Passino CoopSysSJ

ECE 7858 Intelligent Control
(Cooperative Systems for Social Justice)
Social Justice

Syllabus

Dept. Electrical and Computer Engineering, Ohio State University

ECE 7858 Intelligent Control

(Cooperative Systems for Social Justice)

 

  • Enrolled students, see Carmen for more information. Submit all assignments/projects electronically via Carmen (the LaTeX word processing system is preferred).
  • All publications referenced on this page can be obtained at Carmen or here.
  • The syllabus below may change. It has evolved over the years, and continues to evolve, as this is a course to support cutting-edge research, and discuss research methodology.
  • Homework problems below (except Homework #1), will change; check with Carmen before attempting a solution.

Instructor: Prof. Kevin Passino 416 Dreese Laboratory, passino.1@osu.edu

Office Hours: Set an appointment via email, or talk to me before or after class.

Prerequisites: Graduate standing is required by the numbering of the course per OSU policy; ECE 6754 Nonlinear Control Systems is useful but not required; ECE 5759 Optimization is useful but not required

Course Objectives: Social justice design objective quantification.  Feedback controllers based on principles of cooperation, in the dynamics of group agreement, choice, allocation, management, and design teams. Analysis and verification of group properties, and achievement of social justice objectives, via Lyapunov stability analysis and Matlab simulations.

Scheduling: This course is offered in Spring semester of even-numbered years.

Topical Outline:

Background for Analysis, Metacognition, and Learning/Growth Mindset:

Homework 1:

(1) Establish a background in stability analysis of dynamical systems. Read the introduction to stability paper by Michel posted at Carmen. This material is not covered in class; you build your background in this area via this assignment. This material is foundational for all topics covered in class. Prove that three different dynamical systems possess qualitative properties: For one system, asymptotic stability, another exponential stability, and the third, uniform ultimate boundedness, where you choose the dynamical systems, and analysis approaches for your chosen systems, based on the following constraints:

(i) one system must be continuous time, one discrete time, and another of your choice;

(ii) dimensionality: n=1 for one (scalar) system, n>1 and fixed for one system, and n arbitrary for one system;

(iii) one system may be linear, but the other two must be nonlinear;

Prove that invariant sets (e.g., an equilibrium is a simple invariant set) for each of your chosen systems (you find them) possess one of the following qualitative properties: (i) asymptotically stable (globally), (ii) exponentially stable (at least locally), and (iii) uniformly ultimately bounded. Your systems above must be chosen so that at least one of these properties hold for each, and your systems are such that one is for (i), one is for (ii), and one is for (iii).

State all your assumptions, and show all the steps of your three proofs. You may use any source aside from another student in the class, but reference your sources.

(2) Read the slides: "Successful Learning: Mindset and Metacognition" and write a one-page "reaction paper" (e.g., what you think about the ideas, whether they are useful to you in research or not: be specific). Do not dismiss this as unimportant relative to the mathematical part of the class.

Optional: Read the paper, AN Michel, K Wang, KM Passino, "Qualitative Equivalence of Dynamical Systems with Applications to Discrete Event Systems," Proc. 31st Conf. on Decision and Control, Tuscon, AZ, Dec. 1992, can some additional information can be found in Anthony N Michel and Kaining Wang, Qualitative Theory of Dynamical Systems: The Role of Stability Preserving Mappings, Marcel Dekker, NY, 1995. This covers general dyanmical systems on a metric space, qualitative properties, stability preserving mappings, and comparison theory. It is useful in generalizing the ideas covered in this course, that is, in research.

Introduction: Group/Community Dynamics and Cooperation (the basics; Week 1, 2 weeks):

Class:

(1) Overview: Dynamics and the general dynamical system.  Feedback control by distributed computing and a group of social humans (agent interactions over a network, topology), a sociotechnological system. Role of model-free control (e.g., via "intelligent control"). Performance objectives, stability, robustness, and social justice objectives (e.g., diversity, equity, inclusion, common good, rights).  Overview of mathematical and computational analysis methods, stability and the characterization of "emergent" properties.  Discuss paths to implementation: (i) develop strategies (rules) for humans to use; (ii) add technological advisors for processing information and aiding in decisions; and (iii) use only autonomous computers.

(2) Cooperation: (i) examples from behavioral economics and game theory, (ii) the prisoner's dilemma, direct (mutualism) and indirect reciprocity, and other mechanisms of cooperation; (iii) evolution of cooperation (e.g., tit-for-tat) using Axelrod's approach; and (iv) examples in humans, other species.

(3) Relevant fields: (i) Intelligent control methods including fuzzy control, neural networks, planning systems, attentional systems, learning, evolution, foraging; and (ii) social psychology and role theory (see references below).

Homework 2: Simulate the evolution of cooperation using Axelrod's approach with a tournament. Use the bimatrix payoff values given in class for the Prisoner's dilemma called PD*.  You must have at least three different strategies in the tournament, and one must be TitForTat. Your objective is to introduce your own strategy and analyze how it competes against other strategies.  Write Matlab code, provide results (you decide on the form, tables or plots, etc.), and explain each result carefully in terms of what was discussed in class (e.g., explain carefully how cooperation "emerges"). Next, simulate evolution via simulation of a sequence of tournaments, then use the method in class to specify portions of strategies and study which strategy (strategies) evolve to be the best.  Optional: Are any ideas from PID control or other control method (e.g., model predictive control) useful in strategy design, particularly, in trying to come up with a strategy that can beat TitForTat? Can you modify the simulations above to test your ideas? How well do they perform against TitForTat?

 

    Group/Community Agreement (plus Motion and Syncrhonization) (reaching a consensus; Week 3, 2 weeks): Collective/coordinated motion, modeling and stability analysis of swarms (ODE based), distributed agreement/choice and distributed synchronization, human preference/opinion (cognitive variables) vector alignment in human social processes.

    Class:

    (1) Distributed agreement: community elections problem formulation (candidate positions on topics, voter preferences/positions on topics, liberals/conservatives/moderates, inter-voter persuasion, etc.), discuss model and analysis of voter behavior and candidate choice. Relations to a model of democracy from Humanitarian Engineering: Advancing Technology for Sustainable Development. Dynamics of inclusion.

    (2) Movies of flocks, schools, and swarms. See also, Schultz K.M., Passino K.M., Seeley T.D., "The Mechanism of Flight Guidance in Honeybee Swarms: Subtle Guides or Streaker Bees?," Journal of Experimental Biology, Vol. 211, pp. 3287-3295, 2008. (ii) Modeling and stability analysis of swarms (ODE based) via the paper: Yanfei Liu and Kevin M. Passino, “Stable Social Foraging Swarms in a Noisy Environment,” IEEE Trans. on Automatic Control, Vol. 49, No. 1, pp. 30-44, Jan. 2004 covering uniform boundedness, uniform ultimate boundedness, identical agent case, trajectory-following case, simulations (group/individual profile tracking in presence of noise).

    (3) Relations to foraging theory. Effects of diversity. Relations to group formation and group cohesiveness.  See: Group size design by Andrews B.W., Passino K.M., Waite T.A., "Social Foraging Theory for Robust Multiagent System Design," IEEE Trans. Automation Science and Engineering, Vol. 4, No. 1, pp. 79-86, pp. 79-86, Jan. 2007. 

    Homework 3: (i) Swarm simulation for general case (all features, for Liu/Passino "noisy" paper above) for N=2 and N=10 cases. Vary all parameters individually to illustrate impacts on swarm behavior (e.g., increase repulsion gain and show it will keep agents farther apart, generally).  (ii) Simulation of political elections problem (cognitive variables case), case of two cogntitive variables, n=2 (e.g., "positions" on two different policies), N=10 voters, choice of nonlinear interactions, adjustment of parameters, and effects on candidate choice for the case of two candidates. (iii) Summarize and critique the paper by Liu/Passino ("Stable social foraging..."), and derive and state all steps in the proofs for Theorem 1 and Corollary 1.

    Optional: (i) Overview of concepts from other papers on swarms here (just search on the word "swarm" to see which papers); (ii) Liu Yanfei, Passino Kevin M., "Cohesive Behaviors of Multiagent Systems with Information Flow Constraints", IEEE Trans. on Automatic Control, Vol. 51, No. 11, pp. 1734-1748, Nov. 2006, (iii) application to distributed syncrhonization, (iii) discussion on impacts on modeling and analysis for applications. Discussions on mechanisms of cooperation, information flow, and resulting emergent types of cooperation that depend on the goal. Relations to a model of democracy from Humanitarian Engineering: Advancing Technology for Sustainable Development

     

    Group/Community Resource Allocation ("who gets what?;" Week 5, 1.5 weeks): Load balancing in computer networks (brief), The "ideal free distribution." Social foraging by honeybees. Use in communities.

    Class:

    (1) Modeling distributive justice systems and emergent fair/unfair distributions. Overview of how in social foraging a hive can achieve an ideal free distribution. Swarm cognition for allocation per social foraging by honeybees. Relations to distributive justice (and bee pollen regulation).

    (2) Differential equation model, evolutionary game-theoretic, stability, and optimization perspectives: Quijano N., Passino K.M., "The Ideal Free Distribution: Theory and Engineering Application," IEEE Trans. on Systems, Man, and Cybernetics, Vol. 37, No. 1, pp. 154-165, Feb. 2007. Overview of: Finke J. and Passino K.M., "Local Agent Requirements for Stable Emergent Group Distributions," IEEE Trans. Automatic Control, Vol. 56, No. 6, pp. 1426-1431, June 2011.

    (3) Relations to attentional systems, and learning systems and adaptive control using multimodel methods, from Biomimicry for Optimization, Control, and Automation.

    (4) Overviews of (i) Moore B.J., Finke J., Passino K.M., "Optimal Allocation of Heterogeneous Resources in Cooperative Control Scenarios," Automatica, Vol. 45, pp. 711-715, 2009, (ii) balancing modeling and analysis via paper: Burgess K.L., Passino K.M., "Stability Analysis of Load Balancing Systems", Int. Journal of Control, Vol. 61, No. 2, pp. 357-393, February 1995; and (iii) Quijano, Nicanor, Passsino, Kevin M., "Honey Bee Social Foraging Algorithms for Resource Allocation: Theory and Application," Engineering Applications of Artificial Intelligence, Vol. 23, pp. 845-861, 2010.

    Homework 4: (i) Read and summarize/critique (for each critique, write a summary of the paper and then say what is good and bad about the paper): Finke J. and Passino K.M., "Local Agent Requirements for Stable Emergent Group Distributions," IEEE Trans. Automatic Control, Vol. 56, No. 6, pp. 1426-1431, June 2011. Identify at least three differences from the Quijano/Passino paper above. (ii) Simulation of achievement of an ideal free distribution: Consider the case of N=3 habitats (and your choice of topology). Simulate either the ODE approach (Quijano) or distributed computing approach (Finke) and demonstrate achievement of the IFD, and how perturbations off the IFD result in returning to the IFD (suggesting stability). 

    Optional: (i) Study the Kuramoto model of coupled oscillators (synchronization), and simulate the oscillator in Section 1 of that page for your choice of parameters, with N>=10; use a Matlab movie to illustrate the dynamics. (ii) Read and critique the following papers: (i) Finke J., Quijano N., Passino K.M., "Emergence of Scale Free Networks from Ideal Free Distributions," Europhysics Letters, Vol. 82, 28004 (6 pages), April 2008; or (ii) Quijano, Nicanor, Passsino, Kevin M., "Honey Bee Social Foraging Algorithms for Resource Allocation: Theory and Application," Engineering Applications of Artificial Intelligence, Vol. 23, pp. 845-861, 2010.

     

    Group/Community Choice (search/evaluation/selection and choosing the best of N, N unknown; Week 6, 1 week): Nest-site selection by honeybees, "swarm cognition," bias removal by the group compared to the individual(?), human case.

    Class: (i) Passino K.M., Seeley T.D., "Modeling and Analysis of Nest-Site Selection by Honey Bee Swarms: The Speed and Accuracy Trade-off", Behavioral Ecology and Sociobiology, Vol. 59, No. 3, pp. 427-442, Jan. 2006 and Passino K.M., Seeley T.D., Visscher P.K., "Swarm Cognition in Honey Bees," Behavioral Ecology and Sociobiology, Vol. 62, No. 3, pp. 401-414, Jan. 2008.  Social justice goals and multicriterion decision making: Section 4.5 in Humanitarian Engineering: Advancing Technology for Sustainable Development.

    Midterm Project (integrating disparate topics): Consider Section 4.5 (read it all carefully, and teach yourself the Matlab code given there) in Humanitarian Engineering: Advancing Technology for Sustainable Development, and add it into the model of group choice (the one from class based on "Modeling and Analysis of Nest-Site Selection by Honey Bee Swarms: The Speed and Accuracy Trade-off") so that multiple attributes are considered for each candidate nest in a bee's assessment, and study the dynamics in detail (you have to make good choices on what to study computationally using the Passino/Seeley paper, and you will have to write your own code for group choice and add in the approach in Section 4.5). Explain how these ideas could be transferred to a small community, where no technology is available (the "all humans case" discussed in class.

    Optional for Midterm: Explain what a "search-exploit" tradeoff would mean for the choice problem in (i), and (iii) Read and summarize/critique (for each critique, write a summary of the paper and then say what is good AND bad about the paper: this should take about 5 typed pages per critique): Passino K.M., Seeley T.D., Visscher P.K., "Swarm Cognition in Honey Bees," Behavioral Ecology and Sociobiology, Vol. 62, No. 3, pp. 401-414, Jan. 2008. 

     

    Group/Community Task Processing, Scheduling, Assignment ("working together: who does what, and when?;" Week 7, 1.5 weeks): Cooperative management and processing of tasks.

    Class:

    (1) (i) Luis Felipe Giraldo and Kevin M. Passino, "Dynamic Task Performance, Cohesion, and Communications in Human Groups," IEEE Trans. on Cybernetics, Vol. 46, No. 10, pp. 2207-2219, 2016; (ii) Pavlic T.P., Passino K.M., "Distributed and Cooperative Task Processing: Cournot Oligopolies on a Graph," IEEE Transactions on Cybernetics, Vol. 44, No. 6, pp. 774-784, June 2014.

    (2) Overview of: (a) Gil A., Passino, K.M., "Stability Analysis of Network-Based Cooperative Resource Allocation Strategies," Vol. 42, pp. 245-250, Automatica, 2006, (b) Gil A., Passino K.M., Cruz J.B., "Stable Cooperative Surveillance with Information Flow Constraints," IEEE Trans. on Control Systems Technology, Vol. 16, No. 5, pp. 856-868, Sept. 2008. (c) Gil A., Passino K.M., Ganapathy S., Sparks A., "Cooperative Task Scheduling for Networked Uninhabited Air Vehicles," IEEE Trans. on Aerospace and Electronic Systems, Vol. 44, No. 2, 561-581, April 2008. (d) Moore B.J., Passino K.M., "Decentralized Redistribution for Cooperative Patrol," Int. J. Nonlinear and Robust Control, Vol. 18, pp. 165-195, Jan. 2008, (e) Role of task assignment: Moore B.J., Passino K.M., "Distributed Task Assignment for Mobile Agents", IEEE Trans. on Automatic Control, Vol. 52, No. 4, pp. 749-753, April, 2007 and Moore B.J., Passino K.M., "Decentralized Redistribution for Cooperative Patrol," Int. J. Nonlinear and Robust Control, Vol. 18, pp. 165-195, Jan. 2008.

    (3) Relations to planning/learning systems and adaptive control using multimodel methods, from Biomimicry for Optimization, Control, and Automation.

    Homework 5: Summarize and critique: Luis Felipe Giraldo and Kevin M. Passino, "Dynamic Task Performance, Cohesion, and Communications in Human Groups," IEEE Trans. on Cybernetics, Vol. 46, No. 10, pp. 2207-2219, 2016. Optional: Pick a case that illustrates the main ideas in the paper, simulate it, and show how the ideas are valid for that chosen case.

     

    Group/Community Management of Resources ("avoid resource over-use, fair resource use;" Week 9, 0.5 weeks): Cooperation for management of environmental resources, N-person Prisoner's dilemma, management of the use of a common (community) technology.

    Class:

    (1) From Section 1.6.7, 2.4.5, and 3.6.6 in Humanitarian Engineering: Advancing Technology for Sustainable Development cover the tragedy of the commons, nonlinear ODE model, feedback control strategies to manage the commons (environment) (ideas from E Ostrom), along with Section 4.12.1 on "cooperative management of community technology." Relation to the social justice idea called the "common good" in Humanitarian Engineering: Advancing Technology for Sustainable Development.

    (2) Relations to MPC/planning systems. Relations to planning/learning systems and adaptive control using multimodel methods, from Biomimicry for Optimization, Control, and Automation.

    Homework 6: Do homework problems 1.39, 2.38, and 3.32 in Humanitarian Engineering: Advancing Technology for Sustainable Development. You may use code that accompanies that book.

     

    Group/Community Human Development ("lending a helping hand;" Weeks 9-12, 3.5 weeks): Financial cooperation, community change for human development.

    Class:

    (1) Features of communities, (human/community) development approaches.

    (2) Individuals, financial management: Hugo Gonzalez Villasanti and Kevin M. Passino, “Feedback Controllers as Financial Advisors for Low-Income Individuals,” IEEE Trans. on Control Systems Technology, Vol. 25, No. 6, pp. 2194-2201, Nov. 2017.

    (3) Groups, "savings clubs" and cooperative community development: Hugo Gonzalez Villasanti, Felipe Giraldo, Kevin M Passino, “Feedback Control Engineering for Cooperative Community Development,” IEEE Control Systems Magazine, pp. 87-101, June 2018.

    (4) Social dilemmas and group cooperation to complete a common task: Luis Felipe Giraldo and Kevin M. Passino, “Dynamics of Cooperation in a Task Completion Social Dilemma,” PLOS ONE, Vol. 12, No. 1, Jan. 26, 2017.

    Homework 7: Read AF Zambrano, G Díaz, S Ramirez, LF Giraldo, H Gonzalez-Villasanti, MT Perdomo, ID Hernández, JM Godoy. Donation Networks in Underprivileged Communities, IEEE Transactions on Computational Social Systems. 2020. Simulate the networked approach, pick issues to analyze, and illustrate key ideas discussed in the paper.. Compare, via discussion, the approach to: (i) the one in Section 4.12.2 in Humanitarian Engineering: Advancing Technology for Sustainable Development; and (ii) Hugo Gonzalez Villasanti, Felipe Giraldo, Kevin M Passino, “Feedback Control Engineering for Cooperative Community Development,” IEEE Control Systems Magazine, pp. 87-101, June 2018. Identify the similarities and differences.

     

    Group/Community Design ("innovation/building/construction by diverse groups;" Weeks 13-14, 2 weeks): Group formation, justice in the design process and outcomes.

    (1) Philosophy: Participatory design in a community. Co-design. From in Humanitarian Engineering: Advancing Technology for Sustainable Development (Chapter 4).

    (2) Group formation: Diversity, equity, and inclusion (DEI), principles for engineering design team formation and operation. Quantification of D, E, and I (including skills). Roles of bias, exclusions, and diversity on team performance using both business and/or social justice objectives.  Computational method for determining an optimal diverse team composition. Relevance of social foraging theory for the study of inclusion/exclusion per: Andrews B.W., Passino K.M., Waite T.A., "Social Foraging Theory for Robust Multiagent System Design," IEEE Trans. Automation Science and Engineering, Vol. 4, No. 1, pp. 79-86, pp. 79-86, Jan. 2007. 

    (3) Overview and discussion on current research - analytical studies of the group design process: Model and analysis of the group design process? How to incorporate goals, task assignment/scheduling, task completion dynamics, quantify task performance. Is there a role for qualitative analysis per Lyapunov stability theory on a metric space (see above)? Role of human-subjects testing.

     

    Final Project/Exam: Research on Cooperative Systems for Social Justice

    Choose one of the following problems and solve it:

    1. Read: Korman and Vacus, On the role of hypocrisy in escaping the tragedy of the commons, Scientific Reports (2021) 11:17585. Provide a full and complete mathematical/computational analysis to verify each idea in the paper.
    2. Read: Lansing JS, Thurner S, Chung NN, Coudurier-Curveur A, Karakaş Ç, Fesenmyer KA, Chew LY. Adaptive self-organization of Bali's ancient rice terraces. Proc Natl Acad Sci USA. 2017 Jun 20;114(25):6504-6509. doi: 10.1073/pnas.1605369114. Epub 2017 Jun 5. PMID: 28584107; PMCID: PMC5488911. Do research to model, simulate, and analyze the self-organization to verify the results of the paper.
    3. Considering the three approaches: (i) AF Zambrano, G Díaz, S Ramirez, LF Giraldo, H Gonzalez-Villasanti, MT Perdomo, ID Hernández, JM Godoy. Donation Networks in Underprivileged Communities. IEEE Transactions on Computational Social Systems. 2020; (ii) the one in Section 4.12.2 in Humanitarian Engineering: Advancing Technology for Sustainable Development; and (iii) Hugo Gonzalez Villasanti, Felipe Giraldo, Kevin M Passino, “Feedback Control Engineering for Cooperative Community Development,” IEEE Control Systems Magazine, pp. 87-101, June 2018. Do research to extend such community development ideas, and demonstrate performance relative to the evaluations in those three papers (i.e., compare and contrast).

    You may not talk/communicate to anyone except Prof Passino to solve your chosen problem. You may ask Prof Passino questions on the problem, but this is a final exam so only points of clarification can be discussed.

    Grading: Based on homeworks, midterm project, and final project (see Carmen).

     

    Relevant Books (not required):

    • Books treating some topics covered in class:
      1. Cooperation books: Introductions via M Nowak, "Supercooperators" or Axelrod, "Evolution of Cooperation." For a more advanced treatment, see the book by Bowles and Gintis "A Cooperative Species."
      2. Anthony N Michel and Kaining Wang, Qualitative Theory of Dynamical Systems: The Role of Stability Preserving Mappings, Marcel Dekker, NY, 1995.
      3. Veysel Gazi and Kevin M Passino, Swarm Stability and Optimization, Springer-Verlag, Germany, 2011.
      4. Dimitri Bertsekas and John N Tsitsiklis, Parallel and Distributed Computation: Numerical Methods, Prentice-Hall, 1989 (free download, republished in 1997 by Athena Scientific).
      5. Kevin M. Passino and Kevin L. Burgess, Stability Analysis of Discrete Event Systems, John Wiley and Sons, NY, 1998.
      6. Kevin M. Passino. Humanitarian Engineering: Advancing Technology for Sustainable Development, 3rd Edition, Bede Pub., Columbus OH, 2016 (free download).
    • Social groups (from the social sciences):
      1. Forsyth, Group Dynamics, 6th Edition, Cengage Learning, 2013.There is now a 7th Edition out. This book covers human groups, using ideas from many fields, a large range of group sizes, and group objectives.  The author’s background is “social psychology.”
      2. Dale and Smith, Human Behavior and the Social Environment: Social Systems Theory, Allyn and Bacon, 7th Edition, 2013. This book contains a systems-theoretic view of "social work," the profession that focuses on helping individual people and groups of people.
    • Intelligent control:
      1. Kevin M. Passino, Biomimicry for Optimization, Control, and Automation, the web site of which you can go to by clicking here.
      2. Jeffrey T. Spooner, Manfredi Maggiore, Raul Ordonez, and Kevin M. Passino, Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximator Techniques, John Wiley and Sons, NY, 2002
      3. Kevin M. Passino and Stephen Yurkovich, Fuzzy Control, Addison Wesley Longman, Menlo Park, CA, 1998 (free download).
      4. Antsaklis P.J., Passino K.M., eds., An Introduction to Intelligent and Autonomous Control, Kluwer Academic Publishers, Norwell, MA, 1993 (free download, very general hierarchical and distributed “autonomous” controllers”).

     

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