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REVIEW 2 major objections 4 minor 124 references

Partial information decomposition has many competing definitions; this paper claims to map every measure to every proposed axiom with proofs or counterexamples, and to chart which axioms can coexist.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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2026-08-02 19:07 UTC pith:NCZOQI2P

load-bearing objection The PID map is genuinely useful but the central completeness claim is over-promised: several Table 5 entries rest on 'empirical tests' rather than proof/counterexample, and Theorem 2 silently uses (S0). the 2 major comments →

arxiv 2603.06678 v2 pith:NCZOQI2P submitted 2026-03-03 cs.IT math.IT

The mathematical landscape of partial information decomposition: A comprehensive review of properties and measures

classification cs.IT math.IT MSC 94A1594A1762B10
keywords partial information decompositionredundancysynergyunique informationaxiomatic propertiesincompatibility theoremsredundancy latticeinformation theory
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Partial information decomposition (PID) tries to split the mutual information between several sources and a target into redundancy, unique information, and synergy. Since its introduction, at least nineteen competing definitions and a long list of proposed axioms have appeared, and nobody knew for certain which measure satisfies which property, or even which properties can be true at the same time. This paper attempts to close that gap: it gathers the definitions in a common language, and for every measure–property pair that was not already settled it provides a proof or a counterexample in the appendix. It also assembles the web of implications and no-go theorems, adds several new ones, and uses an automatic theorem prover to find the largest sets of mutually compatible axioms. If the table is right, the field gets a single reference: a researcher can look up any measure's axiomatic profile, and any future measure can be tested against the known constraints.

Core claim

The paper's central claim is that it supplies the first systematic, verified classification of PID measures by their axioms: for every measure in the literature and every property — symmetry, monotonicity, target chain rule, identity, Blackwell property, additivity, continuity, and the rest — the paper states whether the property holds, and for every combination not previously settled it gives either a proof or an explicit counterexample. On this basis it derives new relationships among the axioms, most notably that Self-Redundancy (a single source's redundancy equals its mutual information), Target Chain rule (the chain rule for redundancy in the target), and Target Equality (adding the tar

What carries the argument

The load-bearing machinery is the measure–property table (Table 5): each of the nineteen redundancy measures is defined in a standard notation, and every property is verified or refuted for each measure. The proofs and counterexamples live in the appendix; the table itself supports the hierarchically clustered taxonomy of measures and the hypergraph of implications and incompatibilities. Around the table, the paper places an automatic theorem prover encoding of the axioms, which checks compatibility of property combinations and identifies maximal coherent sets. The underlying objects — the redundancy lattice and the Möbius-inversion step that yields the PID atoms — are the shared skeleton th

Load-bearing premise

The load-bearing premise is that every entry of the verification table is correct, including the few justified only by 'empirical tests' rather than proofs or counterexamples — and, separately, that Theorem 2's proof, which silently uses Weak Symmetry, is valid as stated.

What would settle it

Search small probability distributions with exact arithmetic for each of the four empirically tested entries — Target Monotonicity for the information-geometric measure, Strong Symmetry for the dependency-constraint measure, Local Positivity for the maximum-entropy-star measure, Additivity for the causal-tensor measure — and check whether the claimed satisfiability or violation holds; separately, check whether Theorem 2's conclusion (Identity) follows from (Self-Redundancy, Target Chain rule, Target Equality) without Weak Symmetry, since the given proof silently uses it.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • A practitioner can look up any measure's axiomatic profile in one table, rather than tracing original papers.
  • The new incompatibility result (Self-Redundancy, Equivalence-class Invariance, Local Positivity, Target Equality, Target Chain rule) shows that even without the controversial Independent-Identity axiom, target chain rule and target equality cannot be combined with local positivity.
  • The implication (Self-Redundancy, Target Chain rule, Target Equality) ⇒ Identity means any measure that satisfies the chain rule and target equality will also satisfy the contested Identity property.
  • The maximal compatible sets give future measure designers explicit targets: 20 properties are achievable by giving up either Strong Local Positivity or Equivalence-class Invariance; keeping both caps the set at 17.
  • The theorem prover turns the web of axioms into a computable constraint system, so a newly proposed measure can be automatically checked against all known implications and incompatibilities.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the table is correct, the practical lesson is that PID choice is a matter of axiomatic commitment, not a single best measure; the paper's clustering suggests only about a quarter of the axioms distinguish the existing measures, so the real design choices are few.
  • Since no existing measure attains the maximal 20-property set, the framework poses a concrete open problem: construct a measure satisfying all 20 (by dropping Strong Local Positivity and accepting negative atoms), or prove it impossible.
  • The equivalence (Weak Monotonicity + Target Equality) ⇔ Strong Monotonicity implies the axiom catalog can be slimmed, potentially changing how future measures are presented and compared.
  • The encoding of the property web as a computable constraint system means the entire body of PID compatibility knowledge becomes executable, which may accelerate testing of both new measures and new axioms.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 4 minor

Summary. This paper is a systematic review and original-research resource for Partial Information Decomposition (PID). It standardizes notation for 19 PID measures and 20 properties, presents a property–measure classification table (Table 5), compiles a theorem web relating properties (Tables 3–4, Fig. 2), and uses the Z3 SMT solver to verify compatibility claims. The core promise, stated in §1.1 and §3.1, is that every entry in Table 5 not already known in the literature is accompanied by a proof or an explicit counterexample in the appendix. The paper also contributes new theorems (e.g., Theorems 2, 4, 6, 7, 10, 11) and corrects earlier claims in the literature.

Significance. If the completeness claim can be substantiated, this would be the standard reference map of the PID measure/property landscape: it organizes a dispersed literature, makes the property/measure satisfaction structure explicit, and provides machine-checked compatibility results via an open-source Z3 implementation. The inclusion of unpublished measures (I_RAV) and corrections to prior proofs (e.g., Lemma 3 of Ref. [41]) are useful scholarly contributions. However, the central verification claim is currently not met to the letter: a non-negligible set of Table 5 entries rests on "empirical tests" rather than proofs or located counterexamples, so a reader cannot independently certify those entries. The contribution is therefore significant but requires completion before it can serve as a trustworthy map.

major comments (2)
  1. [§3.1, Table 5, Appendix E.1] The promise that every non-known Table 5 entry has a proof or counterexample is not fulfilled. Appendix E.1 contains several entries justified only by "empirical tests" without specifying distributions or parameter values: I_IG "(TM) holds from empirical tests"; I_DEP "(S1) is violated as observed from empirical tests"; I_MES "(LP0) is violated... as seen from empirical tests" and "(TM) is violated as observed from empirical tests"; I_CT "(AD) does not hold as observed from empirical tests"; I_CCS "(TM),(TC),(S1) are violated as observed from empirical tests"; plus similar entries for I_SX, I_do, I_RAV, I_RDR, and a conditional example for I_RR. Numerical observation is not the promised proof/counterexample; if any of these entries is wrong, Table 5 is wrong. Either supply proofs/counterexamples or explicitly downgrade these to empirical claims and revise the §3.1 promise.
  2. [§C.2, Theorem 2] The proof of Theorem 2 (Eq. 49) passes from I∩(X1,X2;X1) to I∩(X2;X1). This requires permuting the sources (S0) together with (TE); the text attributes the step only to (TE). Since Theorem 2 is stated with hypotheses (SR),(TC),(TE) (Table 3), it is under-specified. Theorem 4 inherits this because it relies on Theorem 2. Add (S0) to the hypotheses or state explicitly that S0 is assumed throughout the implication table.
minor comments (4)
  1. [§C.2, Proposition 6] The proof uses (TM) to justify I∩(X1,X2;f(X1,X2)) ≤ I∩(X1,X2;X1,X2), but the (TM) defined by Eq. (32) only concerns adding a target variable, not replacing the target by a deterministic coarsening f(X1,X2). As written, the proof does not follow from the stated axiom; either supply a correct proof or add the needed target-coarsening version of TM.
  2. [§C.2, Theorem 12] The closing remark that Theorem 12 "can also be derived by combining Theo. 1 and Theo. 11" is misleading: that derivation would require (EI), which is not among the hypotheses of Theorem 12. The direct citation to Ref. [19] is sufficient.
  3. [Appendix A] The intended distinction between multivariate and multiple arguments via comma versus semicolon is not visible in the typeset text: "I(X1, X2, X3;Y)" and "I(X1, X2, X3;Y)" appear identical. Please fix the notation so the two quantities are distinguishable.
  4. [Table 5] The footnote apparatus is incomplete: only the I_IG/(TM) entry carries the asterisk for "supported by empirical simulations," yet several other entries in Appendix E.1 also rely on empirical tests. Either flag all such entries or remove the special status of the I_IG entry.

Circularity Check

0 steps flagged

No significant circularity: the central table/theorem claims are derived from measure definitions and external results, not from the paper's own prior work.

full rationale

I walked the claimed derivation chain. The central contribution, Table 5, is a systematic catalogue of which PID properties hold for which measures. Its entries are justified either by direct calculation from the measures' definitions in Appendix D or by external references, e.g. 'I min ∩ (SR),(S0),(M0),(GP),(LP0) follow directly from the definition [10]' and 'I BROJA ∩ (BP),(TE), and (∗) are satisfied: see Ref. [38]'. These are independent support, not self-citations. The newly claimed theorems (Theo. 2, 4, 6, 7, 10, 11) are proved from the axiom definitions; none of the proofs invokes the authors' own prior results as load-bearing premises. Theorem 2's proof is suspect (the step labelled (TE) appears to need additional assumptions), but an unsupported or under-specified proof is a correctness issue, not a circular reduction. The 'empirical tests' entries in Appendix E.1 (e.g. 'I IG ∩: (TM) holds from empirical tests') weaken the promised proof/counterexample completeness claim, but they are not fitted parameters renamed as predictions and they do not make the result equivalent to an input by construction. The Z3 component checks consistency of axiom combinations and is an independent consistency check, not an input to the derivations. Self-citations ([45], [81], [85], [93], [107], [108], [106]) appear in contextual remarks and do not carry the load of Table 5 or the theorems. Therefore no circular step can be quoted with a specific reduction, and the appropriate score is 0.

Axiom & Free-Parameter Ledger

0 free parameters · 4 axioms · 0 invented entities

No new physical or formal entities are postulated, and no free parameters are fitted. The paper organizes existing measures and properties; however it implicitly assumes that the PID lattice framework, the listed property formalizations, and Z3 encodings are correct, and that empirical checks can stand in for proofs in some cells.

axioms (4)
  • domain assumption The Williams & Beer redundancy lattice and inclusion-exclusion principle are the correct decomposition structure for PID.
    The review confines its main results to lattice-based PID; alternative lattices and IEP rejection are discussed but not incorporated (Sec. 4.3).
  • domain assumption The 20 listed properties are a faithful and complete formalization of all properties proposed in the PID literature.
    The central table and theorem map inherit any mis-formalization; e.g., (EI) is formalized via the equivalence relation in Eq. (11).
  • standard math Z3 SMT solver is sound and complete for the encoded property-compatibility problems.
    Used in Sec. 3.2 to compute maximal compatible property sets; results depend on the correctness of the encoding.
  • ad hoc to paper Numerical 'empirical tests' can serve as sufficient evidence for a property holding or failing.
    Several Table 5 entries rely on empirical tests rather than mathematical proof, contradicting the paper's own completeness claim.

pith-pipeline@v1.3.0-alltime-deepseek · 58913 in / 13208 out tokens · 116158 ms · 2026-08-02T19:07:38.989431+00:00 · methodology

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read the original abstract

Partial Information Decomposition (PID) has become one of the most prominent information-theoretic frameworks for describing the structure and quality of information in complex systems. Despite its widespread utility, there exists no unique solution constraining precisely how a PID should be constructed, leading to a multiverse of different formalisms with different mathematical commitments. In this work, we provide a comprehensive overview of the mathematical landscape of PID. By integrating existing PID measures into a common language, we systematically examine all major approaches to the PID framework that have emerged so far, determining for each measure whether or not each known property holds. In addition, we derive a web of all known theorems mapping the relationships and incompatibilities between these properties, before also revealing some novel interdependency results. In doing so, we chart a brief history of the framework, promote a unified perspective for its discussions, and offer a path towards both theoretical refinement and informed empirical applications for the future of this powerful method.

Figures

Figures reproduced from arXiv: 2603.06678 by Alberto Liardi, George Blackburne, Keenan J. A. Down, Matteo Neri, Pedro A. M. Mediano.

Figure 1
Figure 1. Figure 1: FIG. 1: Hierarchical organisation of PID measures induced by properties. a) [PITH_FULL_IMAGE:figures/full_fig_p013_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2: Formal relationships among PID properties. a) [PITH_FULL_IMAGE:figures/full_fig_p014_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3 [PITH_FULL_IMAGE:figures/full_fig_p021_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4: Temporal timeline of the introduction of PID measures, properties, and their relationships. [PITH_FULL_IMAGE:figures/full_fig_p023_4.png] view at source ↗

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Reference graph

Works this paper leans on

124 extracted references · 7 linked inside Pith

  1. [1]

    A mathematical theory of communication,

    C. E. Shannon, “A mathematical theory of communication,”The Bell System Technical Journal, vol. 27, no. 3, pp. 379–423, 1948

  2. [2]

    Certain factors affecting telegraph speed 1,

    H. Nyquist, “Certain factors affecting telegraph speed 1,”Bell System Technical Journal, vol. 3, no. 2, pp. 324– 346, 1924

  3. [3]

    Transmission of information 1,

    R. V. Hartley, “Transmission of information 1,”Bell System technical journal, vol. 7, no. 3, pp. 535–563, 1928

  4. [4]

    Clausius,Ueber verschiedene f¨ ur die Anwendung bequeme Formen der Hauptgleichungen der mechanischen W¨ armetheorie: vorgetragen in der naturforsch

    R. Clausius,Ueber verschiedene f¨ ur die Anwendung bequeme Formen der Hauptgleichungen der mechanischen W¨ armetheorie: vorgetragen in der naturforsch. Gesellschaft den 24. April 1865. Verlag nicht ermittelbar, 1865

  5. [5]

    Weitere studien ¨ uber das w¨ armegleichgewicht unter gasmolek¨ ulen,

    L. Boltzmann, “Weitere studien ¨ uber das w¨ armegleichgewicht unter gasmolek¨ ulen,”Sitzungsberichte der Kaiserlichen Akademie der Wissenschaften in Wien, vol. 66, no. 2, pp. 275–370, 1872

  6. [6]

    Weitere studien ¨ uber das w¨ armegleichgewicht unter gasmolek¨ ulen,

    L. Boltzmann, “Weitere studien ¨ uber das w¨ armegleichgewicht unter gasmolek¨ ulen,” inKinetische Theorie II: Irreversible Prozesse Einf¨ uhrung und Originaltexte, pp. 115–225, Springer, 1970

  7. [7]

    Information theory and statistical mechanics,

    E. T. Jaynes, “Information theory and statistical mechanics,”Physical review, vol. 106, no. 4, p. 620, 1957

  8. [8]

    T. M. Cover and J. A. Thomas,Elements of Information Theory. John Wiley & Sons, 1999

  9. [9]

    Multivariate dependence beyond shannon information,

    R. G. James and J. P. Crutchfield, “Multivariate dependence beyond shannon information,”Entropy, vol. 19, no. 10, p. 531, 2017

  10. [10]

    Nonnegative decomposition of multivariate information,

    P. L. Williams and R. D. Beer, “Nonnegative decomposition of multivariate information,”arXiv preprint arXiv:1004.2515, 2010

  11. [11]

    Disentangling high-order mechanisms and high-order behaviours in complex systems,

    F. E. Rosas, P. A. Mediano, A. I. Luppi, T. F. Varley, J. T. Lizier, S. Stramaglia, H. J. Jensen, and D. Mari- nazzo, “Disentangling high-order mechanisms and high-order behaviours in complex systems,”Nature Physics, vol. 18, no. 5, pp. 476–477, 2022

  12. [12]

    A synergistic core for human brain evolution and cognition,

    A. I. Luppi, P. A. Mediano, F. E. Rosas, N. Holland, T. D. Fryer, J. T. O’Brien, J. B. Rowe, D. K. Menon, D. Bor, and E. A. Stamatakis, “A synergistic core for human brain evolution and cognition,”BioRxiv, 2020

  13. [13]

    Synergistic and redundant information dynamics are modulated by alzheimer’s disease and cognitive impairment,

    K. J. Down, J. Huntley, P. A. Mediano, and D. Bor, “Synergistic and redundant information dynamics are modulated by alzheimer’s disease and cognitive impairment,”bioRxiv, pp. 2026–02, 2026

  14. [14]

    A taxonomy of neuroscientific strategies based on interaction orders,

    M. Neri, A. Brovelli, S. Castro, F. Fraisopi, M. Gatica, R. Herzog, P. A. Mediano, I. Mindlin, G. Petri, D. Bor,et al., “A taxonomy of neuroscientific strategies based on interaction orders,”European Journal of Neuroscience, vol. 61, no. 3, p. e16676, 2025

  15. [15]

    Higher- order and distributed synergistic functional interactions encode information gain in goal-directed learning,

    E. Combrisson, R. Basanisi, M. Neri, G. Auzias, G. Petri, D. Marinazzo, S. Panzeri, and A. Brovelli, “Higher- order and distributed synergistic functional interactions encode information gain in goal-directed learning,” Nature Communications, vol. 16, no. 1, p. 7179, 2025

  16. [16]

    Networks beyond pairwise interactions: Structure and dynamics,

    F. Battiston, G. Cencetti, I. Iacopini, V. Latora, M. Lucas, A. Patania, J.-G. Young, and G. Petri, “Networks beyond pairwise interactions: Structure and dynamics,”Physics reports, vol. 874, pp. 1–92, 2020

  17. [17]

    Synergistic signatures of group mechanisms in higher-order systems,

    T. Robiglio, M. Neri, D. Coppes, C. Agostinelli, F. Battiston, M. Lucas, and G. Petri, “Synergistic signatures of group mechanisms in higher-order systems,”Physical review letters, vol. 134, no. 13, p. 137401, 2025

  18. [18]

    Bivariate measure of redundant information,

    M. Harder, C. Salge, and D. Polani, “Bivariate measure of redundant information,”Physical Review E, vol. 87, no. 1, p. 012130, 2013. 59

  19. [19]

    Shared information—new insights and problems in de- composing information in complex systems,

    N. Bertschinger, J. Rauh, E. Olbrich, and J. Jost, “Shared information—new insights and problems in de- composing information in complex systems,” inProceedings of the European conference on complex systems 2012, pp. 251–269, Springer, 2013

  20. [20]

    Intersection information based on common randomness,

    V. Griffith, E. K. Chong, R. G. James, C. J. Ellison, and J. P. Crutchfield, “Intersection information based on common randomness,”Entropy, vol. 16, no. 4, pp. 1985–2000, 2014

  21. [21]

    Quantifying unique information,

    N. Bertschinger, J. Rauh, E. Olbrich, J. Jost, and N. Ay, “Quantifying unique information,”Entropy, vol. 16, no. 4, pp. 2161–2183, 2014

  22. [22]

    Quantifying synergistic mutual information,

    V. Griffith and C. Koch, “Quantifying synergistic mutual information,” inGuided self-organization: inception, pp. 159–190, Springer, 2014

  23. [23]

    Quantifying redundant information in predicting a target random variable,

    V. Griffith and T. Ho, “Quantifying redundant information in predicting a target random variable,”Entropy, vol. 17, no. 7, pp. 4644–4653, 2015

  24. [24]

    Exploration of synergistic and redundant information sharing in static and dynamical gaussian systems,

    A. B. Barrett, “Exploration of synergistic and redundant information sharing in static and dynamical gaussian systems,”Physical Review E, vol. 91, no. 5, p. 052802, 2015

  25. [25]

    Temporal information partitioning: Characterizing synergy, uniqueness, and redundancy in interacting environmental variables,

    A. E. Goodwell and P. Kumar, “Temporal information partitioning: Characterizing synergy, uniqueness, and redundancy in interacting environmental variables,”Water Resources Research, vol. 53, no. 7, pp. 5920–5942, 2017

  26. [26]

    Measuring multivariate redundant information with pointwise common change in surprisal,

    R. A. Ince, “Measuring multivariate redundant information with pointwise common change in surprisal,” Entropy, vol. 19, no. 7, p. 318, 2017

  27. [27]

    Unique information via dependency constraints,

    R. G. James, J. Emenheiser, and J. P. Crutchfield, “Unique information via dependency constraints,”Journal of Physics A: Mathematical and Theoretical, vol. 52, no. 1, p. 014002, 2018

  28. [28]

    Exact partial information decompositions for gaussian systems based on depen- dency constraints,

    J. W. Kay and R. A. Ince, “Exact partial information decompositions for gaussian systems based on depen- dency constraints,”Entropy, vol. 20, no. 4, p. 240, 2018

  29. [29]

    Unique information and secret key agreement,

    R. G. James, J. Emenheiser, and J. P. Crutchfield, “Unique information and secret key agreement,”Entropy, vol. 21, no. 1, p. 12, 2018

  30. [30]

    Pointwise partial information decomposition using the specificity and ambiguity lattices,

    C. Finn and J. T. Lizier, “Pointwise partial information decomposition using the specificity and ambiguity lattices,”Entropy, vol. 20, no. 4, p. 297, 2018

  31. [31]

    A measure of synergy, redundancy, and unique information using information geometry,

    X. Niu and C. J. Quinn, “A measure of synergy, redundancy, and unique information using information geometry,” in2019 IEEE International Symposium on Information Theory (ISIT), pp. 3127–3131, IEEE, 2019

  32. [32]

    A partial information decomposition for multivariate gaussian systems based on information geometry,

    J. W. Kay, “A partial information decomposition for multivariate gaussian systems based on information geometry,”Entropy, vol. 26, no. 7, p. 542, 2024

  33. [33]

    A partial information decomposition based on causal tensors,

    D. Sigtermans, “A partial information decomposition based on causal tensors,”arXiv preprint arXiv:2001.10481, 2020

  34. [34]

    Towards a framework for observational causality from time series: when shannon meets turing,

    D. Sigtermans, “Towards a framework for observational causality from time series: when shannon meets turing,”Entropy, vol. 22, no. 4, p. 426, 2020

  35. [35]

    Introducing a differentiable measure of pointwise shared information,

    A. Makkeh, A. J. Gutknecht, and M. Wibral, “Introducing a differentiable measure of pointwise shared information,”Physical Review E, vol. 103, no. 3, p. 032149, 2021

  36. [36]

    A partial information decomposition for discrete and continuous variables,

    K. Schick-Poland, A. Makkeh, A. J. Gutknecht, P. Wollstadt, A. Sturm, and M. Wibral, “A partial information decomposition for discrete and continuous variables,”arXiv preprint arXiv:2106.12393, 2021. 60

  37. [37]

    Partial in- formation decomposition for continuous variables based on shared exclusions: Analytical formulation and estimation,

    D. A. Ehrlich, K. Schick-Poland, A. Makkeh, F. Lanfermann, P. Wollstadt, and M. Wibral, “Partial in- formation decomposition for continuous variables based on shared exclusions: Analytical formulation and estimation,”Physical Review E, vol. 110, no. 1, p. 014115, 2024

  38. [38]

    A novel approach to the partial information decomposition,

    A. Kolchinsky, “A novel approach to the partial information decomposition,”Entropy, vol. 24, no. 3, p. 403, 2022

  39. [39]

    Partial information decomposition via deficiency for multivariate gaussians,

    P. Venkatesh and G. Schamberg, “Partial information decomposition via deficiency for multivariate gaussians,” in2022 IEEE International Symposium on Information Theory (ISIT), pp. 2892–2897, IEEE, 2022

  40. [40]

    Decomposing and tracing mutual information by quantifying reachable decision regions,

    T. Mages and C. Rohner, “Decomposing and tracing mutual information by quantifying reachable decision regions,”Entropy, vol. 25, no. 7, p. 1014, 2023

  41. [41]

    Explicit formula for partial information decomposition,

    A. Lyu, A. Clark, and N. Raviv, “Explicit formula for partial information decomposition,” in2024 IEEE International Symposium on Information Theory (ISIT), pp. 2329–2334, IEEE, 2024

  42. [42]

    Reconsidering unique information: Towards a multivariate information decomposition,

    J. Rauh, N. Bertschinger, E. Olbrich, and J. Jost, “Reconsidering unique information: Towards a multivariate information decomposition,” in2014 IEEE International Symposium on Information Theory, pp. 2232–2236, IEEE, 2014

  43. [43]

    Z3: An efficient smt solver,

    L. De Moura and N. Bjørner, “Z3: An efficient smt solver,” inInternational conference on Tools and Algo- rithms for the Construction and Analysis of Systems, pp. 337–340, Springer, 2008

  44. [44]

    On the foundations of combinatorial theory: I. theory of m¨ obius functions,

    G.-C. Rota, “On the foundations of combinatorial theory: I. theory of m¨ obius functions,” inClassic Papers in Combinatorics, pp. 332–360, Springer, 1964

  45. [45]

    Fast m¨ obius transform: An algebraic approach to information decomposition,

    A. Jansma, P. A. Mediano, and F. E. Rosas, “Fast m¨ obius transform: An algebraic approach to information decomposition,”Physical Review Research, vol. 7, no. 3, p. 033049, 2025

  46. [46]

    Bits and pieces: Understanding information decomposition from part-whole relationships and formal logic,

    A. J. Gutknecht, M. Wibral, and A. Makkeh, “Bits and pieces: Understanding information decomposition from part-whole relationships and formal logic,”Proceedings of the Royal Society A, vol. 477, no. 2251, p. 20210110, 2021

  47. [47]

    Information processing and dynamics in minimally cognitive agents,

    R. D. Beer and P. L. Williams, “Information processing and dynamics in minimally cognitive agents,”Cognitive science, vol. 39, no. 1, pp. 1–38, 2015

  48. [48]

    Partial information decomposition as a unified approach to the specification of neural goal functions,

    M. Wibral, V. Priesemann, J. W. Kay, J. T. Lizier, and W. A. Phillips, “Partial information decomposition as a unified approach to the specification of neural goal functions,”Brain and cognition, vol. 112, pp. 25–38, 2017

  49. [49]

    The partial information decomposition of generative neural network models,

    T. M. Tax, P. A. Mediano, and M. Shanahan, “The partial information decomposition of generative neural network models,”Entropy, vol. 19, no. 9, p. 474, 2017

  50. [50]

    Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks,

    A. M. Proca, F. E. Rosas, A. I. Luppi, D. Bor, M. Crosby, and P. A. Mediano, “Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks,”PLOS Com- putational Biology, vol. 20, no. 6, p. e1012178, 2024

  51. [51]

    Partial entropy decomposition reveals higher-order information structures in human brain activity,

    T. F. Varley, M. Pope, M. Grazia, Joshua, and O. Sporns, “Partial entropy decomposition reveals higher-order information structures in human brain activity,”Proceedings of the National Academy of Sciences, vol. 120, no. 30, p. e2300888120, 2023

  52. [52]

    Multivariate information theory uncovers synergistic subsystems of the human cerebral cortex,

    T. F. Varley, M. Pope, J. Faskowitz, and O. Sporns, “Multivariate information theory uncovers synergistic subsystems of the human cerebral cortex,”Communications biology, vol. 6, no. 1, p. 451, 2023

  53. [53]

    Information decomposition and the informational architecture of the brain,

    A. I. Luppi, F. E. Rosas, P. A. Mediano, D. K. Menon, and E. A. Stamatakis, “Information decomposition and the informational architecture of the brain,”Trends in Cognitive Sciences, 2024. 61

  54. [54]

    High-order interdependencies in the aging brain,

    M. Gatica, R. Cofr´ e, P. A. Mediano, F. E. Rosas, P. Orio, I. Diez, S. P. Swinnen, and J. M. Cortes, “High-order interdependencies in the aging brain,”Brain connectivity, vol. 11, no. 9, pp. 734–744, 2021

  55. [55]

    High- order functional redundancy in ageing explained via alterations in the connectome in a whole-brain model,

    M. Gatica, F. E. Rosas, P. AM Mediano, I. Diez, S. P. Swinnen, P. Orio, R. Cofr´ e, and J. M. Cortes, “High- order functional redundancy in ageing explained via alterations in the connectome in a whole-brain model,” PLoS Computational Biology, vol. 18, no. 9, p. e1010431, 2022

  56. [56]

    An integrated computational approach for diversity- sensitive personalized medicine,

    C. Coronel-Oliveros, M. Gatica, R. Herzog, and M. Neri, “An integrated computational approach for diversity- sensitive personalized medicine,”Neuroscience, 2025

  57. [57]

    Beyond pairwise interactions: Charting higher-order models of brain function,

    A. Santoro, M. Neri, S. Poetto, D. Orsenigo, M. Diano, M. Gatica, and G. Petri, “Beyond pairwise interactions: Charting higher-order models of brain function,”bioRxiv, pp. 2025–06, 2025

  58. [58]

    Gene regulatory network inference from single-cell data using multivariate information measures,

    T. E. Chan, M. P. Stumpf, and A. C. Babtie, “Gene regulatory network inference from single-cell data using multivariate information measures,”Cell systems, vol. 5, no. 3, pp. 251–267, 2017

  59. [59]

    Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data,

    S. Chen and J. C. Mar, “Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data,”BMC bioinformatics, vol. 19, no. 1, pp. 1–21, 2018

  60. [60]

    Quantifying information modification in cellular automata using pointwise partial information decomposition,

    C. Finn and J. T. Lizier, “Quantifying information modification in cellular automata using pointwise partial information decomposition,” inArtificial Life Conference Proceedings, pp. 386–387, MIT Press One Rogers Street, Cambridge, MA 02142-1209, USA journals-info . . . , 2018

  61. [61]

    An information-theoretic approach to self-organisation: Emergence of complex interdependencies in coupled dynamical systems,

    F. Rosas, P. A. Mediano, M. Ugarte, and H. J. Jensen, “An information-theoretic approach to self-organisation: Emergence of complex interdependencies in coupled dynamical systems,”Entropy, vol. 20, no. 10, p. 793, 2018

  62. [62]

    Synergistic small worlds that drive technological sophistication,

    H. Rajpal and O. Guerrero, “Synergistic small worlds that drive technological sophistication,”PNAS nexus, vol. 4, no. 4, p. pgaf102, 2025

  63. [63]

    Pearl,Probabilistic reasoning in intelligent systems: networks of plausible inference

    J. Pearl,Probabilistic reasoning in intelligent systems: networks of plausible inference. Elsevier, 2014

  64. [64]

    The identity of information: How deterministic dependencies constrain information synergy and redundancy,

    D. Chicharro, G. Pica, and S. Panzeri, “The identity of information: How deterministic dependencies constrain information synergy and redundancy,”Entropy, vol. 20, no. 3, p. 169, 2018

  65. [65]

    Novel inconsistency results for partial infor- mation decomposition,

    P. H. Matthias, A. Makkeh, M. Wibral, and A. J. Gutknecht, “Novel inconsistency results for partial infor- mation decomposition,”arXiv preprint arXiv:2512.16662, 2025

  66. [66]

    Synergy, redundancy and common information,

    P. K. Banerjee and V. Griffith, “Synergy, redundancy and common information,”arXiv preprint arXiv:1509.03706, 2015

  67. [67]

    On extractable shared information,

    J. Rauh, P. K. Banerjee, E. Olbrich, J. Jost, and N. Bertschinger, “On extractable shared information,” Entropy, vol. 19, no. 7, p. 328, 2017

  68. [68]

    Continuity and additivity properties of information decompositions,

    J. Rauh, P. K. Banerjee, E. Olbrich, G. Mont´ ufar, and J. Jost, “Continuity and additivity properties of information decompositions,”International Journal of Approximate Reasoning, vol. 161, p. 108979, 2023

  69. [69]

    Equivalent comparisons of experiments,

    D. Blackwell, “Equivalent comparisons of experiments,”The annals of mathematical statistics, pp. 265–272, 1953

  70. [70]

    Secret sharing and shared information,

    J. Rauh, “Secret sharing and shared information,”Entropy, vol. 19, no. 11, p. 601, 2017

  71. [71]

    From babel to boole: the logical organization of information decompositions,

    A. J. Gutknecht, A. Makkeh, and M. Wibral, “From babel to boole: the logical organization of information decompositions,”Proceedings of the Royal Society A, vol. 481, no. 2310, p. 20240174, 2025

  72. [72]

    Probability mass exclusions and the directed components of mutual information,

    C. Finn and J. T. Lizier, “Probability mass exclusions and the directed components of mutual information,” Entropy, vol. 20, no. 11, p. 826, 2018

  73. [73]

    An overview of reconstructability analysis,

    M. Zwick, “An overview of reconstructability analysis,”Kybernetes, vol. 33, no. 5/6, pp. 877–905, 2004. 62

  74. [74]

    Ross ashby’s information theory: a bit of history, some solutions to problems, and what we face today,

    K. Krippendorff, “Ross ashby’s information theory: a bit of history, some solutions to problems, and what we face today,”International journal of general systems, vol. 38, no. 2, pp. 189–212, 2009

  75. [75]

    Information geometry on hierarchy of probability distributions,

    S.-I. Amari, “Information geometry on hierarchy of probability distributions,”IEEE transactions on infor- mation theory, vol. 47, no. 5, pp. 1701–1711, 2001

  76. [76]

    Causal diagrams for empirical research,

    J. Pearl, “Causal diagrams for empirical research,”Biometrika, vol. 82, no. 4, pp. 669–688, 1995

  77. [77]

    Causal inference in statistics: An overview,

    J. Pearl, “Causal inference in statistics: An overview,”Statistics Surveys, vol. 3, pp. 96–146, 2009

  78. [78]

    The do-calculus revisited,

    J. Pearl, “The do-calculus revisited,” inProceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, pp. 3–11, 2012

  79. [79]

    “dit“: A Python package for discrete information theory,

    R. G. James, C. J. Ellison, and J. P. Crutchfield, ““dit“: A Python package for discrete information theory,” Journal of Open Source Software, vol. 3, no. 25, p. 738, 2018

  80. [80]

    Predictive information decomposition as a tool to quantify emergent dynamical behaviors in physiological networks,

    L. Faes, G. Mijatovic, L. Sparacino, and A. Porta, “Predictive information decomposition as a tool to quantify emergent dynamical behaviors in physiological networks,”IEEE Transactions on Biomedical Engineering, 2025

Showing first 80 references.