The Secret To Happiness

The American Dream tells us we are free to pursue happiness, but doesn’t give us instructions.  Even life-changing events such as winning the lottery have been shown (Brickman 1978) to only increase happiness in the short-term.

The secret to long term happiness is a concept that seems too sacred to be studied and dissected. However, many researchers devote themselves to this topic, and this paper by Sheldon and Lyubomirsky presents a nice theory about sustainable happiness.

This elusive goal is difficult, and may be impossible. Many past studies have shown that each person has a base level of happiness which they can only deviate from temporarily. Even more unfortunate, is that this base level of happiness is 50-80% inherited.

The researchers in this paper divided events that increase your well-being into: activity changes (intentional acts such as exercising) and circumstantial changes (such as being assigned a great roommate). They performed 3 studies on psychology students who had recently experienced an increase in well-being. These studies showed that sustainable happiness was only possible through activity changes. Intentional changes resulted in a bigger boost in happiness and more varied experiences.

After a period of time, those who experienced the increase in well-being because of an activity change retained their increase more than those who experienced the increase because of a circumstantial change. The ones who became happier by chance became accustomed to the change and were no longer affected by it.

There is no shortcut — effort and hard work are the best route to happiness.

Sheldon, K & Lyubomirsky, S. (2006). Achieving sustainable gains in happiness: Change your actions, not your circumstances. Journal of Happiness Studies, 7, 55 – 86. [PDF]

Edit by Jeff Huang

Blowable User Interface

I just got back from presenting a paper at UIST, a conference that I would describe as a forum for innovative sexy user interfaces. One of the more original talks I attended was by a Georgia Tech student who managed to create a blowable interface on a standard laptop. He takes an ordinary laptop (with no hardware add-ons), blows on the screen, and the laptop identifies where he blew. Seems impossible?

How it works: when the screen is blown, it creates a bit of sound that the microphone picks up, Using a Fast Fourier Transform to create a vector of amplitudes and phase shifts, the system then matches a “blow” (as opposed to a click) with pre-classified vectors using k-nearest neighbor. The best vector match is used to determine where on the screen the user was blowing. During evaluation on a laptop, it was 100% accurate on a 3X3 grid, 95% accurate on a 4X4 grid, and quickly degraded after that. This is pretty good considering you’re blowing on a laptop screen.

One application of this would be birthday cards sent over email, where the recipient could blow out the candles. Or, even an electronic harmonica if the accuracy were improved.

Patel told me on a shuttle ride that he discovered this by accident one day while trying to blow dust off his screen with his sound recorder on.

Given that few people would actually perform the embarrassing act of blowing on their laptop while people were around, I’d give this 10/10 for originality and 4/10 for actual usefulness.

[Video]

Patel, S & Abowd, G. (2007). BLUI: Low-cost Localized Blowable User Interfaces. Proceedings from UIST ’07: ACM Symposium on User interface Software and Technology, 217 – 220. [PDF]

Do Opposites Attract or Do People Look For Similar Partners?

“Opposites attract” is the common response when you see a contrasting couple — a tall woman and a short man, or a party person with a quiet introvert. Yet we all know couples who have the similar personalities — they like the same restaurants or are both neat freaks. So are people attracted to those unlike themselves to complement their personalities, or do people seek out a partner just like themselves because it’s positively reinforcing?

It turns out, neither hypothesis is true. A study of 36 couples found that there was no significant inter-personality similarity or differences. However, a two interesting findings emerged:

1. People had partners who were similarly self-satisfied with themselves
2. People’s perceptions of their partners were biased towards their ideal self

In other words, someone who was low self-esteem has a higher likelihood of having a partner with low self-esteem, while someone who is self-liking will look for a partner who also likes who they are. To elaborate on the second finding, there was no correlation between each individual partner’s personalities, but there was a correlation between a person’s ideal self-concept, and the perception they had of their partner. So if you aspire to be organized, you may believe that your partner is more organized than he or she really is.

Klohnen, E. C. & Mendelsohn, G. A. (1998). Partner Selection for Personality Characteristics: A Couple-Centered Approach. Personality and Social Psychology Bulletin, 24(3), 268-278.

Hard Drive Failure Statistics at Google

Statistics about hard drives from Google’s data centers were published in USENIX, showing how different variables affect the failure rate of hard drives. AFR is “Annual Failure Rate”.

The older drives may be failing more simply because they are a less advanced batch.

Low, medium, and high specify the usage.

Surprisingly, 35-45 degrees C is the sweet spot for hard drives. Colder temperatures actually cause them to fail more.

SMART will catch about half of hard disk failures before the happen, but many drives will fail without any warning.

Pinheiro, E., Weber, W., & Barroso, L. (2007). Failure Trends in a Large Disk Drive Population. Proceedings from FAST ’07: USENIX Conference on File and Storage Technologies, 5. [PDF]

Firstborns have higher IQs than their siblings

This short paper from Science reports tasty findings on the correlation between birth order and intelligence. While it’s generally accepted in the scientific community that older siblings generally have higher IQs due to different environmental influences, numerous possible factors make it tricky to prove causation from correlation. One criticism is that later siblings are likely to come from larger families, which relates to lower socioeconomic status and IQ.

A study of 241,310 Norwegians shows that sibling social order rather than biological factors is what causes the variation of intelligence in siblings. This study supports the popular confluence theory, which claims that intelligence is directly influenced by the intelligence level of the other family members. Thus, older siblings benefit from extra time spent with the parents, while younger siblings are negatively affected by the other children.

In nature vs nurture, nurture takes this round.

Kristensen, P. & Bjerkedal, T. (2007). Explaining the Relation Between Birth Order and Intelligence. Science, 316(5832), 1717. [PDF]

Fluid Temperature has No Effect on Hydration

We reach for a ice-cold beverages on a hot summer day to rehydrate ourselves in the sweltering sun. Frozen water bottles and ice coolers are used to avoid warm drinks, which seem to evaporate once they hit your mouth.

Contrary to the practice of cooling or warming liquids to relieve your thirst, this study shows that temperature has no actual effect on hydration. Fluids administered to infants at body temperature (37°C) versus room temperature (23°C) were equally effective at hydration. This was the same case in both rotating and bolus (all at once) oral rehydration methods.

However, colder fluids pass throgh your body quicker than warm fluids, giving the perception they are better for hydration. This is not necessarily the case. Additionally, your body experiences a temperature cooling when filled with a colder liquid which feels more refreshing on a hot day. However, when it comes to thirst, colder or warmer drinks will quench it just the same.

Pizarro, D. T., Posada, G. S., Levine, M. M., Nalin, D. R., & Mohls, E. V. (1987). Comparison of Efficacy of Oral Rehydration Fluids Administered at 37°C or 23°C. Journal of Tropical Pediatrics, 33(1), 48-51.

Americans Getting Lonelier

In 1985, the General Social Survey reported that Americans had an average of 2.94 close friends (confidants is the term researchers like to use). A recent survey from 2004 found that the number of confidants has dropped to 2.08. In other words, Americans have lost on average one friend with whom they discuss important matters. The relationships with the greatest drop in confidants were neighbors and group/club members. We are less than half as likely to have them as close friends now than in 1985. It’s really unfortunate because everyone could use more friends. The paper suggests that the nature of our social network has changed. Instead of a few strong ties, we have more weak ties.

Americans are less likely to have friends with a different education level, but more likely to have friends of a different race. Furthermore, educated Americans have larger and more diverse networks. That says something about the friends you make during your college years — I find my closest friends are from internships and college.

McPherson, M., Smith-Lovin, L., & Brashears, M. E. (2006). Social Isolation in America: Changes in Core Discussion Networks over Two Decades. American Sociological Review, 71, 353-375. [PDF]

The effectiveness of self-imposed deadlines on procrastination

I often hear of graduate students postponing their research to do other things: play Tetris, read comments on Slashdot, or write a blog. We defer doing something “more important” to do something else and feel guilty and pleased at the same time.

How sweet is it not to do work? Apparently, sweet enough to abate the heavy and bitter costs of procrastinating. Late fines and extra work for missing a deadline seem distant when you can chat online for another 20 minutes right now.

Why do people procrastinate? This is an effect psychologists attribute to “hyperbolic time discounting”: the immediate rewards are disproportionally more compelling than the greater delayed costs. In other words, Procrastination itself is the reward.

However, the eventual cost of neglecting a task has such an impact on people that they learn to impose deadlines on themselves to restrict their own behavior. At what lengths do people do this? This article looks at three questions:

1. Do people self-impose costly deadlines on tasks in which procrastination may impede performance?
2. Are self-imposed deadlines effective in improving task performance?
3. Do people set their deadlines optimally, for maximum performance enhancement?

A few studies are reported in this paper, where students had the opportunity to choose their own deadlines for three tasks they needed to do (write or proofread papers). They were allowed to set separate deadlines for each paper, but they would be bound to the deadlines and be assessed penalties if the papers were submitted late. Logically, the best solution would be to set all the deadlines to be the last day, which would give them the most flexibility and time to work on the three tasks.

However, only 27% of the students chose to submit all three papers on the last day of class. This answers the first question — people are aware of their own procrastination and give themselves earlier deadlines to counter it. The studies show that these deadlines do improve performance over only having deadlines at the very end. Unfortunately, they are still suboptimal because the subjects who were given equally spaced deadlines performed better, thus supporting question two but rejecting question three.

But hey, I’ll push myself to start my taxes earlier, but after a round or two of Winterbells.

Ariely, D. & Wertenbroch, K. (2002). Procrastination, deadlines, and performance: Self-control by precommitment. Psychological Science, 13(3), 219-224. [PDF]

Comic Chat

I remember using Comic Chat when I was in elementary school, trying it out with my new Internet Explorer install. It was the first chat program I had used, and thought it was both exciting and scary to be able to talk to complete strangers. Comic Chat is an application which generates comics from online chat, and uses the IRC protocol.

I was surprised to find this paper on Comic Chat written by the authors in 1996. Interestingly, it was published in SIGGRAPH, the top computer graphics conference in academia. From reading this paper, I find that Comic Chat is a lot more complicated than I initially thought.

Comic Chat creates realistic comics, which mainly consist of characters, speech balloons, and panels.

Characters

Generating a comic requires placing characters in a panel. Comic Chat used cues present in the text to generate the character’s gesture and expression. Things such as smileys :-), use of “I” or “you”, and punctuation would change the appearance of the character. In addition, the position and orientation of the characters is determined by a greedy algorithm. The following strip has examples of position and orientation issues: the first panel is missing a speaker, the characters in the second panel are not facing each other, and the outer two characters in the third panel are talking over the two middle characters. The fourth panel shows a correctly drawn panel.

Balloons

Comics generally use four different types of balloons,

1. Speech balloons for regular text, drawn with a solid outline and tail
2. Thought balloons for what a character is thinking, with a solid online but a tail of ovals
3. Whisper balloons for private conversation, with a dotted outline and tail
4. Shout balloons for shouting text, with a jagged outline (not shown in figure)

 

Determining a balloon’s dimensions and placement is determined by a complex algorithm, which you can find in the paper. There are many things to take into consideration when placing balloons, such as placing them so they are read in the correct order, so they don’t overlap, so they are located somewhat over the speaker’s head, and to leave room for the tails.

Panels

Panel breaks are calculated to accommodate text properly, and to make the comic appear more natural. Breaks can be made when there are too many characters in a panel, or there is not enough room for the text. A break is also introduced when a character speaks twice to ensure a character does not have more than one balloon per panel. Panels are usually close-ups of characters to get a good view of the active character. However, a zoomed out shot is sometimes done to show the surroundings and characters in the scene.

While Comic Chat has become antiquated and few users now use it to chat online, it still has some value today. I realized when reading this paper that the web comic, Jerkcity is constructed from Comic Chat.

You can download a copy of Comic Chat if you want to give it a spin.

Kurlander, D., Skelly, T., & Salesin, D. (1996). Comic Chat. Proceedings from SIGGRAPH ’96: International Conference on Computer Graphics and Interactive Techniques, 225-236. [PDF]

The First Digit Law

If I were to pick a random city in the world, and tell you its population, what might the first digit of that number be?

You may think there’s equal probability for the first digit to be 1 to 9, but over 30% of the time it’s 1 (one).

Why? Think about it this way: let’s say a stock price doubles every year, starting at $100/share; it would spend a year with a first digit of 1 until it reaches $200, a year as $2xx or $3xx until it reaches $400, a year as $4xx, $5xx, $6xx, or $7xx, and then just a month or so at $8xx or $9xx, and all of a sudden it’s at $1,000 and the first digit is 1 again. Now it takes a long time (a year) to reach $2,000. There is a disproportionate amount of time when the stock price begins with the digit 1.

Many things in nature increase logarithmically. Benford observed this first-digit phenomenon in places including populations, addresses, baseball statistics, area of rivers, specific heats of compounds, and death rates. This rule has been used to identify accounting fraud where made-up numbers don’t match the distribution found in real accounting numbers.

Benford sampled over 20,000 numbers, and noticed the distribution of numbers was as follows,

Digit	Occurence
1	30.6%
2	18.5%
3	12.4%
4	9.4%
5	8.0%
6	6.4%
7	5.1%
8	4.9%
9	4.7%

This can be closely modeled using the log distribution of

F_a = log(1 + 1/a)

where F_a is the frequency that the digit a is the first digit in used numbers.

Additionally, the frequency of the n-th digit of a number can also be calculated using a similar formula, presented in the paper.

This is the law of anomalous numbers. We’ve learned to count 1, 2, 3, 4, … but nature counts 1, 2, 4, 8, …

Benford, F. (1938). The Law of Anomalous Numbers. Proceedings of the American Philosophical Society, 78(4), 551-572.