mathematical modeling of choice behavior: from theory to … · 2014. 3. 10. · encyclopedia...
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Mathematical modeling of choice behavior: from theory
to practice
Michel Bierlaire
Transport and Mobility LaboratorySchool of Architecture, Civil and Environmental Engineering
Ecole Polytechnique Federale de Lausanne
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Outline
Outline
1 MotivationLiterature reviewNeed to model behaviorApplicationsImportance
2 Some theoryDecision rule
The random utility model3 Questioning rationality4 Choice data5 Case studies
Market shares of electrical vehiclesValue of timePath to purchase
6 Conclusion
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Motivation Literature review
Literature review
Psychohistory
Branch of mathematics which deals with the reactions of humanconglomerates to fixed social and economic stimuli. The necessary size ofsuch a conglomerate may be determined by Seldon’s First Theorem.
Encyclopedia Galactica, 116th Edition (1020 F.E.)
Encyclopedia Galactica Publishing Co., Terminus
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Motivation Literature review
Literature review
Asimov, I. (1951) Foundation,Gnome Press
Motivation: shorten the period ofbarbarism after the Fall of theGalactic Empire
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Motivation Literature review
Literature review
Albus Dumbledore
It is our choices that show what we truly are, far more than our abilities
Jean-Paul Sartre
We are our choices
Ken Levine
We all make choices, but in the end our choices make us.
In the end what separates a man from a slave? Money? Power? No,a man chooses... a slave obeys.
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Motivation Need to model behavior
Motivation
Human dimension in
engineering
business
marketing
planning
policy making
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Motivation Need to model behavior
Theories and methods
Need for
behavioral theories
quantitative methods
operational mathematicalmodels
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Motivation Need to model behavior
Economic approach
Concept of demand
marketing
transportation
energy
finance
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Motivation Need to model behavior
Transportation
Supply = infrastructure
Demand = behavior, choices
Congestion = mismatch
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Motivation Need to model behavior
Transportation
Usually in operations research:
optimization of the supply
for a given (fixed) demand
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Motivation Need to model behavior
Aggregate demand
Homogeneous population
Identical behavior
Price (P) and quantity (Q)
Demand functions: P = f (Q)
Inverse demand: Q = f −1(P)
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Motivation Need to model behavior
Disaggregate demand
Heterogeneous population
Different behaviors
Many variables:Attributes: price, travel time,reliability, frequency, etc.Characteristics: age, income,education, etc.
Complex demand/inversedemand functions.
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Motivation Need to model behavior
Choices
Concept of choice
Marketing:brand, product
Transport: mode,destination
Energy: type, usage
Finance: buy/sell, product
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Motivation Applications
Applications
Willingness to pay for travel time savings
Swiss Federal Road Office
Compute the Swiss value oftime
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Motivation Applications
Applications
Route choice
How do traveler select anitinerary?
Impact of information andguidance
Data: Nokia
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Motivation Applications
Applications
Market share of electrical vehicles
Renault Suisse
Forecasting of market shares
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Motivation Applications
Applications
Dynamics of vehicle ownership
PSA Peugeot Citroen
Vehicle transactions model
Changes in householdsvehicle ownership
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Motivation Applications
Applications
Path to purchase: the case of ice creams
Nestle Research Center
Impact of the design of theposter
on the choice of ice cream
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Motivation Applications
Applications
Automatic analysis of facial expressions
Images and videos
Signal Processing Lab
Classification algorithm
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Motivation Importance
Importance
Daniel L. McFadden
UC Berkeley 1963, MIT 1977, UC Berkeley 1991
Laureate of The Bank of Sweden Prize in EconomicSciences in Memory of Alfred Nobel 2000
Owns a farm and vineyard in Napa Valley
“Farm work clears the mind, and the vineyard is agreat place to prove theorems”
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Some theory
Outline
1 MotivationLiterature reviewNeed to model behaviorApplicationsImportance
2 Some theoryDecision ruleThe random utility model
3 Questioning rationality
4 Choice data
5 Case studiesMarket shares of electrical vehiclesValue of timePath to purchase
6 Conclusion
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Some theory
Homo economicus
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Some theory Decision rule
Decision rule
Homo economicus
Rational and narrowly self-interested economic actor who is optimizing heroutcome
Utility
Un : Cn −→ R : a! Un(a)
captures the attractiveness of an alternative
measure that the decision maker wants to optimize
Behavioral assumption
the decision maker associates a utility with each alternative
the decision maker is a perfect optimizer
the alternative with the highest utility is chosenM. Bierlaire (EPFL) Modeling behavior 23 / 82
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Some theory Decision rule
Simple example: mode choice
Attributes
AttributesAlternatives Travel time (t) Travel cost (c)
Car (1) t1 c1Bus (2) t2 c2
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Some theory Decision rule
Simple example: mode choice
Utility functions
U1 = −βtt1 − βcc1,U2 = −βtt2 − βcc2,
where βt > 0 and βc > 0 are parameters.
Equivalent specification
U1 = −(βt/βc)t1 − c1 = −βt1 − c1U2 = −(βt/βc)t2 − c2 = −βt2 − c2
where β > 0 is a parameter.
Choice
Alternative 1 is chosen if U1 ≥ U2.
Ties are ignored.
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Some theory Decision rule
Simple example: mode choice
Choice
Alternative 1 is chosen if
−βt1 − c1 ≥ −βt2 − c2
or
−β(t1 − t2) ≥ c1 − c2
Alternative 2 is chosen if
−βt1 − c1 ≤ −βt2 − c2
or
−β(t1 − t2) ≤ c1 − c2
Dominated alternative
If c2 > c1 and t2 > t1, U1 > U2 for any β > 0
If c1 > c2 and t1 > t2, U2 > U1 for any β > 0
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Some theory Decision rule
Simple example: mode choice
Trade-off
Assume c2 > c1 and t1 > t2.
Is the traveler willing to pay the extra cost c2 − c1 to save the extratime t1 − t2?
Alternative 2 is chosen if
−β(t1 − t2) ≤ c1 − c2
or
β ≥c2 − c1t1 − t2
β is called the willingness to pay or value of time
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Some theory Decision rule
Simple example: mode choice
c1 + βt1 = c2 + βt2
t1 − t2
c1 − c2
Alt. 1 is dominant
Alt. 2 is dominant
Alt. 2 is preferred
Alt. 1 is preferred
β1
Alt. 1 is chosen Alt. 2 is chosen
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Some theory The random utility model
Random utility model
Random utility
Uin = Vin + εin.
The logit model
P(i |Cn) =eVin
∑j∈Cne
Vjn
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Questioning rationality
Outline
1 MotivationLiterature reviewNeed to model behaviorApplicationsImportance
2 Some theoryDecision ruleThe random utility model
3 Questioning rationality
4 Choice data
5 Case studiesMarket shares of electrical vehiclesValue of timePath to purchase
6 Conclusion
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Questioning rationality
Homo economicus?
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Questioning rationality
Motivation
Rationality?
Standard random utility assumptions are often violated.
Factors such as attitudes, perceptions, knowledge are not reflected.
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Questioning rationality
Example: pain lovers
Kahneman, D., Fredrickson, B., Schreiber, C.M., and Redelmeier, D., When More Pain Is Preferred to Less: Adding a Better
End, Psychological Science, Vol. 4, No. 6, pp. 401-405, 1993.
Short trial: immerse one hand in water at 14◦ for 60 sec.
Long trial: immerse the other hand at 14◦ for 60 sec, then keep thehand in the water 30 sec. longer as the temperature of the water isgradually raised to 15◦.
Outcome: most people prefer the long trial.
Explanation:duration plays a small rolethe peak and the final moments matter
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Questioning rationality
Example: The Economist
Subscription to The Economist
Web only @ $59Print only @ $125Print and web @ $125
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Questioning rationality
Example: The Economist
Subscription to The Economist
Experiment 1 Experiment 2
Web only @ $59 Web only @ $59Print only @ $125
Print and web @ $125 Print and web @ $125
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Questioning rationality
Example: The Economist
Subscription to The Economist
Experiment 1 Experiment 2
16 Web only @ $59 Web only @ $59 680 Print only @ $12584 Print and web @ $125 Print and web @ $125 32
Source: Ariely (2008)
Dominated alternativeAccording to utility maximization, should not affect the choiceBut it affects the perception, which affects the choice.
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Questioning rationality
Example: good or bad wine?
Choose a bottle of wine...
Experiment 1 Experiment 2
1 McFadden red at $10 McFadden red at $102 Nappa red at $12 Nappa red at $123 McFadden special reserve
pinot noir at $60
Most would choose 2 Most would choose 1
Context plays a role on perceptions
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Questioning rationality
Example: live and let die
Population of 600 is threatened by a disease. Two alternative treatmentsto combat the disease have been proposed.
Experiment 1 Experiment 2# resp. = 152 # resp. = 155
Treatment A: Treatment C:200 people saved 400 people die
Treatment B: Treatment D:600 people saved withprob. 1/3
0 people die with prob.1/3
0 people saved with prob.2/3
600 people die with prob.2/3
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Questioning rationality
Example: live and let die
Population of 600 is threatened by a disease. Two alternative treatmentsto combat the disease have been proposed.
Experiment 1 Experiment 2# resp. = 152 # resp. = 155
Treatment A: Treatment C:72% 200 people saved 400 people die 22%
Treatment B: Treatment D:28% 600 people saved with
prob. 1/30 people die with prob.1/3
78%
0 people saved with prob.2/3
600 people die with prob.2/3
Source: Tversky & Kahneman (1986)
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Questioning rationality
Example: to be free
Choice between a fine and a regular chocolate
Experiment 1 Experiment 2Lindt $0.15 $0.14Hershey $0.01 $0.00Lindt chosen 73% 31%Hershey chosen 27% 69%
Source: Ariely (2008) Predictably irrational, Harper Collins.
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Questioning rationality
Operational models
Behavioral aspects
Attitudes
Habits
Perceptions
Social norms
etc.
Modeling framework
Random utility
Latent variables
Data
Choice data
Psychometrics
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Choice data
Outline
1 MotivationLiterature reviewNeed to model behaviorApplicationsImportance
2 Some theoryDecision rule
The random utility model3 Questioning rationality4 Choice data5 Case studies
Market shares of electrical vehiclesValue of timePath to purchase
6 Conclusion
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Choice data
Choice data
Revealed preferences
actual choice observed
in real market situations
Example: scanner data insupermarkets
Stated preferences
hypothetical situations
attributes defined by theanalyst
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Choice data
Data
Questionnaires
Data about the respondent
Choice data
Revealed preferences
Stated preferences
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Choice data
Data: example of a questionaire
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Choice data
Data
Smartphones
GSM, GPS
Accelerometer
WiFi
Bluetooth
Ambient sound
And more...
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Choice data
Data
Scanner data
Detailed purchaseinformation
Personalized
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Choice data
Data
Eye tracking
Where do people look?
Used in marketing research
Used in driving safetyresearch
Relevant for pedestrianmodels
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Choice data
Data: eye tracking
Movie: Nestle data collection
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Case studies Market shares of electrical vehicles
Outline
1 MotivationLiterature reviewNeed to model behaviorApplicationsImportance
2 Some theoryDecision rule
The random utility model3 Questioning rationality4 Choice data5 Case studies
Market shares of electrical vehiclesValue of timePath to purchase
6 Conclusion
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Case studies Market shares of electrical vehicles
Market shares of electrical vehicles
Glerum, A., Stankovikj, L., Thmans, M., and Bierlaire, M. (to appear)
Forecasting the demand for electric vehicles: accounting for attitudes andperceptions, Transportation Science (accepted for publication on May 29,2013)
Objectives
Demand analysis for two electrical vehicles: Zoe & Fluence (Renault)
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Case studies Market shares of electrical vehicles
Sample
Target groups
Sampling from
Recent buyers
Prospective buyers
Renault customers
Everybody from
Pre-orders
Z. E. newsletter
Sampling protocol: representative for
3 language regions of Switzerland (German, French, Italian)
Gender
Age category (18–35, 36-55, 56–74)
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Case studies Market shares of electrical vehicles
Sample
High response rate - possibility to segment
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Case studies Market shares of electrical vehicles
Sample
Unbalanced sample (gender): need for corrections
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Case studies Market shares of electrical vehicles
Survey
Phase I
Characteristics of car(s) of respondents household
Socio-economic information
Mobility habits
Phase II
Opinions and perceptions on topics related to EV
Choice situations
Willingness-to-pay
Interest in additional services
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Case studies Market shares of electrical vehicles
Design of the choice experiment
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Case studies Market shares of electrical vehicles
Segmentation
A priori higher interest for EV and/or Renault
Pre-orders (1)
Subscribers of the Z.E. newsletter (2)
A priori interest in Renault
Renault customers (3)
No a priori interest for EV and/or Renault
Recent buyers (4)
Prospective buyers (5)
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Case studies Market shares of electrical vehicles
Model specification
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Case studies Market shares of electrical vehicles
Parameter estimates
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Case studies Market shares of electrical vehicles
Parameter estimates
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Case studies Market shares of electrical vehicles
Parameter estimates
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Case studies Market shares of electrical vehicles
Market shares and revenues
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Case studies Value of time
Outline
1 MotivationLiterature reviewNeed to model behaviorApplicationsImportance
2 Some theoryDecision rule
The random utility model3 Questioning rationality4 Choice data5 Case studies
Market shares of electrical vehiclesValue of timePath to purchase
6 Conclusion
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Case studies Value of time
Value of time in Switzerland
Axhausen, K., Hess, S., Koenig, A., Abay, G., Bates, J., and Bierlaire, M.(2008)
Income and distance elasticities of values of travel time savings: new Swissresults, Transport Policy 15(3):173-185.
Data collection
Source for recruitment: survey “Kontinuierliche Erhebung zumPersonenverkehr” (KEP) by SBB/CFF
Stated preferences
Questionnaire designed based on a real reference trip
Three parts:SP mode choice (car / bus or rail)SP route choice (current mode or alternative mode)Socio-demographics and information about the reference trip
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Case studies Value of time
Value of time in Switzerland
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Case studies Value of time
Value of time in Switzerland
Number of observations (1225 individuals)
Business Commuters Leisure Shopping TotalMode : car/bus 6 162 186 126 480Mode : car/rail 426 1716 2538 1104 5784Route : bus for bus users 9 405 450 342 1206Route : car for car users 156 846 1176 660 2838Route : rail for car users 126 594 837 504 2061Route : rail for rail users 324 1008 1881 288 3501Total 1047 4731 7068 3024 15870
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Case studies Value of time
Value of time in Switzerland
Explanatory variables
travel time
travel cost
level of congestion (car)
frequency (TC)
number of transfers (TC)
trip length
income
inertia
car availability
sex
1/2-fare CFF
general subscription
trip purpose
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Case studies Value of time
Value of time in Switzerland
Business Commute Leisure ShoppingTime TC (CHF/h) 49.57 27.81 21.84 17.73Time car (CHF/h) 50.23 30.64 29.20 24.32Headway (CHF/h) 14.88 11.18 13.38 8.48CHF/transfer 7.85 4.89 7.32 3.52
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Case studies Value of time
Value of time in Switzerland
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Case studies Value of time
Value of time in Switzerland
Value of time varies (namely) with
transportation mode,
trip purpose,
income,
trip length.
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Case studies Path to purchase
Outline
1 MotivationLiterature reviewNeed to model behaviorApplicationsImportance
2 Some theoryDecision rule
The random utility model3 Questioning rationality4 Choice data5 Case studies
Market shares of electrical vehiclesValue of timePath to purchase
6 Conclusion
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Case studies Path to purchase
Path to purchase: the case of ice-cream
Collaboration Nestle-EPFL
2006–2008
NestleNestle Research CenterIce cream Business Unit
EPFLTransport and Mobility Laboratory (Prof. Bierlaire)Signal Processing Laboratory (Prof. Thiran)
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Case studies Path to purchase
Path ro purchase
Project
Impact of the stimuli on theconsumers behavior
Example: design of an icecream board
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Case studies Path to purchase
Data collection
Eye tracking
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Case studies Path to purchase
Data processing
From raw video to numerical data
Movie: Original video
Movie: Correct distortions
Identify locations
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Case studies Path to purchase
The model
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Case studies Path to purchase
The model
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Case studies Path to purchase
Results
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Conclusion
Outline
1 MotivationLiterature reviewNeed to model behaviorApplicationsImportance
2 Some theoryDecision rule
The random utility model3 Questioning rationality4 Choice data5 Case studies
Market shares of electrical vehiclesValue of timePath to purchase
6 Conclusion
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Conclusion
Conclusion
Behavioral models
Individual choice model
Disaggregate market segments
Flexible specification
Quantitative and qualitative variables
Usage of revealed and stated preferences data
Wide range of applications
Can account for subjectivity (attitudes and perceptions)
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Conclusion
Short course: Discrete Choice Analysis: Predicting
Demand and Market Shares
March 23 – 27, 2014
Ecole PolytechniqueFederale de Lausanne
Prof. Ben-Akiva (MIT)
Prof. Bierlaire (EPFL)
Prof. McFadden (UCBerkeley)
Prof. Walker (UC Berkeley)
transp-or.epfl.ch/dca
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