Socio-Cultural Modeling and

Collective Behavior14 March 2011 Dr. Joseph Lyons

Program Manager

Dr. Terrence Lyons

Program Manager

AFOSR/RSL

Air Force Office of Scientific Research

AFOSR

Distribution A: Approved for public release; distribution is unlimited. 88ABW-2011-1176

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Portfolio within the Air Force

―Indeed, it is virtually impossible to disentangle culture

and language, which is why I am proud of Air Force

cultural modeling efforts in direct support of the OSD

Human, Social, Cultural and Behavioral modeling

program. Using the National Operational Environment

Model, the Air Force Research Laboratory and Air Force

Office of Scientific Research are helping to explore

cultural contours, representations of governance,

security institutions, critical infrastructure and social

well-being to model and forecast the human terrain, and

offer cultural insights to Joint analysis and planning.‖

--Secretary of the Air Force, Gen. Schwartz, January 26, 2011

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Portfolio within the Air Force

―..the prominence of language skills and regional and

cultural appreciation will continue to grow,

facilitating vital face-to-face interaction for which

there is little substitute. The Air Force has made an

institutional commitment to advancing our

capabilities to address this reality, by designing the

building of partnerships and partner capacity as a

Service Core Function.‖

--Secretary of the Air Force, Gen. Schwartz

January 26, 2011

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2011 AFOSR SPRING REVIEW2313FX PORTFOLIO OVERVIEW

NAME: Dr. Joseph Lyons

BRIEF DESCRIPTION OF PORTFOLIO:

Sponsoring novel research utilizing multiple disciplines to discover the

foundations of group networks, collective behavior and socio-cultural

influence on beliefs, attitudes, and behavior of several populations, mostly

focused on non-state actors.

LIST SUB-AREAS IN PORTFOLIO:

•Understanding the science of behavioral/cultural influence

•Psychology of collective violence

•Computational social science and the study of social networks

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Why Socio-Cultural Modeling?

• Understanding of the ―human terrain‖ is critical to Air Force

Operations (Schwartz, 2011)

– Building partnerships and partner capacity is a service core

function

• Influence Operations (AF IFO Roadmap, May 2008)

– Target audience analysis is a top IFO requirement

– IO doctrine emphasizes decision makers, but precision for

groups is needed (Emery, 2008)

• Quadrennial Defense Review (QDR)

– Succeeding in counterinsurgency (COIN)

– Stability and counterterrorism (CT) operations

– Effectively operating in cyberspace through the study of online

collective behavioral networks

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Strategy

• Fund basic science in three core areas

– Understand culturally-driven methods to shape

behavior/attitudes

– Psychology of collective violence

– Understand and harness the power of social

networks

• Conduct workshops to bridge the gap between

warfighters and scientists

– ACC effects to influence workshop

• Leverage and build collaborations

– Other DoD entities (i.e., Minerva)

– Academia

– AFRL Scientists (Lab tasks)

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Scientific Challenges

• Human behavior is inherently complex

– Collective behavior is difficult to predict

• Kitty Genovese – ―Bystander Effect‖

• Group decision making, conformity

• Emergent, contagious, - social media is an amplifier

– Culture as a construct is elusive

– Cultural behavior/attitudes are driven by a variety of factors

• Rational (i.e., economic models) may be inappropriate – may

result in adverse effects

• Data is very difficult to collect

– International project challenges

– Privacy issues

• Recipe for terrorism/violence is non-linear & complex

– Scope ranges from individual cognition, to social networks,

shared emotions, to societal conditions

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Transformational Opportunities

One hundred victories in one hundred battles is not the most

skillful. Subduing the other’s military without battle is the most

skillful. (Sun Tzu, The Art of War; p.9)

• What if DoD had the foundational science to anticipate violence in

different groups/individuals?

– What if we could quantify this?

• What if the DoD had the capability to alter those courses of action

through influence tactics?

• What if DoD could forecast the secondary, and tertiary effects of

their operations on different groups?

• Social media/networks – great potential yet nascent in

understanding the capabilities and limitations

This is a high-risk, high-payoff domain

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Other Organizations That Fund Related Work

• NSF: Basic research tends to be aligned along disciplines, AFOSR more multi-disciplinary

• DHS: Culturally-independent hostile intent prediction

• NIH: Focused on social network analysis

• ONR: Interests are close to AFOSR but well coordinated

• ARO: Training, mission rehearsal, face-to-face negotiation/interaction, etc. Focus on near-term: ―something for the soldier‖

• DTRA: Focused on WMD scenario

• DARPA: Similar interests, albeit more applied at times, they are a useful transition partner for AFOSR

• OSD: HSCB Modeling, ops analysis, training (6.2-6.3) – related to the effective strategic communication in AFOSR portfolio

• Air University Culture & Language Center: focus is mainly on training related to language and culture

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Interactions Within Air ForceModeling, Cyber, Culture, & Lab Tasks

• ACC—Effects to Influence Workshop at William and Mary and Langley

– AFOSR-Sponsored event to help vector our research

• AFRL/RI: Modeling, mathematical assessment, and cyber domain

– DCT: Influence especially in the cyber domain, AFRL/RI Chief Scientist

helped to draft DCT BAA, RI review of white papers

– NOEM Lab Task (AFRL/RIEA): Holistic nation-state PMESII Modeling

Using a systems dynamics approach (Salerno)

• AFRL/RH: Lab tasks

– Cultural variation in precautionary mechanisms (Mort)

– Dynamic Trust Model (Stokes)

– Avenues of Influence (Sutton)

– Cross Cultural Aspects of Categories and Exemplars (Young)

– Whole body deception (Fullenkamp)

• AOARD & EOARD

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Recent Transitions

• AF Minerva Princeton Project: Direct transition to COIN efforts for ISAF

• AFRL/RI: Modeling, mathematical assessment, and cyber domain

– Singled out by CSAF and AF Chief Scientist

– NOEM Lab Task Holistic nation-state PMESII modeling using a

systems dynamics approach

• CMU: Transition of social network analysis tools to DoD & Intel

– Used by JIOWC in Afghanistan and Iraq: *ORA for dynamic network

analysis was used to train military analysts and support missions;

training of USSTRATCOM and USSOCOM analysts

• Atran/Sageman: Research resulted in credible science-based policy

guidance including a study of evolution of militant networks, impacting

national policies-- U.S. Senate, U.S. State Dept., House of Lords

• Zucker: Transition of work inferring dominant intrinsic dimensions via

geometric harmonics and non-linear dimension reduction techniques to

Google

• UMD MURI: Direct transition of data extraction tools to intelligence

agencies

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Select Projects

• Minerva projects

– Terrorism, Governance, & Development

– Emotion and Intergroup Relations

• Precautionary Mechanisms (Mort Lab Task)

• Dynamic Trust Model (Stokes Lab Task)

• Avenues of Influence (Sutton Lab Task)

• Cultural Dimensions (Saucier)

• Sacred Values (Atran)

• Computational Methods (Zucker)

• Reality Mining (Pentland)

• Young Investigator Program (Juarez)

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Minerva: Terrorism, Governance, and Development

• Eli Berman, (UCS), Jake Shapiro (Princeton), Col. Joe Felter, (Army

and Stanford)

• Objective:

– To understand the impact of policies, security assistance, and

other infrastructure (i.e., education, agriculture, etc.)

development on attitudes and behavior – (i.e., violence)

• Stability Operations are a major part of DoD strategy

• Previous research has shown that interventions sometimes have

unpredicted and deleterious outcomes

– Sometimes increased aid is related to heightened violence

• DoD investment huge > 30 Billion – impact unclear

• Currently working in Afghanistan, Iraq, Columbia, Northern Ireland,

and Philippines

• This research can support new policies based on empirical data

– Already having an influence on COIN strategy

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Minerva: Emotion and Intergroup Relations

• Dr. David Matsumoto (San Francisco State U), and Dr.

Mark Frank (U of Buffalo, SUNY)

• Objective:

– To understand the role of emotions (anger,

contempt, and disgust) in predicting violence

• Approach:

– Linguistic analysis of text

– Analysis of video content (key leaders)

– Controlled laboratory experiments

• Induce emotive states and measures outcomes

• Apply results to group scenarios to predict group

behavior

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Precautionary Mechanisms (Mort Lab Task)

• Focus is on how people react to potential threats, against which one

must take precautions

• Exploring cultural differences in threat detection

- Are there cultural differences in antecedents and reactions

• Leveraging collaborations/data collection venues in South Africa,

Israel, Ireland

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Stokes Lab TaskDynamic Trust Model

• Trust = the willingness of individuals to make themselves

vulnerable to others (Mayer et al., 1995)

• This process may vary by culture, but most studies have relied on

western samples/models

– Research suggests that collectivistic and individualistic

societies have different antecedents to trust (Branzei et al., 2007;

Huff & Kelley, 2003)

• But little experimental evidence exists

• This project will examine the relative weight of the three

trustworthiness dimensions (ability, benevolence, and integrity)

on trust in collectivistic versus individualistic cultures

– Utilize a novel experimental platform

• Leveraging a collaboration with AOARD

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Sutton Lab TaskAvenues of Influence

• This project will examine the relative impact of social influence

tactics and affect-laden messages on different cultures

– Initial work will focus on the social proof bias (Cialdini, 2001)

– Penalties for violating social norms of category memberships

are tougher for collectivists who identify more with social

groups (Markus & Kitayama, 1991)

– Different cultures use and are effected by influence tactics

differently (Fu & Yukl, 2000)

– Emotional arousal via messages can influence behavior (Bator

& Cialdini, 2000; Schneider et al., 2009)

• Current project will leverage the NATO School in Germany

– 9K+ international students/yr

– Conduct a series of experiments

– Dr. Sutton is a Research Fellow at the NATO School

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Cultural Dimensions

• PI: Gerard Saucier, University of Oregon

• Objective:

– Determine dimensionality of psychological measures of personality, attitudes, values, and usefulness of these dimensions for characterizing cultural differences

– Collect data from 45 countries

• Estimated N = 10K

– Should help operationalize culture

– Conduct psychometric analyses

• Factor structure

• Item Response Theory

•Measurement equivalence

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Sacred Values

• PI: Dr. Scott Atran, John Jay College

• Background: Path to collective violence poorly understood

– Thus, attempts to thwart violence may be ineffective

• Objective: Understand the behavioral tendencies and cognitions

associated with collective violence

• Sacred values = moral imperatives drive behavior, attempts to

persuade behaviors that conflict with these are ineffective

– Sacred values may ―trump‖ economic considerations

– Attempts to alter scared values via economic means may have

negative consequences

• Group dynamics – path to violence largely influenced by

immediate peers/network

• Using test cases of inherent inter-group conflict

– Use combination of survey and interviews

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Social Data Analysis Through Non-linear Embedding

• PI: Steven Zucker (Yale)

• Background: Computational methods for mining large datasets is

needed for the social sciences

– Data may be incomplete or inaccurate

• Objective: to develop a novel

approach to organize knowledge

– Reveal knowledge structure

– Identify patterns amid complexity

– Based on diffusion maps

• Leverage existing social databases

– Might reveal intrinsic dimensions

– Support Intel analysts

– Identify trends – i.e., potential problems in advance

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Reality Mining

• PI: Alex Pentland, MIT Media Lab

• Background: Social networks are a powerful source of social

movement/influence

– But how do we harness this power?

• Objective: leverage data mining and machine learning to model

network data over time

– Identify behavioral patterns – ―tribes‖

– Identify influence/leverage points

– Much human activity is predictable

• Seeks to understand the mechanisms that foster social

movement – contagion among individuals

– Tactics that motivate task activity and recruitment of

additional resources effective

– Time Critcial Social Mobilization: The DARPA network

challenge winning strategy – accepted by Science Magazine

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Young Investigator Program

• PI: Ruben Juarez (University of Hawaii)

• Background: Online communities are pervasive but the formation

of these communities is poorly understood

– What drives a coalition to form and how can we model that?

• Objective: To create a model using game theory to predict the

formation of coalitions in distributed communities

– Power and altruism effects

– Validate this model using experimental methods and

culturally-diverse participants

• Use a variety of scenarios (i.e., tournaments) to test the influence

of various agent-based parameters

– Power = multidimensional

– Shared outcomes

– Costs for success

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Program Trends

Experimental Social Science

Field Studies in Social Science

Study of Social Networks

Computational Methods

Agent-based Modeling