard computational complexity theory, descriptive complexity measures complexity in terms of the strength of logical systems. As Immerman argues, time and space are natural complexity measures from an engineering standpoint since they quantify physical resources required of completing a computational task, but neither appears to capture the inherent mathematical complexity of computational problems (Immerman 1999, 1). Thus, when we consider the cognitive complexity of mathematical problem solving processes, descriptive complexity initially appears to have great relevance. The logical systems we use have different strengths in expressing and proving mathematical statements, and it is a priori feasible that processing different logical languages also differs in terms of the complexity of the associated cognitive processes.⁸

What makes descriptive complexity particularly interesting to the present discussion is the way it has proven to be directly connected to results concerning computational complexity classes. The research field of descriptive complexity can be traced back to Fagin’s (1974) result that the complexity class NP is equal to the class of problems describable in the existential fragment of second-order logic (also known as \(\Sigma _{1}^{1}\)-logic).⁹ Many other such connections have since been proven. Full second-order logic, for example, has been shown to yield the complexity class PH, which is the union of all complexity classes in the polynomial hierarchy (Stockmeyer 1977). PH of course contains NP but it is also thought to contain many stronger complexity classes, making full second-order logic more complex than its existential fragment.¹⁰

Here it is not possible to go into all the intricacies of the connections between particular cases, but it is essential to see the general connection between the expressive strength of logical systems and the computational complexity of classes of decision problems. It is particularly important to note that there is a crucial difference between first-order logical systems and second-order systems in terms of their complexity. While existential second-order logic yields the complexity class NP, classical first-order logical systems give us the complexity class P, or one of its subclasses.¹¹ Therefore, only systems of first-order (classical) logic are connected with complexity classes that are considered to be computationally tractable. Even if it turned out to be the case that P = NP, this would still be a problem. It is known that second-order logic with a least fixed point operator yields the complexity class EXP, which we know to be strictly greater than P (Immerman 1999).¹²

Second, the connection between second-order logical systems and intractable computational complexity classes tells us that many important mathematical results can only be expressed with systems of logic that are computationally intractable. For example, the (upward) Löwenheim-Skolem theorem famously shows that first-order theories cannot distinguish between the cardinality of infinite models. If a first-order theory has an infinite model of one transfinite cardinality, it has a model of every transfinite cardinality (Hodges 1993). This would be a huge problem in mathematics because, among other problematic aspects, it means that in first-order theories it is not possible to distinguish between the cardinality of natural numbers and the cardinality of real numbers. There are numerous known differences between first-order and second-order logical theories and, by and large, the latter are seen as an important part of the toolbox of logicians and mathematicians (Väänänen 2019). Thus if, based on the P-cognition thesis, we limit the complexity of acceptable logical languages, it would go against the prevalent use of logical systems in mathematics.

However, before we go deeper into the potential problems in applying descriptive complexity measures for the foundations of mathematics, we need to be more detailed about descriptive complexity and its applications. In particular, we should first consider what kind of systems and problems descriptive complexity concerns. As Grohe (1999) puts it, one main theme of descriptive complexity is to study model-checking problems, i.e., problems of the type:

Given a finite structure \(\varvec{A}\) and a sentence \(\phi\) of some logic \({\mathscr {L}}\), decide whether \(\varvec{A}\) satisfies \(\phi\). (Grohe 1999, 14).

It is important to note that model-checking complexity is just one notion of complexity used in the literature, and there are substantial differences between different notions of complexity. One important notion of complexity, for example, is expression complexity which measures the complexity of a formula in terms of the length of its expression in different models (Vardi 1982). As Szymanik (2016, 104) remarks, expression complexity and model-checking complexity have a potentially important difference: the existential fragment of second-order logic (\(\Sigma _{1}^{1}\)-logic) is of the expression complexity NEXPTIME while its model-checking complexity is NP-complete. This prompts the question why we should focus on model-checking complexity when another notion of complexity like expression complexity (or perhaps a combination of two or more notions of complexity) could be more relevant? This is particularly important when we consider differences in cognitive tasks and the relevant complexity measures for them. For example, Szymanik (ibid.) suggests that while model-checking complexity is suitable for measuring difficulty of the cognitive task of sentence verification, other notions of complexity could be used for measuring the cognitive difficulty of other processes, such as reasoning.¹³

Since the particular area of cognitive processes this paper focuses on concerns mathematical problem solving, we need to assess whether model-checking complexity is indeed a suitable complexity measure for mathematical cognition. The first potential problem is that mathematics (for the most part) is concerned with infinite structures whereas descriptive complexity deals with finite structures, which have a finite universe. This way, descriptive complexity can be treated as a subfield of finite model theory (Szymanik 2016, 103-104), which is hardly representative of mathematics in general. However, we are not here interested in the general question whether a mathematical model \(\varvec{A}\) satisfies a sentence \(\phi\) of a logic \({\mathscr {L}}\). As mentioned in the beginning of this section, we are ultimately interested in cognitive complexity, i.e., the complexity of a computational model of some cognitive entity. Perhaps it is illuminating to think of such computational models as systems of artificial intelligence (AI). If an AI models a human cognitive process, we can use the algorithmic complexity of the AI to characterise the cognitive complexity of the human process. Since all human cognitive processes are obviously finite, we can limit our considerations to finite computational models. This way, the fact that descriptive complexity is restricted to finite structures is not a problem for the present topic.¹⁴

However, the above remark of Szymanik suggests another potential problem. Since the field of study in the present context is mathematical problem solving, the complexity measure we use should be suitable for mathematical cognitive processes. However, at first look sentence verification does not appear to be typically the kind of cognitive task mathematicians are involved with. Instead of verifying sentences in models, mathematicians try to construct proofs. Would another complexity measure, perhaps one based on satisfiability, be more suitable for the cognitive task of proving theorems?¹⁵ This is a legitimate question to ask and I do not want to claim that descriptive complexity is the most suitable measure generally for mathematical cognitive tasks. Indeed, since I will ultimately argue that results connecting descriptive complexity and computational complexity measures should not be used as any kind of general principles when discussing the computational modelling of cognitive capacities, my purpose is not to defend generally the use of descriptive complexity in characterising mathematical (or indeed other) cognitive tasks.¹⁶

Nevertheless, I can envision how descriptive complexity could be seen to be relevant for the question of complexity of cognitive processes involved in mathematical tasks. Taking a (proposed) computational model M of a mathematical cognitive capacity, we can reasonably ask whether for M some mathematical statement \(\phi\) is part of the output. Furthermore, it is reasonable to ask which logical languages do statements like \(\phi\) in a particular model belong to. Thinking of the computational model again as an artificial intelligence, we can ask whether it models human cognitive capacities with regard to a mathematical task by asking whether it provides as output the same mathematical statements as a human mathematician (given the same input). This way, it is possible to understand the question of computational modelling of human mathematical capacities in the context of model-checking problems, even if we do not believe that human mathematical problems standardly are model-checking.

I want to emphasise that for various reasons, which will become apparent in the rest of this paper, I do not advocate the above line of reasoning when it comes to computational modelling. Generally speaking, the connections and differences between different complexity measures and different notions of complexity are a much more complicated topic than there is space to describe here, and I do not want to suggest that the simplistic way above of connecting model-checking problems to mathematical cognitive tasks is valid. Nevertheless, I believe that it carries enough force to require a closer analysis of the connection between descriptive complexity and computational complexity, particularly in the context of computational modelling of cognitive capacities involved in mathematical tasks. Furthermore, as we will see, such a closer analysis is fruitful in explicating the weaknesses of applying descriptive complexity measures in the context of mathematical cognition. Let us therefore continue, for now, with the assumption that descriptive complexity measures can be used in characterizing mathematical cognitive tasks.

If we accept the applicability of descriptive complexity in modelling mathematical cognition as described in the previous section, results from the study of descriptive complexity appear to put us in an uncomfortable position. When subscribing to a strong form of tractable cognition restriction, such as the P-cognition thesis, the conclusion seems to be inevitable: at the very least, second-order logic with a least fixed point operator (since it yields the complexity class EXP) is intractable. As such, according to the P-cognition thesis, it could not feasibly work as the logic of computational models of human cognitive phenomena. The consensus view implies that the same is true of existential second-order logic, since it is connected with the complexity class NP. Only systems of first-order logic yield the complexity class P or one of its subclasses and thus can be considered to be computationally tractable. But first-order systems are too weak mathematically to express important properties, such as the least upper bound property for sets of real numbers or, as in our example earlier, the difference between the cardinalities of sets of natural numbers and real numbers.

Therefore we arrive at a fundamental tension. When we consider the connection between mathematics and logic since at least Frege (1884) and Russell (1903), logic has been thought to provide two types of foundations for mathematics. First of these is expressing mathematical concepts in a system of formal logic. The second foundation comes from logic being formalisation of mathematical thought. In Frege’s (1884) approach, formal logic was meant to provide universally acceptable rules of human thought (free from what he considered to be the arbitrariness of psychologism) that would justify mathematical principles.

However, the complexity considerations above seem to imply that these two foundational roles are in conflict. In order to express many important mathematical concepts, we need to have a system of logic stronger than classical first-order logic.¹⁷ But this connects the required system of logic to a computational complexity class that is considered to be intractable. If we accept the P-cognition thesis, this means that the system of logic used in mathematics can express functions that are too complex to feasibly model human mathematical cognitive capacities. Thus the problem that follows from incorporating the P-cognition thesis, descriptive complexity, and logical foundations of mathematics is as follows: either our logical system is too weak to express important mathematical concepts, or else it is too complex cognitively. This suggests that we need to give up one of the criteria of Frege and Russell. Yet giving up either one is an unappealing prospect. Certainly we do not want to give up important mathematical results because the logical systems used in proving them are considered to be computationally intractable. But from the epistemological point of view, it would seem to be equally problematic to have mathematics built on a system of logic that is considered to be prohibitively complex cognitively.

This prohibitive cognitive complexity can be understood in two different ways. First, we can analyse the strength of a system of logic in terms of its descriptive complexity and connect this to computational modelling of cognitive capacities, as I have been describing above. In this approach, it can be argued that second-order logic cannot be the cognitive basis of mathematics because in it we can express functions that are computationally intractable, which are thought to be unfit to work as computational models. This follows from a direct application of the P-cognition thesis.

The second way to understand the intractability of second-order logic is based on its ability to express and/or decide the truth-value of logical formulas that are too complex for human cognizers to process. This is based on the simple observation that the cognitive process of deciding the truth-value of a logical formula cannot, by definition, be less complex than an optimal algorithm for determining the truth-value run by a Turing machine.¹⁸ As explained in Sect. 2, the computational complexity of decision problems, and hence also the corresponding logical formulas, is defined through optimal algorithms for solving them. There are many ways in which the human problem solving algorithms can be computationally suboptimal (more on this in the next section), but they cannot outperform optimal algorithms.

For these reasons, it may appear that we cannot escape the problem that the logic we need for expressing familiar mathematical concepts can be, following the P-cognition thesis, prohibitively complex for the modelling of human cognitive capacities. As explained above, this can be understood in two ways, both of which end up with the same problem for the proponents of the P-cognition thesis.¹⁹

How can we solve the problem presented at the end of the previous section? How can computationally intractable systems of logic work as foundations of mathematics if they are considered to be unfit for modelling human cognitive capacities? In this section I will argue that this is in fact a pseudo-problem that is the result of unwarranted application of results from the study of computational complexity in the domain of computational modelling of cognitive processes. In particular, I question the general use of tractable cognition theses like the P-cognition thesis.

To see why, we need to make a few important clarifications. The first thing to remember is that not all problems in complexity classes like EXP (or NP) are intractable. Rather, it is the class of EXP-complete (or NP-complete) problems that are considered to be intractable, i.e., those problems that are in EXP (or NP) but not in P. In the case of NP, it is of course possible that this class is empty. But even if that were not the case, the question is not about all the problems of NP; it is about the so-called NP-hard problems, referring to those problems that are at least as hard to solve as the hardest problems in NP.

However, even with this clarification, there seems to be something weird going on. Recall how P and EXP provided us with reference points for tractable and intractable algorithms, respectively, the difference being that algorithms in P have upper bounds for running time that are polynomial functions of the input while algorithms in EXP are lower-bounded by exponential functions. But these may seem like rather useless criteria. We can have, for example, an algorithm that runs for \(n^{999999999999999999}\) computational steps, which is sure to take longer than the \(2^{n^{1/3}}\)computational steps for prime factoring for any humanly feasible input size n. According to Cobham’s thesis, however, the former algorithm is tractable while the latter is intractable. As Aaronson (2012) points out, this characterisation of tractability is accepted in computer science mainly because empirical evidence shows us that it works most of the time. In practice, polynomial time and exponential time have proven be good characterizations for what is considered to be computationally tractable and intractable, respectively.

However, the practice of computer science is potentially very different from the study of tractable cognition. Because of this, we must first put tractable cognition theses in their proper place philosophically. There are two ways in which this should be done. The first of these concerns input sizes. We must acknowledge that the complexity classes and the resulting theses of tractability are practical guidelines in theoretical computer science. In particular cases, however, problems in P can for inputs up to a certain size be more complex to solve than EXP-complete problems. This can continue for a long time as the input sizes grow, but ultimately it will change. It is crucial to remember that the computational complexity measures are asymptotic and measure the complexity of the problem solving task as the input sizes approach infinity. In computer science, the asymptotic complexity measures work in practice, even though problem solving with physical computers is obviously always limited to finite inputs. But the kind of finite inputs computers deal with can be very different from inputs that human problem solvers (without the help of computers) can process. To give just one example, the Goldbach conjecture has been verified for integers up to \(4\cdot 10^{18}\) (Oliveira e Silva et al. 2014).²⁰ If a printout of an integer has 4,000 digits per page, the largest integer alone would take a thousand trillion pages to print. It is obvious that simply reading the integer that is tested would take a prohibitive time for a human problem solver, and thus input sizes of that magnitude cannot be relevant when we consider the complexity of the cognitive processes involved in the treatment of the Goldbach conjecture.

Based on such differences in relevant input sizes, we must be careful about the conclusions we draw based on the computational complexity measures. Even when limiting the consideration to finite inputs, the fact that the asymptotic measures work as a good practical guideline in computer science does not necessarily imply that they provide similar guidelines for studying the complexity of human cognition. When it comes to human mathematical cognition, the relevant input sizes can be very different from what is feasible for computers. As a consequence of this, for humanly relevant input sizes an NP-complete or EXP-complete problem can still be solvable. With this sensitivity to bounded inputs, there is nothing to suggest that a function in NP or EXP could not model the human cognitive process, which goes directly against the P-cognition thesis.

This insight transfers directly to descriptive complexity. It is clear that there are limits to the kind of solutions to mathematical problems that human problem solvers can feasibly provide. If the length of a second-order sentence is beyond the range of what humans can feasibly process, there is no need to consider it relevant for the question what the strength of logical systems can be in order for them to be processable by human cognitive capacities. Based on this reasoning, if the inputs are bounded to humanly relevant sizes, second-order systems could very well be computationally tractable.²¹

Aside from considerations on input sizes, another problematic aspect in applying computational complexity measures to characterise cognitive processes is the fact that the complexity classes are based on optimal algorithms for solving problems. As I have argued in (Pantsar 2019b), in mathematics human problem solvers use many heuristic and didactic methods (e.g., diagrams) that involve suboptimal problem solving algorithms. In (Fabry & Pantsar 2019), we argue that mathematical problem solving is a culturally shaped ability that is tightly connected to spatial manipulation of symbols and other ways of engaging with cognitive tools in the problem solving process. Thus the computationally optimal algorithms that are standardly studied in the research of computational complexity can be a bad fit with modelling human problem solving algorithms.²²

Thus, both in terms of the problem solving algorithms and the input sizes, the standard computational complexity measures fail to be sensitive to important characteristics of cognitive processes as carried out by human agents. When considering the descriptive complexity of systems of logic, the same conclusions apply. General principles, like the P-cognition thesis, that draw directly from the computational complexity measures are simply too coarse to be applied as reliable theses in cognitive modelling. This does not imply, however, that no form of a tractable cognition principle could be used. I am even ready to agree with Szymanik and Verbrugge (2018) that the P-cognition thesis can provide “a fruitful lens for assessing cognitive tasks”. In general, I share their approach that computational complexity measures can provide important tools for the cognitive sciences. Specifically to mathematical problem solving, it is clear that due to physiological limitations there exists a class of problems too complex for human beings to solve as the inputs grow large enough.²³ This class could feasibly intersect in a significant way with the class of EXP-complete or NP-complete problems. I do not want to claim that the computational complexity measures cannot provide useful information, either generally in computational modelling or specifically in the case of mathematical problem solving. I only argue that they cannot be connected to limits in the modelling of cognitive tasks as directly as is done in the case of P-cognition thesis.

In this way, the problem is not only that the P-cognition thesis appears to be too strict. More generally, limits in computational modelling based directly on computational complexity classes should be treated at best as guidelines. The reason for this is that both in terms of input sizes and the applied algorithms, human problem solving processes can greatly differ from those studied in computer science, for which the complexity classes and the Cobham-Edmonds tractability thesis are meant to apply. In addition to the humanly optimal algorithms as studied in (Pantsar 2019b) and (Fabry & Pantsar 2019), this should prompt us to consider bounded inputs, limited to humanly relevant input sizes.²⁴

One such alternative to the P-cognition thesis has been suggested by van Rooij (2008; see Van Rooij et al. 2019 for more details), who argues that by introducing suitable parameters to the computational problem, also super-polynomial time computation can be feasible in modelling cognitive tasks. Van Rooij calls this the “FPT-cognition thesis”, for fixed-parameter tractable. The introduction of such parameters is studied in computational complexity theory (see, e.g., Downey and Fellows 1999) and it can provide more fine-grained complexity considerations to apply to studying the complexity of cognitive tasks. The parameters van Rooij and colleagues discuss are different from limits to input sizes. One example of an NP-hard problem that can be made tractable by fixed parameters is the minimum vertex cover problem in graph theory (Van Rooij et al. 2019, 109). A vertex cover of a graph refers to a vertex set that includes (at least) one endpoint of each edge of the graph. If we limit the size of the vertex cover, we get a parametrised version of the minimum vertex cover problem. Many other parameters can also be used, like the maximum degree of a vertex, etc. Note that this is different from imposing limits to input sizes (which in this case would mean a limit to the size of graphs) since the parameter adds another variable, in addition to the input size, into the analysis (Van Rooij et al. 2019, 202). Thus the difference is that parametrisation determines how the complexity of a function is generally characterised (ibid.), whereas limiting the input sizes simply ignores the complexity of a function after some point in the growth of the input sizes. Of course limiting the input size makes intractable functions fixed-parameter tractable (for some value of the parameter), so the two approaches (FPT-cognition thesis and bounded input sizes) are compatible.²⁵

Another alternative for the P-cognition thesis is proposed by Szymanik (2016), who argues that the relevant complexity class as a limit for cognitive modelling is, at least in some cases, in fact NP.²⁶ Based on Fagin’s theorem and Ristad’s (1993) contention that (human) language computations are NP-complete, he formulates the so-called Ristad’s thesis, according to which everyday language is semantically bounded by properties expressible in existential second-order logic (Mostowski and Szymanik 2012).²⁷ Szymanik then argues that while such languages contain computationally intractable expressions for human agents, there can be indirect mechanisms that make them tractable (in the sense of model checking), meaning that that they can be verified through inferential dependencies with other sentences (Szymanik 2016, 14-16). While Szymanik is concerned with natural languages, Hintikka (1996) has famously argued that his independence friendly (IF) logic can provide a foundation for mathematics. Since IF logic has the same strength as existential second-order logic (Väänänen 2007), combining the views of Szymanik and Hintikka would entail that the foundations of mathematics could be tractable. Hintikka’s contention, however, is not generally accepted and it is unclear whether Szymanik’s view extends to mathematical inferences. As Szymanik himself notes (2016, 14), there are mathematical expressions like “there exist at most countably many” that are not definable by existential second-order formulas, and would thus require a logical language yielding a computational complexity class greater than NP.

One of the reasons why Szymanik (2016) eases the relevant complexity class for tractability considerations from P to NP is that NP-complete problems that are intractable to solve can have solutions whose correctness can be tractably checked. This is important to remember and it is closely connected to the present considerations on what kind of processes mathematical cognition consists of. In Szymanik’s (2016, 10) formulation, we can intuitively think of NP-hard problems as ones that do not have “snappy” algorithmic solutions. Problems become prohibitively complex because the only method for solving them is through brute force by going through all possible combinations. But quite clearly this is not how mathematical problems are solved by human agents, aside from particular cases involving small domains. This is consistent with the way I have argued the P-cognition thesis to be misguided: limits on relevant input sizes and considerations on human problem solving algorithms require thinking of mathematical problem solving in a different context, one in which computational complexity classes are not applied directly. As I see it, this is in line with Szymanik’s argumentation concerning everyday languages, and there is no reason to believe logical languages connected with complexity classes greater than P to be intractable in the relevant human sense. However, while Szymanik argues that the complexity class NP provides a relevant limit for language processing in everyday languages, for mathematical languages I do not see any a priori reason to use even NP as a limit of complexity. It is only after considering the relevant human problem solving algorithms and input sizes (or fixed parameters) that we should consider the computational complexity classes, and the descriptive complexity of logical languages.²⁸

Finally, it should be noted that the present approach is very much in line with how computational-level theories are actually used in cognitive science. Generally speaking, theories are not rejected even though they are known to be intractable (usually NP-hard, but possibly also more complex). Van Rooij and colleagues (2019, 170) list numerous such theories in the literature, ranging from grammar processing (Ristad 1993; Berwick et al. 1987) to bottom-up visual matching (Tsotsos 1990). In the practice of cognitive science, tractability considerations are often ignored, which goes against principles like the P-cognition thesis and the theoretical discussions on them. Based on the considerations in this paper, however, there is not necessarily anything problematic in that. This does not mean that computational complexity is not relevant in the computational modelling of cognitive capacities. As detailed in Van Rooij et al (2019, Chapter 8), computational intractability can be put into use in many ways in revising computational models of cognitive processes. Along similar lines, instead of dismissing the importance of complexity considerations, I have argued in this paper for carefulness and context-sensitivity in making connections between computational and cognitive complexity.

Considering descriptive complexity, the upshot of the considerations in the previous section is clear. If the tractable cognition theses based on computational complexity are translated into the framework of descriptive complexity, it would mean that second-order logic would be (at least partly) unfit as a logical system used in modelling mathematical cognitive capacities, or as a logical system thought to be processable by our cognitive capacities. This would be definitely so if it includes a least fixed point operator (since it yields the complexity class EXP), and likely so already in the case of existential second-order logic (which yields the complexity class NP). This is a troubling prospect, since it would imply that we use in mathematics a system of logic that cannot feasibly work as the basis for the cognitive modelling of mathematical thinking.

Fortunately the matter changes fundamentally if we limit our considerations to humanly relevant input sizes and humanly relevant problem solving algorithms. There are many good reasons for doing that, but one very basic reason is already that processing input always takes time and the longer a logical formula, the more time it takes to process it as input. Here I do not want to suggest any particular limits, because with cognitive tools humans can increase the speed of processing input. Perhaps an even more relevant limit is what human beings can process in their working memory, which is another aspect of cognition that can be enhanced with cognitive tools. Nevertheless, there is always some limit after which the input simply takes too much time to process or the memory load becomes too large. In the simplest case of reading a logical formula, for example, it makes no sense to consider cases where it would necessarily take hours, days, or even years to complete the process of feeding in the input. In computer-assisted problem solving the limits are different, but for every process some such limit to the input size can be established.²⁹

Based on such considerations, I have argued that any philosophical treatment of tractable cognition needs to include considerations on aspects of human mathematical problem solving that the computational complexity measures are not sensitive to. In doing this, I am not claiming that the P-cognition thesis, or another complexity principle, could not be along the right lines for an important amount of cases. It could indeed be a useful guideline to focus on functions computed by P-hard algorithms as models of human cognitive capacities. But this must not be considered to be a strict criterion.

This is not to say that they cannot be used fruitfully at all. The complexity of different logical systems can be relevant for a wide field of issues both in cognitive science and philosophy. In the foundations of mathematics, the descriptive complexity measures can also be relevant. An argument could be made, for example, that the underlying logic of mathematics should not be any more complex computationally than necessary. As a general guideline, it is feasible that the less complex a logical language is computationally, the less complex it is also cognitively. But already at that point we must be very careful to consider complexity in a way that is relevant to the actual cognitive processes of human agents: the kind of algorithms they use and the types of inputs they process. Only then can we assess accurately the question of cognitive complexity of logical languages. In principle, however, there is no reason to believe that, with appropriate restrictions, second-order logical systems could not provide a feasible basis for the computational modelling of mathematical cognition. Consequently, neither is there any reason to believe that we could not have a common logic for the foundations of mathematics and the modelling of mathematical cognition.