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ETH D-GESS: 851-0585-37L Social Modelling, Agent-Based Simulation and Collective Intelligence (Week 2) Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 1 Emergent Residential Segregation using Simple Social Behavior ETH D-GESS: 851-0585-37L Week 2 Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 2 This is a graduate seminar course in a field of scientific inquiry called Computational Social Science, or CSS for short. During the course we will read about, talk about, and consider theoretic and practical instances of complex social processes instantiated as computer code. By the end of the course we will want to discuss our mental models of what is Collective Intelligence and how we might code it. But that is still a long, long way off …. Let’s get started! Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 3 In this course we are going to study Complex Systems, not merely Complicated Systems. So, we may need to draw a practical distinction: A Ferrari is a complicated system but its performance is predictable. A complex system will “almost exclusively” be non-linear, possibly computable, usually stochastic, will be sensitive to initial conditions, will have path dependencies, and will demonstrate probabilistic tendencies. They will often have instance distributions that follow a Power Law and have regime (or phase) changes near so called tipping points. Social models typically involve the study of complex social systems. Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 4 In CSS we begin by constructing mental models of the social systems and social processes that interest us. Then we derive hypotheses regarding the endogenous and exogenous forces that we think may drive the system. Finally, we instantiate our model (and those hypotheses) in computer code. As our code executes, we compare the data produced by our executing program(s) with data collected from the “real-world.” Thus, the “real-world” provides us with empirical data for inspiration and comparison, and the computer becomes our laboratory. We often refer to instantiating social models in software as working in-silico. In Computational Social Science we usually test our models in-silico. Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 5 Working this way, in-silico, allows us to examine social processes, social models, and our assumptions about how the world, our local environment, our social groups, and even we ourselves react to (and within) the world around us. That is what this course is about. This course is here to help you start thinking about, constructing, analyzing, and using social modelling and agent-based simulation as a way to understand, to explain, and to improve your world. Computational social models can give us insights not possible by other means. Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 6 Course Overview Procedure (Parts I & II): 1. Examine a selection of published, formal models of social processes 2. Learn how to analyze and extend simple models and to develop your own social process models using existing computer-coded examples By the end of the course you will be able to: 1. Recognize several well-known, “key,” social process models 2. Understand, analyze, & compare model-outcomes using empirical data 3. Influence the outcome of a coded positive or normative process model Grading: 1. Reading, writing, class participation, running code, 2-exams, & extra-credit. Social Modelling, Agent-Based Simulation and Collective Intelligence Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 7 We will have lectures by: Professor Dr. Dirk Helbing • • • • • • Pedestrian models Game theoretic models Traffic models Internationally recognized scientist and author A thought leader in Computational Social Science A “future-thinker” Dr. Matthias Leiss • Economic and market models • ETH D-GESS program in Computational Social Science Let me introduce myself. Then, I want to meet you … Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 8 I am a technologist turned scientist … Academics by degree, school (year) BS Psychology, College of the Southwest (1996) BS Computer Science, Eastern New Mexico University (1998) Minors in mathematics & electronics technology MS Electrical Engineering, New Mexico State University (2000) Thesis: “Cybernetic intelligence: a return to complex qualitative feedback theory” (Daryl mouse in software) (ZI prototypes) PhD Computational Social Science, George Mason University (2015) Thesis: “Towards Emergent Social Complexity” So, who am I? Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 9 Engineering & Technology by company, location (function) Honeywell, Phoenix (FPGA & EPLD hardware design engineer) GMU, Fairfax (adjunct professor in Electrical Engineering) SRS, Arlington (DARPA SETA) I3, Arlington (JIEDDO & US Army REF SETA) GMU, Fairfax (research project manager, agent-based modeler) Camber, Centreville (agent-based modeler) Harmonia, Blacksburg (machine intelligence MDA) Vencore, Arlington (ONR SETA) Misc., others, (hardware design, simulation technologist, site management, etc.) What are my engineering bona fides? Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 10 Artificial Life: Biologically detailed, autonomous, intelligent virtual agent models Hypothesis: A pristine environment is necessary for the identification of the fundamental principles of small-group social behavior. Experiment: Each situated and embodied agent has its own adaptive, self-organizing, cognitiveaffective artificial intelligence. In turn the entire Virtual World, and its inhabitants, exist within a networked computer cluster. Turing, 1948 P-Type Hypothesis: An environment having a sufficient absence of cultural confounds but also one having a sufficient fullness of socio-environmental stimuli is required for a complete understanding of “emergent” sociality. What do I research? ALife, emergent sociality, and collective intelligence Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 11 Now, who are you? Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 12 After a short break we will start on the models: 1. 2. 3. 4. 5. 6. The Game of Life – (Conway, 1970) Flocking– (Reynolds, 1986) Swarming (Bonabeau, 2001 & 2002) Residential Segregation – (Schelling, 1970 & 1971) Social Segregation on a Realistic GIS Surface – (Crooks, 2011) Pedestrians entering a large building when constrained by small-group behaviors – (unpublished work for a government client Crooks, Hendrey, Rouly, 2011) ... talk about software – the class has no coding requirements ... the weekly reading assignments – typically 1-2 short articles and, of course, we will discuss the class deliverables again. Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 13 break 5-6 minutes Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 14 "Things should be made as simple as possible - but no simpler." Albert Einstein Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 15 Social Modelling, Agent-Based Simulation, and Collective Intelligence reside within the interdisciplinary domain of Computational Social Science (CSS) Graphic from lecture notes Crooks, A. (2015). George Mason University. Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 16 What is Agent-based Simulation? Agent-based systems simulation and agent-based social modeling refers generally to object-oriented software systems which, through the use of computer code, instantiate models of living systems of social entities. These systems, of software agents (objects), tend to have at least two dominant characteristics 1. They are positive representations of the systems they model. Their instantiations attempt to closely recreate or capture the abstract and or detailed essence of the prototype system(s). 2. They can as well be normative, i.e., have control inputs that provide static or runtime exogenous steering of internal feedback loops. Derived from lecture notes Axtell, R. (2016). George Mason University Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 17 What Agent Systems Are *NOT* “Computational” X: when x refers to something from the social sciences, it usually does not refer to agents. (for example…) “Computational economics” refers to a numerical analysis of some conventional (e.g., rational, equilibrium) system model. “Computational finance” involves finding a numerical solution to stochastic partial differential equations (PDEs). “Computational game theory” considers the numerical determination of equilibrium configurations (e.g., Nash, 1950) Finally, “systems dynamics” were once an important computational approach in the social sciences. But, they too are not an agent-based approach. Text paraphrased from lecture notes Axtell, R. (2016). George Mason University. Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 18 Speaking of agent-based simulation generally, and generative agent-based models in particular: There are at least, “three domains of practice wherein and whereupon generative agent-based models are most useful or most usefully built. Those domains of practice are: 1) models of historical systems that either existed or are believed to have existed, but because of their antiquity cannot be revisited for study by some other means, 2) models of "long-lived" systems that, for reasons of test subject controllability cannot be studied, and by extension 3) models of systems whose study involves unethical, illegal, unsafe, or unlikely environmental settings or exogenous stimuli” (Rouly, 2016). Social modeling can give us system insights not possible by any other means. Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 19 The ABM Pedigree • Biology: – John von Neumann: self-reproducing automata (‘50s) – John Conway: Game of Life (late ‘60s) – Chris Langton: Artificial Life (late ‘80s) • Social science: – Simon, March and Cyert: the ‘behavioral school’ and simulation of few agent systems (‘50s and ‘60s) – Thomas Schelling: tipping model of segregation (late ‘60s) • Computer science: – – – – – artificial intelligence (AI) robotics distributed AI (DAI) multi-agent systems (MAS) object-oriented programming (OOP) One possible historic path to the ABM pedigree. (Axtell, 2016) Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 20 Broad CSS paradigms Cellular automata Big data – data-analytic, probabilistic, and actuarial models Social networks Generative models – Agent-Based models & Individual-Based Models Simple tools High-speed computers, parallel & sequential algorithms High-level computer language(s) Heuristics (qualitative and quantitative) Spatial layouts (vector & raster) Concepts Emergence Bottom-up computation “Micro-level rules lead to macro-level behaviors” Some paradigms, tools, and concepts upon which practitioners of CSS rely. Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 21 The Models Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 22 The Game of Life (Conway, circa 1970) Life states: { dead, alive } Rules: Each cell checks the Life State of itself and those of the cells in its local neighborhood at a Moore distance of 1. If alive then display a pixel if dead do not. If this cell has less than two neighbors alive or more than three neighbors alive then, set this cell dead. If there are exactly three alive neighbors, set Life State alive. Randomized activation of cells continues “forever.” Wilensky, U. (1998) Concepts: CSS modeling paradigm – cellular automata Neighborhood types – Moore and von Neumann Distance-neighborhoods – Chebyshev and Manhattan determine set cardinality “Moore neighbors are all-around but von Neumann neighbors are orthogonal.” Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 23 Flocking (Reynolds 1986) Agent Properties: { flight speed, vision distance, turning capacity, separation spacing } Rules: 1. “Collision Avoidance: avoid collisions with nearby flock mates 2. Velocity Matching: attempt to match velocity with nearby flock mates 3. Flock Centering: attempt to stay close to nearby flock mates” Concepts: Wilensky, U. (1998) Simple tools – homogeneous agents, spatial ABM, few rules, torus grid Comparison: Ballerini, M., et al. (2008). Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study. Proceedings of the national academy of sciences, 105(4), pp. 1232-1237. Emergent behavioral complexity: bottom-up computation Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 24 Swarming (Bonabeau 2001, 2002) http://neoswarm.com/bonabeau.html Concepts: Simple tools – homogeneous agents, spatial ABM, few rules, torus grid Example of complex, counter-intuitive, social behavior Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 25 Residential Segregation (Schelling 1969 & 1970) Agent states: { color A, color B } Rules: Execution takes place on a raster surface of arbitrary size but one having a granularity where all areas are “habitable.” Each agent can “see” (has knowledge of) its neighbors out to some distance. Each agent has a variable intensity preference for “color.” And, according to their preference for a “color” can choose to move to be near other agents whose “color” they most prefer. No two agents can occupy the same location. Wilensky, U. (1998) Concepts: Simple tools – spatial layout (raster), torus grid, activation order Social hypothesis – people create segregated neighborhoods by simple choice Emergent social complexity: “micro-level rules lead to macro-level behaviors” Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 26 Residential Segregation Using Vector-based GIS (Crooks 2010) Agent states: { color A, color B } Rules: Execution takes place on a surface of arbitrary size but having vector defined sub-areas; some habitable others not. Each agent can “see” (has knowledge of) its neighbors out to some distance. Each agent has a variable intensity preference for “color.” And, according to their preference for a “color” can choose to move to be near other agents whose “color” they most prefer. No two agents can occupy the same location. Concepts: Simple tools – spatial layout (vector), original Schelling (late ‘60s early ‘70s) Practical hypothesis – Schelling neighborhoods will emerge on (vector) layouts The formal Schelling model in vector space produced an unexpected result. Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 27 (Movie) A spatial-agent based modeling constructed to answer a government question Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 28 Coding Tools High-level languages: Java, MASON, NetLogo, C/C++, MatLab, Javascript, R, Lisp/Scheme, SWARM, Objective-C, Ruby/Rails, BASIC, VBasic, C#, Mathematica, Julia, ... others. Installing and running NetLogo is required (https://ccl.northwestern.edu/netlogo/5.1.0/) Extra-credit: Work can be written in any language if you can prove that you wrote the code, the code operates, and that it instantiates a social system process or model. Minimum requirements: Run NetLogo code, write 1-2 page white papers. Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 29 Deliverables this week Reading assignments: Chapter 9 – in Miller, J. H., & Page, S. E. (2009). Complex adaptive systems: An introduction to computational models of social life. Princeton university press. Chapter 14 – in Helbing, D. (Ed.). (2012). Social self-organization: Agentbased simulations and experiments to study emergent social behavior. Springer. Writing/Coding assignment: Write a 1-2 page White Paper arguing for (and or against) the validity of the Schelling or Reynolds models to describe qualitatively (and or quantitatively) the behavior of the “human” or “boid” objects, respectively. Week 2 deliverables: Read, Write, install and run Schelling and Reynolds. Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 30 REFERENCES • Ballerini, M., Cabibbo, N., Candelier, R., Cavagna, A., & Cisbani, E., et al. (2008). Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study. Proceedings of the national academy of sciences, 105(4), pp. 1232-1237. • Bonabeau, E. and Myer, C. (May 2001). Swarm Intelligence, A Whole New Way to Think About Business. Harvard Business Review. pp. 106-114. • Bonabeau, Eric. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99 (supplement 3), pp. 7280-7287. • Crooks, A. T. (2010). Constructing and implementing an agent-based model of residential segregation through vector GIS. International Journal of Geographical Information Science, 24(5), 661-675. • Crooks, A., Hendrey, M. & Rouly, O. C. (2011). Pedestrians entering a venue constrained by smallgroup social interaction. Unpublished work. George Mason University. • Cyert, R. & March, J. (1992). A behavioral theory of the firm (2 ed.). Wiley-Blackwell. • Gardner, M. (October 1970). Mathematical Games – The fantastic combinations of John Conway's new solitaire game "life". Scientific American 223. pp. 120–123. • Helbing, D. (Ed.). (2012). Social self-organization: Agent-based simulations and experiments to study emergent social behavior. Springer. • Miller, J. & Page, S. (2009). Complex adaptive systems: An introduction to computational models of social life. Princeton university press. • Nash, J. (1950) Equilibrium points in n-person games. Proceedings of the National Academy of Sciences. 36(1):48-49. Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 31 REFERENCES • Reynolds, C. (1987). Flocks, herds and schools: A distributed behavioral model. SIGGRAPH '87: Proceedings of the 14th annual conference on Computer graphics and interactive techniques (Association for Computing Machinery). pp. 25–34. • Rouly, O. (2016). Towards Emergent Social Complexity. Unpublished dissertation. George Mason University, Fairfax, Virginia, USA. • Schelling, T. (1969) Models of segregation. American Economic Review. 59(2). pp. 488–493. • Schelling, T. (1971). Dynamic Models of Segregation. Journal of Mathematical Sociology. 1(2). pp. 143–186. • Simon, H (February 1955). A behavioral model of rational choice. The Quarterly Journal of Economics. 69 (1): 99–118. • von Neumann, J. (1951).The general and logical theory of automata, in L.A. Jeffress, ed., Cerebral Mechanisms in Behavior – The Hixon Symposium. John Wiley & Sons, New York. pp. 1–31. • Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. • Wilensky, U. (1998). NetLogo Flocking model. http://ccl.northwestern.edu/netlogo/models/Flocking. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. • Wilensky, U. (1998). NetLogo Life model. http://ccl.northwestern.edu/netlogo/models/Life. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL. Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 32 In the weeks that follow we will: discuss the vocabulary of ABM and Social Systems Modeling learn about the software tools used by CSS practitioners consider more of the theory behind the model instantiations see more formal and informal models of Social Simulation Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 33 Contact information ETH Zurich D-GESS Computational Social Science Clausiusstrasse 50 8006 Zürich, Switzerland http://www.coss.ethz.ch/ Ovi Chris Rouly, PhD. Email: [email protected] Telephone: (41) 044-633-8380 © ETH Zurich, 27 February 2016 Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 34 LAST SLIDE Department of Hum anities, Social and Political Sciences Program in Computational Social Science Ovi Chris Rouly, PhD | 27.02.2016 | 35