Quantellia announces release of The System Dynamics of Aid: Understanding the Carter Center's CJA Program in Liberia

by Lorien Pratt 28. January 2014 00:44

In the fall of 2012, Quantellia was approached by The Carter Center to study a breakthrough program in Liberia. The Carter Center was The System Dynamics of Aidusing Community Justice Advisors (CJAs)—legal paraprofessionals from local communities—to support plaintiffs and defendants as they navigated through two legal systems in Liberia:  the "formal", or Monrovia-based state-run system; and the "customary" system used in more rural areas.

Since the ultimate goal of this program was peacebuilding, Quantellia built a systems model to explore how spending dollars on this approach, which had substantially lower infrastructure costs than more centralized programs, could help the country to "do more with less", and to move from a "vicious cycle" of conflict to a "virtuous cycle" of economic prosperity and peace.

This paper, and the model shown in the video above, contains our findings. Interestingly, the model showed that a consistent injection of aid along a number of fronts was necessary to overcome the "energy" in state space to move from one phase to another of this complex system. After this injection, the system can become self-sustaining under certain conditions. However, if the aid is not timed correctly, then the system enters a third state of "catch-up", where government money is exhausted on peacekeeping, and is not available for other purposes as the democracy grows. This proof-of-concept model shows these and other dynamics, indicating that this might be the reality in Liberia; extensions include connecting the model to accurate data and cause-and-effect connections.


Development | Economics | Modeling | Politics | Sustainability | Systems analysis | World Modeler

Modeling Energy Strategies for the Cable Industry

by quantellia 14. June 2013 16:11
Cable operators, like many companies, are facing the prospect of steep increases in the cost of energy in the coming years.  In response, they are looking at alternative energy sources. However, navigating the transition to this new world contains hidden dangers, so an evidence-based modeling approach can make a big difference.
Energy in the United States is changing: the cost of on-grid, coal- and petroleum-generated energy is expected to rise rapidly, while the availability of renewable and distributed power sources such as solar, wind, and hydrodynamic energy is decreasing.   These changes are altering the landscape for energy-intensive industries across the world.  Amongst them: U.S. cable operators, who face a sea change in the profile of energy costs, as subscribers grow more hungry for video and bandwidth at the same time that the cost of power to support a typical cable operators’ plant is expected to grow steeply.
Cable operators have an opportunity to “get ahead of the curve” through structured decision analysis of choices about energy usage.  Quantellia’s Mark Zangari and Tim McElgunn, Chief Analyst Bloomberg BNA Broadband advisory services, presented on this topic at the Society for Cable Telephone Engineers (SCTE) Smart Energy Management Initiative (SEMI) forum earlier this year in Atlanta.  Through a combination of data provided by Bloomberg BNA and modeling provided by Quantellia, Zangari and McElgunn were able to demystify some of the important factors involving this complex decision making process.  McElgunn also authored an accompanying analysis report, which is available to Bloomberg BNA subscribers.
Says Bloomberg BNA’s McElgunn, "Within the next five years, the cost of energy to power the U.S. cable industry will become the single largest network cost component.” As shown in the accompanying video and as we’ll discuss below, operators who make good decisions stand to benefit substantially.

Background – The Energy Cost of Success for Broadband Providers

As explained by Zangari and McElgunn, in recent years, U.S. cable operators have steadily expanded their service offerings to both consumers and businesses in such a way that energy demand and associated costs will continue to increase. Bloomberg BNA research shows, for example, that over three quarters of U.S. internet service customers subscribe to “bundles” of two or more services, which may include pay TV, home phone, and wireless service in addition to high-speed data.
Cable operators are responding to this increasing demand by building new data centers and expanding existing ones. These data centers contain equipment with increasingly lower energy footprints – to as low as 3 watts per server blade. However, the total growth in energy demand exceeds the benefit from these technology improvements.
For these reasons, cable operators face difficult decisions in a complex environment. Where should facilities be located? How should existing local and/or regional infrastructure be leveraged? How should cooling and heating be planned? Should operators consider alternative energy sources to utility-delivered power?
Says  McElgunn, “The U.S. cable industry has effectively transitioned to an all-digital video plant, and is preparing for an eventual transition to an all-IP network.” A digital network is less costly to operate than an analog one, however, HDTV impacts this trend in important ways. Says McElgunn, “By the end of 2014, essentially all new television sales in the U.S. will be HDTV-compatible, increasing the number of HD-fed sets per home. Bloomberg BNA estimates that approximately 40 million U.S. cable households will subscribe to HD tiers in 2017, with 1.5 HDTVs connected per home. “A large number of these HDTV sets will be displaying HD linear channels, as well as video-on-demand (VOD). A single HD video stream consumes approximately 8 Mbps, and there are multiple simultaneous such streams delivered to essentially every video household for some portion of each day. “This leads to massive demands on the network,” says McElgunn, “This will impact both distribution and access plant and the resources required for content generation and formatting.”
In addition, U.S. cable operators offer services to businesses, which place additional demands on networks, and the energy required to operate them. However, McElgunn and Zangari write that “Current architectures and software designs require that generation and transmission resources that are preparing and delivering all of that video operate at full power virtually 24 hours a day every day.” This applies to multiple parts of the cable plant: headends, data centers, and outside plant facilities, which together will require substantial growth in energy to power them.

Modeling a Solution

Quantellia’s Mark Zangari worked with Bloomberg BNA to prepare a model that integrates forecasting data with a systems model to allow cable operators to quickly and precisely determine if a proposed energy usage strategy exposes the operator to unacceptable financial and/or technical risks, and to visualize how adjusting specific decision parameters impacts desired outcomes.
Among the variables included in the model are expansion of Network DVR, balancing increasing server efficiency versus equipment replacement costs, evolutionary network and facilities design changes, and energy price changes over time.
Some findings were as follows:
  • Capital costs are a substantial component: Each additional watt of power consumption adds incremental costs due to the increased quantity of energy the operator needs to supply. However, each additional watt also increases infrastructure needs, and the annualized capital costs resulting from higher power consumption can be significantly greater than the effect on power supply charges.
  • Moore’s Law effects are mitigated by power issues: As illustrated below, although the cost of delivering a given unit of computing performance has been dropping rapidly over the years, the cost of power required to deliver that performance has not been dropping at the same rate.
  • Timing is everything: Operators who take too long to change their energy contracts face steep competition through decreasing margins as costs increase. 
  • Sleep mode versus peak shaving: Policies that invoke "sleep mode" for idle equipment can reduce energy usage, but it's important to keep in mind that capital-intensive infrastructure must be built to support the "peaks" of usage, so "peak shaving" is also an important cost containment strategy.

Organizational Challenges

Despite the cost benefits from making good decisions about energy usage, says Quantellia’s Zangari, “most U.S. cable operators have separate organizations with independent budgets for the establishment and operation of facilities, and the operation of the network equipment housed in those facilities.  While power and its supply and assurance is managed as a facilities function, network equipment and its performance falls under the operational and financial responsibility of the Network organization.”  Therefore, there is no single department within a typical cable operator with a holistic view of energy management. 
A model like the one discussed here, provides this kind of cross-organizational view and equips decision-makers with access to all the factors relevant to measuring, understanding, forecasting, and minimizing the cost of energy.
About Bloomberg BNA's Broadband Advisory Services (BAS)
The task of transforming energy supply and consumption is complex and is dependent on a huge number of known and unknown variables. These complexities create significant financial and operational risk for MSOs.
Beginning with our May 2013 report, "Cable Industry Energy Management Strategies", Bloomberg BNA’s Broadband Advisory Service is adding service provider energy strategy to our areas of focus. We will provide ongoing analysis of developments in this space and deliver recommendations based on our research and incorporating data and insight from other divisions within Bloomberg.
In future reports, we will investigate activities spearheaded by the cable industry’s CableLabs research and standards-setting consortium in the areas of energy efficiency for set-top boxes, DVRs, cable modems/EMTAs, and other CPE. We will also assess advances in fleet management and technology and assess the state of the industry’s efforts to approach zero-landfill equipment sourcing and replacement.
Broadband Advisory Services subscribers have full access to these reports, along with our extensive portfolio of reports and databases covering the U.S. broadband services market.
Information on subscribing to the BAS library is available at http://www.bna.com/broadband-advisory-services-p12884902148/.
You can also visit http://www.quantellia.com to learn more.


Business and Management | Modeling | Politics | Cable | Sustainability | Telecommunications

Understanding the US 2012 Presidential Election Result: How Models Triumphed over Big Data

by quantellia 17. November 2012 16:04

Today, opinions of what happened in the 2012 presidential election are beginning to settle.  Since history is often written from the perspective of the winners, many commentators have attributed the American right’s pre-election false confidence to a sort of blindness or group-think.  But there’s another truth.  Given what was known ahead of the election, the prediction that Romney would win was more defensible than the prediction that Obama would be re-elected.
The video above shows why.
The reason is simple. Take a look at this graph, reflecting data published by the social science data analysis network (SSDAN).
As you can see, the percent of African Americans who voted in 2008 is a clear departure from the historical trend.  It’s reasonable to assume that Republicans concluded that the pattern would revert to historical levels, which given the overwhelming amount of data going back over 30 years, is a completely rational, “big data” conclusion.  (Read Nobel laureate Daniel Kahneman’s “Thinking Fast and Slow” for a clear and deep analysis of this phenomenon, which is called Regression to the Mean).
An alternative explanation, of course, is that in 2008, something exceptional happened. And that pattern continued into 2012.  Predicting the future requires an additional ingredient, beyond simply projecting historical trends:
The Future = Historical Trends + Unique events and decisions happening today
Nassim Taleb defines a “black swan” as an event that is 1) a surprise, that has 2) a big effect, and that 3) history rationalizes with hindsight:  “of course the right was ignoring the obvious”.  The reason Black Swans happen: ignoring the second half of the above equation.    Taleb describes dozens of historical events like this one, where misunderstanding exceptions to historical rules has led to disaster. 
Simply put, the future is not always like the past.  It is essential to know when it is, and isn’t.
Obama’s win qualifies on all three Black Swan criteria.  Since such a high percentage of blacks voted for Obama, this miscalculation can be seen as having misled many Republicans to believe in Romney’s guaranteed election, and perhaps even to have caused the loss through leading to an inappropriately small focus on the needs of blacks and other minorities. What’s missing is the ability to clearly analyze the right-hand side of the equation: how do we know when something new is unique enough that it changes the course of history? 
It is clear that the Democrats were masters of this “secret sauce”.  They built an on-the-ground voter turnout operation to bump the curve upward from its expected value in exactly the places that would affect the Electoral College result.  By being able to focus their resources on exactly where they would contribute most towards achieving victory, the Democrats effectively out-modeled the Republicans. 
Before the book is closed on this election, all who chronicle its history should ensure that the modelers on both sides are appropriately acknowledged.  Because from now on, close elections will be won by campaigns who base their strategic decisions on models that are better than those of their competitors.

Want to know more about how great models can drive value for your organization? Drop me a line at lorien.pratt@quantellia.com and/or visit http://www.quantellia.com to learn more.



General | Politics

Under the Hood: How to reach Agreement in Tax Policy and other Important Presidential Matters

by quantellia 30. October 2012 15:59
Instead of "he said, she said" or failing to convince the opposing side based upon an appeal to history or other countries, we can now have an intelligent conversation about the"engine" of the economy: what makes it tick, and the best way to fix it.

A few weeks back, my sister, visiting from Michigan, was driving my car here in Denver.  It's an old  blue Ford Contour, with mileage well into the six figures.  The car began to hesitate.  "Something's wrong for sure", I said.  No, my sister reassured me, "everything's OK".


The Contour sputtered to a halt on the side of Route 6, and we were left arguing.  "I think there's something very wrong here, probably the fuel pump," I said, "I've felt this before".  "No, I think it's the spark plugs." said my sister.


We're smart enough that we didn't debate much further, and left it to the experts at my local shop to open the hood and diagnose the problem.


As we waited for the tow, Faith and I chatted about the economy.  That first argument about the spark plug and fuel pump felt like some political debates we've heard lately: lots of discussion about what's under the hood, without actually taking the time to understand the engine.  Because of that, we end up going around in circles.


Under the Hood


What do I mean by the "engine" when we're talking about political disagreement?  Well, the discussion is often about the economy, tax policy, unemployment, the deficit, and the like.  Usually, we assume this is too complex to understand, and we leave the details to the experts: economists and others.  Not so, with modern tools, which go beyond mind mapping to full simulations on the desktop.  We can be more informed, we can understand the underlying mechanism.   


To begin, we can start by agreeing with our opponents on some basic parts of the systems that drive our world: how do businesses affect governments?  How do governments impact spending?  Where is the fan belt?  How's the starter motor doing?  How is it all connected?



I think that we can use the World Modeler software and the Decision Engineering approach to mapping complex systems to take these arguments to the next level.  Instead of "he said, she said" or failing to convince the opposing side based upon an appeal to history or other countries, we can now have an intelligent conversation about the "engine" of the economy: what makes it tick, and the best way to fix it.


For an easy introduction, watch the video below:


Useful Arguments


So here's the process:


  1. Build a simulation model.  Formerly a world restricted to economists, this is now much easier and faster using modern tools.   Sign up to get a free trial copy of the tool to use to do this at: http://www.worldmodeler.com .
  2. Include your favorite policy in your model. Show how it works.
  3. Send it to your friends and adversaries.  If they disagree, ask them to change the model and the data to show how they see things working instead. And, by the way, this is where "big data", analytics, and machine learning can make their greatest contribution: in helping us to understand the mechanisms inside complex systems like the US economy.  Without understanding the system, focusing on data alone is like diagnosing a car by watching the pattern of leaking fluid on the pavement.
  4. Repeat step (3) until you reach agreement, or at least until the disagreement has been clarified to an assumption that can be tested (for example, my model might assume a certain level of spending amongst wealthy people: something we can measure). 


 As for the model itself, the video shows three basic parts of an economy:

  1. Businesses
  2. The government
  3. Consumers

Connecting the Pieces

At the simplest level, these are connected in the following ways:

  1. The government receives revenues through taxes, and loses money through spending.  Taxes come from businesses and consumers.  Spending is money that goes to businesses (as the government buys things) and consumers (some of whom it hires).
  2. Consumers earn money through investments, government sources (like Medicaid), and wages.  They spend money on goods and services provided by business.
  3. Businesses lose money through taxes, through buying parts from other businesses, and through wages.  They earn money by selling to consumers and the government.

Using a simulator, we can experiment, for instance, with the impact of various tax policies.  These play out over time and impact important factors like unemployment and GDP. 


This is just a starting point.  I invite you to join an important new discussion. Sign up for more information at http://www.worldmodeler.com.



Economics | General | Politics

The opinions expressed herein are my own personal opinions and do not represent my employer's view in anyway.

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