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Modeling the Influence of Information Flow on Social Stability

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  1. Modeling the Influence of Information Flow on Social Stability Points of Contact Ryan Baird, Ph.D. Program Manager ABM Project Social Science Research and Development Office Joint Warfare Analysis Center Phone: 540-653-0348 Email: rbaird@jwac.mil Steven Hall, Ph.D. Research Professor MOVES Institute Naval Postgraduate School Phone: 831-656-1073 Email: sbhall@nps.edu

  2. Our Question … “Let us build a global community in which the people of neighboring countries view each other not as potential enemies, but as potential partners, as members of the same family in the vast, increasingly interconnected human family.” Al Gore, 1994 What is the influence of the media (including the social media) on social stability and the likelihood of insurrection and rebellion? “… Communication technologies often increase, rather than decrease, inequities, and we should be wary of the economic promises of a global information infrastructure. Christine Borgman, MIT Press

  3. Where to Start? • The obvious answer is: “Well … it depends” (Which is apparently remarkably unsatisfying to many) • Saying more with confidence is, however, more challenging • Rebellions are rarely simple causegeffect phenomena • They have to do with … • ideas that have to gain ‘critical mass’ within a ‘group’ • trajectory projections of the future that have ‘tipping points’ • struggles for survival (sometimes versus) identity/meaning making • … • In short they are defined by feedback loops • So … in order to answer our media question • We’ll need to build a dynamic model of rebellion (and its influences) • And then see how tweaking the media effects it

  4. The 1st challenge in construction this model is to design a framework where many/most proposed influences can be represented • *Rebellions are here considered distinct from … • Coups (driven by a ‘consensual’ conviction of governance failure) • Civil Wars (one state group warring against a ‘rival’ state group) Having scanned the substantial literature this is where we’re starting • Groupsrebel* when they believe they have bad leadership, i.e. that: • ‘They’ are relatively deprived (vis-à-vis their fellow groups) • The source of their relative deprivation is the ‘governing body’ of the state • Which is extracting a price (contributing to their relative deprivation) • But not compellingly promising and/or delivering anything of recognized value in return • A responsible party is identifiable (towards which the rebellion is targeted), which is • The State Governance itself … if they believe it is acting ‘selfishly’ or ‘incompetently’ and/or • Their own Group Governance … if they believe it is acting ‘selfishly’ or ‘incompetently’ vis-à-vis the State • (and) The rebellious action’s value proposition is perceived as being positive • Conversely people consent when they believe that … • The immediate sacrifices they are making will yield long-term collective value gain • They believe they have a voice in determining what values are being pursued • The costs of those sacrifices are being fairly bourn by the eventual beneficiaries • Including a belief that the state has the capacity to control ‘cheaters’ • (or) Rebelling isn’t perceived as having a positive NPV (Net Present Value) • Thus insurrections can be managed by … • Undermining the foundation of the rebellious group’s identity • Countering the perception of any state-generated relative deprivation of the group • Explaining how the group’s current relative deprivation will be justly rewarded • Supporting the group’s perception that rebellious behavior has a negative NPV

  5. What is the role of the Media here? *Some argue that the ‘State’, as a chorus of voices, doesn’t have shared collective values except inasmuch as it has a reified identity within the larger community of states. I.e. identities (and their associated values) are socially constructed. • The State can use (mass) media to communicate to the people/groups • Why the sacrifices being called for are in the collective value* interest (NPV > 0)) • How the values of the group(s) are, in fact, being heard by the state • Why the burdens/benefits of sacrifice are (or will be) fairly distributed • How cheaters/criminals are or will be apprehended and punished/rehabilitated to ensure social behavior • How rebellion itself is or will be apprehended and punished/rehabilitated to ensure social behavior • The People can use (social) media to diffuse info amongst themselves • About the identity of those that are being systematically discriminated against • And how ‘we’ are relatively deprived (of the things ‘we’ value) vis-à-vis our neighbors • Who are being favored (or not effectively punished) by the State • About opportunities to improve the group’s relative value deprivation • By changing the way the State pursues the collective interest and/or • By changing the way the Group engages with the State

  6. Key Modeling/Simulation ChallengesHow do we integrate these media influences into a generic rebellion model? • How do we represent the group’s perception of ‘Relative Deprivation’ • The way the group values what it is that they have been allocated and/or promised • The way the group compares what they’ve been allocated vis-à-vis other group’s allocations • The way the group self-defines itself as having a common shared identity (‘against’ or ‘as part’) • How do we represent the perception of the ‘Legitimacy’ of this deprivation • When the deprivation is seen as ‘own responsibility’ versus ‘state produced’ • When short-term sacrifices are seen as supporting long-term gains • When group sacrifices are seen as supporting collective identity/survivability • How do we represent the perception of the value of rebellion • How the probability of success comes to be agreed upon and its associated value gain • How the probability of failure comes to be agreed upon and its associated value loss • How risk is factored into decision making … and what drives risk aversion to change • How collective group value decisions are weighed against expanded group identity formation

  7. Our ABM Modeling Approach Architecture • Key elements (of our ‘FOCUS’ model of stability and rebellion) • The state, people/groups and geographical-parcels are all represented as ‘agents’ • ‘Media towers’ mediate links between people, and with the state, and with each other • Promised and Delivered ‘goods’ and ‘services’ support deprivation assessments • Requests that are made (and fulfilled) support the perception of value recognition • This model explicitly represents … • The dialogic nature of identity formation and value generation • The spatial diffusion of info via both direct contact and (social/mass) media • A psychologically-real model of value assessment and risk (Prospect Theory) • An sociologically-real model of self-organizing collective action (Ostrom) • Additional features … that we’ve found useful • Support for specification of the spatial distribution of agents and network topology • Display of evolving ‘consent’ influences both globally and spatially decomposed • Integration of support for design-of-experiments and post-run statistical analysis • Utilization of real-world GIS data for registration of elements and model validation

  8. FOCUS 1.0 – what it looks like(Flow of Communication upon Society) Specifing the layout of population agents Specifying how population agents value delivered goods and respond to each other Specifying the layout of Faction controlled Mass Media Specifying pop agent’s reluctance to switch loyalties Blue Faction Loyalist Specifying the layout of the population’s Social Media Mass Media Towers Controlling the display of run-time information on the map Specifying the range of unaided networking Social Media Towers Creating (and editing) the Scenario Design Red Faction Loyalist Cross Links Social links Displaying the resulting evolution of population loyalty Specifying Faction behavior Simulation Start Parcel State Data (Blue Security Satisfaction) Provides inputs for analysts to control all key model parameters Provides tools to do sensitivity analyses on critical drivers of consent Provides sufficient dynamic data displays to understand tipping points Controlling the layout of population agents Controlling the layout of population agents Controlling the layout of population agents Specifying how population agents value delivered goods and respond to each other Specifying how population agents value delivered goods and respond to each other Specifying how population agents value delivered goods and respond to each other Controlling the layout of Faction controlled Mass Media Controlling the layout of Faction controlled Mass Media Controlling the layout of Faction controlled Mass Media Specifying pop agent reluctance to switch loyalties Specifying pop agent reluctance to switch loyalties Specifying pop agent reluctance to switch loyalties Controlling the layout of the population’s Social Media Controlling the layout of the population’s Social Media Controlling the layout of the population’s Social Media Controlling the display of run-time information of the map Controlling the display of run-time information of the map Controlling the display of run-time information of the map Determining the spatial extent of unaided networking Determining the spatial extent of unaided networking Determining the spatial extent of unaided networking Specifying Faction behavior Specifying Faction behavior Specifying Faction behavior Displaying the resulting evolution of population loyalty Displaying the resulting evolution of population loyalty Displaying the resulting evolution of population loyalty

  9. A Selected Glimpse Under-the-Hood Overview: • How the FOCUScentral ‘social dialogue loop’ works • How delivered product/service value is calculated • What a GIS data integration looks like

  10. The Central Social Decision Loop … and what the people believe was delivered … and what the people believe was promised … and what the people agree was valuable … and what the people agree would be valued

  11. More on the Logistic Curve(model of Prospect Theory) Expected Utility High Med Low Delivered relative to Expected • Derived from the Generalized Logistic Curve: • Captures the Prospect Theory model of perceived value (Y) representing • The difference between what was delivered relative to what was expected (X) • The human preference towards not losing what you have … over gaining more • The influence of anticipation on expectation (i.e., expectation can be altered by promises) • The strength of the loss avoidance bias can be specified as : • “High”: Y = -1 + 2 /((1 + 0.006955 * exp(-x*10)) ^ 100) • “Med”: Y = -1 + 2 /((1 + 0.4142 * exp(-x*10)) ^ 2) • “Low”: Y = -1 + 2 /((1 + 1.0 * exp(-x*10)) ^ 1)

  12. Understanding FOCUS BehaviorWatching Consent Evolve + Helps analysts/planners see how consent will evolve over time Show uncommitted population agents as well as committed Underlying data can be output for detailed analysis

  13. Validating FOCUS with GIS Data Data for validating ABM social stability models is available Including: resource distribution, communication, ethnicity … Enhancements in ABM tools (e.g. NetLogo and Repast) simplify its use Promises to usher in a new generation of believable models And making their insights accessible in pragmatic circumstances

  14. A Quick Demo Red Attack on Blue Mass Media Tower

  15. Questions/Comments ?

  16. Backup Slides

  17. Backup Contents Topic Page(s) What FOCUS 1.0 Represents 18 Defining the Scenario 19 Modeling Actual Deliveries 20 Modeling Collective Value Assessments 21 Modeling Collective Desire 22 Modeling Promises 23 Tools for Exploring the Behavior Space 24-5 Future Enhancements 26

  18. What FOCUS 1.0 Represents • Behavioral Agents: • Population Agents • Motivated to maximize their realized value of goods and services • Loyal to: Blue, Red or ‘Green’ (uncommitted) … as determined by their ‘gratitude’ and their loyalty hysteresis • Located at specific coordinates within the modeled region … but with the capacity to move • Faction (Blue and Red) Agents • Competing for population loyalty/consent (and the authority to establish policy) • With the capacity to respond differentially to geospatially characterized population needs and expressed desires • Towers (supporting the Social and Mass Media) • Owned by Blue or Red (or they can be shared) … and with the capacity to move as well • Faction Deliverable Goods/Services: Security, Food/Shelter and/or Income • Land Patches: 20x20 array of parcel sub-regions representing both … • The actual state of individual parcels; in terms of delivered good/services and any associated promises made • Population’s perception of good/services delivered to the parcel, their value, future needs and promises made • Communication Links … between: • Population Agents (consistent with their ‘degree’ specification) • Via the within-range non-technology enabled traditional means • Via the within-range local social media ‘towers’ …and/or towers to which the local towers are linked • Social Media ‘towers’ (consistent with their ‘degree’ specification) • Mass Media ‘towers’ (consistent with their ‘degree’ specification) • Interactions • Factions to People • Promises of what will be delivered during the subsequent interval (constrained by the Mass Media capabilities) • Actual deliveries made to the various parcels • People to People • What was delivered; What was its value; What is needed; and What was promised … for each parcel of land • People to Factions • What the population collectively believes that the various parcels need … i.e. would provide the highest value.

  19. Defining the Scenario • The User specifies the number and link degree of people and ‘towers’ • Layout can be (optionally) automated … • People distribution is always randomized but … • It is parametrically subject to ‘preferentially attachment’ (modeling ‘city’ vs. ‘country’) • Tower distribution is either randomized or focused on non-overlapping pop clusters • Linking uses round-robin nearest-neighbor to satisfy degree spec • Candidate ‘people links’ include those within Unaided range and Social Media range • The user may manually edit Media emplacements by … • Enabling or Disabling any Social or Mass Media ‘Tower’ • Moving any Social or Mass Media ‘Tower’ location • Seizing a Mass Media tower and thereby changing its ‘ownership’ • Both People and Towers are mobile • Though they don’t currently move in version 1.0 (except via user input) • The Initial distribution of products/services to parcels is randomized

  20. Modeling Actual Deliveries (and the population’s mediated perceptions of them) • What the Factions actually Deliver • Competing Factions deliver Goods (3 kinds) to Parcels (400 … in a 20x20 grid ) • Goods quantities are measured from ‘0’ (nothing) to ‘1’ (max consumable) • Actual Deliveries are determined by a ‘reliability’ weighting between … • Being responsive to the promises that were made, and … • Being responsiveto localized disparity issues • A ‘generosity’ parameter determines the overall increase/decrease in the total quantity of distributed goods • A ‘credibility’ parameter determines the degree of randomness exhibited by the Faction • What the People perceive/believe was Delivered is … • Calculated on the basis of what they have seen personally • And … what the people they are talking to know about this parcel • Including both what they have personallyseen and what they have learned from ‘hearsay’ • Via personal communications with people in their local neighborhood (i.e. without technology facilitation) • Via aided communications (i.e. with technology facilitation … social media (e.g. cell phone, SMS, Twitter, Facebook …) (As determined by social media connectivity and the link degree specification) • A ‘Population Credibility’ parameter specifies how much weight to put on the beliefs of others • The people’s ultimate goods delivery perception reflects a measure of ‘mutual’ knowledge

  21. Modeling Collective Value Assessment(and consequent loyalty/consent) credibility • People begin by assessing the value of what theybelieve has been delivered • Modeled with a Prospect Theory derived Generalized Logistic Function … with a parameterized steep down side • A ‘Reference Expectation’ (RE) is defined through a parameterized mix of precedent and anticipation • Which is then parametrically blended with a ‘zero expectation’ (absolute) model of value assessment • The consequent value assessment is then submitted to the social network for validation • Value Assessments are propagated, much like Delivery Perceptions, generating a measure of mutual knowledge • Each good/service for each Faction is propagated independently and ^ weighted means/StdDevs are calculated • The consequent collective knowledge is stored in the parcel array (like all other population perceptions/beliefs) • Consent/loyalty is determined by agents using mutual knowledge of local conditions • Loyalty is driven by the sum of the local consensus values (from products delivered and associated promises) • Which ever Faction has delivered the most realizedvalue wins the loyalty of the local residents • Moderated by a ‘ hysteresis’ effect that delays changing loyalties until a threshold has been exceeded

  22. Modeling Collective Desire (and its expression) • People’s Desires are driven by an assessment of their Value Function(s) • I.e., … what is it that they would get the most value from … if it was delivered • People are, individually, notoriously bad at doing this … with much fidelity • Because … the curve is non-linear … and changing expectations are constantly recalibrating it • But they try nonetheless … and succeed to varying degree (with some predictable biases) • Several relatively simple models provide reasonable approximations to this behavior • Very Simple: Desire more of the product/service which provided the most value during the current evaluation interval* • Moderate: Linearly project the values provided over the last two or more intervals • Sophisticated: Fit a curve to the values provided over the last 3 or more intervals • FOCUS 1.0 models expressions of Desire as … • A simple, single collective message per parcel per Faction … • For the good/service that has been determined to provide the most value to the local population • And Factions are assumed to have access to that ‘message’ … even if they choose not to listen * The method currently implemented in FOCUS 1.0

  23. Modeling Promises(Their Mass Communication and Mediated Reception) • Promises are communicated via Mass Media towers • They reach people within range of a Faction supporting tower • (Unless the ‘Media Promise Constraints’ are turned off) • Promise content is defined by a weighting between… • An attempt to reduce localized disparity in goods delivery • An attempt to be ‘responsive’ to the desires of the people • Disparity Leveling Promises … • Use the 8 neighboring parcels to do their leveling • Can parametrically lower or raise expectations while leveling • Can parametrically include an element of randomness

  24. Tools for Exploring the Behavior SpaceSetting up an Analysis • Behavior Space Explorations Tools • Specify values to be analyzed • Specify number of runs to make • Specify what to measure • Specify how long to run • Specify how many cores to use • Monitor the experiment run progress

  25. Tools for Exploring the Behavior SpaceA Sample Analysis of the defined Behavior Space Exploration When Blue’s Promises are Responsive to Pop’s Need then Consent went Up() When Blue focuses on Disparity Reduction (vs. Commitment) then Consent went Down () Any of the parametric input variable can be explored to assess how sensitive ‘consent’ is to their manipulations

  26. Future EnhancementsTopic Areas Recommendations • Existing Behavior Modeling Fidelity Improvement Enhancements • Integrate with PSOM to evaluate relevant stability ops stance intention effectiveness • Provide the ability to import regional/country GIS data and use it to constrain/guide layouts • Enhance the fidelity of each of the FOCUS 1.0 component models (as needed/required for validity) • Build a more ‘intelligent agent’ to define/execute Red and Blue faction plans more reactively • Add multiple ethnicities amongst the population agents to constrain networking (and value determination) • Add a capability to model more than 2 competing Factions • Populate with ‘reasonable’ data for the input parameters from one or more select ROIs • Model Analysis/Exploration and Performance Enhancements • Integrate an ability to do Design of Experiments (DOE) based analysis • Modify the code to use HPC computing resources for both DOE and single run performance improvement • Conduct a DOE analysis of the parameter space to determine what and where each influences consent • Critical Additional Behavior Modeling Capability • Add a Strategic Consent Layer (To capture the conditions when people consent to make (personal) sacrifices for long term collective gain) (Based on the worked of Nobel Prize winning work of Elinor Ostrom) (Highly recommended for capturing an important class of consent giving behaviors not currently modeled in FOCUS 1.0)