Deterministic Chaos and Computational Complexity: the Case of Methodological Complexity Reductions

Some problems rarely discussed in traditional philosophy of science are mentioned: The empirical sciences using mathematico-quantitative theoretical models are frequently confronted with several types of computational problems posing primarily methodological limitations on explanatory and prognostic matters. Such limitations may arise from the appearances of deterministic chaos and (too) high computational complexity in general. In many cases, however, scientists circumvent such limitations by utilizing reductional approximations or complexity reductions for intractable problem formulations, thus constructing new models which are computationally tractable. Such activities are compared with reduction types (more) established in philosophy of science. This revised version was published online in August 2006 with corrections to the Cover Date.     

EVOLUTIONARY THEORIES OF MORALITY AND THE MANIPULATIVE USE OF SIGNALS

Abstract. Several attempts have recently been made to explain moral systems and moral sentiments in light of evolutionary biological theory. It may be helpful to modify and extend this project with the help of a theory of communication developed by ethologists. The core of this approach is the idea that signals are best seen as attempts to manipulate others rather than as attempts to inform them. This addition helps to clarify some problematic areas in the evolutionary study of morals, and it generates new, testable predictions about moral statements.     

基于大数据的文化心理分析

随着大数据技术与文化心理学的融合发展, 计算文化心理学作为一门新兴交叉学科逐渐兴起, 大尺度、近乎全样本的文化心理分析真正得以实现。计算文化心理学关注的文化变量主要围绕个人主义/集体主义这一文化心理学使用最为广泛的维度展开, 分析方法包括特征词典、机器学习、社会网络分析、仿真模拟等, 分析思路包括时间维度上的文化变迁效应以及空间维度上的文化地理效应。当然, 计算文化心理学在为传统文化心理研究提供新方法、新范式的同时, 也存在解码失真、样本偏差、词语多义性、隐私风险等局限, 未来研究应重视变量理论解释、文化动态演化分析、学科深度整合、生态效度等问题。     

RNAi的曲折经历与发展

RNAi是当今分子生物学研究领域内最引人注目的技术之一.已经在植物、线虫、果蝇、锥虫甚至哺乳动物细胞中发现RNAi现象. RNAi同时也是体内抵御病毒入侵、抵抗外在感染和抑制转座子活动的一种重要保护机制.通过介绍RNAi 技术富有传奇色彩的诞生和发展过程以及它对分子生物学乃至整个生命科学界产生的巨大作用,认识到RNAi是多学科联合发展的结果.我们应该加强各学科之间的整合.     

The Epistemological Status of Vision and its Implications for Design

Computational theories of vision typically rely on the analysis of two aspects of human visual function: (1) object and shape recognition (2) co-calibration of sensory measurements. Both these approaches are usually based on an inverse-optics model, where visual perception is viewed as a process of inference from a 2D retinal projection to a 3D percept within a Euclidean space schema. This paradigm has had great success in certain areas of vision science, but has been relatively less successful in understanding perceptual representation, namely, the nature of the perceptual encoding. One of the drawbacks of inverse-optics approaches has been the difficulty in defining the constraints needed to make the inference computationally tractable (e.g. regularity assumptions, Bayesian priors, etc.). These constraints, thought to be learned assumptions about the nature of the physical and optical structures of the external world, have to be incorporated into any workable computational model in the inverse-optics paradigm. But inference models that employ an inverse optics plus structural assumptions approach inevitably result in a naïve realist theory of perceptual representation. Another drawback of inference models for theories of perceptual representation is their inability to explain central features of the visual experience. The one most evident in the process and visual understanding of design is the fact that some visual configurations appear, often spontaneously, as perceptually more coherent than others. The epistemological consequences of inferential approaches to vision indicate that they fail to capture enduring aspects of our visual experience. Therefore they may not be suited to a theory of perceptual representation, or useful for an understanding of the role of perception in the design process and product.     

Schelling on understanding organisms

In this paper, I attempt to reconstruct Schelling’s theory of organism, primarily as it is elaborated in the First Outline of a System of the Philosophy of Nature and the Introduction to the Outline. First, I discuss the challenge that the properties of organisms presented to the dominant scientific viewpoint by the end of the eighteenth century. I present different responses to this challenge, including reductive materialism, metaphysical and heuristic vitalism, and the Kantian response, and I situate Schelling’s account of organism with respect to these responses. I argue that while Schelling agrees with vitalism in that he wants to preserve the specificity of organic phenomena, he rejects principles such as vital forces or the formative drive postulated by vitalism, even for purely heuristic purposes. I argue that Schelling understands organisms fundamentally in terms of the coordinated functioning of their organs. I further clarify Schelling’s account of problematic organic phenomena by focusing on his treatment of the relation between organic activity and organic receptivity. For Schelling, organic activity and organic receptivity mutually condition each other. I provide a detailed account of how this is supposed to work.     

The Crucial Importance of Nancey Murphy’s Deployment of Lakatos’s Methodology for Theology and Science

Russell's paper explores the astonishing fruitfulness of Nancey Murphy’s use of Imre Lakatos’s philosophy of science in the field of “theology and science.” Murphy’s work can be used to choose between competing theologies according to the theologians’ willingness to engage with science, their ability to continue the engagement as scientific theories change, and their ability to make empirical predictions based on this engagement. Topics range from creation and cosmology, the “cosmic Christ”, and non-interventionist objective divine action in quantum mechanics and evolution. Russell has followed Murphy’s lead and used Lakatos to place theology and science into “creative mutual interaction” (CMI).     

Multiple Systems in Decision Making: A Neurocomputational Perspective

ABSTRACT— Various psychological models posit the existence of two systems that contribute to decision making. The first system is bottom-up, automatic, intuitive, emotional, and implicit, while the second system is top-down, controlled, deliberative, and explicit. It has become increasingly evident that this dichotomy is both too simplistic and too vague. Here we consider insights gained from a different approach, one that considers the multiple computational demands of the decision-making system in the context of neural mechanisms specialized to accomplish some of that system's more basic functions. The use of explicit computational models has led to (a) identification of core trade-offs imposed by a single-system solution to cognitive problems that are solved by having multiple neural systems, and (b) novel predictions that can be tested empirically and that serve to further refine the models.