Dr. Jeffrey K. Liker is Professor of Industrial and Operations Engineering at the University of Michigan, owner of Liker Lean Advisors, LLC,  Partner in The Toyota Way Academy, and Partner in Lean Leadership Institute. Dr. Liker has authored or co-authored over 75 articles and book chapters and eleven books. He is author of the international best-seller, The Toyota Way: 14 Management Principles from the World’s Greatest Manufacturer, which speaks to the underlying philosophy and principles that drive Toyota’s quality and efficiency-obsessed culture. The companion (with David Meier) The Toyota Way Fieldbook, details how companies can learn from the Toyota Way principles.

This is the keynote talk given by Jeffrey Liker at the Canadian Manufacturers & Exporters Lean Conference.

https://youtu.be/qvliu21MxK4

The post [VIDEO] A Keynote On TPS Lean Leadership Featuring Jeffrey Liker appeared first on 6sigma.

How Do We Remember

Memorial Day. How do we remember and honor those the day is set aside for? Memorial Day started off as a somber day of remembrance; a day when Americans went to cemeteries and placed flags or flowers on the graves of our war dead. Originally called Decoration Day, this remembrance began following one of the most poignant eras in our country’s history. Between 1864 and 1866, just after the end of the American Civil War, community leaders established a date upon which we could honor both Union and Confederate war dead. It was a day to remember ancestors, family members, and loved ones who gave the ultimate sacrifice. But now, too many people celebrate the day without more than a casual thought to the purpose and meaning of the day.

Memorial Day Is For Reflection

How do we honor the 1.8 million that gave their life for America since 1775? How do we thank them for their sacrifice? We believe Memorial Day is one day to remember. The course of history has been forged by these heroic men and women of the Armed Forces who honorably served and sacrificed for their nation. Their individual acts of selfless bravery should be a reminder to all of us that there is a cost to being a part of this great nation. To put this into a modern perspective, during the past decades of all the volunteer military, less than 1% of eligible Americans have served their country. Of those who served, fewer have sacrificed. It should bring pause and reflection to every American that so few are defending the nation and paying the ultimate sacrifice for freedom. Before setting off on your weekend adventure, reflect on the true meaning of the holiday. It is a solemn occasion deserving of your time and respect.

The post Memorial Day: Honoring Service and Sacrifice appeared first on 6sigma.

Below is the voting chart for that story:

Inspired by Greg’s insightful post, I thought I’d take a minute (or more) on explaining how Digg is a game.

Elements of a Game

In Game Theory, we care about the elements that arise from interactive decisions. Below are the main elements of a game:

• Players – who is interacting?
• Strategies – what are their options?
• Payoffs – what are their incentives?
• Information – how do they know?
• Rationality – how do they think?

In digg, players are the voters; the voters have n options; and, where non-paid voters are concerned, reputation is the over-riding incentive for voting a story (top-digger status, etc.); information come in the form of following the vast web of links of blogs on the web. Rationality — this assumes that the voters are rational & that each voter knows that each voter is rational and that each player knows that each player knows that each player knows that….ad infinitum.

How does Digg work?

I’ll use Philipp’s concise summary of how digg works:

The Digg system of measuring a story’s importance is based on a semi-random set of people voting for or against a semi-random set of news stories.

The Urn Game

In graduate school, I played a really fun and revealing game called “The Urn Game.” The game shows very simply how the concepts of GroupThink, Conformity, Paradigm Shift, and Information Cascades work. Here are the rules of the game:

There are two indistinguishable urns. Urn “W” has two white balls, one yellow. Urn “Y” has two yellow balls, one white. A proctor will flip a coin to choose an urn. You must guess which urn it is after seeing one ball from the urn AND AFTER HEARING ALL THE GUESSES OF THOSE BEFORE YOU. Your goal is to choose wisely.

In our version of the game, 8 students were called per round (new urn-draw each round). At each turn, draw out a ball without looking at any others and without showing the ball to anyone else. Return the ball to the urn, write your guess on the provided sheet, then give the sheet to the proctor.

Playing this game reveals a few things that are relevant to digg and to all social software:

• GroupThink: This game illustrates how conformity can be rational for individuals, even when they don’t care what others do. The decisions made by others convey some information — that is, rational individuals may ignore their own information.

• Conformity: People in a group often believe and do the same thing as people around them. This leads to an Information Cascade — that is, you do what other people do…etc…For example, if you are eating at a fancy restaurant and don’t know which fork to use, you naturally look to see which fork the first person used, and you use the same one. Then, the third person notices which fork you and the first person used, and he does the same. And so on.

• Paradigm Shift: If wholesale conformity occurs, then voters decisions convey no information — that is, if the first 2 people vote for x and everyone follows the first 2 people regardless of their true feelings about the thing voted for, then 100 votes conveys no more information than the first 2 votes. We can further conclude that even when individuals are rational, the group may not be — that is, a few irrational individuals can swing the behavior of an entire group. These irrational individuals are phenomenally first movers also. In the case of Digg, these are the power users — these users commit to a strategy and are known as top diggers.

Digg is not a Prisoner’s Dilemma

In response to Alex Bosworth’s thoughtful post, Digg is not a Prisoner’s Dilemma. Some applications of Game Theory work, and some don’t for certain situations. For example, Nash Equilibrium is not the right concept for some strategic situations.

The Prisoner’s Dilemma is about 2 players, not N players. The two key features of the Prisoner’s Dilemma are (1) both players have a dominant (and rational) strategy to confess AND (2) both players are better off if they don’t both confess. The Prisoner’s Dilemma is the wrong metaphor for Digg.

A Proposed Way of Thinking about Digg

Digg is the online version of The Urn Game. What I see are power users, typically the first movers, wherein a vote is casted by one of the top diggers, and then a flood of comformity follows — that is, voters follow the first movers, typically the power users, and ignore their own rational feelings about the article being dugg and vote anyway, following the power user’s vote. At a wholesale level, this creates an information cascade, such that the Nth vote conveys no more information than the first 2 votes. Philipp is right in his analysis on Groupthink — but, it’s more than that: Digg is a system that allows the power users to swing the behavior of an entire group.

Others Chime In

Here’s Kevin Rose’s solution to the Digg problem:

What is changing however is how we are handling story promotion. While we don’t disclose exactly how story promotion works (to prevent gaming the system), I can say that a key update is coming soon. This algorithm update will look at the unique digging diversity of the individuals digging the story. Users that follow a gaming pattern will have less promotion weight. This doesn’t mean that the story won’t be promoted, it just means that a more diverse pool of individuals will be need to deem the story homepage-worthy.

I’m not sure what he means. But, arrington apparently gets it (or pretends to). I think what Kevin is saying is that Digg will weigh votes, based on the unique user’s historical profile and voting data. Still, it’s vague and I’m not sure what he’s talking about. But, it sounds interesting.

Arrington suggested the following:

I think this is the right thing to do. Digg needs to continue to encourage people to recommend stories to their friends, but also find ways to get truly unique and interesting stories to the home page without the sponsorship of a Digg user group. Hopefully the algorithm changes will help. Another suggestion to improve things that I recently passed on to Digg CEO Jay Adelson: weigh a story digg more if it comes from perusing the upcoming stories area v. someone hitting the story via a direct link. Since friends often email or IM stories around via the direct link, it’s more likely to be a vote from a group. A digg from the upcoming stories page is much more likely to simply be a user reviewing stories and picking the ones that he or she thinks are important.

Arrington’s suggetion is truly lame — to weigh a vote coming from a previously dugg article completely supports the concept of Information Cascade.

Calacanis, of course, has to chime in:

Calacanis — I like you, I really do — you’re level-headed and you go against the tide, but you’re wrong on this one. Digg and most social software smooth over time, and can eventually be approximated by a poisson distribution. This distribution is what Pareto is built on; this means that social software — Digg, ‘Scape, and all of them are not Democratic — they are Republics. A truly democratic society would mean that all votes are equally weighed — a majority rule –, but that’s not the case with first-movers and top-diggers.

My Proposal

If Digg were to be relevant again, it must tackle the problems of GroupThink, Conformity, Paradigm Shift and Information Cascades.

• To tackle Groupthink, make it truly democratic again — do not profile Top Diggers or elevate anybody higher than anyone else. This includes no special weights on previous digging history, etc. — level playing ground for everyone, no monarchies or philosopher-kings.

• To tackle the problem of conformity, do not show profile or # of votes for up-and-coming dugg articles. Just show the article link, with no profiles or votes attached to it. As a compromise, only show the profiles and votes on the articles that make the digg front page, but make them un-diggable from the front page.

Doing the above 2 items will most likely fix the Paradigm Shift and Information Cascade problem.

What’s Left of Digg if Kevin Rose follows my advice?

If Kevin Rose follows my advice, Digg will become a user-generated meme, truly democratic, where the votes are equally weighed and blind to any first-mover, power-user, and irrational individuals that may sway the behavior of the group. The quality of the front page articles will be higher for sure. The social aspect of Digg will be compromised, but it will be relevant again.

+++++

Below are other who have joined the soap opera:

• NicK Carr, hardly an academic post, but quasi-interesting
• Good Stuff from MIT here.
• On how Digg follows the Pareto Principle in its voting behavior
• J. LeRoy on gaming the system
• A history on the Digg controversy
• More on Digg
• Pirillo weighs in
• An excellent article by Clay Shirky
• More from CNET here and here.

The post Digg as a Game: Applying Game Theory to Digg and Voting Systems appeared first on 6sigma.

In Decision Theory, Information Asymetry deals with situations where one or more parties has more or better information than the other.  In the case of Voter decisions, Twitter, the fascinating microblogging platform, is a simple solution to a nasty problem of Information Asymetry.

A really simple example is having to deal with the decision of where and what time to vote: this decision involves one’s personal schedule but also the conditions of the voting area — are the machines working?  super long lines?  weather? — twitter, in this case, becomes an uncomplicated solution to a historically and, theoretically, tough problem.  Specifically, Twitter Vote Report was created to aid the exact problem I just described.

Even more broadly, imagine if more Stock Traders were on Twitter: most likely, Stock Traders would have been sending Tweets about the massive sell-off in the market last week several minutes before CNN could publish the news — the difference in minutes or seconds can be huge.  The problem of “Information Stickiness” or Information Asymetry is becoming less and less of a problem because of Twitter.

Something interesting:

Check out the segment below on Congressional Tweeting – tweets sent out by senators and congressman during a senatorial or congressional session.

The post How Twitter Solves Voter and Other Types of Information Asymetry appeared first on 6sigma.

One interesting property of these social networks or social graphs that isn’t talked about much is the concept of Majority Rule. While I haven’t seen much written about Majority Rule in social networks, we have all used the tools available to us: For Facebook, they have the Like button. For Twitter, there is the Retweet. For Digg, there’s the Digg button. And the same goes with every other tech company and their acquisitions.

One important note: votes can be explicit votes, such as the examples above. But, there are implicit votes also, such as how many times a YouTube video has been seen.

For Foursquare, the check-in can be both explicit and implicit. For example, when I take the kids to the park, my reason is to spend time with the kids. But, while I’m there I can choose to check-in.

But, there are cases where one chooses to go to a specific location for the sole purpose of checking-in. In that case, I consider that explicit. I imagine the commercial viability of Foursquare lies in the explicit check-in: purposely going to a location to check-in, then receive some reward from the place visited (coupon or whatever).

For the purposes of this post, we’re only talking about explicit votes.

So, how does Majority Rule work in a social system?

The Physical World: Majority Rule and Social Graph

In the physical world, majority rule systems appear in electing government representatives, board of directors approval of new policies, and other like instances.

One property that exists in the physical world, but not so much in virtual social graphs, is that the members of the group involved in some voting process are to support and sustain the majority rule.

For example, if a government representative wins from a democratic process and obtains the majority of the votes, he or she wins.  As a member of that group, I am to sustain and support that person, if I choose.  What I don’t have control over, however, is that I become a recipient of that person’s decisions, such as the legislation of a new law.

Indeed, majority rule in the physical world means that we are affected in very real ways.

The Virtual World: Majority Rule and Social Graph

Well, for virtual social graphs such as the Twitter or Facebook, if a majority rule is achieved, there is no contract to compel the members of that group to follow the majority rule. For example, as a member of the broad community of Facebook, if a fan page is Like’d 500 times, I have a choice to like or not to like. And, just because something is like’d a gazillion times, it still has no tangible affect on me.

What a majority rule does do, for me, is pique my interest. It creates curiosity and, if I choose to clickthrough to the thing that has achieved majority rule (majority like’d or majority retweeted), then I might receive some utility such as laughter, entertainment, or joy.

So, what is it exactly that makes Majority Rule work in social systems?

Properties of Majority Rule

There are three main properties of Majority Rule Systems:

1. Option Equality: The characteristics of choice A and choice B are the same.  If the same amount of people vote for choice A, the results will switch.
2. Voter Equality: Voter equality means that no person’s vote is more influential than that of another’s vote.
3. Sensitivity: Sensitivity means that if the voting is tied and one more person votes, then the option he votes for wins.

One major presupposition in the notion of majority rule is that of choice. Social scientists call the method of choosing a social choice function. A social choice function is a rule for choosing between options where some of the citizens prefer one option and others prefer the other.

The majority rule system chooses one option over another when more citizens prefer the one option than the other. From this we get the definition of a social choice function (SCF):

SCF: C is a social choice function for options a and b and citizens V iff a≠b, and V is finite and non-empty, and for all disjoint subsets A and B of V, C(A, B) equals {a} or {b} or {a, b}.

We also get the definition of the majority rule social choice function:

mrSCF: C is a majority rule social choice function for options a and b and citizens V iff C is a social choice function for options a and b and citizens V, and for all disjoint subsets A and B of V, C(A, B)={a, b} if A≈B, C(A, B)={a} if A>B, and C(A, B)={b} if B>A.

In addition to the definition of majority rule social choice function, we should formalize the definitions of the three properties of majority rule.

Option Equality (OE) can be defined thus:

OE: C satisfies option equality for options a and b and citizens V iff for all disjoint subsets A and B of V, C(A, B)={a} iff C(B, A)={b}.

Voter Equality (VE) can be defined as the following:

VE: C satisfies voter equality for options a and b and citizens V iff for all disjoint subsets A and B of V, if A≈B, then C(A, B)=C(B, A).

Sensitivity (S) can be formalized as follows:

S: C satisfies sensitivity for options a and b and citizens V iff for all disjoint subsets A and B of V, if C(A, B)={a, b}, then for every non-empty subset X of V-(A∪B), C(A∪X, B)={a}.

Now, let’s prove that Majority Rule satisfies the three properties above. In other words,

Theorem: C is the Majority Rule social choice function for options a and b and citizens V iff C is a social choice function that satisfies Option Equality, Voter Equality, and Sensitivity for options a and b and citizens V.

Majority Rule – Proof

To prove this theorem, I must proceed in two phases. The first phase will contain the three properties of majority rule. The second phase will contain the three conditions of majority rule social choice function.

Phase I

1.1 (OE) C(A, B)={a} iff C(B, A)={b}

1.2 (VE) If A≈B, then C(A, B)=C(B, A)

1.3 (S) If C(A, B)={a, b}, then for every non-empty subset X of V-(A∪B), C(A∪X, B)={a}

Phase II

2.1 C(A, B)={a, b} if A≈B

2.2 C(A, B)={a} if A>B

2.3 C(A, B)={b} if B>A

The framework I’ve set-up allows me to show how the elements in Phase I satisfy the elements in Phase II. To do this, I will assume the elements in Phase II and prove the elements in Phase I.

Similarly, I need to show how the elements in Phase II satisfy those in Phase I. To do this, I will assume the elements in Phase I and prove the elements in Phase II.

I can prove the theorem this way because it is a bi-conditional statement (principle of transitivity). That is, if I assume the left-hand side, the right-hand side should follow. Similarly, if I assume the right-hand side, the left-hand side should also follow.

By showing how Phase I and II prove each other, I will ultimately show that the majority rule social choice function does satisfy option equality, voter equality, and sensitivity and hence demonstrate fairness and effectiveness.

Phase I : 1.1

Show that C(A, B)={a} iff C(B, A)={b}.

Proof: Assume that C(A, B)={a}; show C(B, A)={b}. If A≈B, then C(A, B)={a, b}. If B>A, then C(A, B)={b}. By process of elimination and cases, since C(A, B)={a}, A>B. So, C(B, A)={b}. Now, assume the other side of the bi-conditional, i.e., C(B, A)={b}; show C(A, B)={a}. If A≈B, then C(A, B)={a, b}. If A>B, then C(A, B)={a}. By cases and process of elimination, B>A. So, C(A, B)={a}. Q.E.D.

Phase I : 1.2

If A≈B, then C(A, B)=C(B, A).

Proof: Assume A≈B; show C(A, B)=C(B, A). So, C(A, B)={a, b}. Also, C(B, A)={a, b}. Hence, C(A, B)=C(B, A). Q.E.D.

Phase I : 1.3

If C(A, B)={a, b}, then for every non-empty subset x of V-(A∪B), C(A∪X, B)={a}.

Proof: Assume C(A, B)={a, b}; show that for every non-empty subset X of V-(A∪B), C(A∪X, B)={a}. Since X≠∅ and x⊆(V-(A∪B)), A∪X>A. If A>B, then C(A, B)={a}.

If B>A, then C(A, B)={b}. So, by the process of elimination and cases, since C(A, B)={a, b}, A≈B. Hence, A∪X>B. Consequently, C(A∪X, B)={a}. Q.E.D.

Thus, by proving Option Equality, Voter Equality, and Sensitivity, I have shown that they are derived from the notion of majority social choice function. But I am only halfway finished.

I now need to prove the elements in Phase II. Remember, that in doing so, I will be assuming the elements in Phase I.

Phase II : 2.1

If A≈B, then C(A, B)={a, b}.

Proof: Assume A≈B; show C(A, B)={a, b}. Since A≈B, by voter equality I get C(A, B)=C(B, A). Also, bear in mind the definition of a social choice function, namely, that C(A, B) must equal {a} or {b} or {a, b}. By option equality, C(A, B)={a} iff C(B, A)={b}. Yet, {a}≠{b}. So, C(A, b)={a, b}. Q.E.D.

Phase II : 2.2

If A>B, then C(A, B)={a}.

Proof: Assume A>B; show C(A, B)={a}. Sensitivity claims that if C(A, B)={a, b}, then for every non-empty subset X of V-(A∪B), C(A∪X, B)={a}. Let A’ be a subset of A such that A’≈B. Accordingly, by Phase 2.1, I get C(A’, B)={a, b}. So, C(A, B)={a}. Q.E.D.

Phase II : 2.3

If B>A, then C(A, B)={b}.

Proof: Assume B>A; show C(A, B)={b}. Sensitivity dictates that if C(A, B)={a, b}, then for every non-empty subset X of V-(A∪B), C(A∪X, B)={a}. Let B’ be a subset of B such that A≈B’. By Phase 2.1, I get C(B’, A)={a, b}. But this means that C(B, A)={a}. So, by option equality, C(A, B)={b}. Q.E.D.

Conclusion

We have proven both phases. The Majority Rule Social Choice function satisfies option equality, voter equality, and sensitivity. By satisfying these three properties, the majority rule proves to be both fair and effective.

The post Majority Rule in Social Networks appeared first on 6sigma.

To view google acquisitions, microsoft acquisitions, yahoo acquisitions, or amazon.com acquisitions, please scroll on the timeline and click on the dots; when you do, a bubble will render showing some details of the acquisition.

Go here If you’re interested in reading my job interview with google

Alternatively, below the M&A timeline is also a table showing the acquisitions.

The post Acquisitions of Amazon, Yahoo, Microsoft, Google appeared first on 6sigma.

Then, I posted on My Experience with Twitter, Part 2, followed by My Experience with Twitter, Part 3, where I offended @darthvader.

Today, is Part 4 of that series on Twitter, Brand Relevance & Brand Intelligence, and Cash Money.

Google is a great search engine, but it is, by de facto, historical.  For example, just by the virtue of the knowledge aggregation process employed by Google, as content hits the web, there is latency in indexing that content.  That very fact makes knowledge based on history.

One area that Twitter has a distinct advantage is in the collection, aggregation, and dissemination of current, real-time, information.  Twitter, as an early feedback system, is valuable to brands in many, many ways.  Here are a couple of ideas:

1. Pharmaceutical & Medical Device: In these industries, there are, by regulation, monitoring systems in-place as new drugs or medical devices are approved by the Food and Drug Administration (FDA) and enter the market.  Twitter can be a very effective and cost-effective monitoring system for Pharmaceutical and Medical Device companies.
2. Market Research: As companies develop new products, these products can be released carefully to a select number of twitterers that act as an army of word-of-mouth buzz agents.  For example, suppose I have a new product called shmula wire (a type of energy drink).  I can recruit a sample of twitterers, give each of them 10 shmula wire products with the commitment that they would give each of those to other twitterers and, hope, that they, collectively, will tweet about shmula wire.  This is literally word-of-mouth that is designed and, because it is within a buzz-system, this data is captureable, measureable, and relevant.  This is huge.
   1. Net Promoter Score (NPS) is designed to be a market monitoring, customer loyalty system.  But, the latency involved in reading verbatim comments and the increasing debate around the score itself causes companies to forget the customer in favor over methodology.
   2. Other feedback systems such surveys (inbound or outbound) have some barrier to entry involved.  Twitter is easy, self-reported, and brutally honest.
3. Communication: This is obvious, but what might be helpful for brands is the notion of pre-wiring the public of an upcoming brand to see what the customer reaction will be.  For example, prior to Coke releasing a new product called shmula wire (again, the energy drink), Coca-Cola might want to release a tweet about shmula wire to see what the reaction might be.  Based on the public reaction, Coca-Cola can make a more intelligent decision on product roll-out, messaging, and sentiment.
4. Government: The Government can monitor sentiment on a public figure, a new bill, a law, or even activities related to national security.  Twitter can be a very dangerous vehicle for terrorist; you get the picture.
5. Centers for Disease Control: Similar to the (1) above, the CDC can also monitor peanut butter salmonella outbreaks or other similar public health concerns.

The above are just some ideas for how Twitter can be more valuable to brands and also how Twitter can monetize this very valuable service.  All of the above is based on this assumption:

Brand Sentiment and Public Perception is a Function of Time; that is, the center of gravity for a brand is greatest when it is more recent.  Over time, brand sentiment loses relevancy.

Graphically, it might look something like the following:

In other words, Peanut Butter Salmonella outbreaks is most important and relevant closer to the event.  It loses importance and relevance the further we get from the event.  The real-time, feedback system that Twitter has become an authority in is ideal for anything that is contingent upon Relevance and Time.  This includes events, brands, products, etc.  This opportunity also presents the potential monetizing-yet-untapped power of Twitter.

The post Can Twitter be Used for Epidemiology Surveillance? appeared first on 6sigma.

A very basic observation I’ve discovered in my 40-something days on Twitter is this: my blog post frequency has gone down and my tweet frequency has gone up.  In other words, I observe an inverse relationship between my blog post frequency and my tweet frequency such as below:

Implications

• My Twitter audience and my Blog audience are different; so, on one respect, one will benefit and the other will not.
• Conveying information and participating in a conversation is tough on Twitter — 140 characters is all one has and needing to convey more complex concepts or trying to make a point on so few characters might be difficult.  Blogging is better for that and the conversation can be had in the comment section but, since Tweeting more can lead to less Blogging, then that is a clear implication of the inverse relationship between the two.
• Cash Money: if you make revenue from your blog and nothing on Twitter, then expect to lose traffic and cash money as you tweet more and blog less.

Either/Or?

As in most things, it’s not an either/or or dichotomous situation.  One can reconcile their tweeting habits with their blogging habits.  In fact, on shmula.com, my tweets are now integrated directly on my blog which, I’ve discovered, is helping like crazy on search engine optimization (SEO) and getting indexed by Google.

Plus, my tweets can be additional or complementary content on my blog.  My tweet content provide a different type of content that is actually more human — more of my everyday life — rather informative content as is typically on my blog.  This means, then, that my readers can see a different aspect of the person behind shmula.com, not just the geek, but the human who does everyday human, non-interesting stuff.

The post Inverse Relationship Between Blog Posts Frequency and Tweet Frequency appeared first on 6sigma.

Tweets come in all forms — useful, useless, agnostic, and bizarre. People who tweet also fall into the same categorization: useful, useless, agnostic, bizarre, and [use your imagination]. One observation is that there are some who tweet that have a lot of followers. As I’ve scoured the people with a lot of followers and tried to make a judgment on the value of their tweets — in general — I came away lacking: why would anybody follow these people?

Case in Point: Guy Kawasaki has almost 47,000 followers. As a former follower for 2 days, I noticed that his tweets were robotic; unnatural; it felt like a bot was tweeting for him. So, I happily unfollowed him — he wasn’t interesting at all & just added noise to my already overcomplicated life.

Then, it dawned on me — that Guy Kawasaki’s tweet behavior was like a rambling drunk at a neighborhood bar: he couldn’t stop tweeting and was, for the most part, rambling stuff nobody wanted to hear.  I also became curious about the quantitative nature of Guy Kawasaki’s tweets — so, I ran some numbers.

• Guy’s earliest tweet I could find was dated November 8, 2008 — 64 days ago.
• Within 64 days, Guy Kawasaki has 16,457 tweets.
• This means the following:
  • 16457/9.14 weeks = 1800 tweets per week (tpw)
  • 16457/64 days = 257 tweets per day (tpd)
  • 16457/1536 hours = 10 tweets per hour (tph)
  • 16457/92,160 minutes = 0.17 tweets per minute (tpm)
  • 16457/5,529,600 second = 0.003 tweets per second (tps)

Looking at the raw numbers above, it’s pretty astounding that a human can tweet that much. It’s pretty overwhelming and, hence, I unfollowed him. But, why does he have almost 47,000 followers?

The Guy Kawasaki scenario led me the following hypothesis:

• Those who tweet the most useless noise have the most followers[1. It would be easy enough to make this quantitative, such that we can actually prove or fail to reject this hypothesis.  What is required is to increase the sample size to something statistically significant and complete the cells in the chi-square categorical table above.  Using the Chi-Square hypothesis test and distribution, we can then conclude whether or not we can fail to reject this hypothesis.  Since I do not that, please take this post with a grain of salt, have a sense of humor, and have some fun with it.].

Perhaps my hypothesis can be displayed as a simple table like below:

To explain,

• the top-left quadrant says that “there aren’t very many followers for low-value tweets”
• the bottom-left quadrant says that “there are a bunch of followers for low-value tweets
• the top-right quadrant says that “there aren’t very many followers for high-value tweets”
• the bottom-right quadrant says that “there are a bunch of followers for high-value tweets”

Based on this table, I’d consider Guy Kawasaki to occupy the bottom-left quadrant.

Conclusion

I’m not picking on Guy Kawasaki at all — in fact, my comments are more of an indictment on the followers than on Guy Kawasaki, the man. He can tweet whatever he wants — but, people have a choice to follow or not to follow. For some reason or other, he still has 47,000 followers.

Let me generalize even further: beyond Guy Kawasaki — my hypothesis is a generalized theory on twitter as a community: maybe twitter is subject to the laws of Game Theory, namely, the paradox of conformity —

Conformity: People in a group often believe and do the same thing as people around them. This leads to an Information Cascade ” that is, you do what other people do, etc. For example, if you are eating at a fancy restaurant and don’t know which fork to use, you naturally look to see which fork the first person used, and you use the same one. Then, the third person notices which fork you and the first person used, and he does the same. And so on.

In other words, perhaps Guy had a bunch of followers, so more joined his bus thinking that, if they didn’t follow him, they might be missing something or not be in the “in-crowd”.

So, I only use Guy Kawasaki as a case study for this post. He can obviously do whatever he wishes with his tweets. I actually like him. He is Asian — which helps and, more importantly, he’s an adoptive father — so am I (baby 1, baby 2, baby 3). So, I like him and my observations are really more on Twitter as a community of conformity than it is anything personal about Guy Kawasaki.

The post The Guy Kawasaki Firehose, AKA, His Twitter Feed appeared first on 6sigma.

Twitter as Asymmetric Follow

James Governor attempts to define a pattern found in Twitter and other social media and calls it “Asymmetric Follow”.  He defines Asymmetric Follow as the following:

Asymmetric Follow is a core pattern for Web 2.0, in which a social network user can have many people following them without a need for reciprocity.  Asymmetric Follow is unlike email for example, which tends to be within small groups, with all users knowing each other (newsletters are a clear exception here). If you see a social network where someone has 5000 followers and only follows 150 back – that’s Asymmetric Follow.

So, if I were to explain James’ definition to a teenager [1. my personal criteria for an atomic and pragmatic definition of a concept is if it can be explained to normally-functioning-and-average human that is 15 human years or younger] , I might say something like:

“Asymmetric Follow is when the popular kid in school is admired by a bunch of less popular kids and when the popular kid speaks, everyone usually listens and when the less-popular kids speak, the popular kid can choose to listen or respond or do nothing”.

If I’ve been charitable in my understanding and summary of James’ definition of Asymmetric Follow, then his explanation and definition makes sense to me.  From my experience as a former High School student of the less-popular type, that’s how life was.

I have to note, however, that James’ definition confounds the behavioral economics definition of Information Asymmetry — but he means something different.  The historical definition has more to do with the direction of communication and the “stickiness” of that information and how that “stickiness” can impact decisions and rational choice.  James Governor doesn’t address the definition in behavioral economics but does use a similar term.

Twitter as Combinatorics

But, let’s quantify what James is talking about.  In fact, when we do, we’ll find out that it’s actually basic combinatorics.

Suppose there are persons A and B, who follow each other.  In this scenario there are 2 communication links (AB, BA).  Add person C who follows and is followed-by persons A and B, now we have 6 communication links, (ABC, ACB, BCA, BAC, CAB, CBA).  So, inductively, as inter-followership [2. I make a distinction between inter-followership and intra-followership where the former is a set where each member follows each other and the latter is a set where the followership is disjointed.  However, for the purposes of Twitter, inter-followership and intra-followership doesn’t matter so much since a follower has the same rank as the non-follower to the one being followed — their voices are not weighted differently] permutation grows, the raw combinatorial communication link counts grows quadratically, not linearly.

To demonstrate this, we use basic statistics of the form n-choose-r, where !, such as n!, is equivalent to n factorial, to arrive at the formula for how many pairs or permutations we can choose from n items:

For the number of pairs, we can reduce the above formula to the following:

Visually, as inter-followership grows, the communication links grows non-linearly, but quadratically (n! grows exponentially) — in either case, the function is clearly not linear:

Mutually Exclusive, Comprehensively Exhaustive (MECE)

JP runs a really fun experiment that validates his hypothesis that tweets in the universe of Twitter are comprehensively exhaustive [3. This is my term that I use to explain his point, but he does not use the terms Mutually Exclusive or Comprehensively Exhaustive in his writings.].  What his experiment does not show is the exclusiveness of the tweets — that is, their uniqueness from each other.  On its face, this is not a big deal, but in scientific inquiry, being able to compartmentalize objects in unique buckets is helpful.

One reason it is difficult to classify tweets as mutually exclusive in content is because there are Replies and Retweets.  There is probably an innovative way to find the unique and mutually exclusive clusters in the corpus that is Twitter — that would be fun work for a computational linguist.

For this post, this is not a big deal, but I just make this point for clarity — really great experiment, JP.

A Conclusion

Ummm, I don’t really have a conclusion or a point, except for that I think Twitter is pretty amazing and that Twitter can and should encourage computer scientists, computational linguists, behavioral economists, combinatorial mathematicians, set theory geekzoids, game theory freakonomica, cultural anthropologists, and others to participate in and learn from this massively human experiment.

I really like Twitter — oh, by the way — retweet this post…

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Okay — maybe that tone is too strong but, generally, PowerPoint is not the most effective medium of communication.  I think most people would agree with that.  There are ways to communicate and effective ways of creating an atmosphere of discussion and debate with PowerPoint, but most people don’t use PowerPoint for that purpose.  It’s unfortunate.

I’ve written about this before — how Bezos doesn’t allow PowerPoint in meetings with him and considers gratuitous clipart  to be anathema, and how Edward Tufte argues strongly and effectively against PowerPoint.

Imagine putting some of the world’s most moving and inspiring speeches in PowerPoint format.   Wouldn’t it be a tragedy if Martin Luther King’s “Freedom” or “I have a Dream” or “Letter from Birmingham Jail” was a PowerPoint deck?  What about the United States Constitution as a PowerPoint deck?  Or, Winston Churchill’s famous “Never, Never, Never Give Up” speech as a PowerPoint deck?  Or, General Douglas Macarthur’s famous “Build Me a Son” prayer as a PowerPoint deck?

Well, one of the more recent and highly acclaimed and inspiring speeches in modern day is the “Yes We Can” speech by Barack Obama.  It is an amazing and inspiring speech.  To test my hypothesis that PowerPoint as an information medium isn’t the best, I decided to completely do Barack Obama’s “Yes We Can” speech injustice by putting it into PowerPoint format.

Below is the “Deck” and, as you’ll see, the PowerPoint version completely strips the speech of any life, emotion, inspiration, and meaning; in other words, PowerPoint is doing what it does well.  Here is a link to the video speech (I strongly urge you to watch this) and you can find the free-text transcript of the Barack Obama “Yes We Can” speech here.

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