Modeling and Simulation of Human Choices: from Utility Theory to - - PowerPoint PPT Presentation

Modeling and Simulation of Human Choices: from Utility Theory to Applications

• Prof. Michel Bierlaire

Director – Transportation Center Ecole Polytechnique Fédérale de Lausanne (EPFL) Switzerland

Introduction : Science Fiction

 Psyc

Psychohistory hohistory: B : Bra ranc nch of h of mathe thematic tics whic s which de h deals ls with the with the re reactions of hum tions of human conglom

• nglomera

rate tes to fixe s to fixed d soc socia ial a l and e nd econom

• nomic

ic stim

• stimuli. Enc
• uli. Encyc

yclope lopedia dia Ga Gala lactic tica, 1 , 116th Edition th Edition (1 (1020 F.E.) F.E.)

Introduction: Prof. McFadden

 La

Laure ureate te of The

• f The B

Bank nk of

• f

Swe Swede den Prize n Prize in Ec in Econom

• nomic

ic Sc Scie ienc nces in Me s in Memory of

• ry of

Alfre lfred N d Nobe

• bel 2

l 2000

 Owns a

Owns a fa farm rm a and vine nd vineya yard in rd in Napa pa Va Valle lley y

 “Fa

Farm rm work work c cle lears the rs the m mind, ind, and the nd the vine vineya yard is a rd is a gre great t pla place to prove to prove the theore

• rems”

”

Introduction : marketing

 Pre

Predic diction of tion of mark rket sha t share res s

 Choic

hoice of bra

• f brand

nd

 Choic

hoice of produc

• f product

t fe feature tures s

 Choic

hoice of re

• f reta

tail il store store

 Etc

Etc. .

Introduction : transportation demand analysis

 Choic

hoice of m

• f mode
• de

 Choic

hoice of pa

• f path

th

 Choic

hoice of

• f

de destina stination tion

 Choic

hoice of pa

• f park

rking ing

 Choic

hoice of

• f

de depa parture rture tim time

Framework

Data Model Simulation

Data: questionaires

 Data

ta a about the bout the re responde spondent nt

 Choic

hoice da data ta

 Reve

veale led d pre prefe fere renc nces s

 Sta

State ted pre d prefe fere renc nces

Data: smartphones

 GSM, GPS

GSM, GPS

 Accele

lerom romete ter r

 WiFi

WiFi

 Blue

luetooth tooth

 Ambie

bient sound nt sound

 And m

nd more

• re...

...

Data: scanner data

 Deta

taile iled purc d purcha hase se inform information tion

 Pe

Persona rsonalize lized d

Data: eye tracking

 Whe

Where re do pe do people

• ple

look look?

 Use

sed in m d in mark rketing ting re rese searc rch h

 Use

sed in driving d in driving sa safe fety re ty rese searc rch h

 R

Rele leva vant for nt for pe pede destria strian m n mode

• dels

ls

Model : assumptions

 Hom

• mo e
• econom
• nomic

icus us

 Rationa

tionality lity

 Utility the

tility theory

• ry

 Ea

Each a h alte lterna rnative tive is is assoc ssocia iate ted with a d with a utility utility

 The

The a alte lterna rnative tive with the with the la large rgest utility is c st utility is chose hosen n

Model : assumptions

 Strong

Strong assum ssumptions ptions

 Unc

ncerta rtainty a inty and nd irra irrationa tionality m lity must ust be be c capture ptured d

 Random

ndom utility utility mode

• dels

ls

 La

Late tent va nt varia riable bles s

Model : features

 Disa

isaggre ggrega gate te – – mark rket se t segm gments nts

 Qua

Quantita ntitative tive a and nd qua qualita litative tive va varia riable bles s

 Can ha

n handle ndle subje subjectivity - tivity - attitude ttitudes- s- pe perc rceptions ptions

Application : simulation of market shares

 Polic

Policy va y varia riable bles s (e (e.g. pric .g. price) )

 Nonline

• nlinear e

r effe ffect t

Application : market segmentation

 Ma

Mark rket sha t share res pe s per r se segm gment nt

 Gra

Granula nularity rity de depe pends on the nds on the da data ta a ava vaila ilability bility

Application : simulation of revenues

 Conc

• ncept of optim

pt of optimal l pric price

 Can be

n be se segm gment nt spe specific ific

Application : pedestrian walking behavior

 Choic

hoice of the

• f the ne

next xt ste step p

 Collision a

• llision avoida

voidanc nce

 Le

Leade der followe r follower r

Application : pedestrian simulation

Application : pedestrian simulation

Applications: route choice

 Com

• mple

plex proble x problem

 Num

umbe ber of pa r of paths is ths is huge huge

 High le

igh leve vel of l of

• ve
• verla

rlapping pping

 Shorte

Shortest pa st path not th not be beha haviora viorally lly meaningful ningful

Application : electric vehicles

 Ma

Mark rket sha t share res s

 Hypothe

ypothetic tical l choic hoice

 Im

Importa portanc nce of

• f

attitude ttitude towa toward the rd the environm nvironment nt

Application : facial expression recognition

 Autom

utomatic tic ide identific ntification of the tion of the emotion

• tion

 Pote

Potentia ntially lly diffe differe rent a nt across ross culture ultures s

 Require

quires a s adva dvanc nced im image ge proc processing ssing algorithm lgorithms s

Application : demand-supply interactions

 Reve

venue nue mana nage gement nt

 Ma

Mark rket e t equilibrium quilibrium

 Com

• mbina

bination of tion of

• pe
• pera

rations tions re rese searc rch a h and nd de demand m nd mode

• dels

ls

Conclusion

 Disc

iscre rete te c choic hoice m mode

• dels

ls

 Adva

dvanc nced a d and ope nd opera rationa tional l

 Accom

• moda
• date

te m mode

• dern da

rn data ta sourc sources s

 Wide

Wide ra range nge of a

• f applic

pplications tions

 Com

• mple

plex m x mode

• dels re

ls require quires sim s simula ulation tools tion tools

Short course : Discrete Choice Analysis: Predicting Demand and Market Shares

 Janua

nuary 2 ry 29- Fe

• Februa

bruary 2 ry 2, , 2012 2012

 Ec

Ecole

• le Polyte

Polytechnique hnique Fé Fédé déra rale le de de La Lausa usanne nne

 Prof. B

• Prof. Ben-A

n-Akiva iva (MIT) – (MIT) –

• Prof. B
• Prof. Bie

ierla rlaire ire (EPFL) (EPFL)

 tra

transp-or.e nsp-or.epfl.c pfl.ch/ h/dc dca