1 A REFERENCE DECISION MODEL FOR ...

A REFERENCE DECISION MODEL FOR SIMULATION OF SITUATIONAL AWARENESS RELATED TO FIRST RESPONSE Tony Rosqvist VTT Technical Research Centre of Finland Ltd. Merik Meriste Tallinn University of Technology Denis Havlik AIT Austrian Institute of Technology ABSTRACT This paper introduces a generic decision model that connects three basic components in the response decision process: the stochastic information flow containing help requests and resource usage; situational awareness in establishing a spatio-temporal risk picture; and decisions to allocate resources for first response. The formal mathematical language utilized is the stochastic Marked Poisson Process. The point of departure is the theory on Recognition-primed Decisions. This paper is written for researchers interested in the formal presentation of situational awareness for simulation applications related to emergency/rescue management training where experienced commanders generate response scenarios for comparison and teaching of non-experienced decision-makers. Paper also briefly discusses the possibility of using such formal model as a point of departure in the development of agent-based models and simulations.

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1. Introduction Situational awareness Situational awareness is a critical concept enabling the successful decision-making across a broad range of complex and dynamic systems, including aviation and air traffic control, emergency response and military command and control operations. In acute emergency and disaster situations, where proper first response is key to saving people in peril decisions related to resource allocation must often be made in a very short time and based on partial knowledge of the situation. A generic definition of situational awareness (SA) has been provided by Endsley (1995) : “the perception of the elements in the environment within a volume of time and space, the comprehension of their meaning and the projection of their status in the near future”. However, to the best of our knowledge, no formalized decision model related to time-constrained resource allocation is available today. The authors note that the main body of research on SA has revolved around the three levels of SA (SAI, SA II, SA III), specified also by Endsley (2000): • Level 1 SA, perception, relies on detection of relevant elements within an environment. Examples include knowing enemy, friendly, and civilian location/movement, terrain, and weather (Endsley et al., 2000). • Level 2 SA, comprehension, involves integrating the acquired perceptual information to gain an understanding of the importance of those Level 1 elements in the context of the decision maker’s goals and task. For example, a soldier may notice enemy tracks along a path (Level 1 SA) and implement a more furtive strategy for further movement, illustrating that the soldier has Level 2 SA (Endsley et al., 2000). • Level 3 SA, projection, involves projecting the near term future situation and its impact based on the information gathered (Level 1 and Level 2 SA). For example, a soldier with Level 3 SA synthesizes perceptual elements such as enemy tracks (Level 1 SA), discerns further movement options and strategies (Level 2 SA), and predicts how long it will take to reach a target destination and how likely it is that detection by an enemy will happen en route. In the context of emergency response it is desirable to reach level 3 SA as fast as possible, and to remain at that level even in a very dynamic situation. The most relevant recent contributions towards understanding SA, and also a critique against the three level approach of Endsley, can be found in Chancey and Bliss (2012) where knowledge and experience of the decision-maker is shown to be crucial for groups’ performances under imperfect information. Level 3 SA can be argued to be maintained when the following two risk management elements are known to the commander(s) of an emergency situation: 1) People and infrastructures at risk, including rescue personnel in action (spatial and temporal distribution) 2

2) Effective resources available (logistic equipment, rescue personnel, command level personnel) Unfortunately, the status of risk and its projection in the near-future are not fully known. Thus, uncertainty about the true risks is also a component of SA. As a consequence, the commander(s) have to form a subjective risk perception based on an overall judgment of the disaster situation that has some analogy to previous experiences of similar disaster situations. Subjective formation of SA in the minds of the commander(s) can be alleviated by effective sharing of information and the establishment of a Common Operational Picture (COP). Important pieces of information are e.g. known damages of infrastructure, known rescue actions that have been initiated, changes with respect to normal patterns of behavior of people, systems, etc. All such events are usually collected and visualized for establishing the COP. Still, perfect information cannot usually be retrieved, and uncertainties will exist regarding risk. Eventually, the implication of uncertainty 1 for the perception of risk and response actions, i.e. the situational awareness, is, individually deduced, very much dependent on the experience of the decision-maker(s)2. Rescue coordination centre Fig.2 shows a generic organization of the emergency or rescue coordination centre (La Posta et al., 2010). The performance of emergency response is strongly influenced by preparedness of the different command levels. Training with available personnel and equipment is the key method for maintaining preparedness. Also post-training assessment of training results in order to improve information processing and sharing, skills in equipment use, communication, etc, is paramount. For example: At the cross-service level, the commanders have to optimize the overall response to the accident assigning situation-aware priorities to each of the subordinate service units as well as allocating available resources; At the level of Medical Service the actors have to optimize the medical response to the accident assigning priorities to medical units as well as allocating available medical resources. Each training event may change the insight of how to develop preparedness further. By the same token, training will reveal operational deficiencies, and their cumulative effects on the outcome of the disaster. All observed deficiencies, can in principle, be attributed to shortcomings of the preparedness and inadequate training. However, it must be acknowledged, that all contingencies cannot exhaustively be trained. Thus, the element of surprise will often enter the COP in training (and real disaster), and hence influence the situational awareness of the commanders, as argued in the previous subsection.

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Uncertainty is due to imperfect information which cannot eliminate all uncertainties in a decision situation Uncertainty does not mean necessarily that the choice of decision is not clear. For instance, the uncertainty of a physically disabled person to die in a fire after 10 to 20 minutes from now does not affect the decision to immediately send in smoke divers to get the person out as all other options are too slow. 2

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It is also clear that training for a large variety of accidents requires proper planning in order to achieve a broad range of accident-specific (context-specific) learning goals. Unfortunately, live simulation of accidents is costly and in the case of a larger disaster not feasible in the first place. Thus it is not surprising that virtual simulation is looked at as a means to replace live simulations. The question addressed in the remainder of this paper is ‘How can situational awareness be modelled in order to support the planning of virtual simulation training’?

Figure 2. Emergency command structure for disasters (ref. EU-project ACRIMAS D4.1)

2. Understanding first response dynamics The first response phase can be split in different sub-phases, as shown in Fig. 3 (La Posta et al., 2010). Each of these sub-phases requires different resources and skills. In different types of disasters, the relative amount of resources to be allocated for the sub-phases with respect to saving lives and protecting infrastructures, vary. For instance, in a slowly advancing hazard, information collection capability during SAR is more important than the site clearing capability.

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Figure 3: Sub-phases of the emergency response (ref. EU-project ACRIMAS D4.1)

Chaotic sub-phase This sub-phase immediately follows the onset of the disaster. It is characterized by fear and lack of leadership among those at the immediate scene. The chaotic sub-phase is most prominent in disasters involving unexpected major disruptions such as bombings, airplane crashes etc. Most efforts of the responders go to establishing a local emergency organization and setting up the chain of command. Consequently, seriously injured victims are at great risk of dying. In addition, the number of victims may rise in this phase due to cascading hazards, e.g. traffic accidents. In urban settings, the chaotic sub-phase of a disaster usually lasts less than one hour. However, this time can be longer due to e.g. -

crowding of hospitals un-coordinated flow of volunteers arriving to the scene people leaving the scene causing gridlock etc.

Initial response and reorganization This phase begins when professional first responders and institutional volunteers arrive at the disaster scene to assume command and control. This sub-phase is characterized by following activities: o

needs assessment involving 5

situation analysis that is the collection of information about the extent and character of the disaster itself and the problems that have to be tackled; resource analysis that involves the collection of information about the resources needed to tackle the problems; o

decision on what disaster plan should be activated;

o

establishment of a command structure that interacts with the central command authority;

o

checking what additional events can make matters worse (cascading hazards)

This sub-phase is crucial in establishing a Common Operational Picture (COP). Site clearing Once the response organization is established, the responders can proceed with a thorough examination of the damage at the disaster scene and clearing of debris, in the case the disaster involves material damage and/or substances that need to be removed before effective Search and Rescue (see SAR below). Generally the firemen and the police have the responsibility to secure the area in order to prevent immediate dangers. This emphasizes the importance of strict control of access to the disaster scene, allowing only those personnel who are trained to address further hazards to enter the area. Important part of site clearing is the establishment of temporary support functions for the responders to operate and the victims to persevere. For instance, electric generators, water cisterns, etc may be in immediate need. Search and Rescue (SAR) Once the disaster scene is reasonably secure, a search for casualties is undertaken. Surviving casualties should immediately be assessed for the nature of their injuries and the urgency of treatment needs after being rescued from the disaster site. In many disaster scenes, extensive extrication must be carried out to safely free these victims from collapsed rubble. The SAR operation can start at the same time as site clearing as, e.g. helicopters can be used before roads are cleared. In practice, site clearing and SAR are partly interdependent, partly progressing in parallell independently. Only after identification of first victims (where, how many, the type of injuries), rescue-decisions will be made. Thus the delay T in Fig. 3 is an important indicator for emergency response performance.

In this sub-phase the social media can be of great value for disaster management if rules have been adopted for information sharing and organization of additional resources. Medical Care The medical care of disaster victims begins at the first moment of rescue and extends for hours, days, weeks, or longer through all phases of definitive care and rehabilitation. This requires early 6

and rapid rescue, evaluation, and evacuation of casualties to hospital facilities. The difference between the normal everyday care of injured patients and emergency medical care is most pronounced within the first minutes and hours following a mass casualty disaster.

3. Modelling response decision-making Globally optimal resource allocation during a specific disaster would imply that the Command Centre has perfect risk information (complete situational awareness); and a selection rule that enables it to find the right decision strategy (resource allocation strategy). The latter would mean solving a strongly NPcomplete problem which is notoriously hard (Karp, 1972). Unfortunately, the decision makers neither have the required data, nor the computational means to discover such optimal resource allocation within the limited time-frame imposed by the disaster situation. Decisions are therefore often based on the combination of available information and the “gut feeling” of the responsible decision maker. Heuristic decision-making strategies for time-constrained situations are thus an essential part of the disaster management. One model for rapid and effective decision making in complex situations is the recognition-primed decision-making (RPD), which has been described in Klein (1998). RPD combines two ways of developing a decision. The first is recognizing which courses of action are feasible in a given situation. In the second evaluating the decision maker tries to imagine the actions resulting from that decision and estimates the effectiveness of the action. As soon as a seemingly feasible and effective solution has been found, the decision is taken to follow this course of action and no further possibilities are evaluated. RPD-type of decision making has been observed in diverse groups including ICU nurses, firefighters, commanders, chess players, and stock market traders, i.e. in situations where globally optimal solutions cannot be deduced due to time pressure, imperfect information and vague goals. The limitations of RPD include the need for extensive experience among decision-makers (in order to correctly recognize the salient features of a problem and model solutions) and the risk of sub-optimal decisions in unusual or misidentified situations (Klein, 1998). RPD type of reasoning will, in general, not lead to a global optimum in the sense of saving as many lives as possible with the available resources in a disaster situation. In time-constrained situations, RDP may nevertheless be the best possible decision making strategy. This explains why experienced and well trained rescue commanders are essential for the disaster management: in unclear and complex situations where a best possible solution can’t be exactly calculated, the experienced decision makers are less likely to misjudge a situation, and to decide on some unfeasible and ineffective courses of action, compared to the inexperienced commanders. We are now ready to formalize emergency decision-making under evolving situational awareness. The mathematical basis is provided by the Marked Point Process (Snyder and Miller, 1991). The relevant decision level is the inter-service coordination level, i.e. the command centre in Fig. 2. Our starting time point is T0 = 0 in Fig.3. 7

Nomenclature: Tk

random time points of arriving ‘marks’, i.e. information and decisions, ordered by index k=1,2,….

Yk

arriving ‘marks’ (help requests, infrastructure status, weather forecasts, assigned rescue tasks, etc) ordered by time index k=1,2,….

Yk , Tk

a ‘mark’ related to a location (ij) created at (random) time point tk, included, as such or

ik jk

filtered, in the Common Operational Picture (COP)

H tq

y1 , t1

i1 j1

,..., y q , t q

t

iq jq 1

t2

....

tq

ij

ij

nxm

complete history of ‘marks’ related to locations (i,j)

[i, j]nxm up to time point tq , i.e. a

Marked Point Process, filtered and visualized for the Common Operational Picture (COP)

X( H tq )

X 11 ( H tq ),..., X nm ( H tq ) situational awareness at time point tq depicted by a risk profile perceived by the commander, as structured by local risk Xij perceived over the whole disaster area [i, j]nxm given ‘marks’ up to time point tq , i.e. local risk interdependence is depicted by the history of ‘marks’, or what is filtered out of it for the COP

X ij H tq

local risk perception at area (ij) at time point tq that obtains qualitative values such as ‘some people are perhaps in distress, ‘one or two people are in distress and require medical care’, ‘several people are in distress requiring medical care’, etc. reflecting the situational awareness at area (ij)

X ( H tq , a tq )

X 11 ( H tq , atq ),..., X nm ( H tq , atq ) situational awareness in terms of perceived risk reduction (potential emergency relief) that can be administered across the disaster area by feasible response decision a q given ‘marks’ up to time point tq , or what is filtered out of it for the COP

a tq

Atq

a feasible response decision possible to make immediately after tq meaning that there needs to be resources free for rescue actions (the set Aq is obviously dependent on past decisions but for clarity of notation this is not explicitly indicated)

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at*q :

V X H tq , a *t

q

1 assign response action 0 wait

recognizion-primed decision made by the commander at time point t q which amounts to the identification of a feasible and effective action for providing relief, given the ‘marks’ , or COP, up to time point tq , possibly entailing multiple resource allocations for operations over many grid elements in the disaster area [i, j]nxm (the decision to wait, due to too much uncertainty, is also a decision)

at*1 ,..., at*q

set of time ordered decisions up to time points tk , k

1,2,..., q , correspondingly

Model structure: The simplest way of modelling the basic influence of field information on response actions at command centre level is illustrated by the influence diagram in Fig. 4 where the information process (observable process) influences situational awareness (not directly observable mental process) which results in response decisions. Also negative information form the SA (Lundin et al. Situational awareness is the linking mechanism between information and response decisions. Note that the above influence diagram is valid for lower level decisions as well. For instance, police teams at the field make their decisions based on immediate local observations and available resources, relying on proper situational awareness (see Fig. 2).

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Common Operational Picture

Atq Situational Awareness

Yk ,Tk

ij

X(Htq ), X(Htq , atq )

at*q

Figure 4. Inference diagram linking the Common Operational Picture with the decision-maker’s Situational Awareness of the disaster and the response decisions

Model application for training disaster response: The above conceptual and mathematical model can be applied for man-in-the-loop simulation and decision-making training as follows: i)

Pre-training setup

The disaster context where disaster response is trained must be specified: the starting time t 0 and the related history of the disaster; H t0 . The set of available resources at the beginning of the training; At0 , must be specified. ii)

During training

The decision-making trainee is continuously subjected to the cumulating information of marked points

H tq

y1 , s1

i1 j1

,..., y q , s q

iq jq

, depending on his/her past decisions, and cumulatively will change

the trainees’ situational awareness, and therefore recognition-primed decisions. 10

In a more realistic setting the information H tq is partly filtered during the team work at the command centre; e.g. time stamps for all events Yk are not necessarily shown in the COP display. In fact, a COP with only time-ordered events would be a filtered marked point process H t'q

y1 , .

i1 j1

,..., y q , . i

q jq

.

Furthermore, filtered COP where some events have been erased completely would be depicted like

H t'q

y1 , .

i1 j1

,..., . , .

ik jk

,..., y q , . i

q jq

. Filtering is mainly influenced by the team work culture and

the display technologies available at the command centre, and may have a bigger effect on situational awareness than assumed. Obviously GIS systems and graphical means to encode and display information play an immense role in the formation of situational awareness. It is important to note that changes in the environment are not triggered by resource management decisions only, also sudden events (e.g. collision of two ambulances), and trends (e.g. fading of the density of toxic plumes) must be encoded and visualized. The CRISMA crisis management simulation framework will support this kind of information management and visualization (Havlik et al. 2015).

iii)

Post-training

The key output is the final history of ‘marks’ H tq which is available when the response simulation ends, at tq. This history contains timely ordered marks on ‘help request’, ‘resource allocation decisions’, ‘equipment failures’, ‘injury & fatality notes’, ‘notes on negative information’, etc. This information stream is a source for computing Key Performance Indicators (KPI) such as ‘average time to first aid after emergency assignment’, ‘number of emergency assignments that did not finish’, ‘ ‘number of deaths during transportation’, etc… which relate to logistic performance. These KPIs are the basis for identifying response improvement options. Especially, we stress the comparative assessment in training, where the KPIs obtained from experienced commanders are compared with those of inexperienced trainees. E.g. If the ‘number of deaths during transport’ is clearly higher for the inexperienced trainees, then management of the logistic medical resources may be a target area for further training. More formally, this means that the decision chains pertaining to a specific scenario run

( a t*1 , a t*2 ,..., a t*q ) traineeA and ( a t*1 , a t*2 ,..., a t*q ) traineeB are linked to their respective KPIs for post-training analysis as follows:

( at*1 , at*2 ,..., a t*q ) traineeA

yields

( at*1 , at*2 ,..., a t*q ) traineeB

yields

KPI 1 , KPI 2 ,... KPI 1 , KPI 2 ,...

traineeA

traineeB

The generated scenarios can also be studied with respect to: 11

applied cues or patterns underlying decisions (from post-training interviews) risks imposed to rescue personnel etc The aim of post-training analyses is to observe whether decisions by experienced commanders and trainees differ in any systematic way. An example A realistic training context, related to accidental bromine release, is provided by Havlik et al. (2015) where typical decision are depicted in Fig. 5 by the arrows.

Figure 5. A disaster situation involving the accidental release of bromine in the air. Commanders manage resources and responders (Havlik et al. 2015).

As an example related to the bromine accident, a trainees’ decisions at*k at time point tk , k = 1,2,3…. , pertaining to the SAR-phase (see section 2), could be as follows Htk: {s1 ; “send medics according to initial estimates of victims provided by the fire brigade evacuating citizens at site A of the accident”, “alert the closest hospitals of seriously harmed victims needing intensive care”} {s2 ; “issue a broadcast in radio/tv where treatment can be received if feeling burns”} {s3 ; “request military to establish additional treatment areas in the stadium as capacity of the initial treatment area is fully deployed”, “ ask for medical help from nearby cities to move to the stadium”, “alert the ministry”} In fact, an experienced commander would probably make a different decision at time point t2 due to the fact that bromine is not fatal, and also because of the uncertainty that a radio/tv broadcast reaches concerned citizens. A better decision, due to better situational awareness, would be: 12

{s2’; “issue a broadcast in radio/tv where treatment can be received if feeling burns after two hours in fresh air”, “send police to the hospital entrance to maintain order and provide information about the hazard”} Such generated scenarios would form a learning basis for calibrating the situational awareness of the trainees as noted above. The scenario sets of experienced commanders can be also useful for training, where recorded scenarios are modified (e.g. increase negative information), or filtered (e.g. mimicking transmission errors), in order to change the information set. An interesting phenomenon might be that decreasing information may lead to a more confident decision-making as the options for different interpretations presumably decrease, simplifying formation of situational awareness. Of course, it will be known only in hindsight whether the decision made was effective, or not.

Model application for exploration of disaster response by agent-based simulation: The above formulation of the response decision model also allows exploring decision heuristics or rulesof- thumb using agent-based simulation model where the COP can be varied as described above. The effectiveness of different decision rules can be studied by extensive simulation runs in variable conditions where decisions by, and interactions between, situation-aware agents, produce scenarios that may reveal surprising emergent situations using the specified rules-of-thumb (Macal & north, 2011; Shvartsman et al.; 2010). Furthermore, computer-agents can be used to produce certain information in training or real emergency situations, e.g. computation of tipping-points; time points in the future that will radically change the development of events (e.g. cascading hazards) if there is no intervention. Such tipping-points mark the temporal boundary for decisions to act. The introduced reference model can straightforwardly be adopted to ‘mark’ such tipping points. Jennings et al (2014) provide further views on the co-operation between human operators and software agents.

A special case One special case related to the information process (Marked Point Process) H tq occurs when all victim and risk related information is available straight after the main hit of the disaster event. In the notation introduced this would imply a fixed information set rather than a stochastic process:

Ht

H0

This would also mean that situational awareness is non-dynamic as all damage is assumed to be known at the very beginning of the response phase. In this case, the globally optimal response decision can be formulated as a NP-problem (Karp, 1972). 13

If the above modelling assumption is far from realistic, than the assessments of response options may systematically be biased towards enhancing medical transportation and care, rather than site clearing and SAR where infrastructure services, especially telecommunication networks, are important factors in the outcome of the response (see Fig. 3).

4. Conclusion This paper introduces a reference model for response decision-making in a real-time disaster situation. It is based on the stochastic Marked Point Process and links conceptually Recognition-primed decisionmaking with situational awareness in emergency response decision-making. The reference model is developed for designers of first response simulators where the man-in-the-loop simulation settings is used to train non-experienced decision-makers based on scenarios and decision paths generated by experienced commanders. Information sets reflecting the Common Operational Picture, can be systematically and transparently tailored for studying the influence of imperfect information on situational awareness and decisions. Especially, agent-based modelling and simulation can be linked to the reference model introduced.

Acknowledgement The work related to this paper was partly supported by the CRISMA project. The CRISMA project is funded from the European Community's Seventh Framework Programme FP7/2007-2013 under grant agreement no. 284552.

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Jennings N.R, L. Moreau, D. Nicholson, S. Ramchurn, S. Roberts, T. Rodden, A. Rogers. “Human-agent collectives”. Communications of the ACM 57(12): 80-88. Klein G. 1998. "Sources of Power: How People Make Decisions", MIT Press, Cambridge, Mass. La Posta G., D. Cecchi, H.M. Pastuszka. 2010. ”Aftermath Crisis Management System-of-Systems Demonstration”. The FP7 ACRIMAS-project; D4.1 Inventory Report. European Commission. http://www.acrimas.eu/attachments/article/5/D4-1_ACRIMAS_Inventory%20Report.pdf Lundin M., P. Hörling, P. Svenson. 2009. “Uncertainty modelling for threat analysis.” In the proceedings of the 14th ICCRTS conference “C2 and Agility”. Paper ID Number 032. Washington, 15-17 June. Karp R.M. 1972. "Reducibility Among Combinatorial Problems". In R. E. Miller and J. W. Thatcher (editors). Complexity of Computer Computations. New York: Plenum. Macal C. M., M. J. North. “Introductory tutorial: Agent-based modelling and simulation”. Proc. Of the 2011 winter simulation conference. Eds. S. Jain,R. Creasey, J. Himmelspach, KP White, M. Fu. December 11 - 14, 2011; Phoenix, AZ. Shvartsman I., K. Taveter, M. Parmak, M. Meriste. Agent-Oriented Modelling. Proceedings of the International Multiconference on Computer Science and Information Technology pp. 209–216 for Simulation of Complex Environments. IEEE. 2010. Snyder, D.L., M.I. Miller. 1991. Random Point Processes in Time and Space, Springer My Copy UK.

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