Optimizing your value to the people that can help you reach your full potential

In “If the average person in the room isn’t smarter than you, you’re in the wrong room“, I asked

a huge part of success is getting involved with smart, successful, big-thinking people, thereby raising your standards and learning from their example. But … the problem is, what’s in it for them?

conditionalcognition replied

Talented folks are not talented in all arenas. Regardless of how successful a particular person may be at certain things (i.e. business, basketball, design), there isn’t a person that can be one of the best at everything. Keeping this in mind, people looking to learn from others in a particular field need to identify what skills/talents can be shared with the “experts,” so they value the exchange of time, ideas and talents.

and I agreed

Yes. And, because intellectual diversity (of thinking styles, backgrounds, etc.) is as vital to social/scientific progress as genetic diversity is to evolutionary adaptability, you can probably optimize your value to the people you want to interact with by cultivating those aspects of yourself that are most authentically you.



Trump elected by Big Data – the impact of Cambridge Analytica

University of South Wales: Information Security & Privacy

Imagine the influence of a London based company, which acted as the catalyst that powered both BREXIT and President Trump’s campaign to success.

What if I told you that Trump was elected by Big Data analysis, carried out by a British company, and that this company can swing elections.  You probably already know the strength of Cambridge Analytica in winning elections, but the video below is for those who may not have realised what was happening.

Here’s the Trump campaign video:

Every single adult in America has been analysed by Cambridge Analytica.  Next they altered the campaign message to each individual’s personality.

So before you get bored.. I always warned you about the dangers of Big Data. This is one of the side effects – one British company can make Presidents.

In Europe there are several elections in the next year.  How much would you pay Cambridge Analytica to win an…

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The cloud never forgets — “You may not know what you do on a regular basis, but I know”

According to Bloomberg’s David Gauvey Herbert in “This Company Has Built a Profile on Every American Adult

IDI, a year-old company in the so-called data-fusion business, is the first to centralize and weaponize all that information for its customers. The Boca Raton, Fla., company’s database service, idiCORE, combines public records with purchasing, demographic, and behavioral data. Chief Executive Officer Derek Dubner says the system isn’t waiting for requests from clients—it’s already built a profile on every American adult, including young people who wouldn’t be swept up in conventional databases, which only index transactions. “We have data on that 21-year-old who’s living at home with mom and dad,” he says.


The reports also include photos of cars taken by private companies using automated license plate readers—billions of snapshots tagged with GPS coordinates and time stamps to help PIs surveil people or bust alibis.


Users and industry analysts say the addition of purchasing and behavioral data to conventional data fusion outmatches rival systems in terms of capabilities—and creepiness. “The cloud never forgets, and imperfect pictures of you composed from your data profile are carefully filled in over time,” says Roger Kay, president of Endpoint Technologies Associates, a consulting firm. “We’re like bugs in amber, completely trapped in the web of our own data.”


“You may not know what you do on a regular basis, but I know,” Rambam says. “I know it’s Thursday, you haven’t eaten Chinese food in two weeks, and I know you’re due.”

See also “Surveillance vs. morality“.



The meaning in our mistake

According to Jonah Lehrer in his excellent “Accept Defeat: The Neuroscience of Screwing Up

Dunbar found that most new scientific ideas emerged from lab meetings, those weekly sessions in which people publicly present their data. Interestingly, the most important element of the lab meeting wasn’t the presentation — it was the debate that followed. Dunbar observed that the skeptical (and sometimes heated) questions asked during a group session frequently triggered breakthroughs, as the scientists were forced to reconsider data they’d previously ignored. […] a single bracing query was enough to turn scientists into temporary outsiders, able to look anew at their own work.

[Discussion of two labs facing the same experimental problem, but one lab diverse, the other not.]

The diverse lab, in contrast, mulled the problem at a group meeting. […] “After another 10 minutes of talking, the protein problem was solved,” Dunbar says. “They made it look easy.”

When Dunbar reviewed the transcripts of the meeting, he found that the intellectual mix generated a distinct type of interaction in which the scientists were forced to rely on metaphors and analogies to express themselves. (That’s because [the diverse] lab lacked a specialized language that everyone could understand.) These abstractions proved essential for problem-solving, as they encouraged the scientists to reconsider their assumptions. Having to explain the problem to someone else forced them to think, if only for a moment, like an intellectual on the margins, filled with self-skepticism.

This is why other people are so helpful: They shock us out of our cognitive box. “I saw this happen all the time,” Dunbar says. “A scientist would be trying to describe their approach, and they’d be getting a little defensive, and then they’d get this quizzical look on their face. It was like they’d finally understood what was important.”

What turned out to be so important, of course, was the unexpected result, the experimental error that felt like a failure. The answer had been there all along — it was just obscured by the imperfect theory, rendered invisible by our small-minded brain. It’s not until we talk to a colleague or translate our idea into an analogy that we glimpse the meaning in our mistake.

You decide what’s possible for you by looking at your neighbors

According to Robert Cialdini

People want to be with the crowd. It tells them something not only about what’s appropriate, but what’s possible for them.

If we send people in San Diego a message saying the majority of your neighbors are conserving energy on a daily basis, that has more effect than telling them to do it for the environment or to be socially responsible citizens or to save money. If your neighbors are doing it, it means it’s feasible. It’s practicable. You can do it—people like you.

It was very important that we say “people in your neighborhood.” If we said “the majority of Americans,” that wasn’t effective. If we said “the majority of Californians,” that was more effective. If we said “the majority of San Diegans,” that was more effective. But the most effective was “the majority of your neighbors.” That’s how you decide what’s possible for you: what people in your circumstance are able to do.

According to John Farrell in “Solar Power is Contagious, Study Finds

The study notes that for every 1 percent increase in the number of installations in a single ZIP code, there’s a commensurate 1 percent decrease in the amount of time until the next solar installation.

See also “If the average person in the room isn’t smarter than you, you’re in the wrong room.” and “Get out of that pickle barrel!” and “The 0-1-2 effect“.

An opinion leader’s global warming epiphany

One of the most popular bloggers in my part of the software industry, Electronic Design Automation (EDA), is Karen Bartleson. In an entry about the annual and influential EDP Workshop, she mentioned

A special part of EDP is the beach walks. There’s nothing like a refreshing walk with stimulating conversations among friends. It was during one of these walks that a colleague convinced me that global warming is real.

In a comment, I asked her to tell more about that experience, and she was kind enough to respond here that

My coworker and I took the beach walk during a break, and I think the venue was important. It brought to mind the environment (perhaps we saw trash on the beach) in a way that sitting inside a building couldn’t. My coworker spoke about global warming, and I spoke about the garbage island that floats in the Pacific and is larger than the size of Texas and growing. I mentioned that I wasn’t sure if global warming was caused by humans or if it was a natural change phenomenon. I hadn’t seen evidence first-hand of global warming caused by people. He asked me if I’d seen the garbage island first hand. I said “no”. He then countered with “if you believe in the garbage island that you’ve never seen, why don’t you believe in manmade global warming?”

Not exactly a mathematical proof, but it was enough for me.

Implicitly, I had asked Karen how she came to accept a scientific reality that her mind would rather have denied, and she didn’t flinch. I think her open, honest introspection was very gracious. We’re playing for big stakes on this issue, and there’s no time for vanity.

I owe her some honest introspection. In my case, I’ve never felt the urge to deny the reality of global warming, so for the reasons here, I conclude that I must have some motives to want to believe it. I’ll think more about what they might be.

Delegating one’s votes

It ought to be possible to delegate one’s votes.

Every election we bemoan the power of the non-voters, but looking at the ballot and the accompanying materials, I can hardly blame them.

The ballot, at least in California, is absurdly confusing, with candidates in random orders instead of party order, non-partisan offices, and a long list of intentionally misleading initiatives.

If the process were sincere, it would be possible to simply vote for a party and leave it at that. In California, we have 6 qualified political parties, enough to fit almost any likely political philosophy.

Another possibility would be to allow voting for a single candidate on the ballot, to whom you would delegate your other votes.

Notes on Kleinberg’s “The Convergence of Social and Technological Networks”

A few notes on Jon Kleinberg survey article “The Convergence of Social and Technological Networks”, CACM, Nov. 2008, pp. 66-72.

 Collecting social-network data has traditionally been hard work […] Social interaction in online settings, on the other hand, leaves extensive digital traces by its very nature. […] the beginnings of a new research area—one that analyzes and builds theories of large social systems by using their reflections in massive datasets.”



Two major areas of social network investigation


  1. Small-world principle, decentralized search, 6 degrees of separation, peer-to-peer
  2. Social contagion, spread of ideas, diffusion of innovations, influencers



Small world


There really are “6 degrees of separation” in human social networks, and those short paths are findable by the people in the network using only local information.  The classic experiment by Milgram, asked someone in, Tulsa, say, to route a letter to someone in Boston by mailing it to someone the Tulsan knew on a first-name basis, who would forward it on to someone he/she knew, etc.


(That term “short path” is used throughout, but I don’t think it’s formally defined in this article.  I assume it means something like a path that has many standard deviations fewer segments than the mean of a randomly chosen path between the source and target.)


Although “adding even a small number of random social connections to a highly clustered network causes a rapid transition to a small world, with short paths appearing between most pairs of people,” that alone is not enough to make these paths findable with only local information. For example, if the random connections were distributed uniformly across all pairs in the whole network, the short paths would seem arbitrary, perhaps from Tulsa to Miami back to Omaha and then to Boston.


I interpret this section to be asking –


    If the local choice is a greedy one that chooses the connection that most improves the closeness to the target, which distribution of random connections is most likely to lead to paths that are short?


and answering –


   An inverse-square power law — if Alice lives twice as far from you as Bob does, then you’re twice as likely to be connected to Bob.


(A version of this reference is available here.)


Real social networks obey this inverse-square power law.  What underlying processes cause social networks to grow this way?  That’s still an open question. (It’s possibly related to the “preferential attachment” mentioned in the concluding “Further Directions” section.)


(On ‘inverse-square’ I still have some confusion though.  What is the metric? Most of the time in this article it seems physical distance, perhaps adjusted for population densities.  But I personally communicate more with technologists in Boston than I do neighbors on my block.  Milgram-type experiments “have tended to be the most successful when the target is affluent and socially accessible”. I’d suppose that my neighbors and I are OK in that regard, yet they are not honestly a significant part of my social network.  Maybe I suppose wrong, especially about my social accessibility? Or maybe I should be asking, ‘What is the social network?’, too, not just ‘What is the metric?’.


And Kleinberg writes “Other research using online data has considered how friendship and communication depend on nongeographic notions of ‘distance.’ For example, the probability that you know someone is affected by whether you and they have similar occupations, cultural backgrounds, or roles within a large organization. Adamic and Adar studied how communication depends on one such kind of distance: they measured how the rate of email messaging between employees of a corporate research lab fell off as they looked at people who were farther and farther apart in the organizational hierarchy. Here too, this rate approximated an analogue of the inverse-square law— in a form adapted to hierarchies—although the messages in the researchers’ data were skewed a bit more toward long-range contacts in the organization than short-range ones.”)






How does information and influence diffuse across a social network?


Not via short paths.  Analysis of chain letter data show that “Rather than fanning out widely, reaching many people with only a few degrees of separation, the chain letter spread in a deep and narrow pattern, with many paths consisting of several hundred steps.” ( ‘Why?’ is an open question, but Kleinberg discusses several hypotheses.)


Naturally, people are influenced by their neighbors in the social network — the more friends you observe doing something the more inclined you are to do it – and, naturally, there’s a diminishing returns effect – if 99 friends are doing it, the seeing the 100th one do it doesn’t change your behavior much.  (Not mentioned, but commonsense says to me that you are more likely to copy people you identify with, but, on the other hand, may not matter because the people in your social network tend anyway to be people you identify with.)


However, there’s an important psychological effect – the 0-1-2 effect — that runs contrary to diminishing returns, because


        “the probability of joining an activity when two friends have done so is significantly more than twice the probability of joining when only one has done so”.  


(Denning defines innovation as “fostering a change of practice in a community”.  The 0-1-2 effect suggests that a successful innovator must not merely make initial converts, but also encourage them to be visible about it.)


This is related to “triadic closure” – “links are much more likely to form between two people when they have a friend in common”.


The last paragraph of this section seems worth focusing on –


         “Large-scale social contagion data […] provides the opportunity to identify highly influential sets of people in a social network—the set of people who would trigger the largest cascade if they were to adopt an innovation. The search for such influential sets is a computationally difficult problem, although recent work has shown that when social influence follows the kind of ‘diminishing returns’ pattern discussed here, it is possible to find approximate methods with provable guarantees”



Effects mentioned in “Further Directions” section


If the number of people in the network grows –


  1.     “densification” – the number of links per person also grows
  2.     “shrinking diameters” – the length of the shortest path can decrease



It’s hard (or perhaps impossible) to predict the growth of a network in detail, because the growth is sensitive to initial conditions.