Results for 'Convexity'

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  1. Measurement of number and average size in volume 129.Convex Bodies - 1968 - In Robert T. DeHoff & Frederick N. Rhines (eds.), Quantitative Microscopy. New York: Mcgraw-Hill. pp. 128.
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  2.  20
    A Convex Mirror: Schopenhauer's Philosophy and the Sciences.Marco Segala - 2024 - New York, US: OUP Usa.
    Schopenhauer is acknowledged as “the philosopher of pessimism” and author of a system that teaches how art and morality can help humans navigate life in “the worst of all possible worlds.” This dominant image has cut off an important branch of Schopenhauer’s tree of philosophy—metaphysics of nature and its constant dialogue with the sciences of the time. Beginning with a reappraisal of Schopenhauer’s system as a whole—which he defined as a “single thought”—this book interprets his metaphysics as a knowledge that (...)
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  3.  89
    T-Convexity and Tame Extensions.Dries Lou Van Den & H. Lewenberg Adam - 1995 - Journal of Symbolic Logic 60 (1):74 - 102.
    Let T be a complete o-minimal extension of the theory of real closed fields. We characterize the convex hulls of elementary substructures of models of T and show that the residue field of such a convex hull has a natural expansion to a model of T. We give a quantifier elimination relative to T for the theory of pairs (R, V) where $\mathscr{R} \models T$ and V ≠ R is the convex hull of an elementary substructure of R. We deduce (...)
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  4.  29
    Convex MV-Algebras: Many-Valued Logics Meet Decision Theory.T. Flaminio, H. Hosni & S. Lapenta - 2018 - Studia Logica 106 (5):913-945.
    This paper introduces a logical analysis of convex combinations within the framework of Łukasiewicz real-valued logic. This provides a natural link between the fields of many-valued logics and decision theory under uncertainty, where the notion of convexity plays a central role. We set out to explore such a link by defining convex operators on MV-algebras, which are the equivalent algebraic semantics of Łukasiewicz logic. This gives us a formal language to reason about the expected value of bounded random variables. (...)
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  5. Adding Convexity to Mereotopology.Marion Haemmerli & Achille C. Varzi - 2014 - In Pawel Garbacz & Oliver Kutz (eds.), Formal Ontology in Information Systems. Proceedings of the Eighth International Conference. IOS Press. pp. 65–78.
    Convexity predicates and the convex hull operator continue to play an important role in theories of spatial representation and reasoning, yet their first-order axiomatization is still a matter of controversy. In this paper, we present a new approach to adding convexity to mereotopological theory with boundary elements by specifying first-order axioms for a binary segment operator s. We show that our axioms yields a convex hull operator h that supports, not only the basic properties of convex regions, but (...)
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  6.  15
    Convexity and Monotonicity in Language Coordination: Simulating the Emergence of Semantic Universals in Populations of Cognitive Agents.Nina Gierasimczuk, Dariusz Kalociński, Franciszek Rakowski & Jakub Uszyński - 2023 - Journal of Logic, Language and Information 32 (4):569-600.
    Natural languages vary in their quantity expressions, but the variation seems to be constrained by general properties, so-calleduniversals. Their explanations have been sought among constraints of human cognition, communication, complexity, and pragmatics. In this article, we apply a state-of-the-art language coordination model to the semantic domain of quantities to examine whether two quantity universals—monotonicity and convexity—arise as a result of coordination. Assuming precise number perception by the agents, we evolve communicatively usable quantity terminologies in two separate conditions: a numeric-based (...)
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  7. Uniformly convex Banach spaces are reflexive—constructively.Douglas S. Bridges, Hajime Ishihara & Maarten McKubre-Jordens - 2013 - Mathematical Logic Quarterly 59 (4-5):352-356.
    We propose a natural definition of what it means in a constructive context for a Banach space to be reflexive, and then prove a constructive counterpart of the Milman-Pettis theorem that uniformly convex Banach spaces are reflexive.
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  8.  56
    A case against convexity in conceptual spaces.José V. Hernández-Conde - 2017 - Synthese 194 (10):4011-4037.
    The notion of conceptual space, proposed by Gärdenfors as a framework for the representation of concepts and knowledge, has been highly influential over the last decade or so. One of the main theses involved in this approach is that the conceptual regions associated with properties, concepts, verbs, etc. are convex. The aim of this paper is to show that such a constraint—that of the convexity of the geometry of conceptual regions—is problematic; both from a theoretical perspective and with regard (...)
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  9.  43
    T-convexity and tame extensions II.Lou van den Dries - 1997 - Journal of Symbolic Logic 62 (1):14-34.
    I solve here some problems left open in “T-convexity and Tame Extensions” [9]. Familiarity with [9] is assumed, and I will freely use its notations. In particular,Twill denote a completeo-minimal theory extending RCF, the theory of real closed fields. Let (,V) ⊨Tconvex, let=V/m(V)be the residue field, with residue class mapx↦:V↦, and let υ:→ Γ be the associated valuation. “Definable” will mean “definable with parameters”.The main goal of this article is to determine the structure induced by(,V)on its residue fieldand on (...)
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  10.  27
    T-Convexity and Tame Extensions II.Lou Van Den Dries - 1997 - Journal of Symbolic Logic 62 (1):14 - 34.
    I solve here some problems left open in “T-convexity and Tame Extensions” [9]. Familiarity with [9] is assumed, and I will freely use its notations. In particular,Twill denote a completeo-minimal theory extending RCF, the theory of real closed fields. Let (,V) ⊨Tconvex, let=V/m(V)be the residue field, with residue class mapx↦:V↦, and let υ:→ Γ be the associated valuation. “Definable” will mean “definable with parameters”.The main goal of this article is to determine the structure induced by(,V)on its residue fieldand on (...)
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  11.  37
    Schur convexity, quasi-convexity and preference for early resolution of uncertainty.Zvi Safra & Eyal Sulganik - 1995 - Theory and Decision 39 (2):213-218.
    This paper deals with decision makers who choose among information systems. It shows that the properties of Schur convexity and of quasi-convexity are equivalent, even when general preferences are considered. Since Schur convexity is closely related to having a willingness to accept information and since quasi-convexity is closely related to having a preference for early resolution of the uncertainty about which information system prevails, then it follows that the equivalence implies that decision makers prefer more information (...)
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  12.  6
    1-Convex Extensions of Incomplete Cooperative Games and the Average Value.Martin Černý & Jan Bok - 2023 - Theory and Decision 96 (2):239-268.
    The model of incomplete cooperative games incorporates uncertainty into the classical model of cooperative games by considering a partial characteristic function. Thus the values for some of the coalitions are not known. The main focus of this paper is 1-convexity under this framework. We are interested in two heavily intertwined questions. First, given an incomplete game, how can we fill in the missing values to obtain a complete 1-convex game? Second, how to determine in a rational, fair, and efficient (...)
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  13.  29
    Externality, convexity and institutions.Andreas A. Papandreou - 2003 - Economics and Philosophy 19 (2):281-309.
    Economic theory has generally acknowledged the role that institutions have in shaping economic space. The distinction, however, between physical and institutional descriptions of economic activity has not received adequate attention within the mainstream paradigm. In this paper I show how a proper distinction between the physical and institutional space in economic models will help clarify the concept of externality and provide a better interpretation of the relationship between externality and nonconvexity. I argue that within the Arrow-Debreu framework externality should be (...)
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  14.  45
    Convexity and Differentiability of Controlled Risk.L. I. Krechetov - 2004 - Theory and Decision 57 (4):291-307.
    We investigate risk associated with the violation of a constraint, which is desirable but hardly satisfiable in all possible states of nature.
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  15.  14
    Convexity and unique minimum points.Josef Berger & Gregor Svindland - 2019 - Archive for Mathematical Logic 58 (1-2):27-34.
    We show constructively that every quasi-convex, uniformly continuous function \ with at most one minimum point has a minimum point, where C is a convex compact subset of a finite dimensional normed space. Applications include a result on strictly quasi-convex functions, a supporting hyperplane theorem, and a short proof of the constructive fundamental theorem of approximation theory.
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  16.  16
    Convexity Is an Empirical Law in the Theory of Conceptual Spaces: Reply to Hernández-Conde.Peter Gärdenfors - 2019 - In Peter Gärdenfors, Antti Hautamäki, Frank Zenker & Mauri Kaipainen (eds.), Conceptual Spaces: Elaborations and Applications. Springer Verlag.
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  17.  13
    "Convex" and "concave".E. Williams - 1971 - Mind 80 (317):132.
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  18. T-convexity and Tame extensions.LouDen Dries & Adam H. Lewenberg - 1995 - Journal of Symbolic Logic 60 (1):74 - 102.
    Let T be a complete o-minimal extension of the theory of real closed fields. We characterize the convex hulls of elementary substructures of models of T and show that the residue field of such a convex hull has a natural expansion to a model of T. We give a quantifier elimination relative to T for the theory of pairs (R, V) where $\mathscr{R} \models T$ and V ≠ R is the convex hull of an elementary substructure of R. We deduce (...)
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  19.  32
    Convex stochastic dominance with finite consequence sets.Peter C. Fishburn - 1974 - Theory and Decision 5 (2):119-137.
  20.  51
    The evolution of convex categories.Gerhard Jäger - 2007 - Linguistics and Philosophy 30 (5):551-564.
    Gärdenfors (Conceptual spaces, 2000) argues that the semantic domains that natural language deals with have a geometrical structure. He gives evidence that simple natural language adjectives usually denote natural properties, where a natural property is a convex region of such a “conceptual space.” In this paper I will show that this feature of natural categories need not be stipulated as basic. In fact, it can be shown to be the result of evolutionary dynamics of communicative strategies under very general assumptions.
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  21. Convex merge of voronoi polygons for neural network design.Ibrahim Esat & Victoria Riao - 1996 - Esda 1996: Expert Systems and Ai; Neural Networks 7:197.
     
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  22.  67
    Fundamental results for pointfree convex geometry.Yoshihiro Maruyama - 2010 - Annals of Pure and Applied Logic 161 (12):1486-1501.
    Inspired by locale theory, we propose “pointfree convex geometry”. We introduce the notion of convexity algebra as a pointfree convexity space. There are two notions of a point for convexity algebra: one is a chain-prime meet-complete filter and the other is a maximal meet-complete filter. In this paper we show the following: the former notion of a point induces a dual equivalence between the category of “spatial” convexity algebras and the category of “sober” convexity spaces (...)
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  23.  4
    Convex optimization for additive noise reduction in quantitative complex object wave retrieval using compressive off-axis digital holographic imaging.Anith Nelleri & B. Lokesh Reddy - 2022 - Journal of Intelligent Systems 31 (1):706-715.
    Image denoising is one of the important problems in the research field of computer vision, artificial intelligence, 3D vision, and image processing, where the fundamental aim is to recover the original image features from a noisy contaminated image. The camera sensor additive noise present in the holographic recording process reduces the quality of the retrieved image. Even though various techniques have been developed to minimize the noise in digital holography, the noise reduction still remains a challenging task. This article presents (...)
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  24.  86
    Calibration and Convexity: Response to Gregory Wheeler.Jon Williamson - 2012 - British Journal for the Philosophy of Science 63 (4):851-857.
    This note responds to some criticisms of my recent book In Defence of Objective Bayesianism that were provided by Gregory Wheeler in his ‘Objective Bayesian Calibration and the Problem of Non-convex Evidence’.
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  25.  63
    Nonclassical Probability and Convex Hulls.Seamus Bradley - 2017 - Erkenntnis 82 (1):87-101.
    It is well known that the convex hull of the classical truth value functions contains all and only the probability functions. Work by Paris and Williams has shown that this also holds for various kinds of nonclassical logics too. This note summarises the formal details of this topic and extends the results slightly.
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  26.  13
    Constructive notions of strict convexity.Douglas S. Bridges - 1993 - Mathematical Logic Quarterly 39 (1):295-300.
    Two classically equivalent, but constructively inequivalent, strict convexity properties of a preference relation are discussed, and conditions given under which the stronger notion is a consequence of the weaker. The last part of the paper introduces uniformly rotund preferences, and shows that uniform rotundity implies strict convexity. The paper is written from a strictly constructive point of view, in which all proofs embody algorithms. MSC: 03F60, 90A06.
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  27.  8
    Vexed convexity.Henry E. Kyburg - 2006 - In Erik J. Olsson (ed.), Knowledge and Inquiry: Essays on the Pragmatism of Isaac Levi. Cambridge University Press. pp. 97--110.
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  28. Convexity and Separability in Representing Consensus.Isaac Levi - 2008 - In Kaushik Basu & Ravi Kanbur (eds.), Arguments for a Better World: Essays in Honor of Amartya Sen: Volume I: Ethics, Welfare, and Measurement and Volume Ii: Society, Institutions, and Development. Oxford University Press.
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  29. Convexity and Separability in Representing Consensus.Isaac Levi - 2008 - In Kaushik Basu & Ravi Kanbur (eds.), Arguments for a Better World: Essays in Honor of Amartya Sen: Volume I: Ethics, Welfare, and Measurement. Oxford University Press.
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  30.  28
    Nonclassical Probability, Convex Hulls, and Dutch Books.Michał Gil Sanchez, Zalán Gyenis & Leszek Wroński - forthcoming - Episteme:1-21.
    We report a solution to an open problem regarding the axiomatization of the convex hull of a type of nonclassical evaluations. We then investigate the meaning of this result for the larger context of the relation between rational credence functions and nonclassical probability. We claim that the notions of bets and Dutch Books typically employed in formal epistemology are of doubtful use outside the realm of classical logic, eventually proposing two novel ways of understanding Dutch Books in nonclassical settings.
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  31. Naturalness and Convex Class Nominalism.Ben Blumson - 2019 - Dialectica 73 (1-2):65-81.
    In this paper I argue that the analysis of natural properties as convex subsets of a metric space in which the distances are degrees of dissimilarity is incompatible with both the definition of degree of dissimilarity as number of natural properties not in common and the definition of degree of dissimilarity as proportion of natural properties not in common, since in combination with either of these definitions it entails that every property is a natural property, which is absurd. I suggest (...)
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  32.  60
    On S-Convexity and Risk Aversion.Michel Denuit, Claude Lefèvre & Marco Scarsini - 2001 - Theory and Decision 50 (3):239-248.
    The present note first discusses the concept of s-convex pain functions in decision theory. Then, the economic behavior of an agent with such a pain function is represented through the comparison of some recursive lotteries.
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  33. Sets of probability distributions, independence, and convexity.Fabio G. Cozman - 2012 - Synthese 186 (2):577-600.
    This paper analyzes concepts of independence and assumptions of convexity in the theory of sets of probability distributions. The starting point is Kyburg and Pittarelli’s discussion of “convex Bayesianism” (in particular their proposals concerning E-admissibility, independence, and convexity). The paper offers an organized review of the literature on independence for sets of probability distributions; new results on graphoid properties and on the justification of “strong independence” (using exchangeability) are presented. Finally, the connection between Kyburg and Pittarelli’s results and (...)
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  34. Truthlikeness and the convexity of propositions.Graham Oddie - 1987 - In Kuipers T. (ed.), What is Closer-to-the-Truth. Rodopi. pp. 197-217.
  35. A Note on Prototypes, Convexity and Fuzzy Sets.Norman Foo & Boon Toh Low - 2008 - Studia Logica 90 (1):125-137.
    The work on prototypes in ontologies pioneered by Rosch [10] and elaborated by Lakoff [8] and Freund [3] is related to vagueness in the sense that the more remote an instance is from a prototype the fewer people agree that it is an example of that prototype. An intuitive example is the prototypical “mother”, and it is observed that more specific instances like ”single mother”, “adoptive mother”, “surrogate mother”, etc., are less and less likely to be classified as “mothers” by (...)
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  36.  6
    Hamilton Connectivity of Convex Polytopes with Applications to Their Detour Index.Sakander Hayat, Asad Khan, Suliman Khan & Jia-Bao Liu - 2021 - Complexity 2021:1-23.
    A connected graph is called Hamilton-connected if there exists a Hamiltonian path between any pair of its vertices. Determining whether a graph is Hamilton-connected is an NP-complete problem. Hamiltonian and Hamilton-connected graphs have diverse applications in computer science and electrical engineering. The detour index of a graph is defined to be the sum of lengths of detours between all the unordered pairs of vertices. The detour index has diverse applications in chemistry. Computing the detour index for a graph is also (...)
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  37. Objective Bayesian Calibration and the Problem of Non-convex Evidence.Gregory Wheeler - 2012 - British Journal for the Philosophy of Science 63 (4):841-850.
    Jon Williamson's Objective Bayesian Epistemology relies upon a calibration norm to constrain credal probability by both quantitative and qualitative evidence. One role of the calibration norm is to ensure that evidence works to constrain a convex set of probability functions. This essay brings into focus a problem for Williamson's theory when qualitative evidence specifies non-convex constraints.
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  38.  48
    Convex models of uncertainty: Applications and implications. [REVIEW]Yakov Ben-Haim - 1994 - Erkenntnis 41 (2):139 - 156.
    Modern engineering has included the basic sciences and their accompanying mathematical theories among its primary tools. The theory of probability is one of the more recent entries into standard engineering practice in various technological disciplines. Probability and statistics serve useful functions in the solution of many engineering problems. However, not all technological manifestations of uncertainty are amenable to probabilistic representation. In this paper we identify the conceptual limitations of probabilistic and related theories as they occur in a wide range of (...)
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  39.  55
    Locative and Directional Prepositions in Conceptual Spaces: The Role of Polar Convexity.Joost Zwarts & Peter Gärdenfors - 2016 - Journal of Logic, Language and Information 25 (1):109-138.
    We approach the semantics of prepositions from the perspective of conceptual spaces. Focusing on purely spatial locative and directional prepositions, we analyze both types of prepositions in terms of polar coordinates instead of Cartesian coordinates. This makes it possible to demonstrate that the property of convexity holds quite generally in the domain of prepositions of location and direction, supporting the important role that this property plays in conceptual spaces.
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  40.  12
    Degrees of convex dependence in recursively enumerable vector spaces.Thomas A. Nevins - 1993 - Annals of Pure and Applied Logic 60 (1):31-47.
    Let W be a recursively enumerable vector space over a recursive ordered field. We show the Turing equivalence of the following sets: the set of all tuples of vectors in W which are linearly dependent; the set of all tuples of vectors in W whose convex closures contain the zero vector; and the set of all pairs of tuples in W such that the convex closure of X intersects the convex closure of Y. We also form the analogous sets consisting (...)
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  41.  5
    Differential marginality, inessential games and convex combinations of values.Zeguang Cui, Erfang Shan & Wenrong Lyu - 2023 - Theory and Decision 96 (3):463-475.
    The principle of differential marginality (Casajus in Theory and Decis 71(2):163-–174) for cooperative games is a very appealing property that requires equal productivity differentials to translate into equal payoff differentials. In this paper we apply this property to axiomatic characterizations of values. We show that differential marginality implies additivity and symmetry under certain conditions. Based on this result, we propose new characterizations of the equal division and the equal surplus division values. Finally, we characterize two classes of convex combinations of (...)
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  42.  38
    Correction to “T-convexity and tame extensions II”.Lou Van Den Dries - 1998 - Journal of Symbolic Logic 63 (4):1597-1597.
    Related Works: Original Paper: Lou Van Den Dries. $T$-Convexity and Tame Extensions II. J. Symbolic Logic, Volume 62, Issue 1 , 14--34. Project Euclid: euclid.jsl/1183745182.
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  43.  7
    Definable Functions and Stratifications in Power-Bounded T -Convex Fields.Erick García Ramírez - 2020 - Notre Dame Journal of Formal Logic 61 (3):441-465.
    We study properties of definable sets and functions in power-bounded T -convex fields, proving that the latter have the multidimensional Jacobian property and that the theory of T -convex fields is b -minimal with centers. Through these results and work of I. Halupczok we ensure that a certain kind of geometrical stratifications exist for definable objects in said fields. We then discuss a number of applications of those stratifications, including applications to Archimedean o-minimal geometry.
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  44.  8
    A new test of convexity–concavity of discount function.Pavlo R. Blavatskyy & Hela Maafi - 2020 - Theory and Decision 89 (2):121-136.
    Discounted utility theory and its generalizations use discount functions for weighting utilities of outcomes received in different time periods. We propose a new simple test of convexity–concavity of discount function. This test can be used with any utility function and any preferences over risky lotteries. The data from a controlled laboratory experiment show that about one third of experimental subjects reveal a concave discount function and another one third of subjects reveal a convex discount function.
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  45. On s-convexity and risk aversion.Denuit Michel, Lefevre Claude & Scarsini Marco - 2001 - Theory and Decision 50 (3).
     
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  46.  15
    Logic of Convex Order.Chenwei Shi & Yang Sun - 2021 - Studia Logica 109 (5):1019-1047.
    Based on a pre-order on a set, we axiomatize the Egli-Milner order on the power set and show how it is related to the Lewis order. Moreover, we consider a strict version of the Egli-Milner order and show how it can be related to the semantics of conditionals based on a priority structure.
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  47.  14
    Conditional Logic is Complete for Convexity in the Plane.Johannes Marti - 2023 - Review of Symbolic Logic 16 (2):529-552.
    We prove completeness of preferential conditional logic with respect to convexity over finite sets of points in the Euclidean plane. A conditional is defined to be true in a finite set of points if all extreme points of the set interpreting the antecedent satisfy the consequent. Equivalently, a conditional is true if the antecedent is contained in the convex hull of the points that satisfy both the antecedent and consequent. Our result is then that every consistent formula without nested (...)
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  48.  35
    Syntactic characterisations of amalgamation, convexity and related properties.Paul D. Bacsich & Dafydd Rowlands Hughes - 1974 - Journal of Symbolic Logic 39 (3):433-451.
  49.  16
    Properties of tree convex constraints.Yuanlin Zhang & Eugene C. Freuder - 2008 - Artificial Intelligence 172 (12-13):1605-1612.
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  50.  4
    Solving connected row convex constraints by variable elimination.Yuanlin Zhang & Satyanarayana Marisetti - 2009 - Artificial Intelligence 173 (12-13):1204-1219.
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