The Art Of Thinking Clearly Pdf Free 167
Travers Guliuzza
You might have seen examples of this approach before, or used it in your own work. You might also have encountered a great deal of discussion around logical forms, reasonable and unreasonable justifications, and so on. What I find most useful about standard form, however, is not so much its promise of logical rigour as its insistence that I break down my thinking into individual steps, and then ask two questions of each one:
What follow from this? When it comes to clarifying your thinking, it means that you need to be very clear about the difference between what follows from your assumptions and the status of those assumptions. To take things step by step:
Working out the implications of your assumptions is, in other words, far from the same thing as being definitively correct; and grasping the difference between these lies at the heart of honestly and persuasively articulating your views.
Indeed, perhaps the most important tool in any attempt at clear thinking is the capacity to test (and to keep on testing and refining) your ideas as if they belonged to someone else: as acts of reasoned persuasion that must stand, or fall, on their own terms.
This is the point at which your personal preferences come into play. For me, the props and routines of clear thinking include (in no particular order) strong cups of coffee, directionless neighbourhood strolls, a desk surrounded by heaped books and scrap paper, and as much serendipitous reading as I can squeeze in between school runs.
Dr. Seema Yasmin, professor at Stanford University School ofMedicine and the Anderson School of Management at UCLA, discusses:information disorder and its treatment, common techniques used forpushing lies, the use of narrative in communicating, the historyand current state of journalism in America, how to best...
Thinking Clearly is a radio show about the process of critical thinking and related topics. The show airs on the first Thursday of every month from 7-8 PM on northern California community radio station, KMUD.
Clear thinking comprises a collection of related skills. Clear thinkers can articulate ideas in an understandable fashion, work logically through problems, infer valid conclusions, and reflect on and account for small details.
Think about ways you can eliminate distractions from your life. Examples might include not using social media during work periods, leaving your mobile phone outside of your office, or blocking news sites during the day.
Meditating for as little as ten minutes per day can have long-lasting and tangible effects. Setting up a regular daily practice, possibly with the help of an app like Headspace, will increase your overall level of calm throughout the whole day.
Working in an orderly environment boosts concentration and cultivates calm, both of which contribute to clear thinking. One study also demonstrated a link between reduced clutter and increased academic performance.
Breaks have an array of benefits, from eradicating decision fatigue to restoring concentration and motivation. Thinking clearly requires mental resources, and taking breaks allows you to replenish these resources.
You can also give your breaks an added boost by going outside. Research shows that undertaking activities in nature leads to short and long-term improvements in mental health, including reduced stress and anger, improved mood, and greater self-esteem.
Over the last several years, a retinue of scientists, business leaders, celebrities and other public figures have come out to promote the benefits of sleep and dispel the myth that working longer means working better.
It reminds me a lot of how difficult beginning algebra seemed when I was about 13 years old. At that age, I had to appeal heavily to trial and error to get through. I can remember looking at an equation such as 3x + 4 = 13 and basically stumbling upon the answer, x = 3.
In high school Mr. Harkey taught us what he called an axiomatic approach to solving algebraic equations. He showed us a set of steps that worked every time (and he gave us plenty of homework to practice on). In addition, by executing those steps, we necessarily documented our thinking as we worked. Not only were we thinking clearly, using a reliable and repeatable sequence of steps, but we were also proving to anyone who read our work that we were thinking clearly.
This was Mr. Harkey's axiomatic approach to algebra, geometry, trigonometry, and calculus: one small, logical, provable, and auditable step at a time. It's the first time I ever really got mathematics.
Naturally, I didn't realize it at the time, but of course proving was a skill that would be vital for my success in the world after school. In life I've found that, of course, knowing things matters, but proving those things to other people matters more. Without good proving skills, it's difficult to be a good consultant, a good leader, or even a good employee.
My goal since the mid-1990s has been to create a similarly rigorous approach to Oracle performance optimization. Lately, I have been expanding the scope of that goal beyond Oracle to: "Create an axiomatic approach to computer software performance optimization." I've found that not many people like it when I talk like that, so let's say it like this: "My goal is to help you think clearly about how to optimize the performance of your computer software."
Googling the word performance results in more than a half-billion hits on concepts ranging from bicycle racing to the dreaded employee review process that many companies these days are learning to avoid. Most of the top hits relate to the subject of this article: the time it takes for computer software to perform whatever task you ask it to do.
Some people are interested in another performance measure: throughput, the count of task executions that complete within a specified time interval, such as "clicks per second." In general, people who are responsible for the performance of groups of people worry more about throughput than does the person who works in a solo contributor role. For example, an individual accountant is usually more concerned about whether the response time of a daily report will require that accountant to stay late after work. The manager of a group of accounts is also concerned about whether the system is capable of processing all the data that all of the accountants in that group will be processing.
Example 1. Imagine that you have measured your throughput at 1,000 tasks per second for some benchmark. What, then, is your users' average response time? It's tempting to say that the average response time is 1/1,000 = .001 seconds per task, but it's not necessarily so.
Imagine that the system processing this throughput had 1,000 parallel, independent, homogeneous service channels (that is, it's a system with 1,000 independent, equally competent service providers, each awaiting your request for service). In this case, it is possible that each request consumed exactly 1 second.
Now, you can know that average response time was somewhere between 0 and 1 second per task. You cannot derive response time exclusively from a throughput measurement, however; you have to measure it separately (I carefully include the word exclusively in this statement, because there are mathematical models that can compute response time for a given throughput, but the models require more input than just throughput).
Example 2. Your client requires a new task that you're programming to deliver a throughput of 100 tasks per second on a single-CPU computer. Imagine that the new task you've written executes in just .001 seconds on the client's system. Will your program yield the throughput that the client requires?
It's tempting to say that if you can run the task once in just a thousandth of a second, then surely you'll be able to run that task at least 100 times in the span of a full second. And you're right, if the task requests are nicely serialized, for example, so that your program can process all 100 of the client's required task executions inside a loop, one after the other.
But what if the 100 tasks per second come at your system at random, from 100 different users logged into your client's single-CPU computer? Then the gruesome realities of CPU schedulers and serialized resources (such as Oracle latches and locks and writable access to buffers in memory) may restrict your throughput to quantities much less than the required 100 tasks per second. It might work; it might not. You cannot derive throughput exclusively from a response time measurement. You have to measure it separately.
Response time and throughput are not necessarily reciprocals. To know them both, you need to measure them both. Which is more important? For a given situation, you might answer legitimately in either direction. In many circumstances, the answer is that both are vital measurements requiring management. For example, a system owner may have a business requirement not only that response time must be 1.0 second or less for a given task in 99 percent or more of executions but also that the system must support a sustained throughput of 1,000 executions of the task within a 10-minute interval.
In the prior section, I used the phrase "in 99 percent or more of executions" to qualify a response time expectation. Many people are more accustomed to such statements as "average response time must be r seconds or less." The percentile way of stating requirements maps better, though, to the human experience.
Example 3. Imagine that your response time tolerance is 1 second for some task that you execute on your computer every day. Imagine further that the lists of numbers shown in table 1 represent the measured response times of 10 executions of that task. The average response time for each list is 1.000 second. Which one do you think you would like better?
Although the two lists in table 1 have the same average response time, the lists are quite different in character. In list A, 90 percent of response times were one second or less. In list B, only 60 percent of response times were one second or less. Stated in the opposite way, list B represents a set of user experiences of which 40 percent were dissatisfactory, but list A (having the same average response time as list B) represents only a 10 percent dissatisfaction rate.
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