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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.