Posts Tagged 'Media'

Organization’s Effects

artguy

If you take a group of people, a subgroup of the larger population, and expose them to focused messages again and again, you will start to change their point of view. If you augment those messages with exposure to other members of the group, the messages will begin to have ever more impact.

We generally tend to align ourselves with those we’re around. We don’t fully understand why. There is a lot of psychology we know, and then other stuff we can’t explain. Yawning, for instance, can be statistically shown to be contagious. It has been studied for years, yet we don’t know why it happens.

Once a group starts to become aligned, and starts acting like a tribe, the messages of the tribe will become self-reinforcing. When someone is born into that tribe, there is a very high probability she will never know the difference. It is simply her common sense about the way the world works.

Programmed.

Advertisements

Probability: Facts, Statistics, and Reality

What is reality? Statistics are based on facts. We can’t deny or ignore them. But they aren’t always factual, or even meaningful.

I am a statistic and so are you. I drive a car. I eat. I buy things. I have an education. I don’t smoke. I was born at a place and time. I am programmed by my surroundings and DNA.

We use prior facts and statistics to reason with uncertainty, to get at probability, but we suck at it. In general we are really, really, staggeringly incompetent at processing all but the simplest statistical data in ways that produce meaningfully accurate evaluations. This is partly because it is much more difficult than it appears on the surface. We aren’t rigorous enough. But it’s also because our intuitive way of understanding complex relationships is…well…it’s too simplistic.

A Quick Lesson in Photography (It’s relevant, run with me)
In photography one learns about the relationship between the glass of a lens, the distance to the subject, and the focal point, which is where the image passing through the curved glass is in focus. The curvature, or varied thickness of the glass, bends light. Distorts it, providing a means to get a reasonably coherent image on to a sensor (without this, all light from all angles will hit the sensor, producing nothing more than a gray blur).

BasicLens

So a lens allows us to pick an area to capture, just like the position of your eye picks an area of the overall scene to project onto your retina through its lens (your brain further filters this into what it chooses to focus on). The focal point is where the image comes into focus after passing through the lens. The focal length is the focal point’s distance from the lens.

Modulating the distance between the lens and the subject changes focal point.

FocalPoint

In short, there is a relationship between the shape of the lens, the distance to the subject, and the distance where light reflecting off of that subject that passes through the lens will come into focus. Five minutes playing with a magnifying glass gives one an intuitive understanding of this. Note: the eye changes focal length by changing the shape of the lens, whereas a camera does it changing the distance between two (or more) lenses. All of these ingredients, and others to follow below, interact with one another to vary the result.

Simple? Well, it’s not that simple. There is more. (Hang in there.)

In around 1000 A.D., Arabic physicist, Ibn el-Haitam penned the first known accurate and comprehensive description of how light is refracted by shaped glass. This led to the development of a myriad of mechanical devices that provided augmentation for human vision. Through a combination of lenses, we can “zoom in” on specific parts of a scene, bringing things far away closer to us for better examination.

ZoomMicroscope

In Astronomy, however, the concept of “zooming in” isn’t as significant, even though it would be cool if we could zoom way in to distant objects. At those great distances our earthly lens ratios don’t accomplish much. And we can’t change the ratios too much due to diffraction. Diffraction, or the scattering effect of light, always exists when light passes through or around something, or is reflected. Diffraction essentially acts to defocus the image, which means that as the magnification or zoom increases, the sharpness and clarity of the image breaks down. This defocusing is inversely proportional to the diameter of the lens, not to mention the optical quality, but is always an issue. It’s one reason why the best telescopes have a large diameter.

DiffractionDiagram

DiffractionImages

Larger f numbers indicate smaller openings. Intuitively one can see that zooming in to any of these images would cause an increased breakdown in apparent focus.

 

The other reason for wider lenses is because they are inherently able to gather more light, which is pretty important for looking at far away stellar objects, some of which are so faint they cannot be seen by human eyes or conventional optics.

StarsTelescopeLightGathering

 

Gathering more light is also useful in earthbound photography. More light reaching the sensor allows it to record the scene quicker, which makes it easier to freeze the motion of moving images. Aesthetically this may or may not be desired. Note how these different capture speeds reflect different interpretations of “reality.”

WaterfallShutterSpeed

As capture speed slows, the motion of the water is revealed in different ways. The image on the left shows the exact position at an instant in time. The rightmost image shows the average position across a period of time.

PinwheelShutterSpeed

The actual speed of the pinwheel was the same in all instances.

 

Wider lenses come at a cost beyond the expense of manufacturing them. As we let more light in, and reduce diffraction, we simultaneously narrow the range of distances from the source to the camera that will appear in focus because light is hitting the sensor from wider angles off dead center.

BasicDOFDiagram

 

Photographers refer to the range of distances that will appear in focus at the focal point as “depth of field.” With larger apertures and lenses more precision in focusing is required, which is generally manageable, but the impact it has on the resulting photograph can be significant.

AperturDOFDiagram

 

More or less of it my be desired, depending upon the look one wants. Like speed, the depth of focus also serves to depict different views of “reality.”

DOFBulbs

DOFGirlBoy

 

It is a very useful consideration for pulling the attention of the observer to a specific part of an image.

DOFExample1

DOFBass

One can look broadly at an image and not see the distortions in the domains of time and clarity, but careful examination will reveal them. They can’t help but be there. We can capture a scene in a way that emphasizes or de-emphasizes certain of those, but in the immortal words of Scotty, “You can’t defy the laws of physics.”

We trust what we see with our eyes, too much. No doubt the reader has now surmised there is a matrix of trade-offs in what we “see” when we capture an image based on these parameters. Photographers make choices, intentionally and unintentionally, that affect the outcome. Observers usually take the photo at face value, with no regard for the actual reality of the scene, instead letting the photo determine the reality we believe existed in the moment.


 

Now, apply that same thinking to statistics and one can begin to see why it is so commonly held that statistics can be used to support nearly any conclusion. Unfortunately what often happens is that they are used too generally. Reputable (*) sources say things like…

  • 10% of our brains are used
  • 50% of marriages end in divorce
  • we share 99% of the genetic code
  • left handers die an average of 9 years younger than right handers
  • 18% of social media users use snapchat
  • 77 cent wage gap between women and men
  • 20% of women are sexually assaulted before they leave college
  • 0.0024% of deaths are from electrocution
  • men think about sex every seven seconds
  • The religion of Islam is growing at a rate of 2.13% per year
  • spousal abuse skyrockets on Super Bowl Sunday
  • The average household income in the U.S. is $70,000, or…
  • Any of the stats that show that the top X% earn [staggeringly large number]%
  • 80% of convicted sex offenders repeat the crime
  • 50% of 18-24 year-olds go on Facebook when they wake up
  • 30.5% of all desktop search traffic between 6/10/16 and 7/7/16 came from searches with the term “pokemon” in them
  • (my personal favorite – LOL) 90% of statistics can be used to say anything 50% of the time

There are so many more I could use: Crime Rates, School Quality, Unemployment, Mortality, Cost of Living, Obesity, Literacy, Birth Rate, Gun Control, Teen Pregnancy, on and on…

They are all generally accepted as true, yet none are precise across the board (stay tuned for a future blog post delving into the difference between precision and accuracy). There are nuances in specific ages, cultures, geography, time, education, temperament, weather, situations, and any of dozens of other variables that come into play. They are generalizations. Averages. Broad snapshots, or maybe narrow ones. Very useful as shortcuts to learning and not basing too much on mere assumptions, but also misleading in some ways. Many sound plausible. Some we believe. Some we would question. Some are subject to varied interpretations. Some are patently false. I’m not just referring to the inevitable weird exceptions that are in the noise of any statistical model. Noise is random. I’m talking about correlatable things with some significance that are missed, ignored, or misinterpreted.

For instance, Psychology Today based an article on the 10% of our brains get used statistic. We’re easy prey for this because we’ve been hearing variations on that number most of our lives. None of them account for what the data really says, which is that a small portion of our brains are used at any given moment, in a given activity, or for a given purpose. Other activities and functions require other parts of our brains to be used. The real number is significantly higher than 10% when all of this is factored in.

I can, to some extent, debunk every one of the above listed items with rigorous research, but then who says my facts are really correct? More significantly, is the interpretation of the data correct? For instance, the divorce rate of 50% seems pretty concrete. We have records to prove it, so there is no questioning the data, right?

If that’s the case, then why are the quoted stats sometimes so “approximate?” The APA website says “40 to 50%.” That’s a massive difference for something that seems so concrete. Further, we all seem to believe it, or do we? We accept it. But…most of us get married anyway!!! If you knew that you had a 50% chance of getting struck by a bus while crossing a street, would you dare to cross in spite of it? A little thinking and reading quickly leads us to realize there are tons of potential nuances to a statistic like this. So many it isn’t practical to try to list them all.

But there are also fundamental issues that skew the data. How is it calculated in the first place? Often the number is reached by simply comparing divorce filings to marriages over a given span of time. Make sense? It does on the surface, but it doesn’t necessarily tell us much because over time things change. Okay, well, what if we compare divorce and marriage rates in a given year? That’s a pretty short span of time and would seem to be a suitable snapshot. So, let me get this right…you want to add up all of the divorces in a year, no matter how many years all of those people have been married, with no consideration to when they were married or under what circumstances, and compare them to marriages this year? Doesn’t really work. As with photography, anytime you capture something moving in a static “image” or focus on a part of it, you’re forced to make decisions about how to present it to meaningfully convey the result. If anything, we should be comparing all people who are currently married to those getting divorces in a year, but it’s almost never done that way.

Yet we buy in. We’ve all heard the statistic so many times that it rings true to us, to the extent that nowadays any data showing something different would be called into question. We have become biased, mostly (in this case) by our own inability to effectively pick the data to look at, or in interpreting what it really means. And yet most of us decide to roll the dice and get married anyway.

We often apply statistics to provide probabilities about how the future will turn out. Seems simple enough. If 20% of children outgrow a childhood allergy to peanuts, then you could assume your peanut allergic child has a 20% chance of doing the same. It’s also easy to see there could be many subsets that could introduce other variables. Is the percentage the same across race? How about across a range of medical treatments or diets or subtle variations in DNA? That’s just scratching the surface – the basics.

Consider this test. Two groups of people engage in a coin flipping exercise. Group A looks for the pattern H-T-T (Heads, Tails, Tails), and Group B looks for the pattern H-T-H (Heads, Tails, Heads). Each member of each group records how many coin tosses it takes before the desired pattern occurs. The two groups then average their respective results. Do you think the average number of tosses between the groups will be the same, or will one of them happen sooner on average? (Don’t worry, most people, even very smart ones, get this wrong.)

It turns out it’s statistically provable that the average number of tosses to reach the pattern H-T-H is 10, while the average number to reach H-T-T is only 8. How could this be? It butts up against our common sense on the matter. If anything we would think that H-T-H would be easier to create, since it overlaps itself. Throw a T between any two occurrences of H, and viola!

But think about what happens the first time you get an H followed by a T. At that point, either of the two results can now occur.

Group A: you’re looking for H-T-H, and you’ve seen H-T for the first time. If the next toss is H, you’re done. If it’s T, you’re back to square one: since the last two tosses were T-T you now need the full H-T-H.

Group B: you’re looking for H-T-T, and you’ve seen H-T for the first time. If the next toss is T, you’re done. If it’s H, this is clearly a setback; however, it’s a minor one since you now have the H and only need -T-T. If the next toss is H, this makes your situation no worse, whereas T makes it better, and so on. Even when you lose you’re 1/3 of the way to winning.

Put another way, in Group B, the first H that you see takes you 1/3 of the way, and from that point on you never have to start from scratch. This is not true in Group A, where a H-T-T erases all progress you’ve made.

People write articles and make declarations every day who do not understand these types of relationships. We read them, and we usually believe them so long as they sound credible (often through citing studies and statistics) and/or match sufficiently with our common sense or already held beliefs.

Bipolar Disorder affects 3% to 5% of the population according to Gary Sachs, director of the Bipolar Clinic at Massachusetts General Hospital. It affects 2.6% of the population over 18 years old according to WebMD. The U.S. National Library of Medicine has been summarized as saying that it occurs more in women. The DBSA (Depression and Bipolar Support Alliance) say it occurs equally in women and men, but women tend to cycle faster. Closer examination, however, reveals that Bipolar II (a predominance on the depressive side) occurs more in women. These are basic examples of statistics being in general agreement, but presented in ways that show differences.

What about clinical testing? What if there is a test that is 99% accurate. When it returns positive, you could assume that there is a 99% chance the positive result is true, no?

Two problems…

  1. I have pointed out that there is almost always a higher level of detail or granularity that can be considered to further classify and characterize the risks of different individuals.
  2. It also depends on how common or rare the condition is. Suppose the condition affects 1 of every 10,000 people. If we sample a million subjects, the test will get the 100 who do have the condition right 99% of the time. 99 of them will test positive. Amongst the 999,900 who do not have the condition, the test will get it right 99% of the time, which means it will improperly show that 9,999 of them have the condition, when in fact only about 99 of them really do. So, less than 1% of the people who test positive actually have the condition!

Consider the case of Sally Clark. She was convicted of murdering her two male children, one of which died in 1996, and the other in 1997. Defense claimed it was likely SIDS that took them, but the prosecution, through expert witnesses, won, in part by showing that there was only a 1 in 73,000,000 chance that SIDS would affect two children in a single household.

Wrong, in two ways.

  1. The calculation was faulty. It was based on the probability of SIDS affecting a child of affluent non-smoking parents being 1 in 8,543, so therefor the odds of two children suffering from it are 1 in 73,000,000 (8,543 x 8,543). But this doesn’t allow for the fact that we don’t completely understand what causes SIDS. There are clearly unknown environmental and/or biological (hereditary) factors. It’s quite likely that if a child dies from SIDS that some of these unknown factors are in play, and may increase the likelihood of it affecting other children by a factor of 5 to 10.
  2. Even if 1 in 73,000,000 is correct, the statistical analysis of murder suffers from the same flaw as the medical example above. There are two parts to the explanation that must be considered independently. 1) That Sally was innocent – which is likely considering most mothers don’t murder two children – and she suffered an incredibly unlikely event. 2) She is guilty, which is unlikely, but if she were trying to kill them, she succeeded.

Figuring that out statistically is much more complicated, and in fact there are significant factors that could have and should have been considered from the get-go, such as the details that boys are more likely than girls to suffer from SIDS. She was eventually released from prison after the second appeal, which more carefully broke down the statistical odds of a double murder to between 4.5:1 and 9:1. Still likely, but hardly conclusive enough for a life sentence based on weak circumstantial evidence.

We can be pretty good at questioning conclusions from people who we believe are unlikely to have competence in a particular area. If the subject matter expert of the court had tried to present arguments about diagnosing an automotive problem, no doubt there would be suspicions. But this expert was in the field, and presumed to understand the data upon which he was drawing conclusions. Yet he got it way wrong, and nobody questioned it.

Sally, after spending a few years in prison as a convicted murderer of her two children (probably not the most enjoyable of prison stays, as far as prison stays go), died of an alcohol overdose a few years later. Her husband said she was never the same after that experience. There is more nuance and information on this story, but the takeaway here is that facts and statistics as used severely mischaracterized the likelihood of a natural death.

Data and stats should be subject to much more scrutiny than they commonly are. But we need them. We need the shortcuts, assumptions, and predictions they provide. It’s a shame we so often get it wrong, because it skews our perceptions and causes faulty decisions. But even where the data and conclusions are relatively good, which as laypeople we often have no way of knowing for sure, we often fail to see into the exceptions.

A whole pile of data can suggest that something is generally true, yet in certain circumstances it isn’t. We don’t or can’t always quantify those circumstances, but we know they exist. Marriage is an example where there appears to be a lot of opportunity. Can we study a detailed matrix of characteristics that will lead us toward better outcomes based on empirical results? I suggest that the big dating sites have an opportunity to do this with the big data they could be collecting. Employers already do it to some extent.

We can do better and learn more, but until then, you have to ask…

What about the exceptions? Now you have to decide. Do you trust what the data is telling you? It’s a lot more tempting to trust statistics at face value when they appear to align with what you already believe. But are you really looking at it the right way? Does it really apply to you, in your unique circumstances?

Is it a big decision? How does the upside balance with the downside, and how does that impact how you think about the stats? Here is where the dreaded lizard brain of fear steps in. We look at it through the filter of what we are in and interpret the facts accordingly, or by finding another’s interpretation that resonates through us, in some cases by stimulating a fear we already have. Fear is so powerful. It can even make us interpret unmitigated facts in a misleading way.

Sometimes you have to trust yourself. Nobody ever got anywhere important through only listening to others, and that includes the stats, the so called experts, the masses, and the common sense. That stuff is useful up to a point, but somewhere along the journey you have to pave your own way, lest you become a different kind of statistic: the person who is eminently forgettable (ironic oxymoron), not happy or satisfied.

Against All Odds

Invariably someone comes along who defies what others believe. The journey that brought them there can later be quantified. We just need the right lens and some effort and skill to be able to see it. It’s much easier, however, to divine it as mysterious or destiny. We look on from the outside with wonder, not always recognizing there is a quantifiable method to the madness. We tranquilize ourselves with belief that the person was lucky, or blessed.

Few achieve the big goal, or true happiness from solely following others, or common sense. Each of those success stories has a foundation of paving one’s own way.

So much of the past five years of this blog has been about the willingness to make a leap of faith. Not in some external force, but in yourself. As I continue to wrap things up and finish off all of the work that has been started, I still find I am frustrated and even hurt by how little progress has been made. I marvel at those who do it better than me, and hurt for those who continue to find excuses to wallow in the status quo. That’s not what life is about. But then, who am I to say? I’m just a guy with a camera and a blog.

Do not put your faith in what statistics say until you have carefully considered what they do not say. ~William W. Watt

 

(*) – I can substantiate every claim and example used here, along with my rebuttals, if necessary (just ask). I chose not to burden the reader with too many links and wild good chases in the interest of focusing on the heart of the message.

 

Passion or Hubris

SteveMcQueen

Just try to find a list of top movie car chases without Steve McQueen’s legendary chase in Bullit near the top. He was a fine actor, but his true passion was auto racing, which culminated in him finally getting to make the movie of his dreams. In 1970, at the height of his career, he set out to make a “real” racing film, and used a real race in which to do it. A car fitted with a special camera (the first of its kind) drove in the actual 1970 Le Mans race – one of the most difficult and grueling races ever devised by man – while other cameras stationed around the course captured the live racing action. Steve himself had wanted to drive in the race, citing “authenticity” as the motivation, but the insurance company underwriting the film would not allow it. He had in fact just placed second to the legendary Mario Andretti in the 12 Hours of Sebring race a few weeks before (with a cast on his foot from a motorcycle accident). That car became the camera car for Le Mans.

He was a real racer. Imagine the intrigue of finally getting to make a major motion picture about it. He went for maximum authenticity. He wanted the film to tell the real story of what his love was all about. This would be the crowning jewel of his career, and arguably his life.

But it all went wrong. Production ran way over budget and time. Of course the scenes captured at the actual race weren’t sufficient to make a movie with a story, so months of additional filming ensued, and by most accounts the process had little to no direction. Steve wasn’t interested in much of a story beyond the race itself, for which his perfectionism in getting the details right drove the crew bonkers. Over 1 million feet of film was used as he tried to orchestrate complicated racing maneuvers at authentic race speeds on the track. Other costs mounted. A few severe driving accidents occurred, resulting in one race driver having a career ending leg amputation and another with burns on his face. He ended up getting divorced around that time. One of the biggest writers in Hollywood, who had worked on prior films with Steve, never worked again. The actress who costarred in the movie never worked again.

Everyone thought they were working on a theatrical release. Steve was making an authentic film about racing. He wanted to show what it was like rather than talk about it. He said, a racer can’t explain why he races, but he can show you. Eventually the financiers backing the production brought in a new director and relieved Steve of all control. The film was finished and released, still with very little dialog, and not much more than hints of a greater story or context beyond that one race. In later years it became a cult film due in large part to how accurately things were portrayed, but at the box office it was a failure, only partly being saved by the cachet of McQueen himself as the star.

His acting career continued, but people close to Steve say he was never the same after that. He seemed to lose his passion for driving and filmmaking. Like Icarus, he had flown too close to the sun and fallen. From that point he was going through the motions until dying of cancer in 1980. A cancer that in the ultimate twist of irony some say may have been caused by asbestos in the fire suits he wore as a race driver. He said in a recording that while the cancer may have been caused by those materials, he also felt he was ready to “let go.” Not sure if that means give up on life, or what, but it sounds ominous on the recording. It sort of reminds me of how when one spouse dies, the other is sometimes soon to follow, as if when a reason to live is taken away, the body may follow suit.

He lost his wife, his film and several friends. He may well have even lost himself.

Was this negative turning point in his life a result of passion or hubris? I suppose those can be two sides of the same coin in some ways. It would be hard to argue he hadn’t lost his objectivity as a filmmaker. But art works that way. The best stuff is inherently not objective. It’s passionate. Visionary. Did HE think the film was a failure? I speculate that the outside world’s interpretation of success had nothing to do with why he wanted to make that art. And lo and behold, it turns out that it has stood the test of time as the de facto standard against which all subsequent racing films have been measured.

What may look like hubris from one perspective, can in fact just be passion when viewed from another. And vice-versa. But when one’s passion starts to result in mounting costs for others, it may well be time for them to take stock and make tough decisions. In the end we all make our choices about how much we will endure, or how much pain we are willing to inflict to fulfill a promise or chase a dream. On either side of that coin these choices are ours. What will you put up with? What are you willing to do?

Steve made his film. It came at great cost to him and others, but he chased his dream and got something close. While there may have been some regrets surrounding it, he never had to face the regret of not having tried.

“Racing is life. Anything before or after is just waiting.”   – Steve McQueen

Duality of Labels

IMG_9048Labels give us an idea about something while also limiting our ideas about it.

We need them. We gain a lot of efficiency by packaging things up into easily recognizable forms. But then it’s really hard for us to see beyond the form which we have given them. Sometimes life requires that we muster the wisdom and will to examine things more deeply. And to even break things, including ourselves, out of the labels that trap us. The closer it gets to our self-imposed boundaries the harder it is to even recognize the need to do it, not to mention mustering the courage to try.

We must remember that while adventure is dangerous, routine can be lethal. Bravery is needed to have contrary opinions and to take unexpected paths. If you’re not courageous, you’re going to be hanging around the water cooler, talking about the person who actually is.

Do not follow where the path may lead. Go instead where there is no path and leave a trail. – Harold R. McAlindon

Of course it’s extremely uncomfortable at times, but with the application of some discipline one can learn to summon the courage necessary to fight the fear and forge ahead. A good goal for the year.

 

Power to the People?

34 years ago today the chartkillspeopleworld changed in a way that isn’t as spectacular or as talked about as some of the major tragedies or accomplishments as they are often portrayed in the drama hungry media.

On August 5th, 1981 Ronald Reagan ushered in a mindset that mass layoffs were acceptable with the firing of over 10,000 employees during the air traffic controllers strike. He also banned the workers from returning to the profession for the rest of their lives!

The merits of their arguments and the various points of view have been debated. It should not be forgotten that the strike itself was illegal, as mandated by the sometimes controversial Taft-Hartley Act of 1947, which prohibits any labor strike that can cause unfair harm to those not involved or negatively affect general welfare and commerce. The union leader at the time has gone on record since, acknowledging that they botched it, were too arrogant, and didn’t understand a lot of the underlying politics, not to mention the power of spinning public opinion in one’s favor (or not).

Up to this time it was not common to use mass layoffs to handle strikes. This incident eased those inhibitions significantly. Reagan broke the union (it should be noted that the union that eventually replaced it got most of the demands the fired employees were asking for), and in a way galvanized a growing mindset that unions were too powerful and not in the best interests of the economy overall. While Reagan may have been ‘right’ in some ways, and was viewed as effective in quickly restoring order to a situation that was getting out of control, the message that was taken has had some dire long term consequences.

We often don’t/can’t know the consequences of perturbing a system, especially one as complex as the economy with the various complications of the corporations and the workforce driving it. Reagan’s actions communicated from on high that it was acceptable to use swift and massive layoffs to help guard against a short-term economic disruption. Though he never intended it as such, there was now a precedence for protecting commerce before protecting people. Social conventions that had restrained CEO’s from doing something many would have liked to do were disrupted. Not surprisingly some began to take advantage of this tacit permission to take such dramatic action affecting the lives of so many. It became gradually more and more common for workers to be viewed simply as assets and liabilities on a spread sheet, with the math at the bottom supporting their actions to make the numbers for a quarter in efforts to appease stockholders and justify, if only in the short term, the CEO’s position to the board.

Protecting the money eventually became a clear imperative over protecting the people. The very concept of putting a number or resource before a person flies in the face of the protection our anthropology says leaders (alphas) are supposed to offer. It’s kind of like a parent putting the care of the car a child rides in before the actual care of the child. On the surface it may appear to make sense sometimes, but our biology knows better.

Now we much more commonly see leaders, whether CEO’s, politicians, record labels, news organizations, or bankers betray the trust of the very people they are supposed to be serving, often by allowing outside, unengaged constituents to have too much influence over decisions and actions, all in the interest of short term gains and the almighty dollar. When people are lower down the priority list they are less able to operate from a secure position and do the really great work. They are more prone to operate from a position of fear and insecurity, which leads to more focus on the short term protection derived from looking good than the profound work that will make the big difference. Differentiation and innovation give way to commoditization, which ironically spells trouble in the long run.

Contrary to what many would say these days, or at least contrary to where their actions take us, the power, or at least the care, needs to be in the hands of the people, and they need to be provided with an environment where humanity and social relations and the accompanying support they provide can prevail. If we listen to our biology, which at this point in time is what it is, we can cultivate environments that take advantage of our core strengths as social animals. At the most practical level, companies who bring a better value to the table will do better in the long run, and studies have shown that companies with more rigorous financial scrutiny tend to have fewer patents, and the ones they have are generally less profound. Give the people the leadership and security to do their best work and the results will come in time.

Or the few who forge their way to power can try to protect it and hang on at all costs, with all of the stresses and difficulties running rampant in our culture today.

There is a better way.

It’s All About Me

Faster Horses Festival Day 1Selfies are great. But the self aggrandizement can get to be a bit much. It lacks depth and detracts from real engagement. We need to teach and learn to apply attention to others. Give them the credit. It’s not really all about us, and (surprise) nobody else cares that much about what you got or even what you’re doing.

Once one accepts that, it becomes possible to more deeply enjoy the moments as you’re in them. Do it. Don’t worry so much about proving it.

Not Logical

Spock

Leonard Nimoy’s character brought wisdom and depth to simple adventure stories, often offering perspective against an ironically emotional backdrop. Axioms to heed by way of their contexts.

 

 

 

After a time, you may find that having is not so pleasing a thing, after all, as wanting. It is not logical.

Not logical indeed, but true. The mood/feeling/emotion of satisfaction being one of the more strangely elusive conditions of the human experience. Fleeting. Wanting tells us there is more. There is better. It pushes us higher and makes us try to live better. But with no easy way to switch it off it torments us throughout our lives. Of course there are many who manage to navigate to a place where a general sense of happiness and satisfaction prevails. When you consider all of the different circumstances of those who are or aren’t happy, it becomes clear that much of it comes from within. For many of us this elusive state hides behind a dark and silent wall between who we are and all that we could be. We’re left wanting to find a way there, or wishing we could have been wired differently. The pontifications of those chirping the logical “be happy with what you have” falling on despondent ears.

It’s important to note that “be happy with…” is not the same thing as “be satisfied with.” In that distinction is a key to a modicum of happiness. While striving for better it’s possible to nestle into a place where there is some happiness found in what is, which feeds the happiness. But we know we can always be and do better, and it’s when we see evidence of this that the unrest buzzes in our minds and hearts like the pesky mosquitos passing by our ears at night, disrupting what would otherwise be a warm and satisfying campfire. Their sound, in itself, not an issue. What it represents, on the other hand, drives us to move.

If we could close ourselves off from all the stimuli it would be easier. But we can’t. And we know that isn’t a real solution anyway. We want to be like Spock. In control of it, even though that, by definition, denies us of the humanity we are. Silly humans…

A few days before his death Leonard Nimoy sent his final tweet: “A life is like a garden. Perfect moments can be had, but not preserved, except in memory.”

Time to turn over some more soil and plant.


Pages

Enter your email address to follow this blog and receive notifications of new posts by email.

Advertisements

%d bloggers like this: