MBA 611 Week 4 Share -- Entropy and You
So, what is your definition of uncertainty?
Let's consider a binary event about most anything we might have experienced. Some examples: bear or bull (about the future itself), investing or waiting (state of the markets), idling or operating (someone's car at an intersection), outage or normal (internet pranks), nasty or nice (waiting for a gift?), important or not (to or about someone or something), stressed or not (work, life), AB or AC (messages), etc.
Our work this week is to forge an example of two separate ways in which the binary event might occur. One way with have a probability structure p, the other a different probability structure q, much like the Kullback-Leibler Divergence example in the reading. We will play them against one another as if they are two scenarios for the same binary event.
Using our new-found tools for information entropy we will compare and contrast the two scenarios. Of course we will also interpret our findings by explaining it someone (here in this site) who knows nothing about information theory, let alone its use of entropy.
Definitely stressed or not! How will this next week unfold?
ReplyDeleteUncertainty I believe can be in some instances be calculated and not calculated. Some uncertainties are emotional, while others are just based on pure odds. This depends on one's event of uncertainty. Perhaps waiting for a job offer to come back, or waiting to find out a pregnancy. These examples are based on human make up and factors. When talking about in the likely event with something happening based on no outside factors or conditionals, these can be calculated. For example, blackjack. Although there is technically a by the book play in any scenario, you truly do not know if you will beat the dealer. There is a statistical calculation that can help sway uncertainty in a decision with the best mathematical odds, but it does not necessarily mean you will win the hand. I think my definition of uncertainty is split into two: one by nature and one by probability structure.
ReplyDeleteUncertainty is unknown information, which can be calculated or not calculated. In data analysis, uncertainty is an estimation of errors present within the data or model. Some examples of a binary event that we might have experienced, would be winning or losing an event. This is seen as a binary event because winning or losing can occur or not occur for people. For instance, when there is a baseball game, some people perform the statistical calculation of the teams and their players to see the probability of who will win the game. But even though they do statical calculations there is always uncertainty present in the data. This can lead to their predictions being wrong.
ReplyDeleteI'd define uncertainty of not knowing the next step in your professional career or not have enough information of what car to purchase. Uncertainty can also be used in a betting context, where the person who bets on a game is uncertain about the outcome of the game, even though the person in most cases has a certain expectation that the team he/she bets on should win the game based on previous results, table position, and so forth. Talking about betting, we also frequently see performed calculated probabilities of a specific team winning a game, the probability of the game to end up in a tie, probability of a player scoring a goal or score a certain amount of points in for example basketball. However, even though these calculations have been made by professional experts, the uncertainity or unexpected outcomes have resulted in many losing its bets
ReplyDeleteWritten by Vetle
DeleteUncertainty is a measure of the probability that a model is wrong. A coin toss is used to make impartial decisions because 50-50 is the most uncertain ratio and thus the fairest between decider and observer.
ReplyDeleteIn another respect, uncertainty is the sum of the probabilities of all the unknown causes of an event. Let us think about the binary event of a light switching on. A cause that we often take for granted as 100% is flipping the light switch. In reality, flipping the switch does not always turn on the light. There are many unknown variables that we do not generally think about which may be affecting the switch's ability to turn on the light. For example, the bulb may be burnt out, the switch might be physically broken, or maybe a mouse chewed through a wire somewhere along the way. We can theoretically assign some sort of probability to each of these events, and any other event that may prevent the light from switching on. Therefore, the sum of such probabilities is the probability that the light doesn't turn on. However, this idea is really only theoretically possible, as we can't possibly know the probabilities of each because which effects an event. It is precisely this lack of this knowledge that is uncertainty.
Uncertainty is a situation where the outcome is difficult to predict, or is not very obviously skewed in one direction or another. Uncertainty is something that many people fear, but is a natural condition of life: and often precedes major growth. Two examples of a binary event in sports might be a field goal being good or no good in a football game, or a team getting a win or loss. Uncertainty in these situations might be related to the odds of these things happening: while they are binary events, a field goal kicker is usually 88-90% likely to hit a field goal, and a good team in football might win a good 70-80% of their games.
ReplyDeleteI wrote this but it showed up as anonymous
DeleteUncertainty refers to unknown factors, which may or may not be quantifiable. In the context of data analysis, it represents an estimate of the potential errors within the data or the analytical model used. An example of uncertainty in action is the outcome of a binary event like a coin toss. Here, winning or losing depends on whether the coin lands on heads or tails. Even with an understanding of probabilities—50% for heads and 50% for tails—each individual toss remains unpredictable, encapsulating the essence of uncertainty. This inherent unpredictability means that outcomes can still defy expectations.
ReplyDelete1. When a once-innovative product or service loses its edge and is no longer considered innovative, managers typically respond in several ways. Managers may revisit their overall strategy, evaluating whether the current approach needs adjustment or a complete overhaul. They might increase their focus on market research and gathering customer feedback to understand why the product/service is no longer perceived as innovative. This helps in identifying where the product/service fell short and what improvements are needed. managers might also seek partnerships with external entities that can inject innovation back into the product or service. Instead of major overhauls, managers might opt for incremental improvements to enhance the product/service’s features, user experience, or performance based on feedback and market trends.
ReplyDelete2. The key drivers in innovation from my experience are creativity and intelligence. I say these two only because they can sometimes contradict each other but its very important to find the balance between both. Too much intelligence can sometimes discourage creativity and creativity without a plan of action is useless. You need to be creative enough to find a niche that is innovative but you also need intelligence to be able to produce and see through the creative idea. I think that's why both of these characteristics are huge drivers in innovation.
1. I think that when an innovative product or service loses its flame, managers often focus on incremental improvements to maintain competitive advantage. They might invest in further research and development to introduce new features, explore new markets, or enhance customer service. There also the possibility of rebranding or changing marketing strategies. I ultimate goal of their technique changing is to generate a new interest in their product.
ReplyDelete2. I believe the key drivers to innovation are competition and ingenuity. I think by having competition as a driver, it will force people, companies, and other subjects to forever push each other to be better at their respective tasks or products. The ability to develop these new products comes from the ingenuity to do things differently or in a new way to produce the same product for cheaper or to develop new products from new ideas to beat their competitors.
Thanks James. When we lookup "entropy," and apply the idea to management as an analogy to thermodynamics, what might be the implications for innovation?
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