A Multiagent Approach to Modelling Complex Phenomena

Designing models of complex phenomena is a difficult task in engineering that can be tackled by composing a number of partial models to produce a global model of the phenomena. We propose to embed the partial models in software agents and to implement their composition as a cooperative negotiation between the agents. The resulting multiagent system provides a global model of a phenomenon. We applied this approach in modelling two complex physiological processes: the heart rate regulation and the glucose-insulin metabolism. Beyond the effectiveness demonstrated in these two applications, the idea of using models associated to software agents to give reason of complex phenomena is in accordance with current tendencies in epistemology, where it is evident an increasing use of computational models for scientific explanation and analysis. Therefore, our approach has not only a practical, but also a theoretical significance: agents embedding models are a technology suitable both to representing and to investigating reality.

Models in Biology and Physics: What’s the Difference?

In Making Sense of Life, Keller emphasizes several differences between biology and physics. Her analysis focuses on significant ways in which modelling practices in some areas of biology, especially developmental biology, differ from those of the physical sciences. She suggests that natural models and modelling by homology play a central role in the former but not the latter. In this paper, I focus instead on those practices that are importantly similar, from the point of view of epistemology and cognitive science. I argue that concrete and abstract models are significant in both disciplines, that there are shared selection criteria for models in physics and biology, e.g. familiarity, and that modelling often occurs in a similar fashion.     

Mathematical Models and Reality: A Constructivist Perspective

To explore the relation between mathematical models and reality, four different domains of reality are distinguished: observer-independent reality (to which there is no direct access), personal reality, social reality and mathematical/formal reality. The concepts of personal and social reality are strongly inspired by constructivist ideas. Mathematical reality is social as well, but constructed as an autonomous system in order to make absolute agreement possible. The essential problem of mathematical modelling is that within mathematics there is agreement about ‘truth’, but the assignment of mathematics to informal reality is not itself formally analysable, and it is dependent on social and personal construction processes. On these levels, absolute agreement cannot be expected. Starting from this point of view, repercussion of mathematical on social and personal reality, the historical development of mathematical modelling, and the role, use and interpretation of mathematical models in scientific practice are discussed.     

Data Model and Classification by Trees: The Minimum Variance Reduction (MVR) Method

O (n ⁴), where n is the number of objects. We describe the application of the MVR method to two data models: the weighted least-squares (WLS) model (V is diagonal), where the MVR method can be reduced to an O(n ³) time complexity; a model arising from the study of biological sequences, which involves a complex non-diagonal V matrix that is estimated from the dissimilarity matrix Δ. For both models, we provide simulation results that show a significant error reduction in the reconstruction of T, relative to classical agglomerative algorithms.     

Creative Strategies Employed in Modelling: A Case Study

This paper examines creative strategies employed inscientific modelling. It is argued that being creativepresents not a discrete event, but rather an ongoingeffort consisting of many individual `creative acts'.These take place over extended periods of time andcan be carried out by different people, working ondifferent aspects of the same project. The example ofextended extragalactic radio sources shows that, inorder to model a complicated phenomenon in itsentirety, the modelling task is split up into smallerproblems that result in several sub-models. This is away of using cognitive resources efficiently and in away which overcomes their limitations. Another aspectof modelling that requires creativity is theemployment of visualisation in order to reassemble,i.e. recreate the unity of, the various sub-models bymeans of visualisation. This illustrates how thecreative effort required to deal with the complexityof the complicated phenomenon of radio sources ischannelled in order to use cognitive resourcesefficiently and to stay within their capacity.     

论纳尔逊·古德曼对“准分析”困境的解决及其效应

作为对肇始于弗雷格的经典分析(构造)计划的进一步推进和实现,卡尔纳普的那个以"准分析"为基本方法的构造系统历来被公认为楷模。然而,古德曼却发现,"准分析"面临着足以威胁其整个构造计划的"同伴关系"和"不完美的共有"困难,并基于多元主义、唯名论和个体计算方法给出了解决方案,不仅因重构了构造主义而回应了对整个构造主义计划的质疑,而且借助于实用主义因素的引入奠基了分析哲学两大学派的融流取向。     

Belief Systems and the Modeling Relation

The paper presents the most general aspects of scientific modeling and shows that social systems naturally include different belief systems (i.e. different models). Belief systems differ in a variety of respects, most notably in the selection of suitable qualities to encode and the internal structure of the observables. The following results emerge from the analysis: (1) conflict is explained by showing that different models encode different qualities, which implies that they model different realities; (2) explicitly connecting models to the realities that they encode makes it possible to clarify the relations among models; (3) by understanding that social systems are complex one knows that there is no chance of developing a maximal model of the system; (4) the distinction among different levels of depth implicitly includes a strategy for inducing change; (5) identity-preserving models are among the most difficult to modify; (6) since models do not customarily generate internal signals of error, strategies with which to determine when models are out of synch with their situations are especially valuable; (7) changing the form of power from a zero sum game to a positive sum game helps transform the nature of conflicts.     

The representation of three-way proximity data by single and multiple tree structure models

Models for the representation of proximity data (similarities/dissimilarities) can be categorized into one of three groups of models: continuous spatial models, discrete nonspatial models, and hybrid models (which combine aspects of both spatial and discrete models). Multidimensional scaling models and associated methods, used for thespatial representation of such proximity data, have been devised to accommodate two, three, and higher-way arrays. At least one model/method for overlapping (but generally non-hierarchical) clustering called INDCLUS (Carroll and Arabie 1983) has been devised for the case of three-way arrays of proximity data. Tree-fitting methods, used for thediscrete network representation of such proximity data, have only thus far been devised to handle two-way arrays. This paper develops a new methodology called INDTREES (for INdividual Differences in TREE Structures) for fitting various(discrete) tree structures to three-way proximity data. This individual differences generalization is one in which different individuals, for example, are assumed to base their judgments on the same family of trees, but are allowed to have different node heights and/or branch lengths.We initially present an introductory overview focussing on existing two-way models. The INDTREES model and algorithm are then described in detail. Monte Carlo results for the INDTREES fitting of four different three-way data sets are presented. In the application, a single ultrametric tree is fitted to three-way proximity data derived from intention-to-buy-data for various brands of over-the-counter pain relievers for relieving three common types of maladies. Finally, we briefly describe how the INDTREES procedure can be extended to accommodate hybrid modelling, as well as to handle other types of applications.     

Non-Monotonic Set Theory as a Pragmatic Foundation of Mathematics

In this paper I propose a new approach to the foundation of mathematics: non-monotonic set theory. I present two completely different methods to develop set theories based on adaptive logics. For both theories there is a finitistic non-triviality proof and both theories contain (a subtle version of) the comprehension axiom schema. The first theory contains only a maximal selection of instances of the comprehension schema that do not lead to inconsistencies. The second allows for all the instances, also the inconsistent ones, but restricts the conclusions one can draw from them in order to avoid triviality. The theories have enough expressive power to form a justification/explication for most of the established results of classical mathematics. They are therefore not limited by Gödel’s incompleteness theorems. This remarkable result is possible because of the non-recursive character of the final proofs of theorems of non-monotonic theories. I shall argue that, precisely because of the computational complexity of these final proofs, we cannot claim that non-monotonic theories are ideal foundations for mathematics. Nevertheless, thanks to their strength, first order language and the recursive dynamic (defeasible) proofs of theorems of the theory, the non-monotonic theories form (what I call) interesting pragmatic foundations.     

Complexity and Context-Dependency

It is argued that given the “anti-anthropomorphic” principle—that the universe is not structured for our benefit—modelling trade-offs will necessarily mean that many of our models will be context-specific. It is argued that context-specificity is not the same as relativism. The “context heuristic”—that of dividing processing into rich, fuzzy context-recognition and crisp, conscious reasoning and learning—is outlined. The consequences of accepting the impact of this human heuristic in the light of the necessity of accepting context-specificity in our modelling of complex systems is examined. In particular the development of “islands” or related model clusters rather than over-arching laws and theories. It is suggested that by accepting and dealing with context (rather than ignoring it) we can push the boundaries of science a little further.     

Can Darwinian Mechanisms Make Novel Discoveries?: Learning from discoveries made by evolving neural networks

Some philosophers suggest that the development of scientificknowledge is a kind of Darwinian process. The process of discovery,however, is one problematic element of this analogy. I compare HerbertSimon's attempt to simulate scientific discovery in a computer programto recent connectionist models that were not designed for that purpose,but which provide useful cases to help evaluate this aspect of theanalogy. In contrast to the classic A.I. approach Simon used, ``neuralnetworks' contain no explicit protocols, but are generic learningsystems built on the model of the interconnections of neurons in thebrain. I describe two cases that take the connectionist approach a stepfurther by using genetic algorithms, a form of evolutionary computationthat explicitly models Darwinian mechanisms. These cases show thatDarwinian mechanisms can make novel discoveries of complex, previouslyunknown patterns. With some caveats, they lend support to evolutionaryepistemology.     

规律的复杂性与规律误差

随着人类实践的深入和拓展,特别是当代科学技术和社会生活的变革,使得规律的复杂性突出起来。规律的复杂性要求我们反思规律的基本理论,走出“必然趋势”或“确定秩序”的观念误区,肯定规律适用的有限性和规律误差的客观存在。对规律误差的正确认识,有助于我们理解唯物辩证法诸范畴和规律,有助于更加有效地用马克思主义指导实践活动。     

复杂系统的隐喻描述与模型描述

由隐喻描述走向模型描述是复杂性科学成熟的标志.本文重点介绍了复杂系统的两种模型描述方法:形式化系统模型与关联论模型,并比较它们各自的优缺点.两种模型是从不同的角度对复杂系统的描述,在复杂系统的理论研究与实际运用中都有着重要的、不可替代的意义.复杂系统的未来研究方向并不是对于两种模型的"整合",而是两种模型交替并用,以多元视角科学地揭示复杂系统的本质.     

Optimal Quantification of a Longitudinal Indicator Matrix: Homogeneity and Smoothness Analysis

NJ by K that represents N individuals' choices among K categories over J time points. The row and column scores of this univariate data matrix cannot be chosen uniquely by any standard optimal scaling technique. To approach this difficulty, we present a regularized method, in which the scores of individuals over time points (i.e. row scores) are represented using natural cubic splines. The loss of their smoothness is combined with the loss of homeogeneity underlying the standard technique to form a penalized loss function which is minimized under a normalization constraint. A graphical representation of the resulting scores allows us easily to grasp the longitudinal changes in individuals. Simulation analysis is performed to evaluate how well the method recovers true scores, and real data are analyzed for illustration.     

Using Neural Network Analysis to Define Methods of DINA Model Estimation for Small Sample Sizes

The DINA model is a commonly used model for obtaining diagnostic information. Like many other Diagnostic Classification Models (DCMs), it can require a large sample size to obtain reliable item and examinee parameter estimation. Neural Network (NN) analysis is a classification method that uses a training dataset for calibration. As a result, if this training dataset is determined theoretically, as was the case in Gierl’s attribute hierarchical method (AHM), the NN analysis does not have any sample size requirements. However, a NN approach does not provide traditional item parameters of a DCM or allow for item responses to influence test calibration. In this paper, the NN approach will be implemented for the DINA model estimation to explore its effectiveness as a classification method beyond its use in AHM. The accuracy of the NN approach across different sample sizes, item quality and Q-matrix complexity is described in the DINA model context. Then, a Markov Chain Monte Carlo (MCMC) estimation algorithm and Joint Maximum Likelihood Estimation is used to extend the NN approach so that item parameters associated with the DINA model are obtained while allowing examinee responses to influence the test calibration. The results derived by the NN, the combination of MCMC and NN (NN MCMC) and the combination of JMLE and NN are compared with that of the well-established Hierarchical MCMC procedure and JMLE with a uniform prior on the attribute profile to illustrate their strength and weakness.     

未来的复杂性

未来是复杂的，不仅具有复杂性的特征，而且遵循其机理。未来的复杂性有着独特的根源和生成途径。认真研究和把握未来的复杂性既具有重要的思想价值，也可以降低和规避由于复杂性带来的发展风险。     

人地关系协调系统的建立--对生态伦理学的一个重要补充

生态伦理学(生态哲学)从生态道德关系的角度,承担协调人与自然关系的任务;强调人类与自然和谐共进的伦理思想。生态伦理学只建立了生态伦理(人类与自然的协调)的思维模式,而没有从理论上建立人地关系协调系统的行为模式。本文从人地关系角度,以行为地理学与复杂性科学的基本理论建立了人地关系协调系统的基本概念与基本理论。这是对生态伦理学理论建设的一个重要补充。     

Error, Error-Statistics and Self-Directed Anticipative Learning

Error is protean, ubiquitous and crucial in scientific process. In this paper it is argued that understanding scientific process requires what is currently absent: an adaptable, context-sensitive functional role for error in science that naturally harnesses error identification and avoidance to positive, success-driven, science. This paper develops a new account of scientific process of this sort, error and success driving Self-Directed Anticipative Learning (SDAL) cycling, using a recent re-analysis of ape-language research as test example. The example shows the limitations of other accounts of error, in particular Mayo’s (Error and the growth of experimental knowledge, 1996) error-statistical approach, and SDAL cycling shows how they can be fruitfully contextualised.     

复杂性与社会分化——卢曼社会系统理论研究

在卢曼看来,社会系统总是处于极端的复杂状态,同时也处于一种复杂化的过程之中。系统的复杂性征使其在面对环境的影响时,出现各种内在的危机和各种不平衡现象,并产生一系列的运作难题,使社会系统无法以其原来对付环境的原则进行实际运作,迫使社会系统不断地进行复杂性的化约和自我的区分化以化解危机,而社会系统也在此一过程中实现自身的演化和进化。     

The location model for mixtures of categorical and continuous variables

Recent research into graphical association models has focussed interest on the conditional Gaussian distribution for analyzing mixtures of categorical and continuous variables. A special case of such models, utilizing the homogeneous conditional Gaussian distribution, has in fact been known since 1961 as the location model, and for the past 30 years has provided a basis for the multivariate analysis of mixed categorical and continuous variables. Extensive development of this model took place throughout the 1970’s and 1980’s in the context of discrimination and classification, and comprehensive methodology is now available for such analysis of mixed variables. This paper surveys these developments and summarizes current capabilities in the area. Topics include distances between groups, discriminant analysis, error rates and their estimation, model and feature selection, and the handling of missing data.