Choice Modelling Perspectives on
Social Influence in Travel Behaviour:
A Behavioural Review with Future Research Directions
Michael Maness, Ph.D.
Graduate Research Assistant
University of Maryland
Centre for Transport Studies Seminar Series
Imperial College London
30 September 2015
Acknowledgements

Cinzia Cirillo, University of Maryland
Elenna Dugundji, CWI
Two anonymous referees

Presentation follows closely from:


M. Maness, C. Cirillo, and E. Dugundji (2015). Generalized Behavioral
Framework for Choice Models of Social Influence: Behavioral and Data
Concerns in Travel Behavior. Journal of Transport Geography, 46, 137-150.
© Michael Maness 2015
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Outline
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Short Example
Introduction
Social Influence: Types and Motivations
Social Networks: Formation and Structures
State-of-the-Art in Transport
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“The Common” Conformity Model
Getting More Out of the Models
Future Research Directions
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Forms of Social Interactions in
Travel-Activity Behavior
An individual’s
decision making
process is altered
by the actions,
behaviors,
attitudes, and
beliefs of others
Social
Influence
Social
Capital
How social
connections bring
value to an
individual by
enabling action and
providing access to
resources
Social
Cooperation
The active
coordination of
travel and
activities between
individuals
Social
Networks
Learning new
behavior and
beliefs through
observation and
experience
Social
Learning
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Motivating Example
College students in the US are
more likely to use a bicycle than
non-students.
1.
2.
Why is this true?
So how do we get more
non-students to cycle?
Theoretical
Practical
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Explanation Types
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Individual-Level Effects
Endogenous Social Influence Effects
Contextual Social Influence Effects
Social Network Structure
Correlated Individual-Level Effects
Correlated Environmental Effects
Social
Non-Social
(Unobservables)
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Individual-Level Effects
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Endogenous Social Influence Effects
Cycling decisions depend on the choices of others
because of social norms and conformity (Dill and
Voros 2007). This can cause a self-perpetuating
cycle of low cycling rates in neighborhoods with
non-students and high cycling rates in
neighborhoods with students. For example, this can
lead to a situation whereby once a few people start
cycling, a critical mass is reached, and cycling
becomes more popular.
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Contextual Social Influence Effects
Preferences for automobiles may be higher among
lower income individuals compared to higher
income individuals (Parkin et al. 2008). Higher
income individuals have higher bicycle ownership
and tend to cycle more often than lower income
individuals. Plus, college enrollment in the US tends
to increase with rising household income…
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Social Network Structure
More Diffusion Paths
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Fewer Diffusion Paths
Note: Circles represent individuals, lines represent ties between connected individuals.
Correlated Individual-Level Effects
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Source: Fowler-Brown, A.G., Ngo, L.H., Phillips, R.S., Wee, C.C., 2009. Adolescent
obesity and future college degree attainment. Obesity, 18(6), 1235-1241.
Correlated Environmental Effects
© Michael Maness 2015
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Sources: Ben Schumin,
Bike Arlington, bikearlington.com
Behavioral Scientists
Travel Demand Modelers
Planners & Policy Makers
explore
estimate
analyze

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How do individuals value
their time differently
between driving and riding?
What is the value-of-time
for ridesharing?
What is the expected
revenue from charging X for
a 12-min ride?
(Discrete)
Choice
Models
Independent decision makers
Connected by markets
© Michael Maness 2015
Behavioral Modeling of
Transportation Systems
13
What is a Choice Model?
Individual
Characteristics
& Alternative
Attributes

Evaluate Payoffs
𝓟𝒏𝒊
A decision maker chooses among
a set of alternatives (Choice Set),
using a:

Payoff Function
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
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Apply
Decision Rule
𝒅 𝓟𝒏𝒊
Choice
𝒚𝒏
𝒫𝑛𝑖 = 𝑓𝑛𝑖 𝑥𝑖𝑛 ; 𝛽𝑖 + 𝜀𝑛𝑖
Decision Rule

𝑑 𝒫𝑛𝑖 , ∀𝑖 ∈ 𝐽 → 𝑦𝑛
Note: 𝑖 ≡ an alternative, 𝑛 ≡ an individual .
Choice Model Example:
Random Utility Model
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Individual
Characteristics
𝒙𝒏
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Utility Function
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𝒰𝑛𝑖 = 𝛽𝑖 𝑥𝑛𝑖 + 𝜀𝑛𝑖
Utility Maximization

𝑦𝑛𝑖 =
1 𝑖𝑓 𝑈𝑛𝑖 = max 𝑈𝑛𝑗
Utility
𝓤𝒏
𝑗∈𝐶
0
𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
Choice
𝒚𝒏
Note: Boxes represent properties that are typically observed, while ovals represent latent (unobserved) constructs.
Behavioral Scientists
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Travel Demand Modelers
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Planners & Policy Makers

(Discrete)
Choice
Models?
Can the sight of other parked
bicycle impact the selection of
a bicycle parking spot?
How many bicycle parking
spaces are needed to satisfy
cyclists using a train station?
How much will illegal bicycle
parking be reduced if police
patrolling increases by X?
Interdependent individuals
Connected by social networks
Social interactions
© Michael Maness 2015
Behavioral Modeling of
Transportation Systems
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© Michael Maness 2015
Illegal Bicycle Parking:
Estimating Social Influence
17
Source: Fukuda, D., Morichi, S., 2007. Incorporating aggregate behaviour in an individual’s discrete choice: an application to
analyzing illegal bicycle parking behaviour. Transportation Research. Part A. 41 (4), 313–325.
Source: Fukuda and Morichi (2007)
© Michael Maness 2015
Illegal Bicycle Parking:
Social Equilibrium
18
Note: Axes are the percent of bicycles parked legally.
Marginal effects of increasing police patrols) on
the expected share of off-street parking in
equilibrium
Source: Fukuda and Morichi (2007)
© Michael Maness 2015
Illegal Bicycle Parking:
Policy Recommendation
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Choice Models of Social Interactions

Social interactions are “direct interdependences in
preferences, constraints, and beliefs of individuals…”
(Durlauf and Ioannides 2010, emphasis added)
𝒫𝑛𝑖 = 𝑓𝑛𝑖 𝑥𝑖𝑛 ; 𝛽𝑖 + 𝑓𝑛𝑖𝑠𝑜𝑐𝑖𝑎𝑙 𝐺𝑛 , 𝑥𝑖𝑛 , 𝑚𝑖
𝑑𝑛 𝒫𝑛𝑖 , 𝒫(−𝑛)𝑖 , 𝐺𝑛 ∀𝑖 ∈ 𝐽𝑛 𝐺𝑛 , 𝐽−𝑛
−𝑛
; 𝜓 + 𝜀𝑛𝑖
→ 𝑦𝑛
© Michael Maness 2015
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Correlation
(non-social)
Individual
Characteristics
𝒙𝒏𝒊
Environmental
Factors
𝑬𝒏
Correlation
(self-selection)
Social Networks
𝑮𝒏 (𝒘)
Contextual
Influence Sources
𝒎𝒏𝒊 (𝑵)
Individual-level
Effects
(Observed &
Correlated)
Endogenous
Influence Sources
∗
𝒎𝒏𝒊
(𝑵)
Social Influence
Mechanisms
𝒔𝒏 (∙)
Environmental Effects
(Observed & Correlated)
Behavioral
Feedback
Social Influence Effects
(Endogenous & Contextual)
Decision Rules
𝒅(𝓟𝒏𝒊 , ∀𝒊)
© Michael Maness 2015
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Choice
𝒚𝒏
Note: Gray boxes denote components exogenous to the model.
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Social influence occurs through tactics* that aim to
satisfy the motivations of an individual
Social Influence Types (Cialdini & Goldstein 2004)
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© Michael Maness 2015
Social Influence: Types,
Motivations, and Tactics
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Conformity
Compliance
Social Influence Motivations (Cialdini & Goldstein
2004)
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Goal for Accuracy
Goal for Affiliation
Goal for Maintenance of Positive Self-Concept
*Pratkanis (2007) describes 107 different social influence tactics classified by influence technique.
Conformity
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Individuals conform when they attempt to match the
behavior of others
Two general types of conformity
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Normative – Norms conveyed directly through the
behaviors of others
Informational – Behaviors of others provide guidance to
appropriate individual behavior
Some Conformity Tactics
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Perceived Consensus
Deindividuation effects (social identity)
Majority and minority influence
(Accuracy)
(Self-Concept)
(Self-Concept)
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Compliance
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Changes in an individual’s behavior caused by the
actions, attitudes, and beliefs of others
Triggered explicitly or implicitly through advice,
commands, and norms
Some Compliance Tactics
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Affect and Arousal
Authority and Obedience
Social Norms
Reciprocation
(Accuracy)
(Accuracy)
(Accuracy)
(Affiliation)
© Michael Maness 2015
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The Link Between
Social Influence & Social Networks
Authority & Obedience
Minority Influence
© Michael Maness 2015
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© Michael Maness 2015
Examples in Transportation:
Abou-Zeid & Ben-Akiva (2011)
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Source: Abou-Zeid, M., Ben-Akiva, M., 2011. The effect of social comparisons on
commute well-being. Transport. Res. Part A: Policy Pract. 45 (4), 345–361.
Tie Generation Process
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Social Safety
• Homophily
• Spatial Proximity (Propinquity)
Brokerage
• Connecting social circles and groups
• Transfer influence, knowledge, social capital
(resources)
Status
• Ranking power and prestige
• Organizational structures & resource allocation
Source: Kadushin, C., 2012. Understanding Social Networks: Theories, Concepts, and Findings. Oxford University Press, Oxford.
Network Generation Example
hierarchy
weak ties
strong ties
Social Safety
Brokerage
Status
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Network Structures
owner
Clique
managers
workers
Hierarchical Network
Small-World Network
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Other Network Structures
events
individuals
N
Spatial – Social Network Overlay
Two-Mode Network
(Bipartite Network)
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State-of-the Art in Transport
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Most models (≥ 27) are conformity-based with the
following form:
Average behavior of
social ties
An individual’s
utility
𝑡
𝑡
Individual-level
Factors


𝑡
𝒰𝑛𝑖 = 𝛽𝑥𝑛𝑖 + 𝛿𝑦𝑛𝑖 + 𝜀𝑛𝑖 ,
𝑡
𝑦𝑞𝑖
𝑦𝑛𝑖 =
𝑞∈𝑔(𝑛)
𝑔(𝑛)
The conformity occurs through the influence source 𝑦𝑛𝑖
Social network forms tend to be selected based on
convenience or data limitations
© Michael Maness 2015
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𝑡
𝑡
𝑡
𝑡
𝒰𝑛𝑖 = 𝛽𝑥𝑛𝑖 + 𝛿𝑦𝑛𝑖 + 𝜀𝑛𝑖 ,
𝑦𝑞𝑖
𝑦𝑛𝑖 =
𝑞∈𝑔(𝑛)

Allows for the measurement of a social effect
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Identification is proven (Brock & Durlauf 2001, 2002)
Cannot differentiate the causes of the social effect
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𝑔(𝑛)
What are the tactics and motivations?
Is this helpful for recommending policy?
© Michael Maness 2015
Conformity Models in Transport:
Consequences
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Dealing with the Consequences
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Better data sources
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Panel data, social network data, attitudes, perceptions
New model specifications
Comparing multiple models
More thorough analysis beyond checking model
estimates and significance
© Michael Maness 2015
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Comparing Models:
Pike (2014)
Egocentric
network of
Nearest neighbors
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recentspatially
contactsranging
with
based
up to250
five–contacts
within
25,000 ft.
Individual
Characteristics
𝒙𝒏𝒊
Environmental
Factors
𝑬𝒏
Conformity
Utility Maximization
(Multinomial Logit)
Current Choices
of Others
(Respondent Reported)
Choice
(Mode Choice)
Source: Pike, S., 2014. Travel mode choice and social and spatial reference groups.
Transport. Res. Rec.: J. Transport. Res. Board 2412, 75–81.
Comparing Models:
Pike (2014)
Egocentric network of
recent contacts with
up to five contacts
Individual
Characteristics
𝒙𝒏𝒊
Environmental
Factors
𝑬𝒏
Conformity
Utility Maximization
(Multinomial Logit)
Current Choices
of Others
(Respondent Reported)
Choice
(Mode Choice)
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© Michael Maness 2015
Examples in Transport:
Kamargianni et al. (2014)
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Source: Kamargianni, M., Ben-Akiva, M., Polydoropoulou, A., 2014. Incorporating
social interaction into hybrid choice models. Transportation 41 (6), 1263–1285.
© Michael Maness 2015
New Data Sources:
Kamargianni et al. (2014)
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Source: Kamargianni, M., Ben-Akiva, M., Polydoropoulou, A., 2014. Incorporating
social interaction into hybrid choice models. Transportation 41 (6), 1263–1285.
© Michael Maness 2015
New Model Specifications:
Kamargianni et al. (2014)
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Source: Kamargianni, M., Ben-Akiva, M., Polydoropoulou, A., 2014. Incorporating
social interaction into hybrid choice models. Transportation 41 (6), 1263–1285.
© Michael Maness 2015
Examples in Transport:
Grinblatt et al. (2008)
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Source: Grinblatt, M., Keloharju, M., Ikäheimo, S., 2008. Social influence and consumption:
evidence from the automobile purchases of neighbors. Rev. Econ. Stat. 90 (4), 735–753.
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Gathered data from the Finnish government
Vehicle Purchase Data
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Tax Return Data (car owners and non-owner)
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Vehicle type, purchase date, new/used
Socioeconomic data, exact address data (including
date of moves)
Analysis performed for each year over a three
year dataset (1999-2001)
© Michael Maness 2015
New Data Sources:
Grinblatt et al. (2008)
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Neighbor Influence Effect:
Grinblatt et al. (2008)
Source: Grinblatt, M., Keloharju, M., Ikäheimo, S., 2008. Social influence and consumption:
evidence from the automobile purchases of neighbors. Rev. Econ. Stat. 90 (4), 735–753.
© Michael Maness 2015
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Where does the influence come from?
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Not Advertising: Influence decay between closest 10 and next 40
neighbors is too great
Not Envy: Influence decay over time was too quick (“[Your
neighbor’s] Mercedes … does not go away after a few days”)
Not Status Signaling: Used cars & rural areas showed most
influence, influenced HHs purchased same make & model
Possibly Normative Conformity: Greater influence among HHs in
similar income-decile
Likely Informational Conformity / Social Learning: Neighbor
influence stronger on used cars, “word-of-mouth” information
may be more prevalent in rural and poorer communities
© Michael Maness 2015
More Thorough Analysis:
Grinblatt et al. (2008)
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Recap: Social Influence
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Social influence occurs through tactics that feed
into the motivation of individuals
Types of Social Influence
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Motivations for Social Influence
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Conformity and Compliance
Accuracy, Affiliation, Positive Self-Concept
The mechanism of social influence is impacted by
relevant social networks that allow dissemination
© Michael Maness 2015
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Recap: Social Networks
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Social networks are about context
Formed by social and behavioral processes
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This results in a structure to social networks
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Social safety, Brokerage, Status
Cliques, Small-worlds, Hierarchical
Social influence occurs through social networks
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Recap: From Individuals to Networks
60
56
© Michael Maness 2015
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33
42
33
33
39
28
33
Woman
Full-Time Worker
Age: 33
High-Income
Single
29
30
Social Contacts: 10
70% Women Contacts
Average Age: 38
60% Middle-Income
Density: 0.31
Note: Number of rings corresponds to income level
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Decision Rules
Heterogeneity in Social
Influence Mechanism
New Social Influence
Mechanisms & Motivations
New Influence Sources
Dynamic Models (Choice &
Networks)
Incorporating Network
Statistics
© Michael Maness 2015
Areas for Future Research in
Social Influence Choice Modeling
46
© Michael Maness 2015
Example: Heterogeneity in Social
Influence Mechanism
47
© Michael Maness 2015
New Social Influence Mechanisms:
Informational Conformity
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Source: Maness, M. (2015). Choice Modeling Perspectives on Social Networks, Social Influence, and
Social Capital in Activity and Travel Behavior. (Doctoral Dissertation, University of Maryland)
The Future?
© Michael Maness 2015
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Source: Illenberger, J. (2012). Social networks and cooperative travel
behaviour (Doctoral dissertation, TU Berlin).
Questions?
Michael Maness
mmaness.com
mmaness.research AT gmail.com
© Michael Maness 2015
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References
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Brock, W.A., Durlauf, S.N., 2001. Discrete choice with social
interactions. Review of Economic Studies, 68(2), 235-260.
Brock, W.A., Durlauf, S.N., 2002. A multinomial-choice model of
neighborhood effects. American Economic Review, 92(2), 298-303.
Cialdini, R. B., & Goldstein, N. J., 2004. Social influence: Compliance
and conformity. Annual Review of Psychology, 55, 591-621.
Dill, J., Voros, K., 2007. Factors affecting bicycling demand: Initial
survey findings from the Portland, Oregon, region. Transportation
Research Record: Journal of the Transportation Research Board, 2031,
9-17.
Durlauf, S.N., Ioannides, Y.M., 2010. Social interactions. Annual Review
of Economics, 2, 451-478.
Parkin, J., Ryley, T., Jones, T., 2007. Barriers to cycling: An exploration
of quantitative analyses. Cycling and society, 67-82.
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