How Far Can We Go Through Social System--论文代写范文精选

库尔特·哥德尔的不完全性定理,影响了科学领域的许多地方,然而,社会科学似乎已经忽略了重要的影响。它可能是探讨了严格的语言严谨的数学系统。从这个角度看,本文阐述了一个应用算法信息理论。下面的essay代写范文进行详述。

Abstract 
The paper elaborates an endeavor on applying the algorithmic information-theoretic computational complexity to meta-social-sciences. It is motivated by the effort on seeking the impact of the well-known incompleteness theorem to the scientific methodology approaching social phenomena. The paper uses the binary string as the model of social phenomena to gain understanding on some problems faced in the philosophy of social sciences or some traps in sociological theories. The paper ends on showing the great opportunity in recent social researches and some boundaries that limit them. 
Keywords: meta-sociology, algorithmic information theory, incompleteness theorem, sociological theory, sociological methods.

Prologue 
Meta-sociology is the term to the inquiries of philosophical issues circling social sciences, the way to build the sociological theory, and the evolution of social sciences regarding the methodology and fundamental principles motivating them. As 1931 saw the issue of incompleteness theorems sounded by Kurt Gödel, they have influenced many parts of scientific domains; however, the social sciences seemed to have been ignoring the important impact. It happened probably because the incompleteness theorems are elaborated in rigorous language to the rigorous arithmetic system. In this perspective, the paper elaborates an endeavor on applying the algorithmic information-theoretic computational complexity – as an effort to see impacts of Gödelian incompleteness theorems to the reality in broader view – to meta-social-sciences. The paper tries to be firmed and not that rigorous. A glance reader can read the paper in the terms of "#" and "*" denoting "definition or axioms" and "deductively causal impact" established in the paper respectively. The paper is structured by noting some definitions considering the construction of social theories, followed by the result or consequence of the implementation of algorithmic information theory to explain some traps that sociological theories face. Aftermath, the paper continues on elaborating recent issue on some contemporary methodology in social sciences.

The Construction of Social Theories
Social sciences are in some epistemological aspects standing for the similar scientific methodology commonly known in the natural sciences. The idea is to grasp the patterns we find in the social phenomena in order to have a sound explanation on them. Qualitatively, we saw a phenomenon as empirical inquiries and then have it analyzed to construct the social theory. The social theory can be regarded as quasi-axioms on understanding many social phenomena. In fact, this procedure could also be thought as finding the reducibility of the social phenomena seemingly random we found in the real life. The social theory is by means of the explanation on social phenomena. As stated by classical sociologist, Emile Durkheim (1895:97), the results of the preceding method concluded as social theory should need verification by demonstrating that the general character of the phenomenon is related to the general conditions of collective life in the social type under consideration. The social theory is meant to be the information that enable us to generate the social phenomena in sociological thoughts in order to have understanding.

Philosophical and Practice of Social Research 
The way to do social research is in short can be described as the way to do the deductive reasoning and empirical inquiries synergistically. However, conventional research method relied merely upon statistical analysis cannot cope with our enlargement of the complexity of social research respect to our previous elaboration. The recent computational techniques are introducing the way to have it in the informally branch of sociology, i.e.: computational sociology. The using of computational sociology is the matter of inter-playing the social factors with the social actors (Macy & Willer, 2002).

It is obvious that the abstraction to constructing the model can distinct the model we are dealing with. Furthermore, the computational model we use to explain social phenomena COLLECTED DATA purpose 1: prediction model 1: statistical model predicted data purpose 2: explanation model 2: computational model simulated data abstraction abstraction similarity similarity estimated parameter simulation Figure 1 The different purposes on prediction and explanation in social research. 8 can factually discovered in the recent methodology of artificial society. In the analysis with artificial society, we capture the social process with its statistical properties and construct the agent-based model emerging the best proximity with the former. There have been many practical discussions in this issue, e.g.: Doran (1997), Axtell (2000), Langton ((2002), as the term coined by Epstein & Axtell (1996). Eventually, we have figured out that there is wide-open opportunity on capturing the social phenomena with compact and explainable theory by taking advantage from the computational technology while in return we realize how the boundaries we have on every possible social research and theory construction through advanced understanding on computation. These are two quite different uses of the computation.

Concluding Remarks 
We present the way to widen the impact of incompleteness theorem by using the algorithmic information theory as originally introduced by Chaitin (1974). We present a model of meta-social-research to be partially recursive computation and show some important impacts on opportunity and boundaries in endeavor of social sciences. In parallel, we show that it is very difficult capturing the social phenomena and explain them using the existing social theories. This is, however, the nature of social sciences as compared to natural sciences. There is also a tendency to be descriptive over social sciences by realizing the uniqueness of social phenomena (highly random social phenomena). However, this tendency – e.g.: postmodern sociology – can be damped by our practical computational technology enabling us to sharpen the way as to construct social theories for a theory just as like a report without ability to explain (or in reverse as a compression of phenomena) remains useless. Furthermore, by applying the algorithmic information theory into social sciences we have newly understanding about the occasional traps of sociological theories, i.e.: the description trap and logical trap. We show that most of social phenomena are maximally unknowable with infinite complexity of social phenomena contrasted to the infinite complexity of social theory. As depicted before, the infinite complexity of social phenomena leaves us boundaries but in return, the infinite complexity of social theories brings us a great deal of opportunity on denouncing the unknowable rest for us for making a better social living.

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