Using Simulations to Validate Emergency Plans
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Simulations are more effective than traditional exercises for validating emergency management plans because they allow participants to: 1) Practice collaborating and problem-solving in a complex simulated emergency environment in a way that develops trust and empathy. 2) Iteratively test plans by making decisions, receiving feedback, and "playing" out scenarios again with changes, which is not possible with traditional exercises. 3) Adequately test plans across multiple jurisdictions and agencies at a large scale, while traditional exercises are too expensive and difficult to organize at that scale.
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Using Simulations to Validate Emergency Plans
- 1. Using Simulations toValidate Emergency Management Plans James Rollins, M.Ed. Copyright Takouba - All rights reserved 1
- 2. a little about me • 27 years experience in training and education • Concurrent experience as a military officer with 10 years active duty • Commanded Homeland Response Force • Experience with multiple incident responses including Oso • Deputy Commander of Seattle District Corps of Engineers for Howard Hanson Dam Rehabilitation Project • Afghanistan 2005 • Experience in education, HR and manufacturing management • Experienced Lean practitioner • Masters Degree in Adult Education – Western Washington University Copyright Takouba - All rights reserved 2
- 3. Problem with the HSEEP Model • Criterion referenced competency model - Great evaluation framework • But it is a not a good “Play” framework for staffs • Functional and full-scale exercises are difficult to organize • They are expensive and do not achieve sufficient scale to adequately validate emergency plans • FSE do not sufficiently test adaptive capacities at local, regional, state and federal level • FSE are difficult to iterate, repeat, back up and try again. • Computer simulation decision spaces are superior ©Copyright Takouba - All rights reserved 3
- 4. Discussion Points • When managing the consequences of a disaster, we are working within a complex ecosystem of interactions between multiple “layers” of entities • Collaboration is the primary way we solve problems in a complex environment where nobody can be in charge • Play is indispensable for developing collaboration skills • Simulations enable playing and learning in a manner that helps participants to develop trust and empathy Copyright Takouba - All rights reserved 4
- 5. 5Copyright Takouba - All rights reserved Complexity
- 6. 6Copyright Takouba - All rights reserved Transportation Layer Complexity can be thought of as layers. Each interacting inside and in between each other all at once.
- 7. It is complicated! So, how do we effectively work together? Copyright Takouba - All rights reserved 7
- 8. Adaptive Management • Communicative Planning versus Command and Control • Different from rational – reductive planning • Typically used in multiple jurisdiction, multiple agency, multiple function incident responses • Purpose is problem solving • Just-in-time collaborative organization • Just-in-time planning oriented • Relies on experimentation Copyright Takouba - All rights reserved 8 Initial Incident Command Meeting Tactics Meeting Prepare for the Tactics Meeting Command & General Staff Meeting Incident Commander & Staff Develop/Update ObjectivesMeeting Incident Brie ng (use ICS-201) Initial Response and Assessment Noti cations InitialResponse BOOM! Prepare for the Planning Meeting Planning Meeting Prepare & Approve Incident Action Plan Operations Brie ng Execute Plan And Assess Progress New Operational Period Begins
- 9. 5 Factors for Effective Collaboration • Recognize and communicate common vulnerabilities • Involve everyone that has “A dog in the fight.” • Use good problem solving tools • Replace belief with knowledge • Maintain and environment of trust Copyright Takouba - All rights reserved 9 Trust
- 10. Recognize and communicate common vulnerabilities • Immediacy • Magnitude • Risk requires an emotional response ©Copyright Takouba - All rights reserved 10
- 11. Involve everyone that has “A dog in the fight” • Group diversity is essential to enhance problem solving • Include everyone associated with the problem • Helps to fully understand the complexities of the problem (versus reducing or distilling the problem) • Pay me now or pay me later . . . ©Copyright Takouba - All rights reserved 11
- 12. Use good problem solving tools • Rules to correct power imbalances within the group • Cognitive map • Value chain • Common perspective is often the most inclusive perspective ©Copyright Takouba - All rights reserved 12
- 13. Replace belief with knowledge • Belief is the acceptance of something as the truth without a basis of fact • Knowledge is the acceptance of something as the truth on the basis of fact or direct experience • Experimentation – scientific method • Modeling ©Copyright Takouba - All rights reserved 13
- 14. Maintain an environment of trust • Trust is the knowledge that each member of the group is dependable and will deliver on expectations • Everyone must be concerned with the problem and share the vulnerability • People involved must be committed to solving the problem • People and the agencies they represent must have a track record of results that demonstrate their competence • Notice that all of these are observable behaviors? ©Copyright Takouba - All rights reserved 14
- 15. ReasonsWhy Play is Indispensable • Play is the basis for human trust, adaptive behavior and acceptance of risk • Play signals • Simulation play is play • Nothing lights up the brain more than play Copyright Takouba - All rights reserved 15
- 16. Use simulations to facilitate play • Use predictive models • End-to-end visualization of the ecosystem • Ability to interact with the variables in order to change initial conditions and see how they play out • See where bottlenecks arise ©Copyright Takouba - All rights reserved 16
- 17. Predictive Models Computer Aided Simulations • Systems dynamics modeling • Agent modeling • Hybrid models Copyright Takouba - All rights reserved 17 Stock • Resource • Has a Potential Flow Feedback Flow dynamics follow a simple mathematical relationship that represents a rate of flow such as “consumption” Agents are individual entities that follow behavioral rules. Often used to load systems models
- 18. Visual - Interaction Copyright Takouba - All rights reserved 18 Below: players interact with panels to input decisions Above: the decisions interact and are played forward on a end-to-end visual
- 19. To summarize • It’s complicated! The interactions in, and in- between layers are complex and difficult to predict • Collaboration is the primary tool we use to address systemic problems • Play is necessary to develop trust • Simulations are a more effective staff training tool than full-scale or functional exercises. Copyright Takouba - All rights reserved 19 G A X
Editor's Notes
- The word “complexity” is a growing part of everyday language. People texting while they drive is an appropriate metaphor of the struggles we have keeping up with information technology in our daily lives. The information industry promises us everything from instant access to our data, to a 3 minute response to a 911 call, to any entertainment we desire such as games and movies – on demand! The systems that deliver these services are complex. And complexity grows exponentially when overlaid on terrain, political and infrastructure topologies and interface with dynamics created by human populations.
- I have over 27 years of experience in training and education – it is my passion. Whether working for ITT Technical Institute or working for a manufacturing company, there were always elements of experiential learning that could be leveraged to enhance problem-solving. I believe that problem solving is what makes us human, and what drives the greatest satisfaction in life (well, besides kids and tax-free interest). I have concurrent experience as a military officer with experience dealing with large scale public emergencies, such as the Howard Hanson Dam Rehabilitation Project, Oso, and the development and validation of the Homeland Response Force.
- The problem with the HSEEP model is that it does not generate adequate capacities to truly test emergency plans. It is hard to do, therefore expensive and difficult to repeat often enough to build those critical relationships necessary in an emergency.
- I am going to describe a disaster as a complex environment with multiple layers interacting constantly. This complexity is compounded when we form incident command, because it is an ad hoc organization (no one has a habitual working relationship). Simulations enable playing and learning in a manner the helps develop not only competence in emergency management, but develops trust and empathy.
- Take a moment and reflect on the idea of complexity. We start with a single node that interacts with other nodes around it. Then it builds out to interact with other systems in three dimensional way. The strength of these connections can vary and differ in terms of duration, and what resources they share. As the network expands, so does the interconnectedness and overall resilience and stability of the system. If any one node is affected, the others may expand in order to fill-in the gap. What is not depicted very well, is that these nodes can also have relationships across and within the grid. Thus - highly networked entities are stable and resilient. Another thing not depicted in the cube, are the nature of the relationships between the nodes. These relationships vary in strength, are often non-linear, episodic and irregular.
- In our “ecosystem” of cyber we can think of our systems as occupying layers. Each layer, such as the transportation layer, represents a capability. Entities on each of these layers have dependencies and relationships with other entities on these layers, and in-between layers. The Transportation Layer overlays the earth and is a network of roads, bridges, electrical, fiber-optic and water and provides a means of conveyance. Above that is the population layer, which represents people and their community and ethnic ties. This layer is particularly dynamic, because people interact with each other and move around very quickly. Furthermore, people are a source of demand and they load resources for our systems. The population layer interacts with the transportation layer, because people move along roads. On top there is a layer of coordinating entities such as businesses, logistics, government, hospitals and others. It is very important for these agencies to be able to communicate and coordinate during a surprise event. They must coordinate to move resources to demand and they too are dependent on the transportation layer to convey instructions to entities that would act on those instructions.
- So to summarize – complexity is, well, complicated! Because of all of the nuances, behaviors, cycles and non-linearity of these types of systems, it requires an adaptable form of management.
- Adaptive Management offer us promise. It focuses on a “Communicative Planning” style that originates from diplomacy, negotiations and consensus building. Diplomacy, negotiation and consensus are perhaps the three most antithetical words in the world, to the idea of speed and progress. Nevertheless, planning is fundamentally agreement to purpose and resources among a diverse group of people. Adaptive management differs from command and control. Command and control uses a reductive planning approach to simplify and prioritize activities in a plan. Command and control is often used because of the speed at which it can be applied, but it is criticized for its lack of granularity and over dependence on a single person’s judgment. Communicative Planning is widely used when plans must be developed between multiple jurisdictions, agencies or in multiple function incident response. This planning style is necessary, because of the diverse group of interests, authorities and resources that are drawn to a single purpose – problem solving. We also see communicative planning used in incident responses, as these are “just-in-time” organizations built with people who may not have ever worked together. These organizations typically use a “just-in-time” planning model, since the situation is novel and the combination of necessary resources is unknown. The main thing to remember about Adaptive Management, is that it relies on trust and active experimentation to work.
- Resilience and rebuilding all depend on effective collaboration. It is collaboration that forms the relationships between interests in a human system.1 As we probably all have experience within collaborations, we recognize that “trust” is an absolutely vital currency in a working collaboration. Trust is built on observable behaviors, actions and experience.2 So how can we build trust before an event occurs? The answer is regular and repetitive training or “PLAY.” 1 Connie P. Ozawa, Planning Resilient Communities: Insights from Experiences with Risky Technologies, in Collaborative Resilience: Moving Through Crisis to Opportunity, edited by Bruce Evan Goldstein, (MIT Press: Cambridge, MA, 2012), p. 22 2Ibid, p. 23
- No one has to explain the need to prepare right after a complex disaster occurs. The risk is right in everyone’s face, and we all feel the vulnerability. People make risk decisions on an emotional level. When folks can see or feel a vulnerability, they are more likely to come together to solve the problem. Communicating risk involves two factors, immediacy and magnitude which of course drives an emotional response.
- Effective collaboration relies on a diverse group. When you leave someone out, or intend to get to them later, you are not fully informing your problem statement. So the complexity of the thorny issue you are dealing with may not be fully understood. Furthermore, down the road, these individuals can potentially become stumbling blocks for the initiative when their factors weren’t considered in the solution.
- Use good heuristics. It is important to rely on good group processes (like the planning P), perhaps use a coginitive map to capture all the variables you are dealing with and others. But above all, choose a way that uses an inclusive method for characterizing the problem.
- Belief creates believers and non-believers and ultimately becomes a source of conflict. Agree on the use of methods and models that will turn supposition into facts. Facts are much more difficult to argue.
- Finally, trust is the lifeblood of effective collaboration. People involved should be dependable, have a dog in the fight, committed and have a track record of demonstrated competence. These are all observable behaviors that give us a sense of whether we can work together with someone. We normally can accomplish developing trust with habitual relationships, such as the people we work with.
- Play. The bottom line is that collaborative groups need to be like, “Kids playing with blocks.” A Stanford University study of children playing blocks found that the benefits to learning go beyond being able to stack blocks. While the term itself may seem trivial, it has much deeper meaning and utility. Play is the dynamic that exists between children that not only provides a laboratory for problem solving, but the social context for working together to solve problems. Children during play, learn how to interact with each other, establish rules for working together and actively experiment with a variety of combinations to solve the problem. By watching each other, they learn about each other’s trustworthiness, competence and commitment. Here’s the link: http://www.npr.org/blogs/ed/2015/02/06/384347659/behold-the-humble-block-tools-of-the-trade Surprise events will push a complex system past its limits. In order to adapt and reconstruct itself, the system will require novelty. Novelty requires active experimentation, interaction and a healthy dialectic in a highly diverse collaborative group. By deliberately designing decision spaces, collaborations can engage in meaningful conversation, actively experiment using simulations and develop trust with playmates.
- Simulations facilitate play by creating a venue where decisions can be entered into predictive models and played over. Simulations provide an end-to-end view of the system, so participants can see how their part affects the whole (empathy). Since it is a simulated in environment, decisions can be modified and replayed for a more effective response (learning). Since the system is reflected by the model, any mistakes do not result in damage or loss (fail-safe environment).
- Predictive modeling or simulations have come a long way. Computers are getting cheaper, our understanding of complexity and adaptive systems is getting better. Modeling has many off-shoots of development, but I will focus on systems dynamics and agent based modeling (and their hybrids). Systems dynamics modeling uses mathematics to account for entities in a system and the relationships they share. These are typically described as “stocks, flows and feedback.” Stocks are accumulations of potential in an entity and flows are those potentials moving to another entity. So for example, if I represent a stock, and I have $100 in the bank that I use to pay a Visa bill, the systems model would show $100 flowing from the “me” entity to the “Visa” entity. Let’s say Visa give me rewards points, then those would flow back to the “me” entity as a feedback loop. Agent modeling is a unique way to account for human behavior. An agent is an individual agent, like a little robot, that follows certain behavioral rules. Interestingly, as you aggregate these behaviors the predictive capabilities become more accurate. Agent modeling is often useful when you are modeling migrations, consumption and loading dynamics in your model. So if you refer to the population layer, this would be modeled using agents as they can move around using various services such as roads and sheltering.
- A visual-interactive environment is integral to sustaining the decision making process. This is a key capability, because quite literally, it can be everything from a dashboard to a 3D simulation that very nearly replicates a physical environment. Visualization is important to sustain collaboration, because it gives the group a method to see the effects of their decision making. Without visualization, it becomes increasingly more difficult for participants to articulate a vision of how the alternatives will work in a systems context.
- I’ll conclude this presentation with a few ideas: The problems we face come from complex systems and the entities in these systems share relationships on and in-between layers that are non-linear, episodic and difficult to predict. To solve these problems we need adaptive management and collaboration enabled by effective decision spaces. Effective decision spaces enable us to be like children playing with blocks – collaborative, experimental and iteratively learning and applying our knowledge Simulations can model the behavior of these systems and allow us to play with blocks, to safely and repetitively experiment and find solutions.