Modeling Crowds: Psycho-history Reinvented (or: crowd modeling and contagion) Gal A. Kaminka The...

Modeling Crowds:Modeling Crowds:Psycho-history ReinventedPsycho-history Reinvented

(or: crowd modeling and contagion)(or: crowd modeling and contagion)

Gal A. KaminkaGal A. Kaminka

The MAVERICK Group

Computer Science Department and Brain Research Center

Bar Ilan University, Israel

September 2012 Gal Kaminka [email protected]

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


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

Social Simulation Approaches

Individuals/Micro(e.g., multi-agent based simulation)


Collectives/Macro(e.g., global dynamics)

September 2012

Social Simulation Approaches

Individuals/Micro(e.g., multi-agent based simulation)


Collectives/Macro(e.g., global dynamics)

Qualitatively model rallies:•Predict violence•Determine actionable factors

[Fridman and Kaminka, SBP 2011, AMPLE 2011, QR 2011, TIST 2012]

Pedestrians, evacuations:•Contagion•Culture effects

[Fridman et al. AAAI 2007, AAMAS 2012, AAMAS 2011, CSR 2011, CMOT 2010, ICCM 09, … ]

September 2012


Need Pedestrian, Evacuation Sims

Training simulations– “Urban noise” to fill virtual streets– Train to spot, track suspects within crowd

Urban planning, architecture Safety decision-support systems

September 2012


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

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


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 Gal Kaminka [email protected]

SCT (Comparing agent Ame, agent set O,

Similarity limits Smin, Smax)

Experiments: Comparison to Human Behavior

Qualitative comparison: Movies of human pedestrians in Paris, Vancouver Movies of simulated pedestrians 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)


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

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


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)

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Indiv

idual

SCT 2-6

.5

SCT 5-6

.5

SCT no

gain

SCT gain

3

SCT gain

4.5

Gra

de

Mean Median

Results: Relative Fidelity


Higher is better

Lower is better

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


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


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


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, …


Base Model

Violence

Willing Personal Price

Hostility for the police

History of Violence

Group Cohesiveness

Fear of Punishment

- + + + +


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


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


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


Base Model

Population

Violence

Willing Personal Price


History of Violence

Group Cohesiveness

Fear of Punishment

Population


Israeli Police Model

Personal Price


History of Violence

Group Cohesiveness

Punishment

Population Number Participants

Group Speaker

Population

Violence

Environment

Weather

Time

Place sensitivity

Time sensitivity

Violent core

Police

Time intervention

Intervention strength

License

Demonstration purpose

United identity


Bar Ilan Model

Population

Violence

Police

Time intervention

Intervention strength

Demonstration purpose

United identity

Personal Price


History of Violence

Punishment

PopulationNumber Participants

Group Speaker

Violent core

License

Order Group Cohesiveness

Anonymity

Visual cohesiveness

Environment

Weather Time

Place sensitivity

Time sensitivity

Light


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)


Typical Qualitative Simulation Graphs


Base model

Police model

BIU model

September 2012

Likelihood of each outcome:

(#behavior paths to specific outcome) (total #paths

Subset of results: prediction accuracy

The results show:1. Police model provides poor results in prediction of Exp42. Base model and BIU model provide good results in all examined cases


Results


• Level 1 errors: Off by one level• Level 2 errors: Off by two levels

September 2012

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


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)

Gal Kaminka [email protected] 2012

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


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


Conclusions

• There are different queries, that build on each other• Prediction: agent-based simulation, qualitative modeling• Analysis: qualitative modeling• Plan: ?

• Key obstacles to progress:• No (open) repository of data• Need for interdisciplinarity• No institutionalized, or funder-guided technology transfer

process


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