Computer and Human Networking (Essay Sample)

COMPUTER AND HUMAN NETWORKING
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PART ONE
Tom Griffiths and Brian Christian discourse how the modest, precise algorithms applied in by computers can correspondingly untie very human enquiries. The underlying premise of Algorithms to Live By is that computer scientists have spent a humongous amount of time and effort researching and honing computer algorithms so as to make them provide results in the shortest time and as efficiently as possible (Konak et al, 2006). The thing is that these strategies that have been honed for computers are also applicable to our daily lives. As the authors say in their introduction (Lorenzo and Glisic, 2013).
As an example, suppose you’ve recently moved to a large city and you have 100 days to look for an apartment, but these little rascals tend to be snapped up almost as soon as they come on the market. Further assume that you are new to the city and you aren’t fully sure what to expect in your price range. You could simply plonk your money down on the first residence that comes along, but then you’ll be left wondering if this was in fact the worst of the bunch and if you could have done much better (Rondeau and Bostian, 2004). Alternatively, you could hold out all the way to the last day, but then the chances are that you’ve already seen and missed out on the best apartment going.
The solution is to look at enough apartments to establish a baseline standard, and then accept the next apartment that comes along that meets, or exceeds, this standard. But how long should you look? The answer as we discover in Chapter 1 is 37% of your time, which would be 37 days in this example.
The end result is an exploration of the workings of both computers and the human mind, including optimal stopping, deciding whether to explore or exploit, sorting, caching, scheduling, overfitting, relaxation, randomness, networking, and game theory. In addition to learning how computers perform their magic, you’ll also discover strategies to optimize your to-do list and organize your bedroom closet (Lin and Labeau,2012).
Computers also use algorithms. However, they’re not only used by computers but also humans because we have our own algorithms in place to help us make decisions and solve problems. The best part is that if you don’t know how to solve a problem, one can turn to computer algorithms for assistance (Konak et al, 2006). The expression “algorithm” dates back to the 19th, when it was original applied by mathematician in Persian. It has been outlined coarsely 4000 years in the past, to the civilization in Sumerian. An algorithm is a set of instructions that helps resolve a problematic. It’s something people use every day, like following a recipe to make dinner or putting together some Ikea furniture.
When we’re trying to resolve whether or else not to agree to take a job proposal, we also weigh the pros and cons of that decision. This is an intuitive algorithm—a way of thinking that isn’t precise but helps us reach decisions anyway. We use these algorithms in times of uncertainty because they help us make the best decisions possible. So, they’re not as objective or precise as the mathematical algorithms a computer uses (Luo ,2010). Apartment hunting is often a tough experience. People usually have an idea of what they want in an apartment, such as the volume of space or distance from work or school. However, this method doesn’t always yield results. The same thing happens when computer algorithms try to solve problems; they don’t always produce good answers. In the next few key points we’ll explore how you can use these methods to your advantage and find a great apartment that meets all your criteria without breaking your budget!
There are a lot of scheduling algorithms out there, and they can help you when you’re having trouble deciding (Selamat et al 2004)
what to do next. For instance, if you have multiple tasks that need to get done and not enough time to work on them all, use Earliest Due Date algorithm. It will prioritize the duty that has the bordering deadline first. If an individual time is ending but one still need more time for some tasks while others don’t really matter anymore, trail Moore’s Algorithm in addition hop the task with utmost amount of time needed (Alba, 2005). But always beware of importance inversion it’s when trivial responsibilities take up all your attention as well as energy while significant ones aren’t getting done at all (Cox, 2005). There are no silver bullets for this problem though; studies show that our schedules cannot be completely optimized so we must learn to make compromises between conflicting priorities instead of trying to schedule everything perfectly from A-Z every day because it’s impossible anyway! So simply emphasis on one thing at a time (overlook any interruptions) during each session until it’s complete before moving onto another one—this way we can accomplish lots without feeling overwhelmed by an endless list or trying too hard to fit everything into our days!
Since you have a better understanding of the steps to take, you can be more productive and focus on achieving your goals (Alba, 2005). To make better predictions, you need to collect more information. However, math has given us tools that can help us do this in a more precise way. For instance, there’s the bell curve model which applies to many phenomena and helps predict things such as average age of people in a group or the time it takes for something to happen. Since most people fall into the middle range of things like age or time, you can be reasonably sure if people are working on a blind date that your date won’t be extremely old or young.
There are other situations where the distribution is not exponential. In those cases, most of the observations fall below the average and only a few extreme outliers exceed it. A brilliant sample of this is means delivery. There are countless poor persons but also a select few who have accumulated most of the world’s money (Goldberg and Holland, 1988). Algorithms support individuals to handle data as well as exchange messages overload. In computer science, there are algorithms to help with problems like the ones that occur when we’re trying to send a message and it keeps getting intercepted. One of those is named retransmitted till collapse, which would be sending in more messengers until one finally gets through. We do something similar by repeatedly texting or calling someone who doesn’t respond right away. But today’s challenges involve overloaded servers instead of captured messengers (Goldberg and Holland, 1988). Exponential Back off can help us deal with these problems by waiting a combine minutes for traffic to ease up before trying again, doubling the time each time if we still don’t succeed after four minutes (and so on). Additive Increase, Multiplicative Decrease is another method that helps prevent overloads from occurring in the first place; this approach increases traffic slowly at first and decreases it quickly as needed later on.
This is a definitive strategic interrogation that signifies game theory, which discovers how rational individuals would reply to such a state of affairs. In this situation, the prisoners may almost surely turn on one another because they each want to get out of jail as soon as possible (De Jong and Spears, 1991). If both prisoners cooperate with the police and say nothing, then both will serve five years in prison. However, if only one prisoner cooperates and tells all about his partner’s involvement in the crime but his partner refuses to talk (cooperate), then there is no guarantee that he won’t be sentenced for ten years in prison while his partner serves just five years for not talking. So, it makes sense for him to risk incriminating himself by telling the police what happened regardless of whether or not his partner talks because even if he does end up serving 10 years instead of 5, at least he’ll have served less time than if he stayed silent and was convicted based solely on evidence against him without any input from his partner about their joint crime.