Philosophy for Computers: Some Explorations in Philosophical Modeling

Philosophical modeling has a long and distinguished history, but the computer offers new and powerful prospects for the creation and manipulation of models. It seems inevitable that the computer will become a major tool in future philosophical research. Here I offer an overview of explorations in philosophical computer modeling that we in the Group for Logic and Formal Semantics at SUNY Stony Brook have undertaken: explorations regarding (1) the potential emergence of cooperation in a society of egoists, (2) self-reference and paradox in fuzzy logic, (3) a fractal approach to formal systems, and (4) on-going explorations with models for the emergence of communication. The attempt is not to give a detailed report on any of these but to give a bird s-eye view of them all, emphasizing the general form that the models take, the philosophical motivations behind them, and the kinds of philosophical results that they may have to offer.     

复杂问题解决中计算机模拟情境的逻辑框架

计算机模拟情境摆脱了实验室研究和现场研究的不足,满足了复杂问题解决的复杂性、动态性和模糊性等特征,受到研究者们的青睐.近年来,各种模拟情境在复杂问题解决研究中得到运用,其内部逻辑结构主要有线性结构方程和有限状态自动化,线性结构方程适用于等距数据,而有限状态自动化适用于称名数据.当前,对复杂问题解决的测量注重结果,而相对忽视过程.未来的模拟情境可以从问题特征、任务逻辑和测量方式来提高信度和效度.     

Information and the complementarity game

Complementarity games are explorative games between two players. The deterministic rules of the game are known to both players. The first player freely chooses among a finite number of states of the game but tells nobody of this decision. The second player accesses the game only by primitive, symbolic input‐output interfaces. The task of the second player is to find out the unknown state by input‐output experiments. The logic resulting from this scenario resembles‐ quantum logic in many aspects. It is the empirical logic of choice for virtual realities.     

Representations of Complexity: How Nature Appears in Our Theories

In science we study processes in the material world. The way these processes operate can be discovered by conducting experiments that activate them, and findings from such experiments can lead to functional complexity theories of how the material processes work. The results of a good functional theory will agree with experimental measurements, but the theory may not incorporate in its algorithmic workings a representation of the material processes themselves. Nevertheless, the algorithmic operation of a good functional theory may be said to make contact with material reality by incorporating the emergent computations the material processes carry out. These points are illustrated in the experimental analysis of behavior by considering an evolutionary theory of behavior dynamics, the algorithmic operation of which does not correspond to material features of the physical world, but the functional output of which agrees quantitatively and qualitatively with findings from a large body of research with live organisms.     

The emergence of personality: Dynamic foundations of individual variation

We conceptualize personality and individual variation from the perspective of dynamical systems. People’s thoughts, feelings, and predispositions for action are inherently dynamic, displaying constant change due to internal mechanisms and external forces, but over time the flow of thought and action converges on a narrow range of states—a fixed-point attractor—that provides cognitive, affective, and behavioral stability. An attractor for personal dynamics develops through two mechanisms: the synchronization of individuals’ internal states in social interaction, and the self-organization of thoughts and feelings with respect to a higher-order property (e.g., goal, self-concept). We present formal models of both processes and instantiate each in computer simulations. Discussion centers on the implications of interpersonal synchronization and self-organization dynamics for issues in personality psychology, including shared vs. non-shared environmental influences on personality development, the expression of personality in social interaction, personal stability vs. change, personal vs. situational causation, and the emergence of self-concept.     

基于元胞自动机的濒危植物明党参种群数量动态研究

根据3年来积累的和峨参野外实验数据,利用元胞自动机模型在计算机上模拟了濒危植物明党参(Changium smyrnioides)的种群生长动态,并选取同科的峨参(Anthriscus sylvestris)作为对照种进行比较分析。模拟结果显示,两个物种在种群中的个体数量均随生殖率m和可利用生境比率h的增大而增加,当生殖率大于0.6732时明党参种群才可在模拟时间尺度上(100年)保持不衰退;峨参生殖率的变化则不会使种群衰退,当m>9.15时,种群密度就不再增加。明党参的最小生境需求值小于峨参。明党参在h大于0.7%时,种群密度不再增加,即明党参最多只能利用生境的0.4%,峨参则可占据生境中位置的81.9%。综合分析模拟参数和结论,我们认为:明党参偏向于K策略,峨参偏向于r策略。本文相关资料对于确定明党参相应的保护措施有重要参考价值。研究元胞自动机在生态学上的初步模拟可以为将来进行更加深入的研究奠定基础。     

The Four Causes of Behavior

Comprehension of a phenomenon involves identifying its origin, structure, substrate, and function, and representing these factors in some formal system. Aristotle provided a clear specification of these kinds of explanation, which he called efficient causes (triggers), formal causes (models), material causes (substrates or mechanisms), and final causes (functions). In this article, Aristotle's framework is applied to conditioning and the computation-versus-association debate. The critical empirical issue is early versus late reduction of information to disposition. Automata theory provides a grammar for models of conditioning and information processing in which that constraint can be represented.     

Comprehension of simple quantifiers: empirical evaluation of a computational model

We examine the verification of simple quantifiers in natural language from a computational model perspective. We refer to previous neuropsychological investigations of the same problem and suggest extending their experimental setting. Moreover, we give some direct empirical evidence linking computational complexity predictions with cognitive reality. In the empirical study we compare time needed for understanding different types of quantifiers. We show that the computational distinction between quantifiers recognized by finite-automata and push-down automata is psychologically relevant. Our research improves upon, the hypotheses and explanatory power of recent neuroimaging studies as well as provides evidence for the claim that human linguistic abilities are constrained by computational complexity.