1 algorithmic decision theory and smart cities fred roberts rutgers university

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Algorithmic Decision Theory and Smart Cities

Fred RobertsRutgers University

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Algorithmic Decision Theory•Today’s decision makers in fields ranging from engineering to medicine to homeland security have available to them:

−Remarkable new technologies−Huge amounts of information−Ability to share information at unprecedented speeds and quantities

•This is particularly true for those managing today’s large, complex metropolitan areas – today’s cities.

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Algorithmic Decision Theory•These tools and resources will enable better decisions if we can surmount concomitant challenges:

−The massive amounts of data available are often incomplete or unreliable or distributed and there is great uncertainty in them

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−Interoperating/distributed decision makers and decision-making devices need to be coordinated−Many sources of data need to be fused into a good decision, often in a remarkably short time

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−Decisions must be made in dynamic environments based on partial information−There is heightened risk due to extreme consequences of poor decisions−Decision makers must understand complex, multi-disciplinary problems

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Algorithmic Decision Theory

•In the face of these new opportunities and challenges, ADT aims to exploit algorithmic methods to improve the performance of decision makers (human or automated).•Long tradition of algorithmic methods in logistics and planning dating at least to World War II.•But: algorithms to speed up and improve (real-time) decision making in urban areas are much less common.

Pearl Harbor

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Outline

1.Climate Change2. Handling Large Health Emergencies3. ADT and Smart Grid

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Example 1: Climate Change: (Emphasis on Health Effects)

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Climate and Health

•Concerns about global warming.

•Resulting impact on health–Of people–Of animals–Of plants–Of ecosystems

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Climate and Health•Some early warning signs:

–1995 extreme heat event in Chicago514 heat-related deaths3300 excess emergency admissions

–2003 heat wave in Europe35,000 deaths

–Food spoilage on Antarctica

expeditionsNot cold enough to store food

in the ice

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Climate and Health•Some early warning signs:

–Malaria in the African Highlands–Dengue epidemics–Floods, hurricanes

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Extreme Events due to Global Warming

•We anticipate an increase in number and severity of extreme events due to global warming.

•More heat waves.

•More floods, hurricanes.

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Extreme Events due to Global Warming: More Hurricanes

Hurricane Irene hits NYC – August 2011

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Hurricane Irene hits NYC – August 2011•To plan for the future, NYC has a climate change initiative.•Using mathematical modeling, simulation, and algorithmic tools of risk assessment to plan for the future•Plan for more extreme events•Plan for rising sea levels

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•NYC climate change initiative is using mathematical modeling, simulation, and algorithmic tools of risk assessment to plan for the future:

–What subways will be flooded?

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•NYC climate change initiative is using mathematical modeling, simulation, and algorithmic methods of risk assessment to plan for the future:

–What power plants or other

facilities on shore areas will

be flooded?

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•NYC climate change initiative is using mathematical modeling, simulation, and algorithmic methods of risk assessment to plan for the future:

–How can we get early warning to citizens that they need to evacuate?

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Special Health Concern: Extreme Heat Events

•Subject of a DIMACS project.•Result in increased incidence of heat stroke, dehydration, cardiac stress, respiratory distress•Hyperthermia in elderly patients can lead to cardiac arrest.•Effects not independent: Individuals under stress due to climate may be more susceptible to infectious diseases

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DIMACS Project on Climate & Health: Problem 1: Evacuations

during Extreme Heat Events•One response to such events: evacuation of most vulnerable individuals to climate controlled environments.•Modeling challenges:

–Where to locate the evacuation centers?–Whom to send where?–Goals include minimizing travel time, keeping facilities to their maximum capacity, etc.–All involve tools of Operations Research: location theory, assignment problem, etc.–Long-term goal in smart cities: Utilize real-time information to update plans

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Problem 2: Rolling Blackouts during Extreme Heat Events

•A side effect of such events: Extremes in energy use lead to need for rolling blackouts.•Modeling challenges:

–Understanding health impacts of blackouts and bringing them into models–Design efficient rolling blackouts while minimizing impact on health

Lack of air conditioningElevators no work: vulnerable peopleover-exertionFood spoilage

–Minimizing impact on the most vulnerable populations

•ADT challenge: Utilize “smart grid” to update plans

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Problem 3: Emergency Rescue Vehicle Routing to Avoid Rising Flood Waters

•Emergency rescue vehicle routing to avoid rising flood waters while still minimizing delay in provision of medical attention and still getting afflicted people to available hospital facilities

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• Work based in Newark, NJ – collaboration with Newark city agencies.

• Data includes locations of potential shelters, travel distance from each city block to potential shelters, and population size and demographic distribution on each city block.

• Determined “at risk” age groups and their likely levels of healthcare needed to avoid serious problems

Optimal Locations for Shelters in Extreme Heat Events

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• Computing optimal routing plans for at-risk population to minimize adverse health outcomes and travel time

• Using techniques of probabilistic mixed integer programming and aspects of location theory constrained by shelter capacity (based on predictions of duration, onset time, and severity of heat events)

• Smart cities: routing plans used quickly; get information to people quickly

• Future: plans quickly modifiable given ADT-generated data from evacuation centers, traffic management, etc.

• (Far from what happens in real evacuations today.)

Optimal Locations for Shelters in Extreme Heat Events

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Example 2: Handling Large Health Emergencies

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Gaming Future Health Emergencies

•One way to prepare for future health crises is to “game” them.•Modelers can help to:

–Develop games–Play in games–Analyze the results of games

•Real-time information can make responses to health emergencies more effective and ways to do this need to be brought into our gaming.

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Developing Games•This is a hot area in computer science as many “exercises” can be “virtual” •It involves

–Computer game design–Immersive games (MIT epi game)–Artificial intelligence–Machine learning–“Virtual reality”–Theories of influence and persuasion from behavioral science

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TOPOFF 3•TOPOFF 3 was an exercise held in April 2005 in New Jersey (and elsewhere)•Goal: provide federal, state, and local agencies a chance to exercise a coordinated response to a large-scale bioterrorist attack.•Some university faculty were invited to be official observers.•We helped with “after-action reports” and made recommendations.•Message: “smart” approaches would make both the exercise better and the outcome in a real emergency better.

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TOPOFF 3•Scenario: simulated biological attack.

•Vehicle-based biological agent.

•Vehicle left in parking lot at Kean University in New Jersey.

•Agent later identified as pneumonic plague.

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TOPOFF 3•Local hospitals involved – patients streaming in.

•All NJ counties became Points of Dispensing (PODS) for antibiotics.

•One POD was at the Rutgers Athletic Center.

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TOPOFF 3: General Observations•Totally scripted or playbook exercise.

•Lacked random introduction of surprise or contradictory information.

–Would ADT-generated models have helped the designers here?

•No flexibility for game controller to change agenda – even after the identity of the biological agent was disclosed a week before the event started.

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TOPOFF 3: General Observations•Very quick identification of the agent as plague – less than 24 hours.•No attempt to use array of databases to help in identification of the agent. In smart cities, this would be done.

–Note: Pneumonic plague takes 2-3 days before symptoms appear

•No “chaos” of responding to

an unknown biological agent.

Pneumonic plague

in India

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TOPOFF 3: General Observations•Lack of truly significant random perturbations

–Underscores importance of randomness in modeling responses to health events; ADT would allow much more sophisticated testing

•No inconsistent information that might lead to refutation of initial hypothesis about cause.

–Would ADT-generated modeling have helped develop a better exercise in this sense?

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TOPOFF 3: General Observations•People were being shipped off to hospitals without any idea (in the “script”) of what the contaminant might have been.

–Models might help us understand the danger of such a decision.–In real emergency, algorithms would absorb data and help us determine where to send people.–Algorithms would help us consider alternatives

Idea of quarantine on Kean University campus was not considered.

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TOPOFF 3: Concept of POD•In a POD: We bring together large numbers of people to receive their materials in one location.

–Hand out antibiotics–Hand out educational materials about the disease and the medicine

•How do you get them there?–Smart Cities: traffic congestion, parking, etc.; models modified

in real time –Smart cities: Instructions to people

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TOPOFF 3: Concept of POD•Other ADT Issues in modeling the POD:

–How do you get enough volunteers?–How do you get food to the volunteers? The patients?–Who gets priority? Triage.

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TOPOFF 3: Concept of POD•Still other issues in modeling the POD:

–How do you handle panic within the POD?–Pushing, shoving.–People on long lines.–People on lines getting sick.–In our observation: TOPOFF 3 had none of these elements.–Modeling challenge: social responses to health events–Better and more rapid information can help avoid panics

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TOPOFF 3: Concept of POD•POD Loading Issues:

–What is maximum capacity of a POD?–How many workers are needed?–How much time is it reasonable to keep patients there?–How to handle short preparation time before masses of people arrive?–What is adequate time to screen individuals?–How do you prevent a secondary attack if a mass of people are gathered in one place?–These are all modeling issues.–Real-time data feedback could really help smart city managers handle these kinds of questions.

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TOPOFF 3: Concept of POD•Some conclusions about PODS:

–The most successful POD violated the rules.–It was a Point of Distribution, not a Point of Dispensing.–Medicines were distributed to a few people in large quantities.–They in turn redistributed the drugs to others – away from the POD.–Smart Cities: Massive databases; record keeping in advance helped distributors know where to go and to whom to give drugs

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TOPOFF 3: Concept of POD•Some conclusions about PODS:

–The most successful POD serviced 67,000 people in 4 hours. This was the one that wasn’t really a POD.–The others serviced 500 to 1000.–Conclusion: Decentralization could be a key

avoid mass movement of people

–Advantages of dispensing drugs and information in local communities. –But: is decentralization always best?–Modeling challenges–Smart City challenge: Information challenges under decentralization

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TOPOFF 3: Closing Comment•Officials in NJ and at Federal Emergency Management Agency (FEMA) were very interested in our observations.

•They seemed quite open to more technical analysis of the exercise and more technical approaches to future planning.

•Published in J. of Emergency Management

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Example 3: ADT and Smart Grid

Many of the following ideas are borrowed from a presentation by Gil Bindewald of the Dept. of Energy to the SIAM Science Policy Committee, 10-28-09

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ADT and Smart Grid•Today’s electric power systems have grown up incrementally and haphazardly – they were not designed from scratch•They form complex systems that are in constant change:

−Loads change−Breakers go out−There are unexpected disturbances−They are at the mercy of uncontrollable influences such as weather

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ADT and Smart Grid•Today’s electric power systems operate under considerable uncertainty•Cascading failures can have dramatic consequences.

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ADT and Smart Grid•The management of a “smart city” faces many challenges in understanding and controlling the electric power available to its citizenry and helping to avoid catastrophic outages and failures.•The smart city can aid citizens in managing their power usage through guidance and directives based on incredibly detailed understanding of their usage:

–Benefits individuals–Benefits the entire city or metropolitan area

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ADT and Smart Grid•Power grid challenges include:

−Huge number of customers, uncontrolled demand−Changing supply mix system not designed forcomplexity of the grid−Operating close to the edge and thus vulnerable to failures

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ADT and Smart Grid•Power grid challenges include:

−Interdependencies of electrical systems create vulnerabilities−Managed through large parallel computers/supercomputers with the system not set up for this type of management

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ADT and Smart Grid•Power grid advantages: “Smart grid” data sources enable real-time precision in operations and control previously unobtainable:

−Real-time data from smart meter systems will enable customer engagement through demand response, efficiency, etc.

Help understand power useHelp conserveHelp power companiesControl use

−This is a good example of aservice provided by a smart city

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ADT and Smart Grid•“Smart grid” data sources enable real-time precision in operations and control previously unobtainable:

−Time-synchronous phasor data, linked with advanced computation and visualization, will enable advances in

state estimationreal-time contingency analysisreal-time monitoring of dynamic (oscillatory) behaviors in the system

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−Enhanced operational intelligence−Integrating communications, connecting components for real-time information and control

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−Sensing and measurement technologies will support faster and more accurate response, e.g., remote monitoring−Advanced control methods will enable rapid diagnosis and precise solutions appropriate to an “event”

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ADT and Smart Grid: Phasor Measurements

•Phasor measurements will provide “MRI quality” visibility of the power system.•Traditional SCADA measurement provides

−Bus voltages−Line, generator, and transformer flows−Breaker Status−Measurement every 2 to 4 seconds

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•Phasor technology and phasor measurements provide additional data:

−Voltage and current phase angles−Frequency rate of change−Measurements taken many times a second−This gives dynamic visibility into power system behavior

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•Phasor technology and phasor measurements provide additional data:

−New algorithmic methods to understand, process, visualize data and find anomalies rapidly are required.−Such measurements will allow rapid understanding of how customers are using electricity: Smart meters.−Raise privacy issues.

What movie am I watching?−Another area for research.

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ADT and Smart GridOther problems/opportunities requiring ADT:

•Grid robustness: How will the grid respond to disturbances and how quickly can it be restored to its healthy state?•Transmission reliability:

−Wide area situational awareness and advanced computational tools can help with quick response to dynamic process changes, e.g., using automatic switching.−Sample challenge: How far are we from the edge? When voltages drop too fast, the entire power system can collapse.

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ADT and Smart GridSome Challenges:

•Need to develop reliable, robust models to help us achieve system understanding.•Need a new mathematics for characterizing uncertainty in information created from the large volumes of data arising from the smart grid.•Need new methods to enable the use of high-bandwidth networks by dynamically identifying only the data relevant to the current information need and discarding the rest. Similar challenges for many smart city applications.

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ADT and Smart GridSome Challenges:

•Security of new software is a priority – same for many smart city applications•Cyber attacks on the electric power grid are a major concern. Needed are methods for

−Prevention−Response−Recovery

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ADT and Smart Grid: SummaryRelevance of ADT

Algorithmic methods needed to aid smart cities:•Improve security of energy system in light of its haphazard construction and dynamically changing character•Find early warning of a changed state – anomaly detection

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Algorithmic methods needed to aid smart cities:•Identify and overcome vulnerabilities in the system•Protect the privacy of individuals under new data collection methods

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Algorithmic methods needed to aid smart cities:•Protect systems operating “close to the edge”•Find new ways to characterize uncertainty in information about the health of the system•Find ways to protect against cyber attacks that take advantage of vulnerabilities created by dependence on massive amounts of data generated through the smart grid.•So: implementation & development of smart city methods requires not only new research on ADT, but research to protect against new vulnerabilities our smart cities create.

1 algorithmic decision theory and smart cities fred roberts rutgers university

Documents

ice slide

health effects slide

global warming

realtime decision

good decision

smart grid slide

short time slide

pearl harbor slide