Behavioral Mereology: A Modal Logic for Passing Constraints
Abstract
Mereology is the study of parts and the relationships that hold between them. We introduce a behavioral approach to mereology, in which systems and their parts are known only by the types of behavior they can exhibit. Our discussion is formally topos-theoretic, and agnostic to the topos, providing maximal generality; however, by using only its internal logic we can hide the details and readers may assume a completely elementary set-theoretic discussion. We consider the relationship between various parts of a whole in terms of how behavioral constraints are passed between them, and give an inter-modal logic that generalizes the usual alethic modalities in the setting of symmetric accessibility.
1 Introduction
Many thinkers, from Heidegger to Isham and Döring have asked “What is a thing?” [Hei68, DI10]. Heidegger for example says,
From the range of the basic questions of metaphysics we shall here ask this one question: What is a thing? The question is quite old. What remains ever new about it is merely that it must be asked again and again.
In this article, our way of asking about things is focused on the mereological aspect of things, i.e. the relationship between parts and wholes. The point of departure is that, at the very least, a part affects a whole: “when you pull on a part, the rest comes with.” For example, wherever my left hand is, my right hand is not far away. A whole then, has the property that it coordinates constraints—or said another way, it enables constraints to be passed—between parts. In this article, we present a logic for constraint passing.
Our approach has roots in categorical logic, and in particular Lawvere’s observation that existential and universal quantification can be characterized as adjoints to pullback, in any topos. In particular, a system, or more evocatively a behavior type , will be a set which we imagine as the set of ways a system can behave over time. If is a dynamical system, then we may think of as the set of lawful trajectories of this system.111More generally, we may take a behavior type to be an object in any topos [MM92]. This allows behavior types which where the behaviors may vary in time (or space). Since we don’t expect our audience to know any topos theory, and since all the ideas we describe will make sense in any topos, we will use the language of sets through this paper, and leave experts to make the topos-theoretic translation themselves. We are inspired here by Willem’s behavioral approach to control theory (see \citesWillems:1998aWillems:2007aWillems.Polderman:2013a).
We work behavior-theoretically; to paraphrase Gump, “X is as X does”. We associate to a system its set of possible behaviors — is the behavior type of . If is a part of our system , then if we know the total behavior of we also know the behavior of ; so, we have a function which we think of as “restricting” the behavior of to the behavior of . However, we are considering as a part of , so every behavior of must come from some behavior of the whole system ; so, the restriction map must be surjective.
We use this analysis of parthood to define a part of the system to be a quotient of , i.e. a surjection from the behavior type. This surjection describes how a behavior of the entire system determines a behavior of the part, and any behavior of the part qua part must extend to the behavior of the whole: for any behavior of my hand, there exists at least one compatible behavior of my whole body. Given a behavior on one part, we can consider all possible extensions to the whole, and subsequently ask how those extensions restrict to behaviors of other parts. In this way one part may constrain another.
To describe the logic of these constraints, we introduce two new logical operators, or “inter-modalities”, closely related to the classical “it is possible that” and “it is necessary that” modalities, known as the alethic modalities [Kri63]. We view a constraint on a part as a predicate on the behaviors of — the predicate “satisfies the constraint ”. We may ask whether satisfying this predicate allows, or whether it ensures, various behaviors on another part : the constraint is passed in these two ways from to . To be a bit more explicit, the first new operator is called allows, written . This describes the set of behaviors in for which there exists an extension in that restricts to some behavior satisfying . The second is the operator ensures, written . This describes the set of behaviors in for which all extensions to restrict to some behavior satisfying in . Our goal in this paper is to describe the properties of these inter-modalities (“inter” because they go from one part to another), and to demonstrate their utility with some elementary examples.
Our inter-modalities allow us to faithfully express concepts of the behavioral approach to control theory as expressed in Willems’ Open Dynamical Systems and their Control [Wil98]. In particular:
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A time-invariant system is controllable if and only if for any two behaviors and , there is a time delay such that the behavior restricted to time before and restricted to time after are compatible in the sense of Definition 4.
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If is a behavior of a part of and a behavior of part of , then is observable from if and only if determines in the sense of Definition 9.
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If is the constraint a controller places on the behavior of a plant , then the controlled behavior is the constraint of behaviors of which are allowed by in the sense of Definition 14.
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The control problem is the problem of choosing a constraint on so that a constraint of the plant is satisfied. The universal solution to this problem is given by the constraint of behaviors on which ensure that behaves according to , in the sense of Definition 14.
We believe the logic for constraint passing presented in this paper can be a useful tool for formalizing arguments in the behavioral approach to control theory.
2 Systems and Their Parts: Behavioral perspective
When we constrain a part of a system, we are constraining what it does. This suggests that we should model a system by its type of possible behaviors, its behavior type.
2.1 Systems as behavior types
Luckily, we won’t need to settle on what precisely a behavior type is, so long as we can reason about it logically. For this, we need the behavior type of our system and its parts to be objects in a topos; then, we can use the internal logic of the topos to reason about our behavior types. A topos can be understood as a system of variable sets; in our case, this allows us the freedom to have sets varying in time, in space, or according to different observers. We will just work in the topos of sets and functions, leaving it to the experts to extend the theory to arbitrary toposes.
So, a behavior type is simply a set whose elements are regarded as possible behaviors of a system. We can think of it as the “phase space” of our system, in a general sense. This terminology is inspired by Willem’s behavioral approach to control theory [WP13], which describes a dynamical system as a subset of lawful trajectories parameterized by time in some value space . The set is the behavior type of this system, where a behavior of the system is simply a lawful trajectory. Many of our examples follow this general form.
Example 1.
We will present a few running examples of systems considered in terms of their behavior types. Let’s introduce them now.
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Consider a bicycle. The bicycle pedals might be moving at some speed , and the bicycle wheels might be moving at some speed , both real numbers. If the pedal is pushing at a certain speed, then the wheels are moving at a least a constant multiple of that speed. Therefore, we will take the behavior type of our bicycle to be
for some fixed ratio .
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Consider a glass of water placed in a room of temperature . The glass of water has temperature for every time . By Newton’s principle, the temperature of the water satisfies the following simple recurrence relation:
Therefore, the behavior type of this glass of water is
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Consider an ecosystem consisting of foxes and rabbits. At any given time , there are foxes and rabbits, where we mask our uncertainty about the precise population by allowing these to be arbitrary real values, rather than integer values. The population of the species at time is determined by its population at time , according to the relation given by the following standard recurrences:
This is known as the Lotka–Volterra predator–prey model, but we could use any model. Here is the death rate of the foxes, is the birth rate of rabbits, and and are rates at which the consumption of rabbits by foxes affect their respective populations. From here, we take the behavior type of this ecosystem to be
2.2 Parts as quotients of behavior type
If we know a whole system (say, my body) is behaving like , then we also know how any part of (say, my hand) is behaving: we just look at what is doing while does . In other words, there should be a restriction function, which we denote , from the behavior type of the whole system to the behavior type of the part.
Moreover, every behavior of a part will arise from some behavior of the whole system: how could a part of the system do something if the system as a whole had no behaviors in which was doing that thing? Remember, we are considering the part as a part of the system , not on its own: my hand, not a severed hand. If we sever from the system , it may be able to behave in ways that have no extension to . But as a part of the system , every behavior of the must be restricted from a behavior of . We will give examples below, but first a definition.
Definition 2.
The behavior type of a part of a system is a surjection out of which we call the “restriction from to ”. We define the category of parts of to have as objects the parts of and as morphisms the commuting triangles
Note that if such a map exists, then it will be unique and a surjection. If there is such a map, we write and say that is a part of . This gives a preorder on parts, which we refer to as the lattice of parts of .222We will see in Section 2.4 that it is indeed a lattice.
For example, suppose that a system is divided into a plant and a controller . For example, might be a set of real variables satisfying a dynamical law , the first of which concern the plant and the last of which concern the controller . Then would be of real variables for which there is some extension of variables (the behavior of the controller) which is valid according to the dynamical law. The projection map gives a surjection from , witnessing that the plant is a part of the whole system.
In practice, we may have certain parts of in mind, and so we may consider a sublattice of that defined in Definition 2.
Remark 1.
Definition 2 may look a little backwards. Usually a “part” is a subset; here we have defined a part to be a quotient. What we have defined to be a part is often called a partition (of ).
What is happening here is a well-known “space/function” duality: we are not considering the system as some sort of object in space, but rather its type of behaviors . Often, the behaviors of a system may be realized as functions on some sort of space ; this gives us a contravariance in , which we see in the definition of part is part of if there is a map “going the other way,” .
Example 3.
Here are some examples of parts.
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The two parts of the bicycle under consideration are the pedal and the wheel. Explicitly, the behavior types of these parts of the bicycle are the types of all possible behaviors which arise as some behavior of the whole bicycle:
In this case, every real number is a possible speed of the pedal, and every real number a possible speed of the wheel.
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In the system of the cup of water sitting in the room, there is just one thing we are considering the behavior of: the cup. But, we can see this behavior at many different times. For every time , we get a part t of the cup at time with behaviors
In fact, for any set of times, we get the behavior type of the cup during :
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The ecosystem consisting of foxes and rabbits is more complicated than the cup, but the principle is the same. We can consider the system at different times, and restrict our attention to just foxes or rabbits as we please. In particular, we let t be the system of foxes at time , and t be the system of rabbits at time . These have behavior types
A surjection out of a set may equally be presented by its kernel pair, the equivalence relation on where iff . We may call this relation observational equivalence; the behaviors with are observationally equivalent relative to . This is clearest when thinking of as some measuring device in a larger system; two behaviors of the whole system are observationally equivalent relative to our measuring device when it measures them to be the same. Two behaviors of my body are hand-equivalent if they are indistinguishable by looking at my hand; and two times are clock-equivalent when they read the same on the clock face.
There is essentially (i.e. up to isomorphism) no distinciton between a quotient of and an equivalence relation on . Thus we are defining parts of to be equivalence relations on -behaviors. This seems to be a novel approach to mereology, though we cannot claim to know the literature well enough to be sure.
2.3 Compatibility
Now we turn our attention to how behaviors of various parts of the system relate to one another. The most basic relation between behaviors of two parts is that of being simultaneously realizable by a behavior of the whole system. We call this relation compatibility.
Definition 4.
If and are parts of , then we say behaviors and are compatible, denoted , if there is a behavior of the whole system that restricts to both and , i.e.
Generally, if is some family of behaviors indexed by a set , then this family is said to be compatible if there is an such that for all .
In other words, two behaviors (one on each of two parts) are compatible if there is a behavior of the whole system that restricts to both of them.
Example 5.
Examples of compatible behaviors are easily obtained by restricting a single system behavior to two parts.
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In the bicycle example, we see that a speed of the pedal is compatible with a speed of the wheel if and only if :
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In the cup of water example, a temperature at time is compatible with a temperature at a later time are compatible if and only if follows from via the recurrence relation. In particular, if , then
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In the ecosystem example, we have a number a different comparisons to choose from. A fox population at time is compatible with at time if and only if there is simultaneous rabbit population so that .
Two simultaneous fox and rabbit populations are compatible if and only if there is some history of the ecosystem which achieves those population at that time. In particular, any two populations of foxes and rabbits at time are compatible.
2.4 Compatibility and the lattice of parts
We can express some of the parts-lattice operations in terms of the compatibility relation.
Proposition 6.
The meet of parts and of has behavior type given by the following pushout.
In other words, a behavior of is either a behavior of or a behavior of , where these are considered equal if they are compatible.
Here, is the disjoint union of these two sets, and we are quotienting out by the smallest equivalence relation for which whenever .
Dually, the join has behaviors given by the image factorization of the induced map .
In other words, a behavior of is a pair of compatible behaviors from and from .
Furthermore, the largest part is , and the smallest part is empty if is empty and a singleton otherwise.
Definition 7.
A part is strongly disjoint from a part if every behavior of is compatible with every behavior of . The two parts and are disjoint if their intersection is the minimal part. Strongly disjoint parts are disjoint:
In general, we will be more interested in joins than in meets because joins are easier to work with (being subsets of a product, rather than quotients of a disjoint union by a non-transitive relation).
Example 8.
Let’s consider some examples of joins and meets of parts.
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In the example of the bicycle, note that we have
since a behavior of the bicycle was defined precisely to be a behavior of a pedal and a wheel satisfying a compatibility constraint.
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In the example of the cup of water, the behaviors over a duration of times are the union of the behaviors for each time :
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Similarly, in the ecosystem example, the parts of the ecosystem at various times are the join of parts at particular times. More interestingly, recall that every behavior of 0 (starting population of foxes) is compatible with every behavior of 0 (starting population of rabbits). Therefore,
This witnesses the fact that the parts 0 and 0 do not at all mutually constrain each other, and so have no shared sub-parts.
2.5 Determination recovers the order of parts
Definition 9.
If and are parts of , and and , then determines if every behavior of the whole system which restricts to also restricts to .
We say that a part determines a part if every behavior , determines some behavior .
This is a much stronger notion than compatibility, and we shall show in Section 11 that it can be used to recover the original order relation on parts.
Lemma 10.
A behavior always determines uniquely: if and , then .
Proof.
We know there is some which restricts to . Since determines and , restricts to both and ; but then . ∎
Proposition 11.
For parts and of , the following are equivalent:
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is a part of , i.e. there is a surjection under .
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-determines , in the sense that for every there is a unique such that is compatible with . In other words, .
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-determines , in the sense that for every there is a such that determines . In other words, .
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For all and , if is compatible with , then determines .
3 Constraints, Allowance, and Ensurance
In this section, we introduce our new logical operators, and , and prove some basic properties about them. We shall see in Section 3.2 that these two operators pass constraints between parts. But first, what is a constraint?
3.1 Constraints as predicates
We will identify a constraint on a part with the predicate “satisfies ” on behaviors of . In other words, we have the following definition.
Let be the two element set of truth values. We think of functions as predicates concerning the elements of — applied to , gives a truth value which says whether or not satisfies the predicate .
Definition 12.
A constraint on a part is a map . The type of constraints on is . We write
to mean that entails , that is, if , then .
For parts , we get an adjoint triple that allows us to transform constraints on to those on , and vice versa, given by the logical quantifiers:
These functors are defined logically as follows:
The fact that they are adjoint means that
We will write , , and for , and respectively. As mentioned, these operations are functorial, meaning that and for ,
Lemma 13.
Recall that for part of system we write for the relation on . Then for any predicate on we have:
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and
Thinking again of as a way to observe behaviors, is the set of system behaviors that our observer says plausibly satisfy : there is something -equivalent to that satisfies . And is the set of system behaviors that our observer can guarantee satisfy .
3.2 The allowance and ensurance operators
Now we turn to the question of how constraints on the behavior of some part of the system constrain the behavior of other parts. We discuss two ways to pass constraints between parts.
Definition 14.
A constraint on a part induces a constraint on a part (of the same system ) in two universal ways:
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“Allows ”:
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“Ensures ”:
A behavior of allows a constraint on if can be doing while is satisfying ; we write this as . A behavior of ensures on if whenever does , must satisfy ; we write this as .
These symbols are chosen due to their relation to the usual modalities of possibility () and necessity () [Kri63]; a behavior allows if it is possible that satisfies while does , and a behavior ensures if it is necessary that satisfies while does . Indeed, in the case that the accessibility relation in the Kripke frame is an equivalence relation, we will be able to recover the usual possibility and necessity modalities from our allowance and ensurance operators (see Section 3.3).
Note that compatibility and determination appear as particular, pointwise cases of the allowance and ensurance operators. For any and , we have
We write for the map that sends to if and only if .
Example 15.
We return to our running examples to see our new operators in action.
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In the example of the bicycle with gear ratio of , we can ask what behavior of the pedal is ensured by the wheels moving slower than mph. We have is the constraint .
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If the cup of water has temperature at time , then it cannot have a temperature further away from the ambient room temperature at a later time. Therefore,
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Suppose that in the ecosystem example, one was given the goal of introducing a fox population at time in order to keep the rabbit population in check after a given deadline . Let’s say that being kept in check means being between two fixed bounds,
so that is a constraint on rabbits at time . The constraint of being kept in check for all times after the deadline is the constraint
on the join . The goal may then be expressed as finding a starting fox population which ensures that the rabbit population is kept in check at all times after the deadline:
Example 16.
We can see a higher-order ensurance in the ecosystem example. If there are any rabbits at time , and if the rabbit population is bounded independent of time, then the rabbits must ensure that there are foxes, and that the foxes ensure there are rabbits:
If there are no foxes, then the rabbit population is unbounded, and if there are foxes, then there must be rabbits for them to eat. We see that this ecosystem model exhibits a rudimentary form of symbiosis; though the foxes eat the rabbits, they counter-intuitively must ensure that the rabbits do not go extinct, lest they themselves go extinct.
Assuming the law of excluded middle, our operators are inter-definable by conjugating with negation.
Proposition 17.
Assuming Boolean logic, allowance and ensurance are de Morgan duals. That is, .
Proof.
The proof uses the law of excluded middle twice:
Note that Proposition 17 does not generalize to arbitrary toposes, where the variation of the sets (in time or in space) means that one must reason constructively in general.
3.3 Allowance and ensurance, possibility and necessity
Finally, we describe the manner in which our intermodalities generalize the classical alethic modalities of possibility and necessity.
Proposition 18.
Let and be parts of the system and a constraint on . Then:
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If a constraint entails , then allowing entails allowing , and ensuring entails ensuring . That is, and are monotone.
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If ensures that does , then allows doing . That is, .
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Allowing entails if and only if entails ensuring . That is, is left adjoint to .
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commutes with and , and commutes with and .
Fix a part . Then for any part , we obtain two modalities on by composing our intermodalities from to with their corresponding intermodality from to .
Proposition 19.
The operators and are a pair of adjoint modalities on . They are the identity modality if and only if .
Proof.
By Proposition 18 item 3, these two modalities are the composites of adjoint pairs of operators, and hence are adjoint themselves. Moreover, is the identity if and only if for all , which occurs if and only if for all , which occurs if and only if . ∎
These modalities describe constraints on as seen through the part . To obtain a description of possibility and necessity, assume that is inhabted — that there is some behavior of the system. We let be the system whose behavior type consists of just a single element. Then the adjoint modalities
describe possibility and necessity on .
For example, for any constraint on , the constraint maps all elements of to if there is some behavior that satisfies , and maps all elements to otherwise. Thus the modality detects whether is possible: that is, whether there is some behavior that satisfies .
On the other hand, is the constraint that maps all elements of to if all behaviors satisfy , and maps all elements to otherwise; thus this modality detects whether is always satisfied, and hence necessary.
More generally, the usual semantics of the “it is possible that” and “it is necessary that” modalities and take place in a Kripke frame , where is a set, known as the set of worlds, and is a binary relation on known as the accessibility relation. The predicate then holds if for some such that , and holds if for all such that [Kri63]. If is an equivalence relation, then we may equivalently describe by an epi . In this case, we have and as modalities on .
4 Outlook and Conclusion
We have presented a logic that describes how constraints—restrictions on behavior—are passed from one part of a system to another. While we have presented this from a set theoretic point of view, we have taken care to use arguments that are valid in any topos (with the noted exception of Proposition 17, which only holds in boolean toposes). As a consequence, our logic retains its character as a logic of constraint passing across a wide variety of semantics. One possibly valuable notion of semantics is one that captures a notion of time.
Indeed, behavior is best conceived as occurring over time, though of course the question of what time is remains an issue. One can imagine that a system has, for any interval or “window” of time, a set of possible behaviors, and that each such behavior can be cropped or “clipped” to any smaller window of time. This is the perspective of temporal type theory [SS18]. While that work uses topos theory in a significant way, the main idea is easy enough.
Whether we speak of a bicycle, an ecosystem, or anything else that could be said to exist in time, it is possible to consider the set of behaviors of that thing over an interval of time, say over the ten-minute window . Above we often discussed an idea which can be generalized to any system that exists in time. Namely, we can consider different parts of time as parts of . Given any behavior over the 10-minute window, we can clip it to the first minute ; this gives a function , which is often called restriction, though we will continue to call it clipping. Let’s assume that every possible behavior at extends to some behavior over the whole interval—i.e. that the universe doesn’t just end under certain conditions on -behavior—at which point we have declared that the clipping function is surjective, and hence gives a part in the sense of section Definition 2. We call it a temporal part.
What then does it mean to pass constraints between temporal parts? The idea begins to take on a control-theoretic flavor: behavioral constraints at one time window may allow or ensure constraints at other time windows. A mother could say “doing this now ensures no dessert tonight”. The child could ask “does our position on the road now allow me to play with Rutherford this afternoon?” A control system could attempt to solve the problem “what values of parameter can I choose, 5 seconds from now, that both allow current conditions and ensure that in 10 minutes we will achieve our target?”
In any case, as mentioned in the introduction, our original goal was to understand what makes a thing a thing, e.g. what gives things like bricks the quality of being cohesive (not two bricks) and closed (not the left half of a brick). We believe that a good logic of constraint passing between parts is essential for that, but perhaps not sufficient. The question of what additional structures need to be added or considered in order construct a viable notion of thing, remains future work.
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