Agent-based Simulation

David MendonçaInformation Systems Department

mendonca@njit.eduweb.njit.edu/~mendonca

LNEC Risk Management in Civil Engineering Workshop, Lisbon, 2008

• Critical infrastructures are combined human-machine systems, encompassing

– human and machine behavior

– human and machine cognition

– often operating interdependently

• Their management engenders collaboration, cooperation, competition.

• Their complexity limits the utility of analytic approaches to modeling them.

Mendonça and Wallace (2006). "Impacts of the 2001 World Trade Center Attack on New York City Critical Infrastructures." Journal of Infrastructure Systems 12(4) 260-270

Some remarks

• Purpose of agent-based modeling is to understand or anticipate the behavior of (semi-)autonomous beings acting– individually or collectively,– dependently or independently,– within or outside a real or simulated environment

• Agent-based simulation involves the use of agents within a real or simulated environment

• Any agent-based simulation is a theory that encompasses agents, the environment and interactions among them.

Foner (1993). "What's An Agent, Anyway?" MIT Agents Report 93-01, Cambridge, MA.

Agent-based Modeling and Simulation

• Understand how behavior of components and processes affects

– vulnerability– resilience– efficiency– effectiveness

under various operating conditions.• Develop prescriptions about CI design in light of likely

– effects of changes in exogenous factors (e.g., market, regulation)

– effects of changes in endogenous factors (e.g., personnel, organization, technology)

Agent-based M&S for CI Management

• A model is only useful to the extent that it is valid.

• Today I will talk about models of cognition, expressed as agents, and a bit about models of the environments with which they interact.

• The bulk of my time will be devoted to a discussion of how to develop and validate models of cognition expressed as agents.

Today

Observation

• Direct impacts

– transportation, emergency services, ...

• Disruptions

– restricted transport network,

– loss of firefighting apparatus,

– injury to personnel, ...

• Agents

– Approximately 15 firefighters

• Task

– Assist FF ops at WTC site

• Preliminary model

– What FF cognition/behavior would you try to model?

– What data would you collect?

– How would you test your prediction?

QuickTime™ and a decompressor

are needed to see this picture.

http://mentalmodels.mitre.org/cog_eng/index.htm

QuickTime™ and a decompressor

are needed to see this picture.

http://mentalmodels.mitre.org/cog_eng/index.htm

QuickTime™ and a decompressor

are needed to see this picture.

• "Methods that produce detailed models of how humans perform complex cognitive tasks that can be run on a computer.

• Such models can provide a priori performance predictions of how well a certain system will support the tasks workers perform by assessing factors such as how easy the system will be to learn and use, the workload it imposes, and the propensity for errors.

• Software agents that perform work tasks in the same way that humans perform work tasks can be used to evaluate proposed system designs without the need to conduct these types of evaluations with actual workers.

• The downside to these methods is that they can be time-consuming and, with the exception of GOMS, have not yet seen widespread use in the engineering community."

• Survey of thirty (30) simulation systems

• Fifteen infrastructure sectors considered

• Continuous and discrete simulation systems– Six agent-based systems

• Example: UIS (LANL)– "Understand

infrastructures’ performance under unusual conditions..."

Illustration

Gu and Mendonça (2006). "Group Information Foraging In Emergency Response: An Illustration Incorporating Discrete-Event Simulation." Winter Simulation Conference.

Feasible Courses of Action

Level of Severity

Property

Object

Object Group

Function

Goal

Ontology

Mendonça and Wallace (2007). "A Cognitive Model of Improvisation in Emergency Management." IEEE Tr. SMC: Part A

Nurses Doctors

Medical Personnel Ambulance Coastal Patrol Boat

Treatment Center

Patient Treatment

Personnel Transport

Patient Transport Shelter

Untreated

Mendonça and Wallace (2007). "A Cognitive Model of Improvisation in Emergency Management." IEEE Tr. SMC: Part A

Decision Logic

1.

3.

2.

4.

5.

6.

7.

Property

Object

Object Group

Function

Goal

Ontology

0. CA, G

Mendonça and Wallace (2007). "A Cognitive Model of Improvisation in Emergency Management." IEEE Tr. SMC: Part A

Goal Index Resource 1 2 3 4

1 Aerial ladder truck 7 70 13 16 2 Ambulance 14 9 18 15 3 Bus* 1 2 3 4 4 Chemical Protection Suits 1 0 2 10 5 CO2 0 3 0 0 6 Coastal Patrol Boat 6 9 18 10 7 Gravel Truck* 2 3 5 6 8 Helicopter* 1 2 3 4 9 Medical Personnel 6 0 0 8 10 Oil Boom* 0 0 0 1 11 Police 5 0 4 2 12 Police Cruiser 5 3 6 9 13 Pumper Truck 6 5 9 11

where Goal1=treat injured2=control access3=rescue trapped persons4=fight the fire

*=non-standard resource

Assessing Fit

QuickTime™ and aVideo decompressor

are needed to see this picture.

Mendonça and Hu (forthcoming). "Cognitive Underpinnings of Resilience," in Resilience Engineering Perspectives.

Role Time Src Content

MO 77 L D

MO 78 C We got, dispatch Da to Z.

CA 79 C Da to Z

MO 80 C Dispatch Db to Z and then also dispatch Aa to D.

CO 81 C Okay and that goal is on patient care so that’s...

MO 82 C Three and four.

MO 83 L Da, 0011

CO 84 L Aa, Da, Z, 0011

Role Time Src Content

MO 77 L D

MO 78 C We got, dispatch Da to Z.

CA 79 C Da to Z

MO 80 C Dispatch Db to Z and then also dispatch Aa to D.

CO 81 C Okay and that goal is on patient care so that’s...

MO 82 C Three and four.

MO 83 L Da, 0011

CO 84 L Aa, Da, Z, 0011

Role Time Src Content

MO 77 L D

MO 78 C We got, dispatch Da to Z.

CA 79 C Da to Z

MO 80 C Dispatch Db to Z and then also dispatch Aa to D.

CO 81 C Okay and that goal is on patient care so that’s...

MO 82 C Three and four.

MO 83 L Da, 0011

CO 84 L Aa, Da, Z, 0011 REC(Da, 0011)

DEC(Aa, Da, Z, 0011))

CCA(Da, Z)

CCA(Aa, Db, Z, 0011

CCA(Da, Z)

CCA(Da, Z), (Aa, D)

CCAs and DECs

Interval

Size

CCAs and DECs

Interval

Size

Validation based on Outcomes

• Given (i) some prior set of decisions and (ii) a mix of feasible resources at time t, to what extent do the model’s choices at time (t+1) match the group’s choices?

Interval

Size

Validation based on Outcomes

• For the session as a whole, to what extent do resource:goal pairings from the model match those of the group?

Case 1 2Goal 1 2 3 4 1 2 3 4 5

RolexCA 0 2 2 1 1 0 0 0 0

FD 0 2 0 2 6 0 0 5 1MO 0 0 0 6 0 0 0 2 0

PD 3 5 3 3 16 0 4 7 2AR 1 3 0 0 5 0 1 5 1

Validation based on Processes

Given (i) some prior set of decisions and (ii) mix of feasible resources at time t, to what extent do the model’s decision processes match those of the group?

Time Src Message Encoding

2602 Msg "CO2 must be escorted by police" –

2751 FD I continue. I have 20 chemical

protection suits at C but no CO2 or

vehicles. So I’ve got suits but no

vehicles.

suits (site = C, transport = 0)

2800 CO No vehicle, so we are going to need

police or fire.

transport = 0 → need (police | fire)

2844 PD Okay, I have got... I will send F

[cruiser] to G to get the suits.

send (F.cruiser -> G) → f (pick up (suits));

2849 FD Well, the suits are at C. suits (site = C)

2850 PD They are at C? test (suits (site = C))

2852 FD The CO2 is at G. CO2 (site = G)

2853 PD Okay, well test (suits (site = C))=true

3654 CO Course of action: "Fa, Ca, Z

(1010)"

send(F.cruiser->C), f(pickup(F.cruiser,

C.suits)), send(F.cruiser, C.suits->Z)

Time Src Message Encoding

2602 Msg "CO2 must be escorted by police"

2751 FD I continue. I have 20 chemical

protection suits at C but no CO2 or

vehicles. So I’ve got suits but no

vehicles.

suits (site = C, transport = 0)

2800 CO No vehicle, so we are going to need

police or fire.

transport = 0 → need (police | fire)

2844 PD Okay, I have got... I will send F

[cruiser] to G to get the suits.

send (F.cruiser -> G) → fxn (pick up

(suits));

2849 FD Well, the suits are at C. suits (site = C)

2850 PD They are at C? test (suits (site = C))

2852 FD The CO2 is at G. CO2 (site = G)

2853 PD Okay, well test (suits (site = C))=true

3654 CO Course of action: "Fa, Ca, Z

(1010)"

send(F.cruiser->C), fxn(pickup(F.cruiser,

C.suits)), send(F.cruiser, C.suits->Z)

Time Src Message Encoding

2602 Msg "CO2 must be escorted by police"

2751 FD I continue. I have 20 chemical

protection suits at C but no CO2 or

vehicles. So I’ve got suits but no

vehicles.

suits (site = C, transport = 0)

2800 CO No vehicle, so we are going to need

police or fire.

transport = 0 → need (police | fire)

2844 PD Okay, I have got... I will send F

[cruiser] to G to get the suits.

send (F.cruiser -> G) → fxn (pick up

(suits));

2849 FD Well, the suits are at C. suits (site = C)

2850 PD They are at C? test (suits (site = C))

2852 FD The CO2 is at G. CO2 (site = G)

2853 PD Okay, well test (suits (site = C))=true

3654 CO Course of action: "Fa, Ca, Z

(1010)"

send(F.cruiser->C), fxn(pickup(F.cruiser,

C.suits)), send(F.cruiser, C.suits->Z)

1.

3.

2.

4.

5.

6.

7.

0. CA, G

Property

Object

Object Group

Function

Goal

Validation Redux

• Decide at onset on where model validation will take place– Outcome

– Process

– Both

• What are the corresponding data needs?• What are the rejection criteria?

Summary

• Complex, dynamic interactions are the rule in management and restoration of critical infrastructures.

• Analytic tools for increasing understanding and informing design are insufficient for generating understanding of these systems.

• Agent-based simulation is one tool for doing so:– extremely detailed descriptions of both human and machine

cognition and behavior are possible

– many avenues for conducting validation

• The resulting simulation models are themselves infrastructure that, with proper maintenance, will provide returns on investment not available from one-shot studies.

Agent-based Simulation

David MendonçaInformation Systems Department

mendonca@njit.eduweb.njit.edu/~mendonca

LNEC Risk Management in Civil Engineering Workshop, Lisbon, 2008

Selected References (1)• Simulation and Agent-based Technologies

– Alessa, L. N., Laituri, M., and Barton, M. (2006). "An ‘all hands’ call to the social science community: Establishing a community framework for complexity modeling using agent based models and cyberinfrastructure." Journal of Artificial Societies and Social Simulation, 9(4), 6.

– Casalicchio, E., and Galli, E. (2008). "Federated Agent Based Modeling and Simulation: an Approach for Complex Critical Systems Analysis." 22nd Workshop on Principles of Advanced and Distributed Simulation. 147-147.

– Foner, L. (1993). "What's An Agent, Anyway." MIT, Agents Report 93-01, Cambridge, MA.– Gu, Q. and D. Mendonça (2006). "Group Information Foraging In Emergency Response: An Illustration Incorporating

Discrete-Event Simulation." Winter Simulation Conference, Monterey, CA.• Critical Infrastructure Management and Restoration

– Little, R. G. (2005). "Organizational Culture and the Performance of Critical Infrastructure: Modeling and Simulation in Socio-Technological Systems." Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS '05). 63b-63b.

– Mendonça, D. and W. A. Wallace (2006). "Impacts of the 2001 World Trade Center Attack on New York City Critical Infrastructures." Journal of Infrastructure Systems 12(4) 260-270.

– Mendonça, D. and W.A. Wallace (2006). "Adaptive Capacity: Electric Power Restoration in New York City following the 11 September 2001 Attacks." 2nd International Symposium on Resilience Engineering.

– Pederson, P., Dudenhoeffer, D., Hartley, S., and Permann, M. (2006). "Critical Infrastructure Interdependency Modeling: A Survey of US and International Research." Idaho National Laboratory Critical Infrastructure Protection Division.

– Sokolowski, J., Turnitsa, C., Diallo, S., and Norfolk, V. A. (2008) "A Conceptual Modeling Method for Critical Infrastructure Modeling." 41st Annual Simulation Symposium, 203-211.

– Tolone, W. J., Wilson, D., Raja, A., Xiang, W., Hao, H., Phelps, S., and Johnson, E. W. (2004). "Critical Infrastructure Integration Modeling and Simulation." Lecture Notes in Computer Science, 3073, 214-225.

Selected References (2)• Agents and Infrastructure

– Balducelli, C., Bologna, S., Di Pietro, A., and Vicoli, G. (2005). "Analysing interdependencies of critical infrastructures using agent discrete event simulation." International Journal of Emergency Management, 2(4), 306-318.

– Brown, T., Beyeler, W., and Barton, D. (2004). "Assessing infrastructure interdependencies: the challenge of risk analysis for complex adaptive systems." International Journal of Critical Infrastructures, 1(1), 108-117.

– Cardellini, V., Casalicchio, E., Tucci, S., and dei Ministri, P. C. (2006). "Agent-Based Modelling of Web Systems in Critical Information Infrastructures." Complex Network & Infrastructure Protection Conference.

– Casalicchio, E., Donzelli, P., and Setola, R. (2006). "Modelling and Simulation of Interdependent Critical Infrastructure: The Road Ahead." Communication Networks And Computer Systems, Imperial College Press, 2006.

– Mendonça, D. and W. A. Wallace (2007). "A Cognitive Model of Improvisation in Emergency Management." IEEE Systems, Man and Cybernetics: Part A, 37(4) 547 - 561.

• Decision Making in Emergency Response– Mendonça, D. and Y. Hu (forthcoming). "Cognitive Underpinnings of Resilience: A Case Study of Group Decision in

Emergency Response," in Resilience Engineering Perspectives. C. Nemeth (Ed.). Ashgate Publishing Ltd., Aldershot, England.

– Mendonça, D. (2008). "Measures of Resilient Performance," in Remaining Sensitive to the Possibility of Failure. E. Hollnagel, C. Nemeth, S. Dekkers (Eds.). Ashgate Publishing Ltd., Aldershot, England.

– Tierney, K. J. (2007). "From the Margins to the Mainstream? Disaster Research at the Crossroads." Annual Review of Sociology, 33(1), 503-525.