Exploratory Modeling of Adversarial Organizational Structure Dynamics

1. Exploratory Modeling of Adversarial Organizational Structure Dynamics Session 7: Understanding and Modeling Human Behavior Wednesday, 09 February 2011 : 8:00-8:30 Steven B Hall, Lockheed Martin Advanced Technology Center O/ABCS B/153, Sunnyvale CA 94089 Telephone: 408 742-2903 Email: steve.hall@lmco.com

2. Introduction : Steven Hall • Principal Scientist, Ph.D in Cognitive Science and AI • Lockheed Martin Advanced Technology Center • Focus: Multi-agent and system of systems (SoS) modeling & simulation • Interests: Complex Adaptive Systems, Acquisition of Cooperative Rules • Recent Program Wins • DHS S&T: ‘Complex Event Modeling, Simulation and Analysis’ • DARPA: System F6 ‘Design Tools for Adaptable Systems’ • This effort is/was … • Inspired by SFI’s Complex Systems Summer School • And the work of Josh Epstein on Adaptive Organizations • Motivated by a desire to understand the influence of ‘context’ on optimal/adaptive (SoS) organizational structure • One of many challenging issues in SoS behavior modeling 2

3. Agenda • Motivation • Related Work • Approach • Results • Discussion 2/2/2011 3

4. Motivating Observations • We’re increasingly encountering ‘self-adaptive self-organizing’ (SASO) organizations • Vulnerabilities (and strengths) are poorly understood. Behavior is difficult to predict. • Compounded by the fact that our response is increasingly similarly ‘self-organizing’ • But it’s clear that how SASOs allocate resources determines important aspects of their behavior • The structure of a SASO influences how resources are allocated … • while a ‘culturally shared’ process influences/determines that structure • The ‘optimal’ allocation structure (degree of centralization) is difficult to determine • Optimal is a tradeoff between short term efficiency and long-term adaptability • Often in the short term ‘optimal’ is neither fully centralized (hierarchical) or decentralized (market oriented) • And, in many cases, its clearly not static … i.e. the optimal structure changes in response to the environment • Need a model to understand (the rules of) this process driven structure dynamic • What do optimal rules (in a particular dynamic context) look like? • What do these rules look like when the context is adversarially dynamic? • How well do these rules evolve towards optimality? • Does co-evolution play a role in the movement towards (adaptive) optimality? It is not the strongest of the species that survive, nor the most intelligent, but the one most responsive to change.  ~Megginson paraphrase of Charles Darwin 2/2/2011 4

5. Related Work and Background • Nobel Prize in Economics (Coase, 1991) • The reason why organizations exist , when markets have been established as remarkable successfully, is … transaction costs. The market takes money to run. • The internet has reduced transaction costs • Pioneering work in adaptive organization structure (Josh Epstein, 2006). • Optimal organizational structure is determined by the dynamics of the organization’s context … and can be modeled as a decentralized agent-based process • Shared policies/beliefs govern emergent behaviors including self-organization • Characterization of the threat of decentralized forces (Alberts, CCRP) • Conflicts with asymmetric adversaries are complex • Characterizable as self-adaptive self-organizing ‘multi-agent’ systems • Fighting the Current Wars (e.g. Kipp, 2006) • Responding to apparent objectives often has unanticipated consequences • Decentralized adversaries are often apparently more agile than Coalition forces • Influence on 3rd parties (often the strategic objective) is often in practice unpredictable 2/2/2011

6. Josh Epstein’s Foundational Work Ch. 13 Growing Adaptive Organizations: An Agent-Based Computational Approach • Investigated the nature of bottom-up self-organizing structures • Specifically the behavior of static ‘genomes’ (i.e., shared organizational rules) … that produce dynamic structure in response to dynamic environments • Interested in laying the foundation for research into the optimal static genome for a particular dynamic environment … i.e. most ‘profitable’ or ‘market-share’ 2/2/2011 6

7. Goal and Approach of this Research Long Range Research Objectives • Understand the nature of cultural genomes that exhibit adaptive, agile, robust and survival organizational behavior • Understand how cultural genomes evolve (and co-evolve) towards adaptive optimality Approach Develop an agent-based exploratorymodelin which adversaries endogenously adaptto the evolving dynamic environment defined by the ‘other’ … • Given simple shared behavioral rules (That define how local decisions to allocate resources should be made) • Encode rule sets (parameters) in a ‘genome’ • Let the ‘genome’ evolve towards ‘long term’ competitive optimality • Explore the resulting behavior space looking for potential real-world correlates • Capture the histories of structural adaptation that they generate • Look for behaviors that emerge from the evolving policies 2/2/2011 7

8. Behaviors of Particular Interest • Survivability: the capability to continue to function during and after a disturbance … sometimes decomposed into susceptibility, vulnerability and recoverability • Robustness: the capability of coping well with variations in its operating environment with minimal damage, alteration or loss of functionality • Agility: the ability to sense and respond to opportunities in order to stay innovative and competitive in a turbulent and quickly changing environment … to change without losing balance or control. • Adaptability: the ability of an organism to alter itself or its responses to the changed circumstances or environment. Adaptability shows the ability to learn from experience, and improves the fitness of the learner as a competitor. Often seen as in conflict with efficiency. Vertical, Centralized, Command Coordination The fundamental questions we are exploring are: What are the characteristics of the ‘cultural genome’ that maximize thesurvivability andagilityof an organization … given a specified (adversarial) environmental dynamic? What influences the emergence of robustness and adaptability? Can salient behaviors emerge during this pursuit of survivability? Horizontal, Decentralized, Market Oriented Coordination 2/2/2011

9. …the Optimal Genome • The Analyst specifies some key ‘invariants’ of the environment: • Nature of the conflict: (max profit | market share | hybrids) • Technology Limitations: perceptual time horizons • Environmental Dynamic (including adaptive adversary) • Cost Structure (labor wages, transaction costs) • The Model evolves the genome (shared cultural rules) • By an 8 dimensional hill-climbing of the global dynamic fitness landscape • Returning a contextually ‘fit’ (adaptable|survivable) genome • Which defines when to construct/deconstruct hierarchical structure • And how to respond to ‘strategic’ changes in adversary behavior • From local conditions like: frequency of ‘incidents’, ‘resistance to change/collaboration’ and ‘profitability’, • And documents … • The short term histories of structural adaptations • The long term history of ‘cultural change’ … i.e., how the rules evolved • The absolute and relative organizational ‘performance’ over time • And the behaviors that emerge from these co-evolved genomes 2/2/2011 9

10. Approach - Visualization Threats are generated by Red relocatable ‘workers’ and move (right) toward the Blue’s defensive ‘worker’ line. Management hierarchy develops ‘bottom up’ towards the margins. Management hierarchy incurs sustainment costs. Authority lines indicate which manager can retask the resource. These parameters define non-evolvable environmental parameters that constrain the co-evolution. They can be manipulated to preferentially benefit one side or the other. These 12 parameters define the policy genomes that determine self-organizing behavior and consequent emergent strategies. ‘Active’ managers can trade with other active managers … but it incurs a transaction cost. This section turns on the policy genome learning evolution and displays its progress. This graph provides a dynamic look into the co-evolution of strategies and how they are correlated. The Bank graphs display the effectiveness of the emergent strategies in both absolute and relative terms 2/2/2011

11. Sample MORAS-SASOS Runs Emulating Epstein’s Model Adversarial context, Static genomes In this short video Blue is first overwhelmed by Red’s Attack and then begins to find an effective counter-strategy As this scenario runs to completions eventually Blue evolves to out-compete Red Adversarial context, Co-evolving genomes

13. Initial Observations • Strategies initially become responsive then sometimes ‘sync’ • Then ‘Gaming’ strategies tend to emerge as policies evolve • They look intelligent and intentional … but they are not. • E.g. “ID promising opportunities; focus resources; decentralize to minimize cost/benefit; then rapidly recentralize, when opposed, to reassess … repeat.” • Such strategies disappear as the genomes continue to co-evolve only to reappear in apparently similar form in later stages. • The evolutionary process at first glance appears to cycle through alternative strategies, but … Initial Strategic Linking Typical Strategic Syncing 2/2/2011

14. Further Experimentation • Exploring the following four evolutionary conditions • Red Evolves, Blue Doesn’t • Blue Evolves, Red Doesn’t • Both Red and Blue Evolve • Neither Red and Blue Evolve • Let the strategy genome evolve over a specified interval • Evaluate the final genome against a novel threat dynamic • Rank the Results (from best to worst): • Co-evolution • Other evolves • Neither evolves • Self evolves • Co-evolution produces Agility! • Better to not evolve than evolve in isolation?? 2/2/2011

15. Hints at Power Law Scaling Log-log plots of the cumulative distributions describing the total number of events with severity greater than , for the ongoing wars in Iraq (blue) and Colombia (green). • Asymmetric attack size vs. frequency known to be a Power Law Relation Johnson, Neil F.; Spagat, Mike; Restrepo, Jorge A.; et.al. (2006). Universal patterns underlying ongoing wars and terrorism ., Cornell University Library, eprint arXiv:physics/0605035v1 • Note: The slope of these distributions vary over time … • e.g. Iraq changed from 2.12 to 2.31 over the course of the war. • Frequency of manager activation has a similar distribution Manager Scope • Is this an explanation for the above? • Will we find similar time-evolved network structures in the data? Initial Frequency of Activity Co-evolved

16. Discussion and Next Steps • A new method/model for studying asymmetric (decentralized) organizational dynamics has been developed • It helps define the questions that are worth asking • It suggests ‘leverage points’ that support operational effectiveness • It suggests engagement styles that could strengthen the adversary • Influence Operations are an obvious domain of application • May predict operation size and frequency • Suggests that organizational process agility may be important • Next Step: How valid is the model (can it be disconfirmed)? • E.g., does it successfully predict the change in slope of the power law distribution of asymmetric attack sizes. • E.g., does it successfully predict known organizational, cultural and/or commercial instances of (non) agility. 2/2/2011 16