What: “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.”
Why: ” 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.”
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.”
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