Myshkin's travel blog

At the Unreasonable Mansion

2011-06-14T22:28:00.001-07:00

Arrived in Boulder, in Daniel's car, accompanied by his dog.

Neat setup, had dinner, talked to a few other folks. Its going to be fun!

Funnily enough, dont feel too tired after the 1.5 day journey. Perhaps the breaks in between helped! :P

With or without you

2011-06-14T22:15:00.000-07:00

Another rest stop.

The bus driver told us 10 mins and then the bus would leave with or without us.

Well, he was true to his word.

The bus set off without 4 passengers!

Somewhere in the front, there were apparently 2 passengers missing: their belongings were strewn across the seats. A boy and his mother. Nearby passengers flagged this to the driver, who pointed out that they had been warned.

Then another passenger pointed out that the father of 2 boys (minors) on the bus was also missing. The boys (9-11) would have no one to look after them during the journey and esp when it stopped at Denver. This time, the driver took the bus around and we picked up the 4.

Overall, I think this particular driver wouldn't be winning Mr Popularity with this lot of passengers anytime soon...

Long after everyone had settled down, the indignant murmurs continued, with some passengers even wanting to report the driver for kidnapping to the cops! (this is the US) Mostly, the indignation was about the "10 minutes" promised - while the bus started off again only after 7-8 mins...

Eventually, I think the worst for the driver is a telling off from somebody at Greyhound (apparently, the 4 passengers abandoned at the rest stop had called Greyhound)

Anyway, we got to Denver at 7 pm (no passengers missing - although some passengers luggage was missing - having been left behind in Kansas City!)

we aint in kansas anymore

2011-06-14T10:34:00.001-07:00

rest stop at the last bit of kansas. lots of wizard of oz memorabilia.
had a subway.

kansas city

2011-06-14T05:56:00.001-07:00

stopover for half n hr in kansas city which i used for a bfast of
orange juice n french fries. hoping my checkin luggage is travelin
with me on the same bus!

off to st louis

2011-06-13T16:33:00.001-07:00

just boarded the bus for st louis finally... at a time i should have
been boarding the bus FROM st louis :P

a few observations about greyhound:
/buses r comfortable with ac reclining seats
/organization around the buses is a shambles
/no signs or schedule info at stations. delays guaranteed. general confusion
/lack of cleanliness at stns seems exaggerated. seem ok.
/large stations but v inefficient system. no wonder chinatown buses r
doing well with only a fraction of greyhounds resources.

walking with kings

2011-06-13T13:20:00.001-07:00

the missed connection landed me with a 6 hr layover in the music city
... nashville.
walked downtown. saw fort nashville. posed with elvis. saw the orig b
b king cafe.

was v interesting despite having luggage to lug around.

so far hv met a bunch of interesting charcters on the hound. Lawrence
of Alabama elementary school teacher n farmer. David the artist with
30 jobs ..frm savannah. orlando frm miami manager of hookah cafes.

enjoying day journey to nashville

2011-06-13T07:30:00.001-07:00

v pleasant. not so full bus. good route. even started out reading the
rough guide to usa on my kindle. may have a 6 hr stopover in
nashville. hope something interesting there. meanwhile had an orange
chivda n chocs!

atlanta to nashville

2011-06-13T05:57:00.000-07:00

as suspected missed my connection to st louis. now on abus to
nashville where i am to catch an evening connection to st louis.

other people in the same boat as well.

back to charlotte

2011-06-13T05:54:00.001-07:00

bus finally started...
thought i was on my way to atlanta finally ... got on the bus n dozed
off... woke up back in charlotte. apparently something wrong with the
bus... driver decided to swap buses. so after a further delay at
charlotte i was away.

got to atlanta finally at 6 30... maybe missed my connection to st louis?

already late

2011-06-13T05:44:00.000-07:00

my first greyhound trip n the bus is late... waiting round for the
atlanta bus to show up....

funny thing is there is no wrtten communication at any greyhound station.

Leaving towards Unreasonable

2011-06-12T17:28:00.001-07:00

Am boarding a Greyhound tonight on a long journey to Denver, Colorado, where I shall join the team at Unreasonable Institute. Excited!

Also, apparently, I am making history: I am the first fellow ever in UR's history to arrive by bus :P

Also, in another first, this is my first initiation into Riding the Hound.

I have been warned: But hey, I've been a regular long distance bus traveler in India - can't get a better education than that!

Can handle uncertainty - check

Can handle random schedule changes - check

Can keep an eye on luggage at all time - check

(not that I have much to keep an eye on!)

Can sit for hours in tight cramped spaces - check

Can rough it when needed - check

All my bags are packed and I'm ready to go. No jet plane, but a hound...

ANOTHER change of tone

2011-06-02T07:43:00.001-07:00

Well, turns out there's not much funny about the IIM FPM! :P ...
Also, I am traveling about at present, so have decided to convert this
into a travel log of sorts.

Will be posting periodically from various places in the US (some
random, others not. All posts, however, will be random).
Most of the time, will be using my kindle to post. So brevity shall be
the soul of wit from here on...
:)

Change of Tone

2011-03-29T15:43:00.000-07:00

I'm back.
I've decided to bring this blog back. Except that there is going to be a change in tone. From now on, it's going to be more "observational humour" - if not my own, then contributed by folks at IIM still in the program.

I am moving

2010-12-11T00:28:00.001-08:00

I am relocating to my micro blog at my homepage

Interesting as it has been to write long(ish!) posts on here, my new job does not give me time to do the same anymore. Besides, I have kind of bought into the whole Less is More philosophy.

So cya all at http://www.myshk.in/blog

An Update

2010-01-08T04:01:00.000-08:00

I have been guilty of not thinking too much about the repercussions of my initial findings in Wikipedia's network analysis, covered by my doctoral work. This was a natural consequence of a very interesting project called "The Copenhagen Wheel" that I was collaborating on, with the SENSEable City Lab at MIT. However, I believe it is important that I carry forward the train of thought I had begun with my findings from Cebuano Wikipedia. Towards this end, I have been running the same process on the revision history dataset of the Bosnian Wikipedia. Why Bosnian? Well, apart from being relatively easy for me to process on the fly, I was genuinely curious as to how volatile external conditions would affect the interaction network structure. As it stands, I believe the structure is remarkable similar to that of the Cebuano Wikipedia. I am not sure whether this is entirely a good thing, as far as my expectations go. Differences would have been interesting! BUT I need to do some more analysis to find out for sure. Will post up the results soon.

...oh, and 2010 is here!

Social Computing

2009-05-20T04:14:00.000-07:00

“Social computing is the use of technology in networked communication systems by communities of people for one or more goals.”

In the spirit of social computing itself, the above definition was agreed upon by a consensus building process at the Social Computing Group & Transliteracies Project at the University of California. As with a Wikipedia article, ‘Social Computing’ definitions are malleable – they may change with time, with technology and its new application, within changing socio-cultural contexts, in sync with the society that defines them. While the term ‘social computing’ has more than one definition, and is constantly evolving in context and scope, it is useful to begin with the very roots of the concept:

Social computing begins with the observation that humans are profoundly social.

From birth humans orient to one another, and as they grow they develop abilities for interacting with one another ranging from expression and gesture to spoken and written language. Different fields of scientific enquiry, ranging from sociology to anthropology to philosophy have addressed the large issue of how these diverse interactions shape - and are shaped by – our perception of - knowledge of - reality. Technology – such as today’s computer tools - may be seen to act as an intermediary, as a moderator, as an enabler, as a tool. Fundamentally though, the process by which humans ‘make sense’ of the world, construct knowledge of the world around them and within them, is social in nature. This activity through which collective human actions organize knowledge has often been referred to as ‘Social Information Processing’ – It is the creation and processing of information into ‘knowledge’ by a group of people. One particular characteristic of this activity is the emergence of what has been termed as ‘collective intelligence’, from the information processing power of networked social systems. This ‘collective intelligence’ emphasizes that as a whole, the networked social system creates its own ‘knowledge’ in a way that no single isolated unit within it could, and proceeds to collectively act on it (Levy, 2001). Tribes of hunter-gatherers, nations, and modern corporations all act collectively with varying degrees of intelligence. And, from some perspectives, even collections of bacteria, bees, or baboons can also be viewed as collectively intelligent (Bloom 1995).

From this perspective, Social Computing research focuses on methods for harvesting the collective intelligence of groups of people (“wisdom of the crowd”) in order to realize greater value from the interaction between users and information. In recent times, the social potential of computer networks has come to the fore through the rapid development of such network applications as wikis, blogs, social networking sites, social bookmarking sites, and online collaborative editing suites that encourage people to engage in collective resource-building, action, and work. People are interacting more often and in more ways than ever before in human history (Malone 2004). These developments of the last decade have aroused tremendous interest in this field. Are we seeing merely an amplification, an acceleration of activities that have existed since time immemorial, or are we witnessing the birth of something completely new? Is this an ancient phenomenon now occurring in dramatically new forms, or is it something fundamentally different? As the underlying technologies continue to advance forward, it is more important than ever for us to understand collective intelligence at a deep level so we can create and take advantage of these new possibilities.

Research on Social Computing is by its nature, interdisciplinary. Along with the broad canvas of sociology, psychology, anthropology and cultural studies, social computing researchers have been studying the dynamics of interaction mediated typically by computer tools such as:

* Authoring tools: e.g., blogs

* Collaboration tools: e.g., wikis, in particular, e.g., Wikipedia

* Tagging systems (social bookmarking): e.g., del.icio.us, Flickr, CiteULike

* Social networking: e.g., Facebook, MySpace, Essembly

* Collaborative filtering: e.g., Digg, the Amazon Mechanical Turk, Yahoo answers

* Social Information Aggregation: e.g., scratchmysoul.com

While these tools and the ‘knowledge’ artifacts they have helped produce (large, complex encyclopedias to diverse, fine-grained folksonomies) are certainly impressive, it must be remembered that although computers are often used to facilitate networking and collaboration, they are not always required. For example the “Trictionary”, a collaboratively generated 400-page trilingual English/Spanish/Chinese translation wordbook, in 1982 was entirely paper and pen based, relying on neighborhood social networks and libraries. The creation of the Oxford English Dictionary in the 19th century was done largely with the help of anonymous volunteers organized by help wanted advertisements in newspapers and slips of paper sent through the postal mail. That having been said, it is notable that modern societies, with information and communication technologies, are vastly better at collective cognition than earlier ones. The degree of organization, and its precision, which we take for granted would have been astonishing for even the in- habitants of the most advanced societies of previous centuries. Historians have explored some of the technical and institutional underpinnings of these organizational revolutions (McNeill 1982; Beniger 1986; Yates 1989), but at a deeper level we have little idea of the mechanics, or why what we do works (when it does work!), and what role information and communication technologies play. On large scales, market economies, corporations and other bureaucracies, scientific disciplines, and democratic polities all have something of this collective information-processing character. Knowing how they accomplish this would be deeply rewarding, and, if that understanding can be used to make them work better, of profound economic and political importance. A frontal assault on this problem, as represented by one of those grand institutions, is unlikely to succeed (though, as Shalizi, 2007 points out- it may be a magnificent failure!) Fortunately, social information processing also occurs in much humbler institutions, such as tagging systems and collaborative filtering, where issues of data collection and even experimental manipulation are much more manageable, and where we might hope to learn more, before tackling the fundamental problems of social science.

The research focus of my thesis, therefore, is predominantly on the ‘social’ aspect of social computing, and on the interplay between social interactions and technology, rather than the technology alone.

Conference Time

2009-05-15T02:40:00.000-07:00

My papers at both AMCIS 2009 and PACIS 2009 have been accepted. It's always nice to receive external validation and constructive feedback for one's ideas. I was really impressed with the depth of some of the reviewers' analysis: Really a lot of time and effort had gone into them. As a result, I got some interesting insights on the papers, and can possibly incorporate these in my next work.

Apart from the utility of the comments, I have of course got a sudden 'happy adrenalin' rush because of 2 acceptances on the trot (I seem to recollect a particularly funny phdcomics strip about this a while ago!) and am really looking forward to hobnobbing with some of the big names in my field at the conferences, getting more ideas.

And of course, free food!! :P

2009-04-21T04:08:00.000-07:00

This is a very important time for yours truly, as some things are ending and others are beginning.

The going in April has been good so far. I have got some incredibly detailed and helpful reviews for my paper on Small Worlds in Wikipedia from AMCIS. I have in mind a more in-depth and comprehensive article, which I hope to get around to putting to paper shortly ( new things!) , using some of my results and the new feedback from the conference.

The old things that are ending might well include my time at IIM Calcutta, as I make my transition from a PhD student into _____ .

I guess by May, the blank would be filled.

A Resolution

2009-03-18T04:31:00.000-07:00

Much inspired by PhizzleDizzle , I also am setting an ambitious but attainable deadline.
1 April is the date by which I shall have finished the first version of my Thesis.

So there then, there it is, in writing. Hopefully now, schedule pressure will act its magic.

An Update

2009-03-04T23:50:00.001-08:00

I have not been posting regularly for the past few weeks, on account of feeling the whooshing sound of deadlines going by.
Thankfully, I have been able to meet the dates for the PACIS 2009 and AMCIS 2009.
I already had a fair number of things to say about Collaborative Knowledge Creation in general and Wikipedia in particular, based on the findings of my longitudinal network analysis studies of different language Wikipedias. (Abstracts uploaded here )
I will put up the full papers shortly, as soon as the review process concludes.

Meanwhile, here's one of the visualizations from my network analysis of the interaction graphs of Cebuano Wikipedias. More shortly (one more deadline coming up the hill!)

Writers Block

2009-01-13T06:03:00.001-08:00

I have abount 3 interesting points to make. At this stage, I am
a) Gripped by Writers Block - how and where do I start to communicate my points?
b) Not entirely certain I can bake my half baked points. To make sense, I need first to clear things up in my mind.
c) I can't remember the 3rd point. (There's an attempt at irony/humor in there somewhere!)

The AoM general meeting deadline is coming up, and I really should have been done with my draft a while ago....
Oh well, here's hoping last min pressures work again, as they have in the past ...:)

Wikipedia and Agent Based Modeling

2009-01-07T07:12:00.000-08:00

This is something that I had been meaning to do for a while.
I even penned down some of my thoughts on how ABM made sense for understanding Wikipedia:

http://portal.acm.org/citation.cfm?id=1458550.1458565
Understanding the wikipedia phenomenon: a case for agent based modeling
Abstract:
"Wikipedia, the user led and monitored "open" encyclopedia has been an undoubted popular success. Of particular interest are the diffusion process of the innovation throughout the "contributor" community, and the question as to why unpaid, often well qualified, volunteers contribute content and time. Explanations for 'altruistic' contributor behavior based on the positivistic paradigm, and with roots in organizational psychology, while heavily researched and documented, have not been readily transferable to quantitative models of sufficient predictive value, in relation to Wikipedia's metrics. For despite the wide range of types, ages, locations and motivations of its contributors and seekers, investigators on Wikipedia have identified certain definite and often surprisingly universal trends ('laws') in its overall growth curve, organization structure, community and article formation. Models based on aggregated top-level relationships between entities on and around wikipedia suffer from assuming relationships between these entities as inputs to the wikipedia process, rather than emergent phenomena that evolve and change with the output. We argue for an Agent Based Model of Wikipedia, with the end objective of our work being a tool with diagnostic and/or prescriptive value for decision makers in organizations using or planning to use Knowledge Management Systems."

However, ultimately, I think it would still be difficult to make a clear case for the rationale, especially since ABM would only give me 2nd order predictions.

I've since then managed to bark up the right (hopefully!) tree. Have some interesting findings...
Will post lots more on that soon.

...deadlines are making a whooshing sound....

Wikipedia's Kittens

2008-07-29T03:42:00.000-07:00

Just found a great blog
http://wikip.blogspot.com/search/label/sociology

Quoting:

"....a generalized unit of contributor motivation called a kitten.

1 kitten = the amount of motivation needed to get 1 person to spend 1 minute trying to improve an article

We can say, quite literally, that Wikipedia runs on kittens. In fact, entrepreneurs discover this every day when they try to start a "crowdsourcing" site and nobody shows up. So, what generates kittens? Foremost, it's the possibility of someone else learning from what you wrote -- not just immediately, but at any time in the future"

Kittens are born when there is a perception that the words one write will survive for some time.... something like this, to go by

Number of views on a given day = (Number of views per day).(Chance of surviving one day) ^ (Number of Days that have passed)
With a 1-in-ten-thousand chance of being destroyed each day, the article will rack up exactly seven million views over its lifetime.

Like the author says - thats a LOT of kittens!

Why Wikipedia Succeeded

2008-07-25T03:05:00.000-07:00

Larry Sanger (Wikipedia's cofounder)'s take on why Wikipedia succeeded.
Although rather old (2005), the feature has some great insights.

http://features.slashdot.org/article.pl?sid=05/04/19/1746205&tid=95

In short, these are the factors

Open content license. We promised contributors that their work would always remain free for others to read. This, as is well known, motivates people to work for the good of the world--and for the many people who would like to teach the whole world, that's a pretty strong motivation.
Focus on the encyclopedia. We said that we were creating an encyclopedia, not a dictionary, etc., and we encouraged people to stick to creating the encyclopedia and not use the project as a debate forum.
Openness. Anyone could contribute. Everyone was specifically made to feel welcome. (E.g., we encouraged the habit of writing on new contributors' user pages, "Welcome to Wikipedia!" etc.) There was no sense that someone would be turned away for not being bright enough, or not being a good enough writer, or whatever.
Ease of editing. Wikis are pretty easy for most people to figure out. In other collaborative systems (like Nupedia), you have to learn all about the system first. Wikipedia had an almost flat learning curve.
Collaborate radically; don't sign articles. Radical collaboration, in which (in principle) anyone can edit any part of anyone else's work, is one of the great innovations of the open source software movement. On Wikipedia, radical collaboration made it possible for work to move forward on all fronts at the same time, to avoid the big bottleneck that is the individual author, and to burnish articles on popular topics to a fine luster.
Offer unedited, unapproved content for further development. This is required if one wishes to collaborate radically. We encouraged putting up their unfinished drafts--as long as they were at least roughly correct--with the idea that they can only improve if there are others collaborating. This is a classic principle of open source software. It helped get Wikipedia started and helped keep it moving. This is why so many original drafts of Wikipedia articles were basically garbage (no offense to anyone--some of my own drafts were sometimes garbage), and also why it is surprising to the uninitiated that many articles have turned out very well indeed.
Neutrality. A firm neutrality policy made it possible for people of widely divergent opinions to work together, without constantly fighting. It's a way to keep the peace.
Start with a core of good people. I think it was essential that we began the project with a core group of intelligent good writers who understood what an encyclopedia should look like, and who were basically decent human beings.
Enjoy the Google effect. We had little to do with this, but had Google not sent us an increasing amount of traffic each time they spidered the growing website, we would not have grown nearly as fast as we did. (See below.)

Defining Knowledge

2008-07-24T06:11:00.000-07:00

Came across this quite elucidating (yes, I've used the word "elucidating"!) definition, explanation about Knowledge...

http://www.systems-thinking.org/kmgmt/kmgmt.htm

"

A collection of data is not information.
A collection of information is not knowledge.
A collection of knowledge is not wisdom.
A collection of wisdom is not truth.

The idea is that information, knowledge, and wisdom are more than simply collections. Rather, the whole represents more than the sum of its parts and has a synergy of its own.

We begin with data, which is just a meaningless point in space and time, without reference to either space or time. It is like an event out of context, a letter out of context, a word out of context. The key concept here being "out of context." And, since it is out of context, it is without a meaningful relation to anything else. When we encounter a piece of data, if it gets our attention at all, our first action is usually to attempt to find a way to attribute meaning to it. We do this by associating it with other things. If I see the number 5, I can immediately associate it with cardinal numbers and relate it to being greater than 4 and less than 6, whether this was implied by this particular instance or not. If I see a single word, such as "time," there is a tendency to immediately form associations with previous contexts within which I have found "time" to be meaningful. This might be, "being on time," "a stitch in time saves nine," "time never stops," etc. The implication here is that when there is no context, there is little or no meaning. So, we create context but, more often than not, that context is somewhat akin to conjecture, yet it fabricates meaning."

Model for Viral Growth

2008-07-21T05:24:00.000-07:00

One of the things in my mind is to create a sufficiently accurate model to predict viral growth - While digging, I came across this very interesting blog post
http://lsvp.wordpress.com/2008/03/10/an-excellent-excel-model-of-viral-growth/
with a link here:
http://andrewchen.typepad.com/andrew_chens_blog/2008/03/facebook-viral.html?cid=106420002#comment-106420002

Some others
http://www.insidefacebook.com/2007/07/17/predicting-growth-with-appaholic/
http://www.skelliewag.org/the-butterfly-growth-model-224.htm

Watson and Crick

2008-05-16T07:28:00.000-07:00

On Feb. 28, 1953, Francis Crick walked into the Eagle pub in Cambridge, England, and, as James Watson later recalled, announced that "we had found the secret of life."....

Slightly off-topic, but worth it for the 1959 picture alone

Full TIME article here.

Tracking Memes in the Infosphere

2008-05-16T06:40:00.000-07:00

Infosphere is a term used since the 1990s to speculate about the common evolution of the Internet, society and culture. It is a neologism composed of information and sphere. More about its origins here.
The difficulties with memetics are many - and it has been bogged in controversy since a long time. One of the main problems is how to isolate a "meme" ; What IS a meme, anyway?
Start here, but don't expect to find a definite answer - it's not there yet!
The whole discipline (if it can be called that!) is in a similar state to that of genetics in the 1950s. What was a gene? It took Watson and Crick to come up with the molecular structure - the double helix - of DNA, before genetics really took off.
The meme sounds very vague when defined as "a unit of cultural information". An abstract but precise mathematical notion is required - perhaps it can be found in information theory?
So, not even knowing precisely what a meme is, how are we supposed to track them, and build theories around them, and maybe even try and predict stuff with them?

The meme-tracking problem.....
Some possible routes:
Web publication volume and search trends

Hitwise tracks search data of all major search engines, including Google.
Google Trends also tells us the history of search volume on keywords, i.e. how many searches were executed on these keywords over time. This sounds like a good indicator of what's "hot". I am not entirely sure it is a truly accurate indicator of "meme" though. For example, Breaking News of any kind will cause a peak in News Coverage - But News is not Meme!

Published Reports by Professional Market Research Firms:
E.g The Harris Interactive Annual RQ™ study, conducted yearly since 1999, assesses the reputation of the 60 most visible companies in the United States, as perceived by the general public. Changes in reputation are what we want to learn. Perceptions of 'brand' by consumer are one part of it, of course - but again, this is not quite enough or good enough data.

WOM data: 'Positive word of mouth' data can be sourced from places like Keller and Fay's Talk-Track, a research service that tracks consumer conversations via a weekly survey
sample of 700 consumers aged 13+. Online brand mentions data can be sourced from a service like Nielsen Buzzmetrics. It searches the net for mentions of specific words or phrases on discussion boards, blogs or other places where consumers communicate online.

Advertising Spending: Weekly advertising spending data for television and national magazines can be had from Nielsen's Monitor + database. Online advertising spending can be obtained from AdRelevance, owned by Nielsen Netratings.

Agencies like ComScore MediaMetrix track website visits through a representative panel of 2 million users - another valuable storehouse of data but not "ready made" for meme-tracking by any means.

One problem for any non-US study is the possible difficulty in getting location-specific non-US data.

Research design incorporated open-ended, discoveryoriented in-depth interviews are another option, with the obvious limitation of being impossibly difficult to scale up, or even trust.

The meme-tracking space is supposed to be HOT round about now...
http://www.techcrunch.com/2006/02/04/a-look-at-the-memeorandum-killers/
It’s not easy to define this space.....but as Alex Barnett says, these are not meme-trackers - there seems to be no real meme-tracker around. I agree.
"Memeorandum, Megit and Chuquet are not 'meme' trackers. They are news trackers. Or tittle-tattle trackers. Or gossip trackers. Again, generally speaking, there are no 'memes' being tracked at these sites". I especialy like his comment that "The idea that these are 'memetrackers' is actually quite a good example of a meme."
Which brings us back to the question: How does one define a meme, at least in a way for a bot can measure it? (I think if you can define something that a machine can understand then you have done a good job at the definition!)

Some interesting papers, using innovative means to find and interpret data can be found in the Journal of Advertising Research, December 2007. I am going to fish around for those again.

Hint : Technology Memes

2008-05-16T06:09:00.001-07:00

While learning quite a lot about Project Management from DavidT, I quite accidentally ran across his post on adoption .

"There are a couple of largely accepted theories that model or predict technology lifecycle and adoption patterns:- The Diffusion of Innovations theory offers a model for how a given technology gets accepted and spreads through markets. Its central point is that technologies spread by gradually addressing the needs of 4 types of users: innovators, early adopters, the early majority, and the late majority (a fifth category, the laggards, might just never get it)- The Technology Acceptance Model (TAM) offers some prediction to End User adoption. The key concept here is that individual users adopt a given technology based on its perceived usefulness and its perceived ease of use.To my knowledge, there isn't an established theory or framework that models evolution trends of Technologies.When looking at the history and evolution of web services, we seem to be in front of species that are spreading, adapting, and diverging much like finches in the Galapagos.The immediate thought that then comes to mind is whether Darwin's Theory of Evolution has some or any relevance to Technology.The theory of evolution defines three basic mechanisms of evolutionary change:. Natural Selection is a process by which traits that are more useful in a given environment become more common over time (because they give better chances of survival), while traits that are harmful become rarer. Gene Flow is the exchange of genes within and between populations, which translates in traits being transferred between populations and species.. Genetic Drift is a purely random shift of the frequency of traits within a population - traits become more or less common in a population because of the long-term statistical effect of the random distribution of genes in each generationHow could these mechanisms apply to technology?- Natural Selection is probably the mechanism most relevant to technology trending.The fitter a technology is to the needs of its market, the more likely it is to stick around, and potentially supersede other technologiesThis is why PCs are more likely to be found today than mainframes, why java is more often used than Fortran, and why soap-based web services have replaced xml-rpc.- Gene Flow is also common in the tech field (although we'd probably want to call it something else).Features and concepts are constantly exchanged between complementary or competing technologies.That's how C# got a memory garbage collection mechanism similar to the one in java, and how row-level locking made it in MS SQL Server after years of Oracle claiming it as a key differentiator.Gene flow is also at the root of hybridization, where traits of different species end-up being combined. This is what might be truly going on right now with REST - which is applying concepts of simpler web protocols, most notably HTTP and RSS, onto Web Services.- Genetic Drift seems at first least relevant to the tech field, but might in fact be the most interesting bit.The core concept in genetic drift is that the random distribution of genes in each generation can have a long-term effect on the frequency of traits in a population (because of the statistical law of large numbers, genetic drift is less likely to occur in large population than in smaller ones).What, if anything, could have a similar impact in the evolution of technologies? What type of mechanisms, if any, can have an effect on the evolution and adoption of a technology, without being connected to its intrinsic fit or value?Obviously there are a lot more forces that dictate the success or demise of technologies than just their core virtues.A strategic alliance with IBM propelled MS-DOS into market dominance; technology companies like Oracle spend millions trying to influence the market; and there is a whole ecosystem of media, analysts, and venture capitalists who strive on generating buzz (PointCast or Twitter come to mind).Who knows - if LISP had been able to be more hip, we might all be using more parenthesis today."

Again, my "meme theory" interest (obsession?) means I cannot but help notice 'one more case that fits'. Technology memes playing out their game of survival in the world....
The question is: How can I model, simulate, and more importantly - validate, prove....and Predict the future?

Learning about the Social Networks behind Wiki

2008-05-15T23:30:00.000-07:00

Reference: Korfiatis, Poulos and Bokos, “Evaluating authoritative sources using social networks: an insight from Wikipedia”, Online Information Review Vol. 30 No. 3, 2006 pp. 252-262

Two Layers of Network

(1) The articles network.
Every article in the Wikipedia contains references to other articles as well as external references. A set of links used for classification purposes is also available in most of the active articles of the encyclopedia. Every article represents a vertex in the article network and the internal connections between the article edges of the network.

(2) The contributors network.

Wikipedia is a collaborative writing effort, which means that an article has multiple contributors. We assume that a contributor establishes a relationship with another contributor if they work on the same article. In the resultant signed network, a vertex represents each contributor, and their social ties (positive or negative) are represented by an edge denoting the sequence of their social interaction.Visualization of the Social Network of contributors behind the article "Immanuel Kant"

...And of course, I had already mentioned Chris Harrison's WikiViz Project. Talk about beautiful graphs!

You can also have a look at the Clusterball Project. Dont miss the movie.

10 Questions, with Jimmy Wales

2008-05-15T12:31:00.000-07:00

This might be an oldish article, but just thought this might be a good time to dig this up.

http://www.time.com/time/business/article/0,8599,1601491,00.html

Jimmy Wales' answer to "Why do people contribute" is especially interesting

"...It's realizing that doing intellectual things socially is a lot of fun—it makes sense. We don't plan on paying people, either, to contribute. People don't ask, "Gosh, why are all these people playing basketball for fun? Some people get paid a lot of money to do that."

He also says "It turns out that people aren't as horrible as the Internet made them seem for a while."

All the Interesting Questions about Wikipedia on One Page

2008-05-15T12:00:00.000-07:00

Just quickly, off the top of my mind, I can think of these

Growth
How fast will Wikipedia continue to grow in the near and far future?
Is there a limit to the growth (as per the logarithmic growth model)?
(...Or is "To know all is not permitted" !!?)
How will Quality of Content be affected in the near and far future, as Wikipedia grows?

Content
Is Wikipedia truly Reliable? Would you bet your life on a Fact from Wikipedia?
Can the regulatory mechanism be improved? How?
Do "Editing Wars" always lead to the "unbiased Truth"? (Can the Truth itself oscillate?!)
Does the process of "finding consensus" always lead to the best entry? (Is the "Average" always the right answer? Are there cases where the 'populist decision' may not be the 'right one' ?)

Motivation
What motivates Contributors? (The question "What motivates Seekers?" is fairly trivial)
What is the "carrot" at the end of the stick for contributors?
Will a contributor contribute content that has a high personal cost (or opportunity cost!) associated with sharing? (e.g: A stock trader chancing upon and then disclosing a piece of positive/negative news about a listed firm before it has broken on any other news channel, and without profiting personally, Or an inventor publishing on Wikipedia without thought of personal gain from his/her invention)

Culture
(I use the term very loosely here, as the "sum total of all human social knowledge".)
Can one uncover hidden cultural facets by studying the topography of the wikipedia network, by observing clusters, by deducing from "association" something of value? (somehow, again, I think - "Meme Theory"!)
What is the contribution of Wikipedia to Culture?

Computational Trust in Web Content Quality

2008-05-14T06:27:00.000-07:00

Interesting points I found in Pierpaolo Dondio and Stephen Barrett, "Computational Trust in Web Content Quality: A Comparative Evalutation on the Wikipedia Project",
Informatica 31 (2007) 151–160

Abstract
The problem of identifying useful and trustworthy information on the World Wide Web is becoming increasingly acute as new tools such as wikis and blogs simplify and democratize publication. It is not hard to predict that in the future the direct reliance on this material will expand and the problem of evaluating the trustworthiness of this kind of content become crucial. The Wikipedia project represents the most successful and discussed example of such online resources. In this paper we present a method to predict Wikipedia articles trustworthiness based on computational trust techniques and a deep domain-specific analysis. Our assumption is that a deeper understanding of what in general defines high-standard and expertise in domains related to Wikipedia – i.e. content quality in a collaborative environment – mapped onto Wikipedia elements would lead to a complete set of mechanisms to sustain trust in Wikipedia context. We present a series of experiment. The first is a study-case over a specific category of articles; the second is an evaluation over 8 000 articles representing 65% of the overall
Wikipedia editing activity. We report encouraging results on the automated evaluation of Wikipedia content using our domain-specific expertise method. Finally, in order to appraise the value added by using domain-specific expertise, we compare our results with the ones obtained with a pre-processed cluster analysis, where complex expertise is mostly replaced by training and automatic classification of common features.

I thought interesting:
Ciolek, T., Today's WWW, Tomorrow's MMM: The specter of multi-media mediocrity, IEEE
COMPUTER, Vol 29(1) pp. 106-108, January 1996.
Predicted a seriously negative future for online content quality by describing the World
Wide Web (WWW) as “a nebulous, ever-changing multitude of computer sites that house continually changing chunks of multimedia information, the global sum of the uncoordinated activities of several hundreds of thousands of people”

.....On one hand, recent exceptional cases have brought to the attention the question of Wikipedia trustworthiness. In an article published on the 29th of November in USA Today , Seigenthaler, a former administrative assistant to Robert Kennedy, wrote about his anguish after learning about a false Wikipedia entry that listed him as having been briefly suspected of involvement in the assassinations of both John Kennedy and Robert Kennedy. The 78-year-old Seigenthaler got
Wikipedia founder Jimmy Wales to delete the defamatory information in October. Unfortunately, that was four months after the original posting. The news was further proof that Wikipedia has no accountability and no place in the world of serious information gathering .

How much do you trust wikipedia? (March 2006)
http://news.com.com/20091025_3-5984535.html
In December 2005, a detailed analysis carried out by the magazine Nature compared the accuracy of Wikipedia against the Encyclopaedia Britannica. Nature identified a set of 42
articles, covering a broad range of scientific disciplines, and sent them to relevant experts for peer review. The results are encouraging: the investigation suggests that Britannica’s advantage may not be great, at least when it comes to science entries. The difference in accuracy was
not particularly great: the average science entry in Wikipedia contained around four inaccuracies; Britannica, about three. Reviewers also found many factual errors, omissions or misleading statements: 162 and 123 in Wikipedia and Britannica respectively.
Gales, J. Encyclopaedias goes head a head, Nature Magazine, issue N. 438, 15, 2005

Trust
“trust is a subjective assessment of another’s influence in terms of the extent of one’s perceptions about the quality and significance of another’s impact over one’s outcomes in a given situation, such that one’s expectation of, openness to, and inclination toward such influence provide a sense of control over the potential outcomes of the situation.” - Romano
Computational trust was first defined by S. Marsh, as a new technique able to make agents less vulnerable in their behaviour in a computing world that appears to be malicious rather than cooperative, and thus to allow interaction and cooperation where previously there could be none.
Ziegler and Golbeck studied interesting correlation between similarity and trust among social network users: there is indication that similarity may be evidence of trust.
The most visited and edited articles reach an average editing rate of 50 modifications per day..."Speed" is one of the requirements that conventional techniques do not match up to.
In general, user past-experience with a Web site is only at 14th position among the criteria
used to assess the quality of a Web site with an incidence of 4.6% . We conclude that a mechanism to evaluate articles trustworthiness relying exclusively on their present state is required.
Alexander identified three basic requirements: objectivity, completeness and pluralism. The first requirement guarantees that the information is unbiased, the second assesses that the information should not be incomplete, the third stresses the importance of avoiding situations in which information is restricted to a particular viewpoint.

Modeling Wikipedia

I won't elaborate on their experiment in detail, but jump straight to the conclusion.
They claim to have proposed a transparent, noninvasive and automatic method to evaluate the
trustworthiness of Wikipedia articles. The method was able to estimate the trustworthiness of articles relying only on their present state, a characteristic needed in order to cope with the changing nature of Wikipedia.

Is Wikipedia TrustWorthy?

2008-05-14T04:33:00.000-07:00

Some people, especially academics, are uncomfortable with Wikipedia as a "source" of knowledge. Notwithstanding the regulatory and control mechanisms to prevent "vandalism" of content, there is still skeptism among most academics about how far Wikipedia can be a trustworthy resource.
In COMMUNICATIONS OF THE ACM September 2007/Vol. 50, No. 9, Neil L. Waters explains "Why You Can’t Cite Wikipedia in My Class"
A recent post on SlashDot quotes another IT professor saying:
"People are unwittingly trusting the information they find on Wikipedia, yet experience has shown it can be wrong, incomplete, biased, or misleading"

There was an interesting case recently of a "circular reference" created by Wikipedia. "Ali G", claimed a wikipedia entry, had worked for Goldman Sachs. No sources were given. This found its way into a popular mainstream media journal and Wikipedia became a reference to itself!
http://techdebug.com.nyud.net/blog/2008/04/19/wikipedia-article-creates-circular-references/

Where does that leave us?
I'll leave you with a quote from http://tech.slashdot.org/comments.pl?sid=521670&cid=23103370 (reference link above)
The real Wiki-vandals are the companies, governments and lobby groups of all sorts that flood Wikipedia with their squeaky clean corporate profiles (yes, corporate governments), whipped straight from their websites … These entities are the true threat to the laudable goal of Wikipedia to provide a freely accessible forum for the production and storage of (hopefully well-referenced) articles for the masses and a forum that does not restrict the privilege of contribution to those that have jumped through the all the right hoops. … The printed word is no more reliable than the plasma. Lies may be propagated on Wikipedia, but not without debate. Politicians spouting their sludge find their propaganda sitting side-by-side with those that mock them… If knowing that anything in a Wikipedia article is as likely to be crap as correct, the average reader becomes more vigilant in clicking through to the supporting sources; then Wikipedia has served the purpose of bringing to the masses the healthy skepticism that is, after all, the cornerstone of all academic pursuits.Dark eyes look down from ivory towers. Do they cheer or do they fear?

Visualizing Wikipedia

2008-05-14T04:04:00.000-07:00

Chris Harrison, over at the Human-Computer Interaction Institute at Carnegie Mellon University has some beautiful visualizations of Wikipedia's network structure. He calls this project WikiViz.
http://www.chrisharrison.net/projects/wikiviz/index.html
Apart from the stunning visuals, I am thankful to Chris for two ideas that came to me:
- Can Visualizations in the form of Graphs of Wikipedia be analyzed from the point of view of Meme Theory?
- Can I use GraphViz (or similar tools) to develop visualizations for concepts in Religious Texts?
Chris has, in another interesting project, undertaken a visualization of the social network present in the Bible (http://www.chrisharrison.net/projects/bibleviz/index.html).
Similar visualizations for the Gita and Quran should be possible. What would be the motive?
Some vague thoughts swimming in my mind at this stage- Memes, Clusters, Selective Pressures, Evolutionary theories of Culture.

Modeling Wikipedia's Growth

2008-05-14T03:42:00.000-07:00

Wikipedia is one of the most interesting phenomena of current times.

One interesting area of study is the modeling of its growth.

If Wikipedia's growth follows the exponential growth model the average rate of growth would be proportional to the size of the Wikipedia. However, it appears that the rate of growth is slowing Maybe Wikipedia's growth follows the logistic growth model better. This model is based on:
- more content leads to more traffic, which in turn leads to more new content
- however, more content also leads to less potential content, and hence less new content
- the limit is the combined expertise of the possible participants.

Interestingly enough, while the number of articles may not be strictly following the exponential curve, we may consider that the quality of articles is, i.e if we assume that the number of edits per article is a measure of its quality.

The graph is plotted in logarithmic scale, and this data also fits well with exponential growth starting from October 2002. The number of edits per article has since doubled once every 504 days.

Reference: http://en.wikipedia.org/wiki/Wikipedia:Modelling_Wikipedia