Index
Bounded Rational Choice Behaviour: Applications in Transport
ISBN: 978-1-78441-072-8, eISBN: 978-1-78441-071-1
Publication date: 31 January 2015
This content is currently only available as a PDF
Citation
(2015), "Index", Rasouli, S. and Timmermans, H. (Ed.) Bounded Rational Choice Behaviour: Applications in Transport, Emerald Group Publishing Limited, Leeds, pp. 265-268. https://doi.org/10.1108/978-1-78441-072-820151017
Publisher
:Emerald Group Publishing Limited
Copyright © 2015 Emerald Group Publishing Limited
INDEX
Activation level
, 196–197, 199–200, 202–205, 210
Activity-based model
, 126, 139–141, 190, 214
Activity-based travel demand models
, 2
Activity-travel behaviour
, 117, 158, 189–210
Activity-travel choice task
, 122
Advantage maximization
, 10
Agent-based models
, 213, 228–229
Albatross
, 138, 140, 151–154, 157–159
Aspiration levels
, 7
Aspirations
, 189–191, 195, 198–201, 204–206, 209–210
Association pattern technique
, 120
Attribute
, 1, 3–12, 14–20, 22, 24–25, 32–37, 46, 55–56, 63, 66, 68, 73–76, 79–85, 87–89, 91–93, 96–105, 111, 119–121, 123, 125–126, 129–130, 147, 157, 193–194, 196, 198, 236, 246
Attribute non-attendance (ANA)
, 1, 17–18, 73–81, 83–92
Attributes
, 1–10, 13–21, 23–25, 31–33, 35–39, 41, 46, 51, 53, 59, 62, 66, 68–69, 73–84, 86–89, 92, 96, 98–102, 105, 115, 117–121, 123, 125–131, 157, 192–194, 197–198, 215–218, 229, 235–237, 244, 246–249
Awareness
, 195, 199–200
Awareness reinforcement parameter
, 195
Awareness retention rate
, 195, 200
Bayesian Decision Network
, 118
Bayesian learning
, 218
Bayesian networks
, 137–138, 141–142, 144–146, 149, 152–156, 158–159
Bayesian updating
, xviii
Behavioural change
, 222, 246–248
Behavioural economics
, 56, 235, 244
Behavioural equilibrium
, 190, 229, 247
Beliefs
, 102–103, 105–107, 116, 191, 193, 199–200, 215–219, 225–226, 228
Benefits
, 115–119, 121, 123, 125–126, 128–131, 221, 237
Bias
, 59, 76, 81, 92, 122, 239
BNT classifier
, 137, 143, 149–150, 152–159
Bounded rationality
, 1, 3, 5–7, 9–11, 13, 15, 17, 19, 21, 23, 25–26, 51, 68–69, 74, 95–96, 98, 102, 111, 137–141, 143, 145, 147, 149, 151, 153, 155, 157, 163–165, 167, 169–171, 173, 175, 177, 179, 181, 189, 191, 209, 213, 215, 222, 226, 228, 234–235, 244, 249
Causal knowledge
, 115, 118
Causal network
, 115, 117–118, 120, 129
Causal Network Elicitation Technique (CNET)
, 115, 117, 121–122, 132
CB-CNET
, 122
Censored normal distribution
, 75
Centrality of variables
, 129
Choice complexity
, 74–75
Choice set
, 1, 3–8, 10, 12–14, 19–21, 23–25, 38, 50–55, 57–58, 62–64, 66, 68–69, 97, 111, 119, 123, 126, 131, 190–198, 200, 202–204, 206, 208, 210
Choice set formation
, 19, 23, 25, 191–192, 194
Choice set generation
, 23–24, 54–55, 62–63
CNET card game
, 122
Cognitive mapping
, 119
Cognitive maps
, 116
Cognitive responses
, 193
Cognitive space
, 4, 6–8, 20, 25
Cognitive subsets
, 123, 125–126, 128–130
Compensatory choice
, 25, 75
Competing destination model
, 11
Compromise alternatives
, 10
Conjunctive decision rule
, 6–8, 20, 23
Consideration set
, 4, 6, 15, 19–21, 23–24
Constrained latent class model
, 76
Construal Level Theory
, 119
Context-dependent choice models
, 10–11
Cumulative Prospect Theory (CPT)
, 142–143, 235, 240–242, 245, 248–249
Decision process
, 1–4, 19, 38, 77, 95–98, 100–101, 111–112, 117–118, 140, 215, 220, 225
Decision rule
, 1, 6–8, 10, 20, 23, 25, 31–33, 38, 41, 46, 97, 137–138, 141, 146–147, 149, 154, 156, 158–159, 213–217, 222–223, 225, 235
Decision rules
, 1, 6–7, 10, 23, 25, 31–33, 38, 41, 46, 97, 137–138, 141, 146–147, 149, 154, 156, 158–159, 213–217, 222–223, 225
Decision strategies
, 74, 107, 111
Decision trees
, 137–138, 146, 149, 152–154, 157–159
Descriptive theory
, 163, 213, 215, 229
Dirichlet distribution
, 218
Disjunctive rule
, 7–8, 14, 96
Dynamics
, 2, 163, 168, 189–193, 195, 197–199, 201, 203, 205, 207, 209–210, 213, 223, 227–229
Dynamic traffic assignment
, 163–165, 167, 169, 171, 173, 175, 177, 179, 181
Dynamic user optimal
, 163, 169, 172
E-Commerce
, 123–128, 130–131
Elimination by aspects
, 38
Emotional responses
, 191, 193–194, 199, 206, 210
Entropy measure
, 74, 149
Error variance
, 74–75
Expected utility
, 23, 137, 193–194, 196–197, 200–201, 203–205, 207–208, 210, 233, 235–237, 239
Expected Utility-maximization (EUT)
, 137–138, 193–194, 235–237, 239, 242
Exploitation
, 197–198, 200, 202–205, 207–209
Exploration
, 132, 191, 197–198, 200, 202–210, 229
Feasibility
, 2, 177, 179
Forgetting
, 189, 191, 199–200, 202, 209, 216, 218
Four-step model
, 2, 214
Gap function
, 175, 177
G-RRM
, 33–39, 41, 46
Habit formation
, 189, 191, 196, 199
Hard laddering
, 120
Heterogeneity
, 4, 18, 25, 31–33, 41, 46, 53, 58–60, 69, 76–77, 80–81, 92, 95–96, 111, 116–117, 131–132, 248
Heuristic
, 15, 20, 22–25, 74, 95–97, 99–107, 109, 111–112, 140–141, 171, 177, 214, 228
Hidden Markov chain
, 213, 222–223
Hierarchical value maps
, 120
Hyperbolic response curve
, 9
Inertia
, 190, 196
Inferred attribute non-attendance
, 73
Influences on attribute non-attendance
, 73–93
Information load
, 73–77, 79–81, 83–85, 87–89, 91–92
Instrumental Variable
, 220–221
Interview protocol
, 121
Laddering
, 116, 120, 132
Latent Class
, 5, 17–18, 31–33, 37–39, 46, 54, 74, 76–78, 96, 116
Latent class model
, 17, 54, 74, 76, 96
Learning
, 132, 137–138, 140–143, 145–146, 152, 163, 189, 191, 199, 209, 213–218, 223, 227–229, 248–249
Lexicographic rule
, 1, 14–17, 23, 75, 95–97, 100, 111
Lexicographic choice
, 14, 23, 75
Lexicographic semi-order
, 15
δ-logit
, 19
Lognormal distribution
, 81, 84, 91
Long-term change
, 190–191, 198–199, 207
Maximax decision rule
, 9
Maximin decision rule
, 9
Means-end-chain
, 120–121
Means-end-chain theory
, 120
Memory decay
, 189, 195
Memory retrieval ability
, 195
Mental effort
, 25, 95, 97, 102–103, 105–107, 111–112, 119, 217
Mental maps
, 116
Mental representations
, 115–117, 119–123, 125–127, 129–132
Mind reading
, 119
Minimax decision rule
, 9
Minimum awareness level
, 195
Minimum difference lexicographic rule
, 15
Mixed Logit
, 116
Mixture model
, 31–33, 37
Multidimensional decisions
, 214
Multinomial logit
, 2–3, 5, 10, 22–24, 50, 52, 77, 95, 98
Multiple context dependency
, 49
Needs
, 33, 91, 103–105, 115–119, 125–126, 131–132, 151, 159, 192, 198, 200, 210, 214, 217, 220, 229, 233
Network equilibrium
, 245–247
Non-compensatory model
, 23, 96
Nonlinear complementarity problem
, 163, 171, 174
Nudge
, 226, 247
Number of alternatives
, 20, 25, 54, 73–75, 80, 86–87, 89, 92, 206
number of attribute levels
, 15, 73, 75, 80, 87, 92
number of attributes
, 18, 32, 73–75, 80–82, 86–87, 89, 92, 96, 123, 125–126
number of choice tasks
, 80, 86–87, 89
range of attribute levels
, 81, 89
Online shopping
, 122, 123, 126, 130, 131, 132
Ordered heterogeneous logit model
, 75, 87
Passive bounded rationality model
, 74
Perceived search cost
, 217, 219–221
Predicted irrationality
, 247
Preference heterogeneity
, 76–77, 80–81, 92, 116
Priming
, 132
Prospect
, 49, 51, 56, 64, 66–68, 233–235, 237–241, 243–250
Prospect Theory (PT)
, 51, 56, 64, 233–235, 237–250
Random parameters attribute non-attendance (RPANA) model
, 73–74, 76–78, 80–81, 83–88, 90–92
Random Utility Models (RUM)
, 25, 31, 33–39, 41, 45–46, 49, 51, 132, 234, 250
Rationally adaptive model
, 74–75, 92
Recall techniques
, 119, 120, 132
Recognition techniques
, 119
Reference point
, 5, 9, 13, 49–52, 68–69, 237–238, 242–244, 246–249
Regret
, 1, 3, 9–13, 25, 31–37, 39, 41, 43, 45, 49, 56, 64–68, 103, 250
Regret-based choice models
, 3
Regret minimization
, 13
Regret-weight
, 32–37
Reinforcement
, 195
Relative advantage model
, 13
Relative utility
, 10, 13–14, 25, 49–69
Reluctance to change
, 196
Reporting error
, 73, 75, 84
Risk aversion
, 236, 238
Risk/loss asymmetry
, 238
Route choice principle
, 169, 172, 174
Route-swapping algorithm
, 177
RRM
, 32–39, 41, 45–46, 64–69
Rule complexity reduction
, 137
RUM
, 31, 33–39, 41, 45–46, 132, 234, 250
SAM
, 156–157
Satisficing states
, 7
Satisficing theory
, 195
Shopping behavior
, 95, 97, 112
Short-term change
, 196, 198
Similarity
, 10–12, 58, 60–61, 68, 74, 119, 157
Situational dependence
, 117, 131
Stated adaptation
, 210, 224, 229
Stated adaptation experiment
, 210, 224
Stated attribute non-attendance
, 17, 75, 83, 84
Stated choice
, 17, 31, 46, 66, 75, 80–81, 92
Stress
, 189–191, 195–201, 210
String recognition algorithm
, 122
Subjective search gain
, 217, 219, 222, 228
Supervised learning
, 137, 146
Taste heterogeneity
, 18, 46, 116
Threshold utility value
, 7
Tolerance
, 163, 171–174, 176–177, 179–181, 191, 196–197, 199, 210
Tolerance based principle
, 181
Traffic flow component
, 166–167, 174
Transformation function
, 173, 176–177
Travel behavior
, 1–3, 31, 138, 139, 141, 190, 234, 235, 244, 248–249
Travel behavior forecasting
, 2, 214
Type I strategy
, 74
Type II strategy
, 74
Unsupervised learning
, 146
User equilibrium
, 164, 168–172, 179, 181, 229, 246–247
Utility
, 1–3, 5–11, 13–20, 22–25, 31–33, 35, 37, 39, 41, 43, 45, 49–70, 78–79, 95–97, 104, 111–112, 116, 137, 140, 158, 169, 193–198, 200–201, 203–205, 207–210, 214–215, 223, 228, 233–239, 242, 250
Utility maximization theory
, 13, 158
Utility-maximizing behavior
, 111
Utility space
, 6–8, 13, 15, 20, 25
Valence framing
, 246, 248
Value function
, 109, 238, 240–241, 247–248
Value judgments
, 5–6
Weight function
, 241
Willingness to accept (WTA)
, 246–247
Willingness to pay (WTP)
, 73–74, 83, 88, 90–91, 244, 246–247
Working memory
, 117–118
- Prelims
- Chapter 1 Models of Bounded Rationality under Certainty
- Chapter 2 Utility Maximisation and Regret Minimisation: A Mixture of a Generalisation
- Chapter 3 Relative Utility Modelling
- Chapter 4 The Influence of Varying Information Load on Inferred Attribute Non-Attendance
- Chapter 5 The Heterogeneous Heuristic Modeling Framework for Inferring Decision Processes
- Chapter 6 Investigating Situational Differences in Individuals’ Mental Representations of Activity-Travel Decisions: Progress and Empirical Illustration for the Impact of Online Alternatives
- Chapter 7 Towards a Novel Classifier for the Representation of Bounded Rationality in Models of Travel Demand
- Chapter 8 Bounded Rationality in Dynamic Traffic Assignment
- Chapter 9 Incorporating Bounded Rationality in a Model of Endogenous Dynamics of Activity-Travel Behaviour
- Chapter 10 Multidimensional Travel Decision-Making: Descriptive Behavioural Theory and Agent-Based Models
- Chapter 11 Prospect Theory and its Applications to the Modelling of Travel Choice
- About the Authors
- Index