Information, Information theory If you ask me ‘what is information’ in a course provided by department of information engineering, the first several things come to my mind is some infrastructure related to communication networking, or some computer technology about big data analytics and data warehousing, after all, I am not an engineering school student in my undergraduate, nor in my master. In the second class of social networking, I had the chance to get an overall of Shannon’s theory. From my own comprehension, Shannon guess information is not only an event itself, he raised a communication system model (in the figure 1 ), the conception of Entropy as well as three interrelated levels (Shannon’s Information Theory) , to explain the word ‘Information’ in the degree of the transformation, measurement and some judge criteria related or unrelated to technology. Figure 1 The communication system model Source of Figure 1 My comprehension of Information Theory Honestly, it is the first time to learn Shannon’s Information Theory, however, I guessed I was looking something familiar at that time, for I find something in Information Theory is interrelated to some topics in physics and economics, which are my major and minor during my undergraduate, and it is unexpected for me. Figure 2 Physics ‘Entropy’ (Source of Figure 2) In Shannon’s theory, the measurement of information transformation should make use of Entropy, that is, higher entropy means more information is sent, and more information involves more uncertainty. Personally, as a student majoring in physics during undergraduate, I am familiar with the word ‘entropy’ in some other degrees, for ‘Entropy’ is a key element word (Physics Entropy) for Thermology , and in physics, higher entropy means more mess of an energy system (you can see it in the figure 2 ). In my own view, there is some potential connections between two conceptions, for it is easy for us to link the mess, uncertainty and much information together. Figure 3 Carnot Cycle in Thermology (Source of Figure 3) However, what emphasize in physics ‘Entropy’ and information ‘Entropy’ is still a little different. From the ‘Family Invitation’ case in Social Networking class, I guess information ‘Entropy’ is related to probability theory, which means that different degree of uncertainty reveals various probability distributions, then leading to different amount Entropy, and that is, different amount of information. This application of probability is absent in physics ‘Entropy’, while it reminds me of Game Theory in economics, my minor in undergraduate. Figure 4 The Prisoner’s Dilemma (a special case in Game Theory, Source of Figure 4) In Game Theory, how well an applicant knows his opponent and the probability distribution he estimates before decision-making (in the figure 4) is something similar with the case in class(Game Theory), apart from this, some experts use the formula
H=-∑p(x) log⁡p(x)
to define the utility function in Game Theory, which makes it closer between Game Theory and Information ‘Entropy’ (<the information value and Entropy theory of investment portfolio> Chenguang, LU). Mutual promotion between information and its application is what I desire The information comes from all kinds of fields in the word, so it is the same as the construction of the information theory, as an ITM student in business school, I guess information and information theory should be apply not only in the IE/EE/CS field, but also in the business processes in our daily life, which forms the back-feeding. Here are some brief summery from what I learned in publications. The information theory can be applied as a plug in the measurement in the financial risk management, in some publications, some financial expert raises the formula
λH_x (θ)+(1-λ) S_x (θ) H_x (θ): the Entropy of an event S_x (θ): the standard deviation of an event
to measure the overall risk in a financial decision , which performs better than only use the standard deviation (<Research on Game Payoff and Nash Equilibrium Selection based on Information Entropy> Xiaojian, ZHU). In translation field, the information theory can also make sense, according to the model of the communication system, some translation experts guess that reduce some original noise in language before translation and add some noise in the form of objective language may make the receivers have better understanding of the original meaning of the text, which reaches the non-technical aspects of communications(<On redundancy in translation from the perspective of information theory: a case study of E-C translation of under the net> Ye,WU). Figure 5 Noise in Communication System(Source of Figure 5) In transportation field, some experts treat the entropy as the weight to evaluate the degree of the traffic congestion (<A fuzzy comprehensive evaluation model of road traffic state based on entropy weight> Zhenchao, TAN) . In conclusion, there is mutual promotion between the information theory and other fields, and making this connections in business application more solid is what an ITM student should do. Figure 6 The relationship between information theory and other fields(Source of Figure 6)