Recently I’ve needed to get mail on the road, and my employer’s policy forbids to connect a non-standard device to their network. So, a GPRS connection through the phone is an OK alternative. Nothing as the 3G connection I used to have in the United Sates, but it is OK. My provider is China Telecom, but the instructions here work for any other provider.

1. Pair the Treo and the Mac, if you haven’t already do so. The Bluetooth Setup Assistant should do everything required. Just follow the steps. Choose “Mobile Phone” as the type of device

   

2. Check the services on the newly added device. When selected on the Bluetooth panel, the Dial-up networking service must be present

   

3. Go back to see all the control panel modules, choose Network, and now you should have a Bluetooth connection (if it wasn’t already present). Add a new configuration for your service provider - handy if you travel a lot

   

4. Now fill in the required login information. For China Telecom, account and password are guest, check this page if you don’t know the correct info for your phone provider. Bear in mind that data rates are expensive if you don’t have the appropriate plans.

   

5. Click on Advanced. In my case, the Treo worked fine with the Generic script as shown below. If not, find out what works for your phone. For instance, for a RAZR V3xx I used in the US, Ross Barkman’s scripts worked well, as mentioned in a post some time ago.

   

6. Finally test, try to connect.

   

Hope this post helps someone else. If you have other suggestions, please leave me a comment.

I have attended his seminars, and definitely recommend his method. For anyone interested in improving their presentation skills, and generating action out of their presentations, it is must-read.

Here it goes:

And the makeover:

Problems:

• Vertical labels: Even when the list of items is long and is tempting to use columns to use the width of the screen versus the height to show the items, it forces the audience to turn their heads to read the labels (or to not read them at all)

• Clutter: Does your audience need to know to the dollar the GDP in each region? The new chart uses ‘000s. People will remember more easily data they get interested in. Will someone interested in the Shangdong province remember the GPD per capita is $3,162? More chance they will recall it was like 3.2 thousand, right?
• More clutter: Once you label the bars/columns, why keeping the axis? Also, the arrow to highlight the 9x difference adds clutter to the chart
• Lack of labels/legends/source: The original chart has an average line, which is evident only on close inspection because it is not labeled. Also, the columns are grouped in colors, without legends, so a reader, like me, is left clueless about their meaning. Finally, every chart using data must show its source (I’d have noted one if I knew where the original came from)

In charts, Simplicity is King: I’m assuming in the context of the presentation it was necessary to include every province by name.
“Is this cart needed? Is this level of detail needed?” That’s a question you should ask yourself before including any chart. The answer is completely situation-specific. What is the key message to communicate? Who is the audience? What will they look for from their specific viewpoint?

So, what do you think? How would you further improve my proposal?

It allows students to experience first-hand how structure of business organizations impact their behavior, to some extent irrespectively of how skilled their managers may be. In the game, a 3 tier distribution system delivers beer from a factory to consumers. Only the retailer knows the consumer demand. As information and goods propagates through the chain with delays, oscillation occurs.

Students play competitively, as a pool of money collected from them at the start of the game will be awarded to the winners. A very entertaining and active dynamic develops during the 4 hour exercise.

The Systems Dynamics Society offers kits for playing the beer game, that I recommend. They include everything you need to run the game, as well as videos of Professor Sterman running a session.

I have attended many of his sessions, both as student and observer - my employer sponsors the Beer Game during incoming MBA orientation at MIT. Professor Craig Kirkwood at Arizona State University also has very good materials and hints.

I decided to run with a low-budget version that uses beans, pieces of paper and boards printed on common plotter paper. Everything is in Spanish language. I believe publishing the materials here may be useful to colleagues in Latin America and other Spanish-speaking countries.

Here is the board for the game, and here are the slides

For editable versions of these files, please contact me directly.

Michael Bloomberg, New York City’s mayor writes on The Economist

…we drew on the experiences of Berlin for our renewable-energy and green-roof policies; Hong Kong, Shanghai and Delhi for our innovative transit improvements; Copenhagen for our pedestrian and cycling upgrades; Chicago and Los Angeles for our plan to plant 1m more trees; Amsterdam and Tokyo for our transit-oriented development policies; and Bogotá for our plans for Bus Rapid Transit

Avg_Profit = (1000*1200 + 200*300 + 500*2500 + 10*600 + 100*300) / (1000 + 200 + 500 + 10 + 100)

or

Avg_Profit = SUMPRODUCT(UnitsSold,ProfitPerUnit)/SUM(UnitsSold)

I’m using Excel notation, and assuming it is clear from the context that UnitsSold is a range that covers the second column, for all models, etc.

A less known way of averaging are Harmonic Averages. It is relevant when the data to aggregate is actually a ratio whose denominator is proportional to the weighting factor. A typical case is miles per gallon (MPG) for a bunch of vehicles. Gas consumption is directly proportional to the number of units.

Let’s add some MPG data to the table above.

Using Weighted Averages for an inverse ratio like MPG is plain wrong (24.3 MPG is NOT the average fuel economy)

The right thing is to use Harmonic Average:

Harm_Avg_MPG = (1000 + 200 + 500 + 10 + 100) / (1000/22.5 + 200/15.0 + 500/32.0 + 10/12.0 + 100/24.0)

As Excel doesn’t have a similar function to SUMPRODUCT for adding 1000/22.5, 200/15.0, etc. I will not use Excel notation, but plain math notation:

UPDATED formula

If you have to deal with Harmonic Averages, you may find interesting this note on how to do PivotTable Multidimensional Analysis with Harmonic Averages. There’s a similar one for Weighted Averages as well.

Let me know what you think.

I haven’t been back to Bogota since 1999, except a quick stop-over during our honeymoon.

This is also exciting as I will add more content to this website in preparation for the classes. Stay tuned.

Here are the links to the courses:

Escuela internacional de verano / Facultad de Ingeniería - Universidad de Los Andes

Definición de políticas y toma de decisiones en organizaciones globales - Una perspectiva de dinámica de sistemas
Juan Carlos Méndez
Global Portfolio Strategy Manager – General Motors
Junio 30 - Julio 11 (40 horas)

Escuela de Verano - Facultad de Ingeniería

Políticas y decisiones en organizaciones globales desde la dinámica de sistemas
Julio 01 al 11 de 2008
Invitados Internacionales:
Juan Carlos Méndez: Gerente de Desarrollo de Negocios para Asia Pacífico de la compañia General Motors, basado en Shangai, China.

There is no simple answer, and this post is an attempt to point readers to ways to think about what they want to model, as well as giving helpful resources for further study

In my opinion, one of the best approaches to understand market adoption is through system dynamics. One of the advantages of the methodology is that it allows you to conceptually link business effects and relationships to the equations. I touched on this issue on on a previous entry, and here I will try to explain further.

The logistic equation (shown below) is a commonly used way to model market adoption.

From a System Dynamics perspective, the logistic model can be explained looking at the following model (click for full size): The boxes, called “stocks” in SD terminology, represent an accumulated quantity over time. One way to think of stocks is a bathtub. The amount of water in the tub is the accumulation over time of how much water you added through the faucets, less how much water you let out through the drain.

On the model, there are two stocks: how many potential adopters are out there (left side) and how many adopters are (right side). The pipe that connects the boxes is called a “flow”, and it shows a valve, whose value represents how fast potential adopters turn into actual adopters (thus we call it Adoption Rate). Again, in the bath tub analogy, we can think of the value of the flow as how open or closed the faucet is.

Adoption rate depends on how big the population is (the larger the population, the larger the adoption rate), how much the adopters interact with potential adopters (creating the “word of mouth” benefits), etc.

As stocks are accumulations of whatever flows in minus what flows out, from a mathematical perspective, the value of a stock is calculated integrating over time the values of the net flow. On the logistic model, the arrow that links the stock and the adoption rate flow means that the flow changes proportionally to the stock - i.e. if I have more potential adopters, there are more possibilities for contagion, when a user talks favorably to a potential user about the product. The net result is an exponential behavior, which, after some mathematical reduction, is represented by the formula above.

If I want to explain a business audience some market adoption dynamic, it possible to do it talking in terms of stocks and flows (once the audience is comfortable with these terms). It’s almost a guaranteed failure if I try to explain it by using a mathematical formula with exponentials and integrals

The Bass model addresses one limitation of the simple logistic model, regarding how the system “gets started”: with no adopters, there is no chance for interactions, so there is no inflow to the adopters stock. It does it through the use of an external force, like advertising.

Below is a Systems Dynamics interpretation of the Bass model. As you can see, the only difference is that now the Adoption Rate is the addition of two elements, adoption rate from advertising and adoption rate from word of mouth. The latter is exactly the same as the AR in the logistic model.

Returning to Reader Vince’s specific question on how to extend the logistic or Bass models to comprehend effects like cross-segment interactions, I would frame it like this:

• Identify the most important cross-segment interactions - How much “cross-shopping” exists between the segments? (using data like second choice selection); are there characteristics of the upper segment that consumers will translate into the lower segment favorably/unfavorably? consumers replace their vehicles within segment or they try to go up segment? etc.
• Incorporate the key cross-segment interactions on the model - They will most likely affect the Adoption Rate. It also may be necessary to model another stock or stocks (Upper Segment Adopters and Lower Segment Adopters, for instance)
• Check sensitivity of cross-segment assumptions - Understand how different the results are when the cross-segment assumptions are considered versus when they are not. What are the assumptions that most impact the results? A tornado diagram, as discussed in a previous entry, may provide a good way to show the sensitivity to the assumptions

As more dynamic effects are considered for inclusion in a model, it is better to move from a tool like Excel to something like Vensim, or iThink. Chapter 9 of John Sterman’s excellent book “Business Dynamics” talks about both the logistic and Bass models as shown here, and expands on ideas on how to extend them.

Here are some other very good references on the topic

• Forrester, J. W. 1980. Information Sources for Modeling the National
  Economy. Journal of the American Statistical Association 75 (371):
  555-574.
  Argues that modeling the dynamics of firms, industries, or the economy requires use of multiple data sources, not just numerical data and statistical techniques. Stresses the role of the mental and descriptive data base; emphasizes the need for first-hand field study of decision making.
• Legasto, A. A., Jr., J. W. Forrester & J. M. Lyneis, eds. 1980. System Dynamics. TIMS Studies in the Management Sciences. Vol. 14. Amsterdam:
  North-Holland.
  Collection of papers focused on methodology. Includes Forrester and Senge on Tests for Building Confidence in System Dynamics Models and Gardiner & Ford’s discussion on Which Policy Run is Best, and Who Says So?
• Randers, J., ed. 1980. Elements of the System Dynamics Method.
  Cambridge MA: Productivity Press. Includes Mass on Stock and Flow Variables and the Dynamics of Supply and Demand; Mass & Senge on Alternative Tests for Selecting Model Variables; and Randers’ very useful Guidelines for Model Conceptualization.
• Richardson, G. P., and A. L. Pugh, III. 1981. Introduction to System Dynamics Modeling with DYNAMO. Cambridge MA: Productivity Press.
  Introductory text with excellent treatment of conceptualization,
  stocks and flows, formulation, and analysis. A good way to learn the
  DYNAMO simulation language as well.
• Morecroft, J. D. W. 1982. A Critical Review of Diagramming Tools for
  Conceptualizing Feedback System Models. Dynamica 8 (part 1): 20-29.
• Critiques causal-loop diagrams and proposes subsystem and policy
  structure diagrams as superior tools for representing the structure of
  decisions in feedback models.
• Roberts, N., D. F. Andersen, R. M. Deal, M. S. Grant, & W. A. Shaffer.
  1983. Introduction to Computer Simulation: A System Dynamics Modeling
  Approach. Reading MA: Addison-Wesley.
• Easy-to-understand introductory text, complete with exercises.
• Homer, J. B. 1983. Partial-Model Testing As A Validation Tool for
  System Dynamics. In International System Dynamics Conference: 920-932
• How model validity can be improved through partial model testing when
  data for the full model are lacking.
• Sterman, J. D. 1984. Appropriate Summary Statistics for Evaluating the
  Historical Fit of System Dynamics Models. Dynamica 10 (2): 51-66.
• Describes the use of rigorous statistical tools for establishing model
  validity. Shows how Theil statistics can be used to assess
  goodness-of-fit in dynamic models.
• Forrester, J. W. 1985. ‘The’ Model Versus a Modeling ‘Process’. System
  Dynamics Review 1 (1): 133-134.
• The value of a model lies not in its predictive ability alone but
  primarily in the learning generated during the modeling process.
• Richardson, G. P. 1986. Problems with Causal-Loop Diagrams. System
  Dynamics Review 2 (2 ): 158-170.
• Causal-loop diagrams cannot show stock-and-flow structure explicitly
  and can obscure important dynamics. Offers guidelines for proper use
  and interpretation of CLDs.
• Forrester, J. W. 1987. Fourteen ‘Obvious Truths’. System Dynamics
  Review 3 (2): 156-159.
• The core of the system dynamics paradigm, as seen by the founder of the field.
• Forrester, J. W. 1987. Nonlinearity in High-Order Models of Social
  Systems. European Journal of Operational Research 30 (2): 104-109.
• Nonlinearity is pervasive, unavoidable, and essential to the
  functioning of natural and human systems. Modeling methods must
  embrace nonlinearity to yield realistic and useful models. Linear and
  nearly-linear methods are likely to obscure understanding or lead to
  erroneous conclusions.
• Barlas, Y. 1989. Multiple Tests for Validation of System Dynamics Type
  of Simulation Models. European Journal of Operational Research 42 (1):
  59-87.
• Discusses a variety of tests to validate SD models, including
  structural and statistical tests.
• Barlas, Y., & S. Carpenter. 1990. Philosophical Roots of Model
  Validation: Two Paradigms. System Dynamics Review 6 (2): 148-166.
• Contrasts the system dynamics approach to validity with the
  traditional, logical empiricist view of science. Finds that the
  relativist philosophy is consistent with SD and discusses the
  practical implications for modelers and their critics.
• Wolstenholme, E. F. 1990. System Enquiry - A System Dynamics Approach.
  Chichester: John Wiley.
• Describes a research methodology for building a system dynamics
  analysis. Emphasizes causal-loop diagramming, mapping of mental
  models, and other tools for qualitative system dynamics.
• Mass, N. 1991. Diagnosing Surprise Model Behavior: A Tool For Evolving
  Behavioral And Policy Insights (written in 1981). System Dynamics
  Review 7 (1): 68-86.

Two features that have not received as much publicity but were very pleasant surprises (I know, I never Read The Fine Manual) were the inclusion of the A2DP profile in the Bluetooth stack, and TextEdit’s ability to read OpenOffice .odt files.

Nice touch.  As usual with Apple, new functionality is rolled out in a seamless, intuitive and non-intrusive way.

During the next few weeks expect scarce new activity