Measuring the Good and the Bad in Inconsistent Information

1 Introduction

The need for more intelligent ways to deal with inconsistencies in information is an important issue in many areas of computer science including data and knowledge engineering, software engineering, robotics, and natural language processing. This has then raised the need for ways to analyze inconsistent information in order to be able to decide how to act on inconsistency intelligently.

If we have some items of information, we want more than just to say that the set of items is "consistent" or "inconsistent". Intuitively, some sets of information are more inconsistent than others, (e.g. in some respects {a, b, ¬a, ¬b} is more inconsistent than {a, b, c, ¬a}), and this has given rise to a number of proposals for measuring the degree of inconsistency. Furthermore, just because some set of information is inconsistent does not mean that it fails to convey information. Intuitively some sets of information (irrespective of whether they are consistent or inconsistent) convey more information than other sets (e.g. in some respects {a, b, c, ¬a} is more informative than {a, b, ¬a, ¬b}), and this has given rise to a number of proposals for measuring the degree of information. These measures are potentially important in diverse applications such as belief revision, belief merging, negotiation, multi-agent systems, decision-support, and software engineering tools. Already, measures of inconsistency have

Anthony Hunter

Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK

been considered for analysing conflicts in ontologies, in requirements specifications, and in ecommerce protocols.

Since classical logic is trivialized by inconsistency, each measure has to obviate this problem when analysing inconsistent information. For both inconsistency and information, the measures are either syntactic, for instance based on maximally consistent subsets or minimally inconsistent subsets, or semantic, for instance based on three-valued models.

In this paper, we explore the space of measures of inconsistency and measures of information by considering minimal properties of such measures, and then reviewing existing and new proposals for such measures to show that none can be entirely replaced by any other. This shows in a formal way that none of the measures is redundant, and each provides a particular perspective on the (inconsistent) information. We then explore how measures of inconsistency, and measures of information, are inter-connected in a trade-off when revising inconsistent information, since reducing the amount of inconsistency tends to also reduce the amount of information.

2 Preliminary Definitions

We assume a propositional language L of formulae composed from a set of atoms A and the logical connectives ∧, ∨, ¬. We use φ and ψ for arbitrary formulae and α and β for atoms. All formulae are assumed to be in conjunctive normal form. Hence every formula φ has the form ψ1 ∧ . . . ∧ ψn, where each ψi, 1 ≤ i ≤ n, has the form βi1 ∨ . . . ∨ βim, where each βik, 1 ≤ k ≤ m is a literal (an atom or negated atom). A knowledgebase K is a finite set of formulae. We let ⊢ denote the classical consequence relation, and write K ⊢ ⊥ to denote that K is inconsistent. Logical equivalence is defined in the usual way: K ≡ K′ iff K ⊢ K′ and K′ ⊢ K. We find it useful to define also a stronger notion of equivalence we call b(ijection)-equivalence as follows. Knowledgebase K is b(ijection)-equivalent to knowledgebase K′ , denoted K ≡b K′ iff there is a bijection f : K → K′ such that for all φ ∈ K, φ is logically equivalent to f (φ). For example, {a, b} is logically equivalent but not b(ijection)-equivalent to {a ∧ b}. We write R≥0 for the set of nonnegative real numbers and K for the set of all knowledgebases (in some presumed language L).

For a knowledgebase K, MI(K) is the set of minimal inconsistent subsets of K, and MC(K) is the set of maximal consistent subsets of K. Also, if MI(K) = {M1, ...,Mn}

then Problematic(K) = M1 ∪ ... ∪ Mn, and Free(K) = K \ Problematic(K). So Free(K) contains the formulae in K that are not involved in any inconsistency and Problematic(K) contains the formulae in K that are involved in at least one inconsistency. The set of formulae in K that are individually inconsistent is given by the function Selfcontradictions(K) = {φ ∈ K | {φ} ⊢ ⊥}). In the next section we will use these functions in definitions for syntactic measures of inconsistency.

The corresponding semantics uses Priest's three valued logic (3VL) [Priest, 1979] with the classical two valued semantics augmented by a third truth value denoting inconsistency. The truth values for the connectives are defined in Figure 1. An interpretation i is a function that assigns to each atom that appears in K one of three truth values: i : Atoms(K) → {F,B, T}. For an interpretation i it is convenient to separate the atoms into two groups, namely the ones that are assigned a classical truth value, (i.e. Binarybase(i) = {α | i(α) = T or i(α) = F}), and the ones that are assigned B. (i.e. Conflictbase(i) = {α | i(α) = B}). For a knowledgebase K we define the models as the set of interpretations where no formula in K is assigned the truth value F: Models(K) = {i | for all φ ∈ K, i(φ) = T or i(φ) = B} Then, as a measure of inconsistency for K we define

So the contension gives the minimal number of atoms that need to be assigned B in order to get a 3VL model of K.

Example 1 ForK = {a, ¬a, a∨b, ¬b}, there are two models of K, i1 and i2, where i1(a) = B, i1(b) = B, i2(a) = B, and i2(b) = F. Therefore, Conflictbase(i1) = 2 and Conflictbase(i2) = 1. Hence, Contension(K) = 1.

Finally, we consider some useful definitions based on the notion of implicants. A consistent set of literals X is an implicant for a knowledgebase K iff for each φ ∈ K, X ⊢ φ. A minimal implicant is called a prime implicant. For example, for K = {a, ¬b ∨ c}, the prime implicants are X1 = {a, ¬b} and X2 = {a, c}. A proxy for K is a set of literals X such that X is a prime implicant of a maximal consistent subset of K. Let the set of proxies for K (denoted Proxies(K)) be defined as follows.

Proxies(K) = {X | X is a prime implicant of K′ ∈ MC(K)}

We see that each proxy represents an "interpretation" of the possible literals that hold, and so the number of proxies rises by increasing the number of disjuncts in any formula, and by increasing the number of conflicting formulae. The cardinality of each proxy rises with the amount of information in each alternative, and so adding conjuncts to a formula will increase the size of one or more proxies (as long as the conjunction is consistent).

3 Inconsistency and InformationMeasures

In this section, we study inconsistency and information measures. We consider both existing and new proposals. Our main result is that for both inconsistency measures and information measures, the various measures are incompatible with one another. This result strongly implies that unlike some other intuitive concepts, such as the concept of effective computability, where different definitions using recursion, λcalculus, and Turing machines are equivalent, both inconsistency measure and information measure are too elusive to be captured by a single definition. Additionally, for information measures we also consider various plausible constraints and investigate which measures satisfy them.

3.1 InconsistencyMeasures for Knowledgebases

An inconsistency measure assigns a nonnegative real value to every knowledgebase. We make three requirements for inconsistency measures. The constraints ensure that all and only consistent knowledgebases get measure 0, the measure is monotonic for subsets, and the removal of a formula that does not participate in an inconsistency leaves the measure unchanged.

Definition 1 An inconsistency measure I : K → R≥0 is a function such that the following three conditions hold:

1. I (K) = 0 iff K is consistent.
2. If K ⊆K′ , then I (K) ≤ I (K′ ).
3. For all α ∈ Free(K), (I (K) = I (K\{α}).

The above requirements are taken from [Hunter and Konieczny, 2006] where (1) is called consistency, (2) is called monotony, and (3) is called free formula independence.

Next we introduce five inconsistency measures: the rationale for each is given below.

Definition 2 For a knowledgebase K, the inconsistency measures IC, IP , IB, IS, and IR are s.t.

• IC(K) = |MI(K)|
• IM(K) = (|MC(K)| + |Selfcontradictions(K)|) - 1
• IP (K) = |Problematic(K)|
• IB(K) = Contension(K)
• IQ(K) = { 0 if K is consistent ∑X∈MI(K) 1 |X| otherwise

We explain the measures as follows: IC(K) counts the number of minimal inconsistent subsets of K; IM(K) counts the sum of the number of maximal consistent subsets together with the number of contradictory formulae but 1 must be subtracted to make I (K) = 0 when K is consistent; IP (K)

counts the number of formulae in minimal inconsistent subsets of K; IB(K) counts the minimum number of atoms that need to be assigned B amongst the 3VL models of K; and IQ computes the weighted sum of the minimal inconsistent subsets of K, where the weight is the inverse of the size of the minimal inconsistent subset (and hence smaller minimal inconsistent subsets are regarded as more inconsistent than larger ones). Each of these measures satisfies the definition of being an inconsistency measure (i.e. Definition 1).

There is a rationale for each inconsistency measure. We cannot require these differently defined measures to give identical numerical values but it would be reasonable to assume that at least some of them place the knowledgebases in the same order with respect to inconsistency. Define Ix and Iy to be order-compatible if for all knowledgebases K1 and K2, Ix(K1) < Ix(K2) iff Iy(K1) < Iy(K2) and orderincompatible otherwise. The next theorem shows that ordercompatibility doesn't hold for any pair of the inconsistency measures we have defined, leading us to think that inconsistency is too elusive a concept to be captured in a single measure.

Theorem 1 1 IC, IM, IP , IB, and IQ are pairwise orderincompatible.

Although the five inconsistency measures are quite different, four of them give identical results on bijection-equivalent knowledge bases.

Interestingly, b-equivalence does not guarantee equality for IB. The problem is with self-contradictions. For instance, if K = {a ∧ ¬a} and K′ = {a∧¬a∧b∧¬b}, then K ≡b K′ , but IB(K) = 1 ̸= IB(K′ ) = 2.

The use of minimal inconsistent subsets, such as IC, IP , and IQ, and the use of maximal consistent subsets such as IM, have been proposed previously for measures of inconsistency [Hunter and Konieczny, 2004; 2008]. The idea of a measure that is sensitive to the number of formulae to produce an inconsistency eminates from Knight [Knight, 2001] in which the more formulae needed to produce the inconsistency, the less inconsistent the set. As explored in [Hunter and Konieczny, 2008], this sensitivity is obtained with IQ. Another approach involves looking at the proportion of the language that is touched by the inconsistency such as IB. Whilst model-based techniques have been proposed before for measures of inconsistency, IB is a novel proposal since it is based on three-valued logic, and as such, is simpler than the ones based on four-valued logic (e.g. [Hunter, 2002]).

3.2 InformationMeasures for Knowledgebases

Another dimension to analysing inconsistency is to ascertain the amount of information in a knowledgebase. The following novel proposal for an information measure assigns a nonnegative real number to every knowledgebase. The constraints ensure that the empty set has measure 0, the measure is subset monotonic for consistent knowledgebases, and a consistent knowledgebase that does not contain only tautologies has nonzero measure.

1 All proofs and additional references are given in the report www.cs.ucl.ac.uk/staff/a.hunter/papers/stepwise.pdf.

Definition 3 An information measure J : K → R≥0 is a function such that the following three conditions hold:

1. If K = ∅ then J (K) = 0.
2. If K′ ⊆K, and K is consistent, then J (K′ ) ≤ J (K).
3. If K is consistent and ∃φ ∈ K such that φ is not a tautology, then J (K) > 0.

The above definition is a general definition that allows for a range of possible measures to be defined. Next we introduce seven information measures; the rationale for each is given below. We note here that in the definition of JB we will use the concept of Models as previously defined for 3VL. However, in the case of JL we will need a model concept using classical 2-valued interpretations. We write 2VModels(K) = {i| is a 2-valued interpretation and for all φ ∈ K, i(φ) = T }.

Definition 4 For a knowledgebase K, the information measures JA, JS, JF , JC, JB, JP , and JL are such that

• JA(K) = |Atoms(K)|
• JS(K) = |K|
• JF (K) = |Free(K)|
• JC(K) = Max{ |M| | M ∈ MC(K)}
• JB(K) = Max{ |Binarybase(i)| | i ∈ Models(K)}
• JP (K) = Max{ |X| | X ∈ Proxies(K)}
• JL(K) = log2 2n | ⋃{2VModels(K′ )|K′∈MC(K)}| where n = |Atoms(K)| if n ≥ 1, else JL(K) = 0.

The first two measures do not actually deal with inconsistency at all: JA counts the number of atoms and JS counts the number of formulae. For the other four measures: JF counts the number of free formulae; JC finds the size of the largest maximal consistent subset; JB finds the maximum number of atoms that need not be assigned B in the 3VL models; JP finds the size of the largest proxy; and JL uses an information-theoretic approach that is discussed further at the end of this section. All seven measures are information measures according to Definition 3.

In analogy to inconsistency measures, we can define ordercompatibility and order-incompatibility for information measures. Similarly, we find that order-compatibility does not hold for any pair of information measures, leading us to think that information is also too elusive a concept to be captured in a single measure.

Theorem 2 JA, JS, JF , JC, JB, JP , and JL are pairwise order-incompatible.

Next we prove some results concerning information measures followed by some that relate information measures with inconsistency measures.

Proposition 2 If K is consistent, then JS(K) = JF (K) = JC(K).

Proposition 3 If K is a set of literals, then JA(K) = JC(K) = JP (K).

Proposition 4 For any knowledgebase K, JS(K) JF (K) = IP (K).

Proposition 5 For any knowledgebase K, JA(K) JB(K) = IB(K).

-

-

Proposition 6 No information measure is also an inconsistency measure.

Since our definition of information measure (i.e. Definition 3) is rather weak we consider additional constraints that can be useful for comparing information measures. For an information measure J, and for any knowledgebases K,K′ ⊆ L, we call J:

• (Monotonic) If K ⊆K′ , then J (K) ≤ J (K′ ).
• (Clarity) For all φ ∈ K, J (K) ≥ J (K ∪ {ψ}), where ψ is the cnf of ¬φ.
• (Equivalence) If K is consistent and K ≡ K′ , then J (K) = J (K′ ).
• (Bijection-Equivalence) If K ≡b K′ , then J (K) = J (K′ ).
• (Closed) If K is consistent, and K ⊢ φ, then J (K) = J (K ∪ {φ}).
• (Cumulative) If K ∪ {φ} is consistent, and K ̸⊢ φ, then J (K) < J (K ∪ {φ}).

A monotonic measure is monotonic even for inconsistent knowledgebases. A clarity measure does not increase when the negation of a formula in the knowledgebase is added. An equivalence measure assigns the same value to logically equivalent consistent knowledgebases. A bijectionequivalence measure (which was first proposed in [Knight, 2001]) has the same value for a pair of knowledgebases when the formulae are pairwise equivalent. A closed measure (which was first proposed in [Lozinskii, 1994]) does not have increased information for a consistent knowledgebase when entailed formulae are added. A cumulative measure (which was first proposed in [Lozinskii, 1994]) has increased information for a consistent knowledgebase when a non-entailed formula is added that is consistent with it. We note that if an information measure has the equivalence property then it is closed because if K ⊢ φ then K ≡ K ∪ {φ}.

Theorem 3 Figure 2 indicates the constraints that hold for each of the information measures JA, JS, JF , JC, JB, JP , and JL.

Depending on which constraints one considers important, one may choose from those measures that satisfy them. In particular, JP satisfies all seven constraints.

The JA, JS, JF , and JC measures are simple syntactic measures that have been considered in some form before (see for example [Hunter and Konieczny, 2004] for a discussion)). However, the JB and JP are novel proposals for information measures. There have also been proposals for measures of information for propositional logic based on Shannon's information theory (see for example [Kemeny, 1953]). Essentially, these measures consider the number of models of the set of formulae (the less models, the more informative the set), and in case the set of formulae is consistent, the result is intuitive.

However, when the set is inconsistent, the set is regarded as having null information content. To address the need to consider inconsistent information, Lozinskii proposed a generalization of the information-theoretic approach to measuring information [Lozinskii, 1994] that we called JL earlier.

4 Stepwise Inconsistency Resolution

Generally, when a knowledgebase is inconsistent, we would like to reduce its inconsistency value, preferably to 0. The problem is that a reduction in inconsistency may lead to a corresponding reduction in information. Consider, for instance, JS. This measure counts the number of formulae in the knowledgebase. Hence any deletion reduces it. Our goal is to reduce inconsistency with as little information loss as possible, a task that depends on the choice of both the inconsistency measure and the information measure.

Here we focus on stepwise inconsistency resolution. Often it is not possible or desirable to irradicate all inconsistency at the same time, and therefore we need a process that reduces the degree of inconsistency over a number steps.

To illustrate some of the key issues in stepwise inconsistency resolution, we consider the following example. Let K = {a, ¬a∧¬b∧¬c, b, d}. K has two minimal inconsistent subsets: M1 = {a, ¬a∧¬b∧¬c} and M2 = {¬a∧¬b∧¬c, b}; and two maximal consistent subsets N1 = {a, b, d} and N2 = {¬a ∧ ¬b ∧ ¬c, d}. As we want to show how to reduce the inconsistency of K in a stepwise fashion, one formula at a time, we will apply three inconsistency resolution functions: delete a formula, weaken a formula, and split a formula.

• DeletionWe delete a formula that is in a minimal inconsistent subset. Thus we can delete either ¬a∧¬b∧¬c or a or b. In the first case, since ¬a∧¬b∧¬c is in both minimal inconsistent subsets, the result is consistent. This is the most drastic of the three options because this operation loses the most information.
• Weakening We change a formula to another formula logically implied by it. Typically, we add a disjunct or change a conjunction to a disjunction. For instance, we can weaken ¬a ∧ ¬b ∧ ¬c to (¬a ∨ ¬b) ∧ ¬c or ¬a ∨ ¬b ∨ ¬c. We can weaken a to a ∨ b or even a ∨ ¬a, and so on. While this operation may reduce the number of minimal inconsistent subsets, the size of the minimal inconsistent subsets may rise, as seen here, where the first weakening results in one minimal inconsistent subset {a, (¬a ∨ ¬b) ∧ ¬c, b}.

• SplittingWe split a formula into its conjuncts. This may isolate the really problematic conjuncts. For instance, we can split ¬a ∧ ¬b ∧ ¬c into ¬a, ¬b, and ¬c. In this case, we get a new knowledgebase {a, ¬a, b, ¬b, ¬c, d} that is still inconsistent, though by some inconsistency measures it is less inconsistent. Also, this allows us at a later step to delete just the portion of the conjunction that causes the inconsistency.

In an inconsistent knowledgebase, any one of the formulae in the knowledgebase can be selected for one of the resolution operations (of deletion, weakening or splitting). So there is a question of how to choose a formula and which operation to apply. In general, inconsistency and information measures offer possible answers to this question. Our guiding principle is to minimize information loss while reducing inconsistency as we resolve an inconsistent knowledgebase by stepwise resolution.

We start by formally defining the three functions that we allow in the process of inconsistency resolution. They appear to be representative of all options. These operations will be applied to inconsistent knowledgebases.

Definition 5 An inconsistency resolution function irf, is one of the following three functions d(φ) or w(φ, ψ) or s(φ) where φ ∈ K:

• (Deletion) d(φ) = K \ {φ}.
• (Weakening) w(φ, ψ) = (K \ {φ}) ∪{ψ} where φ ⊢ ψ.
• (Splitting) s(φ) = (K \ {φ}) ∪ {φ1, . . . , φn} where φ1, . . . , φn are the conjuncts in φ.

Then irf (K) is the knowledgebase obtained by applying irf to K. Also irf (K) = K in case φ ̸∈ K.

In the stepwise inconsistency resolution process we will usually have multiple applications of such functions. A stepwise resolution function sequence (abbr. function sequence) F = 〈irf1, . . . , irfn〉 is a sequence of such functions. A stepwise inconsistency resolution knowledgebase sequence (abbr. knowledgebase sequence) KF = 〈K0, . . . ,Kn〉 is a sequence of knowledgebases obtained by using F such that K0 is the initial knowledgebase and irfi(Ki-1) = Ki for 1 ≤ i ≤ n. We also write F(K0) = Kn and observe that Kn = irfn(. . . irf1(K0) . . .).

The goal of stepwise inconsistency resolution is to reduce the inconsistency of the knowledgebase. Next we define a simple way to measure the reduction . We will be interested in applying this definition to the case where F(K) = K′ for some function sequence F.

Definition 6 Given an inconsistency measure I, an inconsistency resolution measure RI : K × K → R is defined as follows:

For illustration we give two examples. The example given in Figure 3 corresponds to deletion, and Example 2 corresponds to splitting a formula.

Example 2 Let K = {a, ¬a ∧ ¬b, b}. Splitting K by applying s(¬a ∧ ¬b) we obtain K′ = {a, ¬a, b, ¬b}. Here we see that splitting does not reduce inconsistency according to any of the five inconsistency measures. Indeed, for several measures it causes an increase in inconsistency .

Some simple observations concerning the RI measure are the following: (1) If φ ̸∈ K, then RI(K,K \ {φ}) = 0 and (2) If φ ∈ Free(K) then RI (K, K \ {φ}) = 0.

In the stepwise resolution process we try to minimize the loss of information as well. For this reason we now define a way to measure the loss of information.

Definition 7 Given an information measure J, an information loss measure RJ : K × K → R is defined as follows.

Our general goal is to simultaneously maximize RI and minimize RJ . In the following subsections we consider some of the issues for each of the options we have (i.e. for deletion, for weakening, and for splitting).

4.1 Inconsistency Resolution by Deletion

Deletion is the simplest, and yet most drastic, of the options we have for dealing with inconsistency. In terms of deciding of how to proceed, if deletion is the only function used, it is just a matter of choosing a formula to delete at each step. The following result describes the possibilities for both RI and RJ when K′ is obtained fromK by a single deletion.

Theorem 4 Let K′ be obtained from an inconsistent K by deleting a single formula.

(a) For all 5 inconsistency measures RI (K, K′ ) ≥ 0.

(b) For the information measures JF , JB and JL, RJ (K, K′ ) may be negative; in the other cases RJ(K,K′ ) is a nonnegative integer.

The following result follows immediately from the second constraint of an information measure and will be useful in narrowing the knowledgebases that need to be considered for minimal information loss when inconsistency resolution is done by deletions.

Proposition 7 If K is consistent then RJ(K,K \ {φ}) ≥ 0.

This result shows that once we delete enough formulae from an inconsistent knowledgebase to make it consistent (and thereby make any inconsistency measure 0), we might as well stop because additional deletions may only cause information loss. This gives the following result.

Corollary 1 Suppose that stepwise inconsistency resolution is done by deletions only. To find a consistent knowledgebase with minimal information loss (i. e. where RJ(K,K′ ) is minimal) it suffices to consider only those function sequences F where F(K) ∈ MC(K).

4.2 Inconsistency Resolution byWeakening

In this subsection we investigate the case where the inconsistency of a knowledgebase is resolved by using weakenings only. Thus we start with an inconsistent knowledgebase K and by applying one or more weakenings we obtain a consistent K′ . Our concern here is what happens to the information measure during this process. In order to analyze this situation we will exclude the case where a formula is weakened by using an atom not in K such as by applying a disjunction with such an atom. We do this because it does not seem reasonable to change the language of the knowledgebase when our purpose is to weaken it for consistency. Also, by excluding this case we make sure that the information measure cannot become arbitrarily large by simply taking bigger and bigger disjuncts with new atoms.

Our result is summarized in the following theorem.

Theorem 5 Let K be an inconsistent knowledgebase that is transformed to a consistent knowledgebase K′ by one or more weakenings without introducing any atom not already in K. Then (1) JA(K′ ) ≤ JA(K), (2) JS(K′ ) ≤ JS(K), (3) JF (K′ ) ≥ JF (K), (4) JC(K′ ) ≥ JC(K), (5) No inequality holds between JB(K′ ) and JB(K), (6) JP (K′ ) ≤ JP (K), and (7) JL(K′ ) ≥ JL(K).

4.3 Inconsistency Resolution using Splitting

Here we consider what happens when splitting is applied. First we note that unlike deletion and weakening, splitting by itself cannot resolve inconsistencies. Hence splitting must be used in conjunction with deletion or weakening. We start by considering what happens when just splitting is applied. Just as in the case of deletions and weakenings, we split only formulae in Problematic(K).

Theorem 6 Let K′ be obtained from an inconsistent knowledgebase K by splitting a single formula in Problematic(K). Then (1) IC(K′ ) ≥ IC(K), (2) IM(K′ ) ≥ IM(K), (3) IP (K′ ) ≥ IP (K), (4) IB(K′ ) = IB(K), (5) No inequality holds between IQ(K′ ) and IQ(K), (6) JA(K′ ) = JA(K), (7) JS(K′ ) > JS(K), (8) JF (K′ ) ≥ JF (K), (9) JC(K′ ) ≥ JC(K), (10) JB(K′ ) = JB(K), (11) JP (K′ ) = JP (K) and (12) No inequality holds between JL(K′ ) and JL(K).

This theorem shows that splitting decreases neither inconsistency nor information (except possibly for IQ and JL), and for some measures it increases both. Anyway, as pointed out earlier, splitting must be combined with another operation to eliminate inconsistency.

5 Discussion

In this paper, we have clarified the space of inconsistency and information measures and then shown how a wide variety of proposals conform to some general properties. It is surprising that all the different measures are incompatible with one another. We have also shown how inconsistency and information measures can be used to direct stepwise resolution of inconsistency so that inconsistency can be decreased whilst minimising information loss.

In future work, we want to better elucidate the different dimensions for measuring inconsistency and measuring information. Ideally, we would like to identify the "elementary" measures of inconsistency and the "elementary" measures of information, and then identify functions that calculate the other measures of inconsistency and information as composites of the elementary measures.