Algorithmic Decision Theory and Smart Cities

1. Algorithmic Decision Theory and Smart Cities Fred Roberts Rutgers University

2. 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.

3. 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

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

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

6. 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

7. Outline • Climate Change • Handling Large Health Emergencies • ADT and Smart Grid

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

9. Climate and Health Concerns about global warming. Resulting impact on health Of people Of animals Of plants Of ecosystems

10. Climate and Health Some early warning signs: 1995 extreme heat event in Chicago 514 heat-related deaths 3300 excess emergency admissions 2003 heat wave in Europe 35,000 deaths Food spoilage on Antarctica expeditions Not cold enough to store food in the ice

11. Climate and Health Some early warning signs: Malaria in the African Highlands Dengue epidemics Floods, hurricanes

12. 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.

13. Extreme Events due to Global Warming: More Hurricanes Hurricane Irene hits NYC – August 2011

14. Extreme Events due to Global Warming: More Hurricanes Hurricane Irene hits NYC – August 2011

15. Extreme Events due to Global Warming: More Hurricanes Hurricane Irene hits NYC – August 2011

16. Extreme Events due to Global Warming: More Hurricanes 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

17. Extreme Events due to Global Warming: More Hurricanes 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?

18. Extreme Events due to Global Warming: More Hurricanes 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?

19. Extreme Events due to Global Warming: More Hurricanes 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?

20. 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

21. 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

22. 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 conditioning Elevators no work: vulnerable people over-exertion Food spoilage Minimizing impact on the most vulnerable populations ADT challenge: Utilize “smart grid” to update plans

23. 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

24. Optimal Locations for Shelters in Extreme Heat Events • 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

25. Optimal Locations for Shelters in Extreme Heat Events • 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.)

26. Example 2: Handling Large Health Emergencies

27. 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.

28. 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

29. 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.

30. 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.

31. 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.

32. 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.

33. 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

34. 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?

35. 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.

36. 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

37. 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.

38. 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

39. 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.

40. 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

41. 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

42. 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

43. 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

44. 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

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

46. 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

47. ADT and Smart Grid • Power grid challenges include: • Huge number of customers, uncontrolled demand • Changing supply mix system not designed for complexity of the grid • Operating close to the edge and thus vulnerable to failures

48. 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

49. 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 use • Help conserve • Help power companies • Control use • This is a good example of a service provided by a smart city

50. 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 estimation • real-time contingency analysis • real-time monitoring of dynamic (oscillatory) behaviors in the system