We are about to begin an experiment in Podcasting for Learning. Josh
Weiss, our faculty for the CDM Re-Framing Conflict online learning (now
in its fourth year) will be starting a weekly podcast that will contain
a negotiating tip. Each podcast should run no more than three minutes.
Josh will record his weekly 'cast on a Creative Labs MP3 player (with
built-in voice recorder) that I picked up for him at the Cambridge
Galleria Mall. Once he captured the recording on the player, he can
upload it to his computer with a USB connection. He will then post it
the Josh Weiss Negotiating Tip category to our on Ottergroup weblog
site. 

When
Aixa and I come up for air next week, we'll put together a window on
our site that will hold the folder containing Josh's podcasts and an
explanation for “listeners” on how to subscribe to the category feed.We
will then promote the podcast through various elearning blogs and pubs,
as well as on the podcast blogs.

I am envisioning this as a way for us to promote our use of podcasting
in training and as a model for other portals where experts can 'cast
their knowledge in the tip-of-the-week format. I hope and believe the
podcasts will most importantly promote Josh as a terrific expert in the
field of negotiation and result in new projects for him (and for us in
the online learning space).

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I have been reading Peter Drucker's Management Challenges for the 21st Century. In his final chapter, entitled “Managing Oneself,” Peter makes a compelling case for how most knowledge workers will have to manage themselves:

They will have to place themselves where they can make the greatest contribution; they will have to learn to develop themselves. they will have to learn to stay young and mentally alive during a fifty-year working life. They have to learn how and when to change what they do, how they do it and when they do it.

Knowledge workers have to ask themselves:

1. Who am I? What are My Strengths? How do I work?

2. Where do I belong?

3. What is my contribution?

4. What is my relationship responsibility?

5. How do I plan for the second half of my life?

Drucker describes Managing oneself as a “REVOLUTION in human affairs:

It requires new and unprecedented things from the individual, and especially from the knowledge worker. For in effect it demands that each knowledge worker think and behave as a Chief Executive Officer. It requires an almost 180-degree change in the knowledge workers' thoughts and actions from what most of us–even the younger generation–still take for granted as the way to think and act.”

My own experiencing with blogging is that it can help people think and behave like CEOs of their own careers. While journalism may be the first draft of history, my blog(s) are the first draft of my career and business plans. I use my blog to work out my ideas, share them, and get feedback. And as I come to my own conclusions about things and write about them, like-minded people find me, expanding my network. My blog helps me answer the critical questions Drucker poses. By going public with my ideas, I am in a constant process of developing myself–ever planning for the second half of my life, which is now just around the corner.

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I received an email this morning from a woman who has a web site for a nonprofit organization setting up a traveling museum exhibition on contemporary American glass art beads. She was confused about how and why she should switch to a blog or how she could use a blog network. Here is my reply to her email:

Here are the reasons to consider switching to a blog from your html web site:

Blogs are built on RSS which is the next generation of web technology. Each entry is given a unique and permanent URL which means that information can be indexed and searched on an entry by entry basis (as opposed to page by page).

RSS which stands for Really Short Syndication is a technology that enables readers of your site to get information from it in new ways: each time you post an article, your blog will ping central servers that store data that there is new information on your blog. Readers of your blog will use a new tool called a news aggregator or RSS aggregator to poll your blog to see if there is anything new. (You can also notify them by email.) If so, their aggregators will show the updated information. This is very useful for tracking multiple blogs and web sites. (Many newspapers now publish information in RSS.)

Each blog also has an RSS feed—a URL that is used by the aggregators and ping servers to track new information. For your blog network, you can aggregate these feeds into glass bead aggregator that would track new information in all blogs in your network. (To see an example, go to http://pingwellesley.com, and in the upper left hand corner click on the newsgator icon. When you log in, you will see all the new posts from every blog in the Pingwellesley network.) You can also use our “advanced search” tool on Pingwellesley to search only the blogs in this network.

One of the biggest advantages for someone in your situation is how the blog manages photos. It is very easy to post an image within an article. And it is simple to upload a group of photos into an automatic slideshow. For an example of how this works, check out my blog http://kathleengilroy.com and click on the topic Hawaii in the right hand column. If you click on the slideshow icon under “next” the blog will automatically create a slide show for you.

The blog is not a “dumb” windows program. It is actually a very powerful content management system that allows non-programmers to have a sophisticated, easily updated web site with no programming skills.

Pingware is our blogging platform designed specifically for groups. We have built a set of components (like the aggregators and search tools referenced above) that make it easy for groups to find information about one another.

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Here is a link to a case study of retrieving sales leads from salesforce.com via RSS. Instead of having to go to the web site and look for leads, a custom aggregator brings this information directly to the sales person. This is an example of where enterprises will surely go as they begin to adopt blogs and RSS. Despite massive investments made in IT over the past decades, communications problems persist in getting the right information to the right people at the right time. As Charlie Wood, who developed this app says, “By fitting the source of information in the enterprise–both people and automated systems–into a common network with ubiquitous clients, you unlock a lot of power.”

This blog has links to a few interesting embryonic case studies for field sales, product marketing, and information brokers and how they can begin to use blogs and enterprise RSS in their operations and workflow.

This is the direction we are headed with The Ottergroup's new Ping Network Service.

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According to Rebecca McKinnon, South Korea is a leader in new communications technologies and media trend. With its 90% broadband penetration, South Korea is a place to look for trends in media and technology. Rebecca references a new study that indicates new forms of communication spell the end of email in Korea. Here are some surprising trends coming out of Korea. This article references mini home pages, which I believe are the Korean equivalent of blogs:

The email era is coming to an end because replacement communication means such as Internet messengers, mini-homepages (dubbed “one-man media”), and SMS are wielding their power. As a consequence, the stronghold of email, once the favorite of the Internet, is being shaken from its roots.

Leading the big change, unprecedented in the world, are our teens and those in their 20's. The perception that “email is an old and formal communication means” is rapidly spreading among them. “I use email when I send messages to elders,” said a college student by the name of Park. For 22-year-old office worker Kim, “I use email only for receiving cellphone and credit card invoices.”

A poll conducted by Chungbuk University computer education professor Lee Ok-hwa on over 2,000 middle, high school and college students in Gyeonggi and Chungcheong provinces in October revealed that more than two-thirds of the respondents said, “I rarely use or don't use e-mail at all.”The ebb of email is confirmed by a diminishing trend in pageviews, a tabulation of frequency in service used by email users. Daum Communication, the top email business in the country, saw its email service pageviews fall over 20 percent from 3.9 billion in October last year to 3 billion in October this year. By contrast, with SK Telecom, the nation's No. 1 communication firm, monthly SMS transmissions skyrocketed over 40 percent in October from 2.7 billion instances last October. Cyworld, a representative mini-homepage firm, witnessed its pageviews multiply over 26-fold from 650 million instances in October last year to 17 billion in October this year.

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Here's the second part of my notes from the paper, INFORMATION AGGREGATION
MECHANISMS: CONCEPT, DESIGN AND IMPLEMENTATION FOR A SALES FORECASTING PROBLEM,
by Charles R. Plott of CalTech and Kay-Yut Chen of Hewlett Packard
Laboratories,which describes how they set up a prediction market for sales forecasts
at HP.

Advantages of
Prediction Market Over Other Forecasting Methods

·       
The methodology is flexible. It can be used to
aggregate any type of information possessed by different people. It involves a
natural methodology for quantifying subjective, qualitative, information and
giving weights to the opinion of different people for the purpose of
information aggregation. The task is performed giving not only a point forecast
but also a complete probability over the range for which the value of some
unknown variable is to be predicted.

·       
The methodology is scalable by number of participants,
timing of participants and location of participants. There are no practical
limits to the number of people that can participate. With markets conducted
over the Internet, hundreds and even thousands of people can participate either
at the same time or at different times. Traditionally, businesses collect and
aggregate information through a process of meetings, which not only limits the
number of participants but also the time frame for information collection.

·       
The methodology tends to be incentive compatible.
Incentives to hide information, misrepresent information or simply ignore
requests for information are either eliminated or limited. Furthermore the
markets are designed to give incentives to the participants’ to acquire
information about future events and use this information wisely in the market.

Observations

·       
Theoretical arbitrage profits existed. In all
the experiments, prices summed to be greater than the winning payoff. However,
to take advantage of the arbitrage conditions, individuals needed to execute
multiple trades when fluctuations of prices were substantial. So it is likely
that there were actually no practical arbitrage opportunities. Why in all 12
experiments was the sum of the prices always above the winning payoff?

·       
No significant trends in the sequences of
predictions are observed. So it doesn’t appear there was any response to
changing market information during the trading. Maybe all the information
aggregated quickly at the beginning.

Scientific issues

·       
How is the performance of the system related to
the psychology and decision biases of individuals?

·       
How can one deal with incentive problems in
which individuals might large incentives to conceal or misrepresent what they
know?

·       
What rules and mechanisms might be needed for
different underlying information structures?

·       
If markets are thin or the number of
participants few, how will the performance of the system be affected?

·       
How can we find the people with the relevant
information and how do we know that they knew something of relevance anyway? If the participants know nothing, the mechanism
will produce nothing.

·       
Can a prediction market not only produce a prediction but
also simultaneously help management ascertain which participants have
information. That is, can it be designed to attract those with good information
and discourage those with bad information?

Scientific Literature

The
experimental demonstration is first found in Plott and Sunder (1982, 1988).
This early paper demonstrated that the ability of markets to aggregate
information is sensitive to the market architecture. In particular, this early
work demonstrated that compound securities are not as reliable as indicators as
a complete set of state dependent instruments. The conditions under which a
single compound security is reliable are isolated in Forsythe and Lundholm
(1990) The need for selecting proper instruments is underlined by demonstrations
of markets that can equilibrate at patterns that are not fully revealing of information
such as cascades (Anderson and Holt, 1997; Hung and Plott, 2001) or misleading
such as mirages (Camerer and Wiegelt, 1991) or bubbles ( Smith et al, 1988;
King et al. 1993; Porter and Smith, 994; Lei et al, 2001). In fact, some types
of market organization facilitate no information aggregation at all as is the
case of the winners curse in sealed bid auction markets (Kagel and Levin, 1986;
Lind and Plott, 1991). See Sunder (1995) for a summary, or aspects of search
(Sunder, 1992).

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Here's the closest thing I've found to an explanation of how
to set up and conduct a prediction market. This paper, INFORMATION AGGREGATION
MECHANISMS: CONCEPT, DESIGN AND IMPLEMENTATION FOR A SALES FORECASTING PROBLEM,
by Charles R. Plott of CalTech and Kay-Yut Chen of Hewlett Packard
Laboratories, describes how they set up a prediction market for sales forecasts
at HP with the following results:

·       
In 6 out of 8 events for which official forecasts
were available the market predictions were closer to the actual outcome than
the official forecast.

·       
The probability distributions calculated from market
prices were consistent with actual outcomes.

·       
The market made accurate qualitative predictions
about the direction that the actual outcome will occur (above or below)
relative to the official forecast.

I’m separating my notes into two posts. First, the nuts and
bolts about how it was done and second, some of the scientific issues.

How it was Done

The Prediction

Typically, the
prediction was for monthly sales for a month three months in the future.

Business Constraints

·       
Hesitation
to engage employees in an exercise in which they might lose money. Solution: provide
a small amount of cash to each participant before the market sessions - this
constrains the amount of stakes a participant can have in the market and affects
incentives to trade.

·       
Market
has to offer useful information. E.g., if forecasts are not valuable if they
are made with horizons less than 3 months then market sessions need to be
conducted 3 months before the event to be predicted.

Who participates

·       
Relatively small number of participants chosen.
Selected specifically from different parts of the business operation because
they were thought to have different patterns of information about the targeted
event. These patterns of information, including market intelligence, specific
information about big clients, and pricing strategies, were in need of 
aggregation. No public summaries of information available to the participants
during the operation of the IAM. The official forecasts were not known until
after the IAM closed.

·       
Participants need to be selected carefully –
don’t want to “miss” a person with much information but it might not be
efficient to include many people without any relevant information. Little is
known theoretically about the information size relative to the market that
might be required for effective information aggregation to take place.

·       
Laboratory
experiments have suggested that a small number of uninformed participants
provide both market liquidity and a function of adding “consistency” to the
market through a process of “reading” and “interpreting” the actions of others.
So, around five subjects was recruited from HP Labs (with little or no
information) in each experiment.

Preparing the participants

15-20 minute
instruction session from: explained the structure of incentives, the market
mechanism and the web interface. In addition, the participants were told the
goals of the experiment and were told that their participation was important
for HP business. Contact information provided and participants were encouraged to
call if they encountered difficulties.

Defining the
Contracts to be Traded

Most similar to the IEM “winner take all” markets (state
contingent securities).Traded a
complete set of state contingent contracts (Arrow-Debreu securities). The space
of possible outcomes was partitioned into about 10 intervals. Each interval was
given a name and with each interval there was an associated security with the
same name that traded in a market with that name. Thus the interval 0-100 would
be associated with a security named 0-100 that traded in a market named 0-100.
The interval 101-200 would be associated with a security named 101-200, etc.

Payoff

If the final outcome
fell in an interval, the corresponding security would pay, say, one dollar per
share at the end of the experiment. All other securities would pay nothing. A
higher payoff per share would place more value on the share but the payoff per
share interacts with the total cost of the exercise and the potential volume of
trades and related market liquidity.

How to Start

Each participant is given
a portfolio of shares in markets and cash to start. Could start with equal
shares in all securities or could start with shares in every other security,
alternating which security was first across participants. The unequal
distribution of endowments was used to encourage trading by attempting to make
sure that the initial endowments of securities did not approximate the ultimate
equilibrium.

Market Mechanism

Web based, double
auction markets. Marketscape software (Laboratory of Economics and Political
Science at Caltech.)

·       
All the
markets for an event were organized on a single web page for easy access.

·       
Links to
a complete time series of trades available.

·       
Links available
to HP data bases, which allowed participants to review data held by HP.

·       
A
participant could enter a buy offer, a sell offer or acceptance of an offer
through the web form on the page. Orders were compared to the other side
immediately. If a trade was possible, it was executed and if not the order was
placed in an order book. The best offers were listed on the main market web
page. The whole book of offers was available for each market at the click of a
button.

·       
Participation
was anonymous. However, each participant was assigned a subject ID number for
each experiment. During the experiment, the subject ID number of the person who
made offers and transactions were public knowledge. Participants had the
ability to track behavior of other subjects with   in the same experiment if they wished to.

When should market be open?

·       
In all
cases the information was gathered for a week with the markets being open
during lunch and in the evening every day. Management did not want participants
being preoccupied with the task during the working day when pressing issues needed
attention.

·       
It is
desirable to have a schedule (for example, 24 hours for a week) to minimize
conflict with other activities.

·       
It is
not desirable to leave market open for long periods because participants will
often find a lack of activity in the market and thus lose interest.

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From the commerce.net blog


Chris Masse's year end summary
of Prediction Markets activity for 2004 gave a pointer to an article
from Time Magazine back in July that I had skimmed earlier. When I
looked at it again, I found references to internal markets at
Microsoft, Eli Lilly, and Intel that I hadn't noticed before. It seems
worth the time to gather together references to all the internal market
experiments I've heard about, since most of them haven't been written
up formally as far as I've been able to tell.

* I should mention that the BP case was trading internal pollution credits, while all
the others seem to have been Idea Futures markets.

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From Chris Masse's Prediction Markets Portal

Play-Money Exchanges

• NewsFutures
  (Maryland, U.S.A.)
• Innovation
  Futures by MIT's Technology Review
  (Massachusetts, U.S.A.)
• The
  Foresight Exchange Prediction Market
  (Alberta, Canada)
• Hollywood
  Stock Exchange (L.A., California, U.S.A.)
• U.S. Idea Futures Exchange
  - (a defunct exchange, alas)

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Art Hutchinson pointed me to his blog, Mapping Strategy, and the collection of articles he's been writing about prediction markets since last September. Here are my notes:

“The strong consensus - supported by a compelling body of academic
research - is that these mechanisms deliver uncannily accurate
forecasts across a wide range of topics, time horizons, and approaches
to participation. Even more interesting is that they appear to do so at
a fraction of the cost of conventional techniques for generating
business foresight, (e.g., trend extrapolation, market research, polls,
expert opinion and even sophisticated models and simulations).“

“They need not be perfect in order to be compelling. Compared to any
pragmatic forecasting alternative, prediction markets remain remarkably
resilient to manipulation, and uniquely (if not perfectly)
efficient at assessing the impact and importance of vast amounts
distributed information.“

Examples:

• The Iowa Electronic Markets (IEM). IEM traders with real money at stake called the presidential race for Bush in September.
  “This is the
  fifth
  straight
  presidential election that the Iowa Electronic
  Markets has called
  correctly. Other markets did as well or better - including forecasting
  the tightness of the race.

  What I find even more
  fascinating is that each candidate took every single one of the states in which
  he was running over 50% likelihood according to a smoothing formula I
  applied to pricing data gleaned from Tradesports in the final days. Every one. As I watched it play out through the
  night, it felt like I'd been let in on
  prophecy.“

• The Des Moines Register carried this story two weeks ago, describing how the Iowa Electronic Markets are involving physicians in markets to predict the location, timing, and severity of flu outbreaks this Winter. …It was 90 percent accurate during a short pilot project last year

Following are from an article in the Australian paper, The Age

• The Hollywood Stock
  Exchange, an affiliate of online trading firm Cantor Index Ltd,
  allows people to buy and sell virtual shares in movies, celebrities
  and music. To pay for pseudo-shares, they use pseudo-money in the
  form of “Hollywood Dollars”. This allows people to bet on such
  questions as total box office returns and Oscar winners. Because
  the data outperforms industry forecasts, it is also syndicated as
  market research.
• Three years ago, Goldman Sachs and Deutsche Bank launched a
  market for economic statistics futures including employment,
  industrial output, retail sales and inflation. The Chicago
  Mercantile Exchange now trades in inflation futures contracts.
• Some
  companies have also experimented with prediction markets.
  Hewlett-Packard, for example, set one up that reportedly generated
  more accurate forecasts of sales than its own internal
  processes. Siemens had one that predicted the German conglomerate would
  fail to deliver on a software project in time, in defiance of its
  established management systems that insisted the deadline would be
  met. Management was wrong.

Prediction markets for business

• “By accepting the superiority of managed, credential-based, hierarchical
  information flows across the board, organizations are handicapping
  themselves in evaluating early signals on some of the most important
  open ended strategic questions they need to confront. And while such
  mechanisms are powerful and necessary for many kinds of tasks, they're
  poorly suited to the task of harvesting, assimilating, and assessing
  distributed intelligence in the near real-time. I.e., on questions with
  high uncertainty and little or no past precedent, it's only smart
  business to acknowledge the possibility that the best answers may
  sometimes arrive from truly unexpected sources.“
• “…where I think prediction markets will find traction in a corporate
  context is in highlighting where management teams ought to pay more
  attention (i.e., do more research), and in making groupthink denials of
  emerging external trends and inflection points far more difficult to
  sustain. This will be particularly true of prediction markets that are
  populated across corporate boundaries -e.g., including customers.“

Comparing a prediction market to the stock market: the importance of making contracts specific
“I suspect that other hypothetical contracts, written so as to avoid
clear moral hazard issues could have (and still might) shed
strategically important light on how the market for over-the-counter
pain medications will evolve. For example, a winner-takes-all contract
for 2006 stating that: “Two companies hold 90% share in Cox-2
therapies”, might have signaled much earlier that this was a market
bound to consolidate. The reasons could have had nothing to do with the safety of any
particular drug (e.g., difficulty in establishing a compelling third
brand, patient-perceived efficacy, mergers and acquisitions, etc.) What
keeps the idea of trading in such specific contracts interesting vs.
simply watching the share price of major pharmaceutical companies on
Wall Street is that…such markets can highlight the potential for
specific change events with much greater precision.“

Critical success factors

• Select the right kinds of (non-random) questions
• recruit/attract
  a solid pool of informed/active tradesr
• Only rely upon the
  predictions of markets that show robust and honest trading activity
• Limit the stakes so as not to create moral hazard. There's plenty of evidence to suggest that web games and carefully
  constrained real money markets are as or more effective than high
  stakes wagers at predicting the future outcome of uncertain but
  non-random events
• Make it easy to figure out what's
  going on
• Be clear about trading parameters, e.g., market open and close
• Make it obvious how trading
  values translate into probabilities or vote shares
• Make it easy
  to compare the prices of the underlying commodities

Problems with Prediction Markets

• Skewed participation
• Poor question articulation
• Inadequate
  information input
• Thin floats and big spreads.
• Can prediction markets be deliberately influenced?
  “…prediction markets are proving as
  resilient to deliberate influence as theorists have long said they
  would be.” Example: The Bush relection futures at Tradesports survived a speculative attack: “'There is now no question whatsoever that the Bush re-election futures contract at Tradesports.com
  is being manipulated. Yesterday the price of the futures were sold down
  from about 55 (indicating the market's estimate of a 55% probability of
  Bush's re-election) to 10 (indicating on a 10% probability) with a
  single 10,000-lot order entered by a single trader. An order that size
  represents twice the normal volume of an entire typical day's trading'….The 'price' of a Bush future quickly rebounded to the mid 50's.“

Robin Hanson on “Manipulators increase information market accuracy“.

“Why does… manipulation seem to be less of a problem than many
fear it should be? One possible explanation is the view that a
manipulative trader is in essence a 'noise' trader in the sense that his trades are based on considerations other than his best estimate of asset value…
when potentially informed traders have deep pockets relative to the
volume of noise trading, increases in trading noise do not directly
effect price accuracy… by inducing more traders to become better
informed, an increase in noise trading indirectly improves the accuracy of market prices.” (emphasis added)

How prediction markets can separate the wheat from the chaff
“…let
prediction markets directly incent information gathering and sharing,
highlighting those individuals with the best grip on reality in
particular areas, i.e., those with knowledge that’s objectively
valuable to the enterprise. Mary Murphy-Hoye of Intel (a pioneer in
using prediction markets) made this point directly in a Time Magazine feature article last summer entitled “The End of Management”:

'I can now tell if planners are any good, because they're making money or they're not making money.'

She highlights a little talked-about, but important flip side to the
whole debate: shouldn't good knowledge management discipline
marginalize those who obfuscate, dilute or detract from institutional
knowledge-building to the same degree that it elevates (and enriches)
those who add to it?“

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