# 1 Expected learning outcomes After the course students will be able - PDF document

## Ume University Department of Computing Science Emergent systems Spring-15 Jonny Pettersson http://www.cs.umu.se/kurser/5DV017/VT15 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU Course Description Element 1, theory, 4,5

1. Umeå University Department of Computing Science Emergent systems Spring-15 Jonny Pettersson http://www.cs.umu.se/kurser/5DV017/VT15 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU Course Description ❒ Element 1, theory, 4,5 högskolepoäng ❍ The course deals with systems where the system's behavior arises as an emergent property of interactions between system components. Emergent properties can be observed in all non-linear systems that are sufficiently complex, both natural and artificial. Fractals, chaos, complex systems, evolution and adaptation are examples of topics. ❒ Element 2, assignments, 3 högskolepoäng ❍ The element consists of a number of mandatory assignments 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU 1

2. Expected learning outcomes ❒ After the course students will be able to: ❍ exemplify how the parts of the system can interact and give rise to a more complex behavior than is apparent in the description of each part ❍ explain how fractals, chaos, complex systems, evolution and adaptation can be used to model, simulate and understand emergent systems ❍ compare different techniques and algorithms such as cellular automata, ant algorithms, genetic algorithms, boids, swarm algorithms, evolutionary methods, autonomous agents, producer-/consumer dynamics ❍ explain different game theoretical models ❍ apply the algorithms and models to simulate and visualize simple natural and artificial systems that express emergent properties 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU Last time ❒ Tenth time, different approaches used ❒ An interesting course with good result ❒ Average workload ❒ The scientific approach in the assignments are appreciated ❒ Literature ❍ Good and not so difficult to read ❒ Assignments ❍ Interesting 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU This Course ❒ Text book + papers ❍ Computer programs ❒ Focus on ❍ Fractals ❍ Chaos ❍ Complex Systems ❍ Evolution and Adaptation ❍ Doing science ❒ Contents ❍ Lectures ❍ Assignments ❍ Project ❍ Textbook, papers 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU 2

3. Assignments and Project ❒ Assignments ❍ Termites ❍ Boids ❍ 2 and 2 in the assignments ❍ NetLogo as a tool • First (?) contact with NetLogo ❒ Project 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU Teaching ❒ Pedagogical thoughts ❒ Lecture materials ❍ Source ❍ Contents ❒ What should you learn? 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU The rest of this lecture ❒ Concepts ❍ Emergence, emergent systems, … ❒ Life ❒ Topics ❒ Scientific approach ❒ NetLogo ❒ Assignments 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU 3

4. Emergence ❒ Definitions ❍ (The whole is more then a sum of parts) ❍ (The global behavior can not be predicted from lower levels) ❍ Something that emerges in the interactions between (simple) parts and the environment, and that is not described in the parts ❒ Emergent properties ❒ Emergent systems ❒ Example: Ants 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU Emergent Behavior ❒ Bottom-up ❒ Distributed ❒ Local determination of behavior ❒ On all levels ❒ Example: The human body 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU Properties of Emergent Systems ❒ Many interactions ❒ Decentralized ❒ Non-linear ❒ Dynamic ❒ Competition and cooperation ❒ Emergent properties 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU 4

5. Many interactions ❒ In space and/or time ❒ Societies made of many people, people made of many organs, organs made of many cells ❒ A system of parts because of interactions ❒ Number of parts may differ ❒ Massive parallelism ❍ Often many simple parts doing the same thing ❍ Complexity comes from interaction ❒ Example: Weather 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU Decentralised ❒ Self-organization ❍ The order emerges from the system itself ❒ Advantages of decentralization ❍ Easier to adapt to changes ❍ No need for a smart leader ❒ Example: ❍ WWW 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU Non-linear ❒ Do not obey the superposition principle ❍ Output is not proportional to input ❒ Interactions between parts ❒ Example: Phase transitions ❍ Solid – liquid - gas 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU 5

6. Dynamic ❒ Often the interactions continues on and on ❍ Does not always come to a ”fixed” state ❍ Can be better to handle changing environments ❒ Dynamic systems can have different amounts of complexity ❒ Example: Society 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU Competition and Cooperation ❒ Some agents may cooperate ❒ Some agents may fight for the same resource ❒ Some agents may destroy what others tries to do ❒ Example: Producer-consumer systems 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU Discussion ❒ Questions to consider: ❍ What is the emergent property? ❍ What is the goal of the system? ❍ Does each agent know the goal? ❍ How was the system created? ❒ Emergent systems in the society ❒ Emergent systems in the nature 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU 6

7. Complex Systems ❒ (Almost) like emergent systems ❍ Many parts ❍ Interdependent parts ❒ Difficult to understand ❍ The behavior of the whole system understood from behavior of the parts ❍ The behavior of the parts depends on the behavior of the whole system ❒ Example: Family 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU Adaptation ❒ Can lead to improved fitness and performance, or just to be able to survive ❒ Adaptation can happen in three ways ❍ Improved handling of an event by the agent ❍ Learning – in the lifetime of the agent ❍ Evolution – across generations 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU Complex Adaptive Systems ❒ A complex adaptive system is a system consisting of many interacting parts. The behavior of the system emerges out of the parallel interactions between the parts and the environment without any global plan. The parts adapt and evolve over time. ❒ Example: Ecosystems 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU 7

8. Life – What is Life? ❒ Vitalism, - 1600 ❍ Life is ”something” extra over and above the detailed organization of a material organism ❒ Langton, 1988 ❍ ”…living organisms are nothing more than complex biochemical machines. … A living organism … must be viewed as a large population of relatively simple machines.” ❍ ”Life is a property of form, not matter” ❒ Flake, 1998 ❍ ”Nature appears to be a hierarchy of computational systems that are forever on the edge between computability and incomputability.” 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU From Chaos to Life ❒ Living organisms are nonlinear systems! ❒ Living organisms are complex systems! ❒ How has nature achieved this? 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU From Chaos to Life - Naturally ❒ Evolution through natural selection ❍ Darwin, The Origin of Species , November 24, 1859 ❍ Genotype – phenotype ❍ Criteria for evolution • Heredity • Variability • Fecundity ❒ The role of environment ❒ Co-evolution ❍ A necessary condition? ❒ Self-similarity ❒ Self-organization 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU 8

9. From Chaos to Life - Artificially ❒ How to do this artificially? ❍ Can not predict the global behavior of simple interacting subparts ❍ Can not decide which subparts to use to get a predetermined global behavior ❒ You must ”run” the system to see what kind of global behavior it generate ❒ You need methods to search through the solution space of a nonlinear system 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU From Chaos to Life - Artificially ❒ Methods ❍ Lindenmayer systems ❍ Cellular Automata ❍ Boids, herds and flocks ❍ Ant Algorithms ❍ Genetic Algorithms ❍ And more… 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU Simulation and Modelling ❒ Emergence is explained ❒ Want the simplest system that produce the emergent behavior ❍ Ockham’s razor (1285 – 1347/49) ❒ Not complete models of reality ❍ Focus on the essence of the system ❍ Not clones 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU 9

10. Scientific approach Communicate the model Formulate the question Assemble hypotheses Patterns Compare Patterns Analyze the model Choose model Implement the structure model (Adapted from Grimm and Railsback 2005) 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU Why Study Emergent Systems? ❒ Fundamental to theory and implementation of massively parallel, distributed computation systems ❒ Goals/challenges ❍ Efficiency ❍ Self-optimizing ❍ Adaptive ❍ Robust to failures ❍ Security ❒ Nature as a source ❍ Try to understand nature ❍ Use nature as an inspiration 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU Assignments ❒ NetLogo ❍ A multi-agent modeling language ❍ A parallel extension of Logo ❒ Rules for assignments ❍ See the links on the course homepage ❒ Assignments ❍ Termites ❍ Boids 19/1 - 15 Emergent Systems, Jonny Pettersson, UmU 10

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