Information Dynamics

Though we have access to a wealth of information, the main issue is always how to process the available information. How to make sense of all we observe and know. Just like the English alphabet: we know there are 26 letters but unless we put these letters together in a meaningful way, they convey no information. There are infinitely many ways of putting these letters together. Only a small number of those make sense. Only some of those convey exactly what we wish to convey though the message may be interpreted differently by different individuals. That same issue comes up with information: how can we process the information we have? How can we infer and reason under conditions of incomplete observed information? In his seminal book on the philosophy of information, Floridi (2011a) raises a number of open questions. I discuss here one of these questions. That question is how to process information. To do so, I take the more realistic view that information is always limited, incomplete and possibly noisy. I define types of information, relate it to Floridi’s definitions and discuss a basic formulation for processing information under a unified framework. I relate it to some of the basic concepts discussed in the book.     

The Logic of Knowledge and the Flow of Information

In this paper I look at Fred Dretske’s account of information and knowledge as developed in Knowledge and The Flow of Information. In particular, I translate Dretske’s probabilistic definition of information to a modal logical framework and subsequently use this to explicate the conception of information and its flow which is central to his account, including the notions of channel conditions and relevant alternatives. Some key products of this task are an analysis of the issue of information closure and an investigation into some of the logical properties of Dretske’s account of information flow.     

Annotated Logic Applications for Imperfect Information

Imperfect information is a very general term that comprises different types of information, such as uncertain, vague, fuzzy, inconsistent, possibilistic, probabilistic, partially or totally incomplete information [2]. In the literature of knowledge representation we find a different formal model for each one of these distinct types. For example, annotated logic is a formal model to represent inconsistent information.Annotated logics are non-classical logics introduced in [20] as a logic programming theory. They were proved to be paraconsistent. Based on [5], we present in this work the annotated logic programming theory and some of its applications in Artificial Intelligence (AI). We present it as a formalism to reason with inconsistent information and investigate its possibility to represent other types of imperfect information, such as possibilistic and non-monotonic reasoning. Our main goal is to verify and confirm the importance of annotated logics as a tool for developing knowledge-based and automated reasoning systems in AI.     

Logic and Computational Complexity for Boolean Information Retrieval

We study the complexity of query satisfiability and entailment for the Boolean information retrieval models WP and AWV using techniques from propositional logic and computational complexity. WP and AWV can be used to represent and query textual information under the Boolean model using the concept of attribute with values of type text, the concept of word, and word proximity constraints. Variations of WP and AWP are in use in most deployed digital libraries using the Boolean model, text extenders for relational database systems (e.g., Oracle 10g), search engines, and P2P systems for information retrieval and filtering     

Evolutionary Synthesis of Logic Circuits Using Information Theory

In this paper, we propose the use of Information Theory as thebasis for designing a fitness function for Boolean circuit designusing Genetic Programming. Boolean functions are implemented byreplicating binary multiplexers. Entropy-based measures, such asMutual Information and Normalized Mutual Information areinvestigated as tools for similarity measures between the targetand evolving circuit. Three fitness functions are built over aprimitive one. We show that the landscape of Normalized MutualInformation is more amenable for being used as a fitness functionthan simple Mutual Information. The evolutionary synthesizedcircuits are compared to the known optimum size. A discussion ofthe potential of the Information-Theoretical approach is given.     

Information and Dynamics: Sequences of Call Markets

This paper is part of a wider research project with the objective of creating computational testbeds for designing and testing new mechanisms—new economic and political institutions. Here we illustrate the power of such an approach by testing two call market designs in a repeated demand-supply environment. We find there to be significant differences in performance depending on the information provided to the traders between calls. In particular, we find that both dynamic and static performance is better, less volitility and higher gains from trade, if traders receive less information between calls.     

领域值信息表上的邻域逻辑及其数据推理

引入了一种基于邻域值信息表的邻域逻辑,它是用邻域拓扑内点和邻域拓扑闭包作为逻辑算子的一种逻辑。其内点和闭包是先经二元关系定义了邻域系统,然后用这种邻域系统来定义它。这种逻辑被定义在信息表上,其表上的每个个体关于属性不是取单独一个值,而是扩充到取一个值的领域。公式的真值被扩充为一个区间或邻域,因此讨论一个公式可满足性的三种类型：邻域内点可满足、邻域闭包可满足和邻域可满足,即将公式的真值扩充为多值,并讨论了这种真值关于逻辑联结词的运算和公式的语义模型。最后还给出了这种逻辑的数据推理。     

Practical Handling of Exception-Tainted Rules and Independence Information in Possibilistic Logic

This paper provides a survey of possibilistic logic as a simple and efficient tool for handling nonmonotonic reasoning, with some emphasis on algorithmic issues. In our previous works, two well-known nonmonotonic systems have been encoded in the possibility theory framework: the preferential inference based on System P, and the rational closure inference proposed by Lehmann and Magidor which relies on System P augmented with a rational monotony postulate. System P is known to provide reasonable but very cautious conclusions, and in particular, preferential inference is blocked by the presence of irrelevant properties. When using Lehmann's rational closure, the inference machinery, which is then more productive, may still remain too cautious, or on the contrary, provide counter -intuitive conclusions. The paper proposes an approach to overcome the cautiousness of System P and the problems encountered by the rational closure inference. This approach takes advantage of (contextual) independence assumptions of the form: the fact that is true (or is false) does not affect the validity of the rule normally if then . The modelling of such independence assumptions is discussed in the possibilistic framework. Moreover, we show that when a counter-intuitive conclusion of a set of defaults can be inferred, it is always possible to repair the set of defaults by adding suitable information so as to produce the desired conclusions and block unsuitable ones.     

The Information Dynamics of the Diffusion Processes of Population Genetics

With no prior knowledge assumed, the Wright-Fisher-Kimura diffusion process on gene frequency space is introduced and discussed from the geometric symmetries point of view. This derives from consideration of the covariance matrix of this diffusion as dynamical information analogous to Mahalonobis' static covariance matrix for informational discrimination of data in statistics. This model allows one to obtain Antonelli's ray solution in closed form for all dimensions of the gene frequency space, and to study the more difficult Felsenstein natural selection diffusion in higher dimensions. Of particular interest are the effects of positive and negative curvature in various regions of the frequency space. For the first time, several computer generated 3-dimensional geometries are presented in the collection of figures for the Felsenstein process of 3 alleles. Regions of negative curvature exhibit weak chaos in natural selection, while those of positive curvature exhibit meandering paths. An open problem is stated at the end.     

Day-to-Day Assignment Models and Traffic Dynamics Under Information Provision

Networks and Spatial Economics - Traffic networks are often perturbed by temporal events, which generally cause capacity on the links to drop. A capacity drop can lead to fluctuations in flows and...     

Dynamic Relation Logic Is the Logic of DPL-Relations

In this paper we prove that the principles in the languagewith relation composition and dynamic implication, valid forall binary relations, are the same ones as the principlesvalid when we restrict ourselves to DPL-relations,i.e. relations generated from conditions (tests) and resettings.     

符号动力学和信息熵在暴雨监测中的应用研究

暴雨是世界上最主要的灾害之一,每年都会给国民经济和人民的生命财产造成巨大的损失。但是,暴雨预报是非常困难的,造成这种情况的根本原因是对大气环流短时间的突变和强对流天气系统发生、发展的机理了解得非常少。这其中包括对大气内部的动力机制及其与外部环流之间相互作用的物理过程和热带、中纬度的各种天气系统异常变化的机理,特别是对多尺度相互作用的研究还非常浅显。假如忽略一些随机因素的影响,大气系统可以被认为是一个确定性的非线性系统。符号动力学是非线性科学的一个分支,有着深厚的理论基础,它已慢慢成为实际工作中所要掌握的工具之一。信息熵是信息论中最重要的物理量之一,它把非线性科学与统计学结合起来,已经成为了一种分析非线性问题的重要工具。利用符号动力学和信息熵对暴雨过程进行研究具有一定的探索意义,通过分析大量暴雨过程的熵曲线,发现大部分暴雨过程在发生前10 d内,其熵值达到了极小值。这一征兆的发现表明,该方法在对暴雨事件进行监测和预报方面具有一定的研究价值。