Modeling Crowds:
Psycho-history Reinvented
(or: crowd modeling and contagion)
Gal A. Kaminka
The MAVERICK Group
Computer Science Department
and Brain Research Center
Bar Ilan University, Israel
September 2012
Gal Kaminka [email protected]
Psycho-history
(in Isaac Asimov's Foundation Series)
“The branch of mathematics which deals with the reactions
of human conglomerates to fixed social and economic
stimuli.”
– Gaal Dornick, “Foundation” by Isaac Asimov
September 2012
Gal Kaminka [email protected]
Making decisions (that affect crowds)


Making decisions involves weighing uncertain outcomes
To reduce uncertainty: want predictions, and more
Types of Queries:

“What if” (predictions)

Analyze (determine actionable factors influencing the outcomes)

Plan (propose action plans to affect outcomes)
• State of the art: surveys, fact-finding missions, experts, …
• Limited automation

Social simulation: Automation
September 2012
Gal Kaminka [email protected]
Social Simulation Approaches
Collectives/Macro
Individuals/Micro
(e.g., global dynamics)
(e.g., multi-agent based simulation)
September 2012
Gal Kaminka [email protected]
Social Simulation Approaches
Collectives/Macro
Individuals/Micro
(e.g., global dynamics)
(e.g., multi-agent based simulation)
Qualitatively model rallies:
• Predict violence
• Determine actionable factors
Pedestrians, evacuations:
• Contagion
• Culture effects
[Fridman and Kaminka, SBP 2011, AMPLE
2011, QR 2011, TIST 2012]
[Fridman et al. AAAI 2007, AAMAS
2012, AAMAS 2011, CSR 2011,
CMOT 2010, ICCM 09, … ]
September 2012
Gal Kaminka [email protected]
Need Pedestrian, Evacuation Sims

Training simulations
–
–

“Urban noise” to fill virtual streets
Train to spot, track suspects within crowd
Urban planning, architecture

September 2012
Safety decision-support systems
Gal Kaminka [email protected]
The Goal:
Individual Agent with Social Capabilities
 Endow each agent with capability for social reasoning
 Creates crowd phenomenon when used by many
 Able to account for different crowd behaviors

Task independent
 Factors influencing action selection of agent

Goal-oriented selection (most agent literature)

Contagion (we do this via social comparison)

Culture

Emotions (e.g., Tsai, Tambe, Marsella et al.)
September 2012
Gal Kaminka [email protected]
Social Comparison Theory (SCT)
(Festinger 1954)

SCT: Theory in social psychology, actively researched



Originally given as a set of axioms (Festinger 1954)
Still active research topic in psychology
Key: If lacking objective means to evaluate their progress:



People compare their behavior with those that are similar
They take actions to reduce differences with others
Tendency to reduce difference increases with similarity
Hypothesis:
Social comparison is the underlying mechanism of contagion
8
Gal Kaminka [email protected]
SCT (Comparing agent Ame, agent set O,
Similarity limits Smin, Smax)
Lines 1-4: Select agents not too dissimilar or too similar
Line 5: Select a representative agent Ac to compare against
Line 6: Determine differences with Ac
Line 7: Determine power of attraction to Ac
Line 8: Select an action to minimize differences
9
Gal Kaminka [email protected]
Experiments: Comparison to Human Behavior
Qualitative comparison:
 Movies of human pedestrians in Paris, Vancouver
 Movies of simulated pedestrians


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Different variants of SCT, also non-contagion model
Asked 39 subjects to rate each model:

How close to human (this measures absolute fidelity)

Whether it was best or worst of all models (relative fidelity)

Ordinal Scale: 1 (least similar) … 6 (most similar)
Gal Kaminka [email protected]
11
Gal Kaminka [email protected]
Experiment Design

Compared models from literature:
Individual choice: Each agent makes decision independently
 SCT with wide (2-6.5) and narrow (5-6.5), both with gain
 SCT with no gain, constant gains (2, 3, 4, 5)
 Pilot experiment threw out some of these models


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Subjects: 39 subjects (male 28)
Movies were randomly selected
From several clips of horizontal (Vancouver), vertical (Paris)
 From several clips of each of the simulation movies
 Compare horizontal to horizontal, vertical to vertical

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Gal Kaminka [email protected]
6
5
4
3
2
1
0
Mean
Median
In
di
vi
du
al
SC
T
26.
5
SC
T
5SC
6.
5
T
no
ga
SC
in
T
ga
SC
in
3
T
ga
in
4.
5
Grade
Results: Absolute Fidelity
Higher results: Greater similarity to human pedestrian behavior
SCT2-6.5 significantly different than Individual and SCT 5-6.5 (two tailed t-test)
SCT 5-6.5 significantly different than Individual (two tailed t-test)
13
Gal Kaminka [email protected]
Results: Relative Fidelity
Higher is better
Lower is better
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Gal Kaminka [email protected]
Adding Culture
A Variety of documented cultural phenomena:
• Passing side
• Movement in groups, vs. independently
• Family formations
• Leisurely walking speed
• Personal space (proxemics)
• Tendency to communicate information
• Upward/downward comparison tendencies
• …
A very small subset of culture results
• Use webcam data, tourist videos from various locations
•
England, France, Israel, Iraq, Canada
• Measured mean parameters based on data
• Were able to show good fidelity of simulation
•
Also, simulated mixed-culture crowds
September 2012
Gal Kaminka [email protected]
Modeling macro phenomena
View of psycho-history:
“Implicit […] is the assumption that the human conglomerate
[…] is sufficiently large for valid statistical treatment.”
– Gaal Dornick, “Foundation” by Isaac Asimov
September 2012
Gal Kaminka [email protected]
Modeling Demonstrations, Rallies, …
Goals:
• Predict violence level (none, property damage, casualties)
• Assist police decision making process
Constraints
• Expert knowledge not accurate nor complete
• Mostly partial macro-level qualitative descriptions
• Simulation is of large groups
Proposal:
Use QR (qualitative reasoning) modeling
September 2012
Gal Kaminka [email protected]
Qualitative Reasoning (QR)
[Kuipers AIJ 84, 86, Forbus AIJ 84]
• Ordinal variables: qualitative values rather than real numbers
• Monotonic functions (increasing/decreasing, derivatives)
• Algorithms simulate how variables affect each other
• With partial and imprecise information
•
Draw useful qualitative conclusions
• Physics, economics, …
September 2012
Gal Kaminka [email protected]
Base Model
Fear of
Punishment
Willing
Personal Price
-
Hostility for
the police
+
+
Group
Cohesiveness
+
History of
Violence
+
Violence
September 2012
Gal Kaminka [email protected]
Qualitative Simulation
Develops all possible behaviors from initial conditions
Input: Initial state of the world
• Contains a structural description of the model
Output: State transition graph
• Captures the set of all possible behaviors
• developed from initial state
September 2012
Gal Kaminka [email protected]
What are the influencing factors on violence level?
•
Several theories regarding influencing factors
• Each theory: focuses on a sub-set of factors
• Challenge: combine all of them to one model
•
To address this challenge:
• Israeli police initiated a comprehensive study, based on:
• database of 102 demonstrations
• interviews with 87 officers
• Result: report which provides collection of factors and their influences
We use this report as source of knowledge
To develop QR models which enable reasoning
September 2012
Gal Kaminka [email protected]
Comparison of following models:
•
Base model: Based on literature review provided to us
•
•
By Israeli Police
Israeli Police model
• Extension of the Base model
• Based on the review conclusions
•
Bar Ilan model
• Extension of the Israeli Police model
• Based on consultations with social and cognitive scientists
September 2012
Gal Kaminka [email protected]
Base Model
Population
Fear of
Punishment
Willing
Personal Price
Hostility for
the police
Group
Cohesiveness
History of
Violence
Population
Violence
September 2012
Gal Kaminka [email protected]
Israeli Police Model
Population
Group
Cohesiveness
Number
Participants
License
United identity
Personal
Price
Hostility for
the police
Punishment
History of
Violence
Group
Speaker
Violent core
Demonstrat
ion purpose
Population
Violence
Police
Time
intervention
Environment
Intervention
strength
September 2012
Weather
Time
Time
sensitivity
Place
sensitivity
Gal Kaminka [email protected]
Bar Ilan Model
Population
Personal
Price
License
Group
Speaker
Number
Participants
Order
Hostility for
the police
History of
Violence
United
identity
Group
Cohesiveness
Visual
cohesiveness
Punishment
Anonymity
Violent core
Demonstrat
ion purpose
Population
Violence
Police
Time
intervention
Environment
Intervention
strength
September 2012
Weather
Time
Light
Place
sensitivity
Time
sensitivity
Gal Kaminka [email protected]
Query 1: Predictions
Compared the models on 24 real-life events
• 20 demonstrations in Israel (wikipedia, in Hebrew)
• 3 reported riots, with expert analysis:
• Violence in Heysel stadium (1985, reported in Lewis 1989)
• Los Angeles riots (1992, reported in Useem 1997)
• London riot (1990, reported in Stott and Drury 2000)
•
Calm protest
• Petach Tikva (Israel) protest (2009, video taped by us)
September 2012
Gal Kaminka [email protected]
Typical Qualitative Simulation Graphs
Base model
Police model
Likelihood of each outcome:
BIU model
(#behavior paths to specific outcome)
(total #paths
September 2012
Gal Kaminka [email protected]
Subset of results: prediction accuracy
The results show:
1. Police model provides poor results in prediction of Exp4
2. Base model and BIU model provide good results in all examined cases
September 2012
Gal Kaminka [email protected]
Results
• Level 1 errors: Off by one level
• Level 2 errors: Off by two levels
September 2012
Gal Kaminka [email protected]
Experiment 2: Sensitivity Analysis
Expert analysis of reported cases:
• Police used too much (case 1,2), or
• too little (case 3) force.
Overall, BIU model changes classification when police strength is changed
September 2012
Gal Kaminka [email protected]
Sensitivity Analysis (more results)
Changed “police strength” variable in all 24 cases:
• Police model: distribution change in 3 cases, outcome change in 2
• BIU model: distribution change in 24 cases, outcome change in 7
Compared to decision-tree learning:
• Use Weka J48 (C4.5) for learning
• Variables as attributes, so learning DTs for Police model, for BIU model
• Use all 24 cases for learning (specialization is a conservative assumption)
• 100% accurate on original cases
• Outcome change in 3 cases (no distribution)
September 2012
Gal Kaminka [email protected]
Analysis query: What affects outcome?
• Only subset of variables are actionable
•
•
Cannot change weather
Can change police strength used
• Want to know what actionable variables affect outcome
•
And when, under what set of conditions
Algorithm analyzes simulation graph:
• Find nodes with high entropy over outcomes
•
i.e., nodes in which outcome is uncertain yet
• Contrast variables in node and in children
• Determine variable changes that shift outcome
September 2012
Gal Kaminka [email protected]
Analysis query: Results
• Partial agreement between algorithm and experts
• Algorithm does not contradict the experts
•
•
Algorithm specifies settings in which actions should be taken
Experts accounted for general conditions
September 2012
Gal Kaminka [email protected]
Conclusions
• There are different queries, that build on each other
•
•
•
Prediction:
Analysis:
Plan:
agent-based simulation, qualitative modeling
qualitative modeling
?
• Key obstacles to progress:
•
•
•
No (open) repository of data
Need for interdisciplinarity
No institutionalized, or funder-guided technology transfer
process
September 2012
Gal Kaminka [email protected]
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