IBM to Help Cities Predict Long-Term Effect of Municipal Policies

"Service app" identifies unintended consequences, "virtuous cycles," and future impact on citizens

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ARMONK, N.Y., - 09 Aug 2011: IBM (NYSE: IBM) today introduced new analytics software and services to help cities predict the result of policy decisions and their positive and negative spill over consequences up to a quarter century in the future.

System Dynamics for Smarter Cities is designed to help mayors and other municipal officials reduce the unintended negative consequences of municipal actions on citizens, as well as uncover hidden beneficial relationships among municipal policies. A more thorough understanding of how policies affect each other over time will enable officials to reduce or avoid negative results before they happen. Leaders will also be able to "double down" on policies that are projected to have positive ancillary results.

Using sophisticated analytics, System Dynamics for Smarter Cities addresses the dynamics among the complete spectrum of municipal policies and their effect on citizens, such as the association between:

As a decision support system, the solution provides an intuitive interface that enables government officials to create countless "what if" scenarios that quickly model the effect that a proposed policy change could have on the city as a whole and its citizens.

"Municipal government is still very much a world of silos," said Michael Littlejohn, Vice President of Strategy for Smarter Cities at IBM. "The various departments -- transportation, education, public works, and so forth -- often have very little interaction with each other, dramatically increasing the possibility that an action in one area of government will have an unexpected affect on another area."

Littlejohn continued: "Sometimes these unintended consequences are negative – perhaps the policies of two departments are unknowingly working against each other. Sometimes the consequences are positive. In fact in the best case you stumble on a 'virtuous cycle' where an improvement in one department like public safety helps another like transportation and then that improvement comes right back around to help further improve public safety.  But the problem has always been that it has taken years for decision makers to comprehend the result of their actions.  We can't just leave that to accidental learning anymore."

Addressing this problem, System Dynamics for Smarter Cities is a repeatable, scalable synthesis of IBM services and software – a very sophisticated "services app," one of many standardized services products offered by IBM Global Business Services.

Here's how it works: a project starts by using the existing dynamic engine which contains over 3,000 equations from past work with cities.  At the beginning of a new engagement with a municipality, IBM government experts conduct a series of knowledge-gathering workshops with dozens of people who have expertise about that particular city, including economists, educators, police officers, city planners, demographers, elected officials, business leaders, electric and water utility providers, real estate developers, transportation experts, health care providers, and other community leaders. This vital information – representing decades if not centuries of hard-won expertise -- is codified and combined with existing government data such budget allocations, number of K-12 students, unemployment rates, population growth and density, number of grocery stores, vehicle miles traveled, and city GDP to create a deep corpus of information about that city.

Next, the input from city subject matter experts and data is analyzed with software specialized for determining how systems evolve over time, incorporating feedback and delay.  The resulting system of simultaneous differential equations is calibrated and evaluated against up to 10 years of historic data from the client city.  The result is a model that builds on experiences from past clients but uniquely simulates the dynamics of the client city.  For instance, the dynamics surrounding water policies might look very different for a city like Phoenix than it would for Seattle.  The revenues of a city that relies on a sales tax will have different funding cycles and patterns over time from one that uses a property tax.  Yet each can be represented in the system dynamic model.

Complementing the rollout of System Dynamics for Smarter Cities, IBM today also announced a number of related services offerings:

For more information about IBM Smarter Cities, please visit

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