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Modeling Crowds: Psycho-history Reinvented (or: crowd modeling and contagion) Gal A. Kaminka The MAVERICK Group Computer Science Department and Brain Research.

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Presentation on theme: "Modeling Crowds: Psycho-history Reinvented (or: crowd modeling and contagion) Gal A. Kaminka The MAVERICK Group Computer Science Department and Brain Research."— Presentation transcript:

1 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 2012Gal Kaminka

2 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

3 Gal Kaminka 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

4 Social Simulation Approaches Individuals/Micro (e.g., multi-agent based simulation) Gal Kaminka Collectives/Macro (e.g., global dynamics) September 2012

5 Social Simulation Approaches Individuals/Micro (e.g., multi-agent based simulation) Gal Kaminka 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

6 Gal Kaminka 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

7 Gal Kaminka 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

8 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 Gal Kaminka 8

9 Lines 1-4: Select agents not too dissimilar or too similar Line 5: Select a representative agent A c to compare against Line 6: Determine differences with A c Line 7: Determine power of attraction to A c Line 8: Select an action to minimize differences Gal Kaminka 9 SCT (Comparing agent A me, agent set O, Similarity limits S min, S max )

10 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) Gal Kaminka 10

11 Gal Kaminka

12 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 Gal Kaminka 12

13 Results: Absolute Fidelity Gal Kaminka 13 Higher results: Greater similarity to human pedestrian behavior SCT2-6.5 significantly different than Individual and SCT (two tailed t-test) SCT significantly different than Individual (two tailed t-test)

14 Results: Relative Fidelity Gal Kaminka 14 Higher is better Lower is better

15 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 …

16 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

17 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

18 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

19 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

20 Base Model Violence Willing Personal Price Hostility for the police History of Violence Group Cohesiveness Fear of Punishment September 2012Gal Kaminka

21 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 2012Gal Kaminka

22 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

23 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

24 Base Model Population Violence Willing Personal Price Hostility for the police History of Violence Group Cohesiveness Fear of Punishment Population September 2012Gal Kaminka

25 Israeli Police Model Personal Price Hostility for the police 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 Demonstrat ion purpose United identity September 2012Gal Kaminka

26 Bar Ilan Model Population Violence Police Time intervention Intervention strength Demonstrat ion purpose United identity Personal Price Hostility for the police History of Violence Punishment Population Number Participants Group Speaker Violent core License Order Group Cohesiveness Anonymity Visual cohesiveness Environment Weather Time Place sensitivity Time sensitivity Light September 2012Gal Kaminka

27 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

28 Typical Qualitative Simulation Graphs Gal Kaminka Base model Police model BIU model September 2012 Likelihood of each outcome: (#behavior paths to specific outcome) (total #paths

29 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

30 Results Gal Kaminka Level 1 errors: Off by one level Level 2 errors: Off by two levels September 2012

31 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

32 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 September 2012

33 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 Gal Kaminka September 2012

34 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 Gal Kaminka September 2012

35 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 Gal Kaminka September 2012


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