**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*()*, apart from this, some experts use the formula

H=-∑p(x) logp(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)*

Unlike others, your post first explains information in an academic way and introduces applications of information theory in diverse fields, e.g. financial scenario. According to me, information theory can be regarded as a tool to help info engineers better understand how people utilize existing information. And the duty of IE/ITM student is to enable designed application to realize the true value of information and work more effectively for users. Therefore, I totally agree with your views in the last part that IE/ITM students are expected to design and develop diverse applications with use of information theory to create values for users and companies. ‘Coz I have little idea about the financial part, I firmly believe that it will be more appealing to introduce relevant vivid real-life examples in your article.

LikeLiked by 2 people

Your blog post has a lot of information about professional knowledge, but it is easy to understand and gives people a good understanding of the importance of information. At the same time, you also use icons and formulas to confirm your point of view, which makes your article more rigorous and persuasive. Even readers who have not studied relevant theories can have a clearer understanding of the relationship between information theory and ITM through your articles. In addition, I also like your own opinion. It is hoped that information theory will play a greater role in our professional field.

LikeLike