multi scale human modeling ¾modeling individual behavior is now extended to model organizational or...
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Resilience Informatics for Innovation Multi Scale Human Modeling
RERC/TMIKazuo FURUTA
How can we understand others ?
◎ ☆ ※
Human Model
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What is human model?
Description of some aspects (not necessarily all aspects) of a human or humans for particular purposes
n Mannequin for crash test of automobile designn Poll data for prediction of outcome of election
Perspectives of human modellingn Descriptive ⇒ For understanding (science)n Predictive ⇒ Design tool (engineering)n Normative ⇒ Not to fail (decision-making)
History of human modelling
Human modelling as sciencen Cognitive psychology & cognitive science
Human modelling as engineering n For productivity and usabilityn For safety and security
Human modelling for better society (2000- )n Multi-scale human modellingn Interactive systems design process
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Approaches of human modeling
Control theory
Statistics and probability theory
Decision theory
Information processing
Artificial intelligence
Ecological psychology (holistic)
Situations and issues of our society
Situations surrounding our societyn Strong connections between sci. & technol. and societyn Globalization and competitive economic environmentn Constraints on energy and environment (3E problem)n Highly aged society in industrialized countries
Issues our society is facingn Reduction of environmental loadn Reform of pension systemn Reform of medical systemn Reform of civil service and financen Urban design that is resistant against disasters
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What is social function?
Social function is a combination of institutions and operation of society. n Organizations that perform social activitiesn Laws and rules that govern social activitiesn Flow of goods, people, energy, information, and moneyn Communication among people
Modern civilization realizes our welfare.n modern civilization = social function + sci. & technol.
To solve the issues our society is now facing requires rational design of social functions.
Society design
Conventional society designn Based on learning from past experiencesn Inapplicable to complex and fast changing society
New approach of society designn Mathematical: positive and predictiven Cognitive: consideration of human behaviorw Humans adaptively respond to changes of social context;
social reforms sometimes result in failure.w Description, understanding, and prediction of human
behavior in the real context are required.n Objective and scientific approaches may not work.w The designer is a part of society: the design target.
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Multiple aspects of human behavior
Human behavior has not only individual but also group aspects in social context.
Human modeling that started from modeling individual behavior is now extended to model organizational or social behavior of humans.
Multi-scale modeling of fluid
Pt
∇−∇=∂∂
=∇
uuu
2
0
µρ
ρ
Microscopic Mesoscopic Macroscopic
Molecules
MD
Cluster of moleculesVirtual particles
LGA, BGA, MPS
Infinitesimal volume of fluid
NS-Equation
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Multi-scale modeling of human behavior
Microscopic Mesoscopic Macroscopic
Individual Group Society
Knowledge Model
causality
configuration
goalstateContext
Control Model
opportunistic
tactical
scrambled
strategic
Process Model
interpretation
observation execution
planning
memory
Individual level
Targetn Internal cognitive process of an individual
Key conceptsn Perception, affordance, situation awareness,
memory, knowledge, mental model, inference, attention, decision-making, adaptation
Supporting technologiesn Expert system, adaptive interface, EID, HRA,
speech recognition, personal VR
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Components of human model
Knowledge modeln What types of information (concepts) do
humans utilize?
Process modeln How do humans process information?
Control modeln In what order do humans execute the
processes?
Human model
Knowledge Model
causality
configuration
goalstateContext
Control Model
opportunistic
tactical
scrambled
strategic
Process Model
interpretation
observation execution
planning
memory
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Construction of cognitive model of individual air traffic controller
Backgroundsn Increase of task loads dues to increase of air
traffic demands
n Prevention of human errors in ATCw Near miss of two JALers in Japan (2001)w Crash of DHL-BAL in Germany (2002)w Many accidents and incidents reported worldwide
n Lacking scientific knowledge on ATC taskw Cognitive model is a basis for preventing human errors
and improving safety and reliability of ATC
Experimental study on cognitive model of air traffic controller
Simulator
Recorder etc Microphone
VTR camera
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ANA896FL240G48
JAL542FL390G51
ANA744FL350G51
1X-A11X-B2
1X-C3
1X-A41X-B5
1X-C6
1X-A7
1X-C8
1X-B9 1X-C10
1X-A11
1X-C121X-B131X-B14
1X-B15
1X-C16
ATC strategy for Team 1
ANA896FL240G48
JAL542FL390G51
ANA744FL350G51
1Y-A1
1Y-B2
1Y-C3
1Y-B4
1Y-A5
1Y-B61Y-C7
1Y-C8
1Y-B9
1Y-B101Y-B11
ATC strategy for Team 2
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ANA896FL240G48
JAL542FL390G51
ANA744FL350G51
1Z-B11Z-A2
1Z-C3
1Z-A4
1Z-C5
1Z-B6
1Z-C7
1Z-B81Z-C9
1Z-B10 1Z-C11
1Z-B12
1Z-A131Z-B14
1Z-C15
ATC strategy for Team 3
Concept of routine
Finding from task analysisn Details of ATC direction are different between
subject teams, but there exists some basic common strategy for each typical situation.
Routine as a model of separation strategyn A model or a schema for decision-making and
prediction of the futuren A routine is a task image that consists of
description of situation, strategy for processing targets, method of separation, implementation timing, and other factors to be considered.
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Strategic image for the case
Cognitive model of controller
Perception of
parameter
Comprehension of
situation
Projection of future situation
Decision of
command
Number of parameters
Relation between of
target and other aircrafts
Projection based on
experience(accuracy of projection)
Action
Target search
Search for basic
pattern
Routine matching of strategy and method
No
Call-in
Traffic routine
matching
Additional information
Obtaining additional information from flight strips, coordinator, etc.
Knowledge based process
Timemargin
Strategic/Tactical Thinking
Opportunisticthinking
Long
Short
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Cognitive model of NPP operators
execution
perception
action
belief
signal
hypothesis
similarity matching
ruleexplanationrethinking
observation
goal
planningvalidation
rule
script
confirmation
plan
skill
script
skill
Assessment of NPP MCR design
L-type97m (107±28m)
U-type85m
Integrated11m
Reactor operatorTurbine operatorAux. sys. operator
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Group level
Targetn Group or team of a few or several people
Key conceptsn Group cognition, organizational structure,
communication, human interface, mutual belief, group decision, knowledge distribution
Supporting technologiesn Large display, media room, PDA tool, group ware,
CSCW, team simulation
Team intention and team SA
Team intention (Tuomela & Miller)Team intention = Individual intention
+ Mutual beliefs
Team SA (Shu & Furuta)n Two or more individuals sharing the common
environment, up-to-the-moment understanding of situation of environment, and another person’s interaction with the cooperative task.
TSA = Individual SA + Mutual beliefs
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We-intention (R.Tuomela and K.Miller)
(WI) A we-intends to do X with B iff
(a) A intends to do his/her part of X (IaXa).(b) A believes that B will do his/her (B’s)
part (BaXb).(c) A believes that B believes that he/she
(A) will do his/her part (BaBbXa).
Abilities required for we-intention
X XA’s mind B’s mind
Xa XbXa’ Xb’
BaBbXa BbBaXb
IaXa IbXb
Planning
BaXb BbXaIntent
Inferencing
IaXa’ IbXb’
MutualResponsiveness
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Team intention inference
Candidates of A’s intention
Candidates ofbelief on B’s
intention
Candidates of B’s intention
Candidates ofbelief on A’s
intention
IbXbIaXa
BbXaBaXb
ExpectationExpectation
T. Kanno
Architecture of team intention inference system
Intention inference
Belief inference
T. Kanno
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Working situation of ATC
Radar controller
Coordinator controller
Radar screen Flight data strips
Cognitive Model of ATC Team
Choosingscheme ofinteraction
Belief Acquisition
Informing
Q&A
Observation
Acquisition of TSAAcquisition
of individualsituation
awareness
Decision-makingbased on TSA
Inte
r-pe
rson
al p
roce
ssIn
tern
al p
roce
ss
Execution of controlinstruction or coordination
from Partnerto Partner
Situation oftarget sector
Task model(Role model)
Detection and repair ofinsufficiency & inconsistency
in TSA
Complementing
Verification
Mental simulation using knowledge and experience
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Scenario
Creation of task list Prioritizing tasks
interruption
Mutual belief model Task list Task
Task selection
前回のインタラクション新しい話題Previous task
New topic
Memories of each agent
scoring
Modification
Modification
Radar
Coordinator
Task execution (interaction)
Ordered task list
Flow of Simulation
Interactions on MBM
1st layer
2nd layer
3rd layer
A’s cognition
A’s beliefs on B’s cognition
A’s beliefs on B’s beliefs on
A’s cognition
B’s beliefs onA’s beliefs on
B’s cognition
B’s beliefs on A’s cognition
B’s cognition
InferenceComplementing
Transmission / Observation
A’s mind B’s mindEnvironment
Perception
Assumption
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R_perception(Focus2)
R_transmission(Focus2)R_assumption(Focus2)
H_complementing(Focus2)H_inference(Instruction1)
H_transmission(Instruction1)H_assumption(Instruction1)R_complementing(Instruction1)R_inference(Instruction2)R_transmission(Instruction2)
R_perception(Focus2)
H_observation(Focus2)
H_complementing(Focus2)H_inference(Instruction1)
H_transmission(Instruction1)R_assumption(Instruction1)R_complementing(Instruction1)R_inference(Instruction2)R_transmission(Instruction2)H_complementing(Instruction2)
H_transmission(Instruction2)[C2→R3]
(80)
82
106
109
110
Time Transcribed observation data Simulation result
Essential match
Predictive match
Perfect match
Assessment of TSA (2nd MB layer)
0 10 20step
0 10 20step
30
0
50
100
0
50
100
com
plet
enes
s (%
)co
mpl
eten
ess
(%)
C2 R2
C2 R2i
p
p
ip
p i
ip
i ip
p
i
p perceptioninferencei
p perceptioninferencei
p
Observation
Simulation
Assessment measureCompleteness(R2) = [R2∩C1]/[C1]Completeness(C2) = [C2∩R1]/[R1]
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Society level
Targetn Organization, group of organizations, society
Key conceptsn Multi-agent model, emergent behaviour, evolution,
networking, social structure, market, consensus, organizational knowledge, culture
Supporting technologiesn Mass media, Internet, mobile phone, social ware,
universal web, artificial society
Background and objective
Backgroundn Increasing demands for participatory approaches
of social decision-making n Crucial role of public opinion in such approachesn Insufficient knowledge on dynamics of public
opinion development
Objectiven Analyze dynamics of public opinion development
with a multi-agent model considering cognitive and social factors in decision-making
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Simulation model (1)
Agents are arranged in a square grid of 100X100.Each agent is connected with and influenced by the four neighbors.Each agent has opinion p, which stands for the probability to take an affirmative position on a certain assertion.
Simulation model (2)
Individual decision-making
External factor (conformity to others)
IwEwdtdpi
21 +=
)()( 43 imin ppwppwE −+−=
internal factorexternal factor
opinion of mass mediaopinion of neighbors
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Internal factor
)12()21( 1111)( −− +
−+
−=
iii pki
pki
i ep
eppI
0.5-0.5
0.5
0.0
1.00.0
k1 = 3k1 = 7k1 = 15
pi
I (p
i )
Self-organization of communities
Initial Final
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Balance between external and internal factors
w2/w1 = 0.67
other parametersk1 = 7w4 = 0D = 0c = 0
w2/w1 = 0.82
w2/w1 = 1.2w2/w1 = 1.0 w2/w1 = 1.5
Characteristic function of mass media
)5.0(211
avepkm ep
−+= 0.5 1.00.0
pave
p m(p
ave)
0.0
1.0
0.5
k2 = 6k2 = 10k2 = 30
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Effects of mass media
0.0
0.6
0.3
0.0
0.6
0.3
0.0
0.6
0.3
0.0
0.6
0.3
0.0 0.5 1.0 0.0 0.5 1.0
0.0 0.5 1.00.0 0.5 1.0
fraction of affirmative opinion
freq
uenc
yw4 /w3= 0.0 w4 /w3= 0.1
w4 /w3= 0.2 w4 /w3= 0.4
Network models
regular random
small-world scale-free
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Final opinion distribution
0.0
0.6
0.3
0.0
0.6
0.3
0.0
0.6
0.3
0.0
0.6
0.3
0.0 0.5 1.0 0.0 0.5 1.0
0.0 0.5 1.00.0 0.5 1.0
fraction of positive opinion
freq
uenc
yregular random
scale-freesmall-world
Overview of Analysis Method
goal
mean
B
A
C D
E
Deliberation Space
A
B
C
BB
DA
E
Pieces of Schemata
Minutes
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Model of Consensus Development Process
(1) Divergent articulation (2) Search of preliminary consensus
(3) Persuasion or compromise (4) Embodiment of consensus
Multi-agent simulation of emergency response (MASTERD)
Aims to provide design and assessment support for emergency response systemIntegrated simulation of various factors of disastersFocuses on human activities and behaviours in emergency
OrganizationalActivity
Simulator
Disaster PhenomenaSimulators
PresentationTools
EvacuationSimulator
Conceptual design of MASTERD
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Organizational activity simulatorSimulates activities of response organizations.n Task:Call, Dispatch, Meeting, Monitoring, Traffic Control, etc.n Communication:inform, request and queryn Resource exchange:workforce, telephone, iodine, car, etc.
Each organization is implemented as one agent with a normative decision-making model.
Info.section
Public info.section
EvacuationsectionFacility
LocalGovt.
information(accident!)
PoliceDept.
Mass-Media
resource (ambulance)
Announcemnt
Receive anEmergencyCall
FireDept.
Dispatch
Meetingstarts !
Meeting
Criticality!Accident
TEL
SenderReceiverTitleTimeKindSituationRadiationFacilityActionRegionMeetpointMedia
Points of today’s lecture
New approach for society design, more positive and predictive, is highly desired to solve issues our society is now facing.
Human modeling that can cover a wide range of human behavior is promising for considering human behavior in society design of the next generation.