The overview slide of games you proposed for Assignment 1 is now available in the Dropbox folder. The paper describing the evolutionary model (Lindgren, 1991) presented yesterday is available under the recommended reading, and the Mathematica code for the simulation is also available.
The games you proposed for Assignment 1 are now available in the Dropbox folder.
Also, some instructions for the midterm exam:
The deadline for forming project/seminar groups is postponed to Wednesday 21 November. This change is now reflected in the documents below.
Lectures 2 and 3 on Basic concepts will primarily be based on Chapters 1, 2, and 4 in the course book. Since the book is short and concise, we recommend a broader introduction to the area that is found in two texts by Roger Myerson, covering work by John Nash and Thomas Schelling. The texts are found in the Dropbox folder under "Recommended reading".
Welcome to the course! We will start with an introductory lecture on Monday, November 5, in FL51.
The schedule with lecture rooms is available in TimeEdit.
Documents
Kristian's implementation of the Schelling segregation model: download (Mathematica notebook).
The course responsible for this course has chosen not to move his or her course web page to Studycas yet.
Course page for 2013/2014 (link updated 2014-08-28):
The course information this term will be published on pingpong.chalmers.se. If you have any requests regarding the course, please contact This email address is being protected from spambots. You need JavaScript enabled to view it..
Much of modelling in the sciences focuses on simple models, highlighting key mechanisms using small sets of moving parts. However, in complex systems the interesting features are often a direct result of having large sets of particles or agents with different characteristics. This makes new tools a necessity. The course introduces simulation techniques frequently used in complex systems to handle models with many heterogeneous parts. Specifically, we will look at agent-based modelling, evolutionary game theory, cellular automata, and networks, with application to physics, biology and social science. We also learn how to validate the outcomes of simulation models in order to reach scientifically sound conclusions.
There is no single book that covers all the topics in the course so we have no required books. However, we aim to provide a set of compulsory (and additional) readings assigned to each lecture that covers and perhaps broadens the content of the lectures.
If you still want something for your shelf, here are some recommendations:
Mark Newman's book on Networks is very good, both comprehensive and well-written. It covers much more than this course, but if you're the type that reads non-fiction and textbooks for fun, I can recommend it. If you want something shorter, the review article Statistical Mechanics of Complex Networks by Albert and Barabasi are available for free and covers most of the network topics we discuss.
For agent-based models we have used Epstein's Generative Social Science for a reading project earlier years. It gives a good overview of what kind of questions agent-based models can be used to handle in the social sciences and also discusses a range of fairly deep issues regarding e.g. what it means to explain a social phenomenon. Epstein's and Axtell's Growing Artificial Societies is shorter and focuses more on a single type of model, but can also serve as an introductory text.
For cellular automata, Ilachinski's Cellular Automata - A Discrete Universe is very comprehensive and covers almost everything even remotedly related to CA. I'm not very impressed with its quality though, and would only recommend it to someone who only need the coverage and can afford to read it with a skeptical mind. You can also take a look at Wolfram's A New Kind of Science (full book available online), though even stronger caveats apply here (see e.g. this review). Wolfram's papers are much more measured, though a bit dated by now. You could for example take a look at his review Statistical Mechanics of Cellular Automata.
Main lecturer: Kolbjørn Tunstrøm (This email address is being protected from spambots. You need JavaScript enabled to view it.)
Tutors: Erik Edlund, Vilhelm Verendel, Clas Andersson, Jonas Einarsson, Martin Nilsson Jacobi
Examiner: Martin Nilsson Jacobi (This email address is being protected from spambots. You need JavaScript enabled to view it.)
You can find us in our offices at the division of Physical Resource Theory, in the EDIT-building.
Ordinary lectures are 8.00-10.00 in MC on Wednesdays and Fridays. Computerlabs replace Friday lectures some weeks and take place in F-T7204 (in Forskarhuset Fysik, street address Fysikgränd 3).
Variations can occur, please check the lecture plan below.
Lecture 1: Wednesday Nov 05 The role of simulations in the study of complex systems; Course information; Introduction to agent based modeling; Collective motion.
Compulsory reading:
Additonal reading:
Lecture slides:
Lecture 2: Friday Nov 07 Agent based models continued; SIR model of disease spreading; Schelling's model of segregation.
Compulsory reading:
Additonal reading:
Lecture 3: Wednesday Nov 12 Evolutionary game theory (guest lecture by Kristian Lindgren).
Compulsory reading:
Lecture 4: Wednesday Nov 19 Modeling of complex systems (guest lecture by Philip Gerlee).
Compulsory reading:
Lecture 5: Friday Nov 21 Networks Part 1; Preferential growth and scale free networks; The small world model.
Compulsory reading:
Additonal reading:
Lecture slides:
Lecture 6: Wednesday Nov 26 Introduction to cellular automata (CA); CA as modeling technique (guest lecture by Martin Nilsson Jacobi).
Compulsory reading:
Additonal reading:
Lecture 7: Wednesday Dec 03 Networks part 2; Community structure and modularity; Percolation and disease spreading on networks.
Lecture 8: Wednesday Dec 10 Stability of complex systems.
See also TimeEdit. But note the change we have made in presentation date from Jan 09 2015 to Dec 19 2014.
The purpose of the problem sets is to provide you with hands on experience of the different methods presented in the course. You will work in groups of 3 and have your work assessed in a series of scheduled computer labs. The problem sets account for 50% of your total course grade.
Grading
There are five problem sets available, each accounting for 25 points. The homework deadlines are set with the assumption that you choose one of Homework 1 and Homework 2, followed by doing the other three. A perfect score is therefore 100.
Note 1: A necessary (but not sufficient) requirement for passing grade is that 10 points are achieved in each homework 1 or 2, 3, 4, and 5.
Note 2: You can build your score anyway you want, attaining a maximum of 125 points (that's right; 25 points more than a perfect score).
The grade limits will be 45 points for grade 3, 65 points for grade 4, and 85 points for grade 5. For GU the limits will be 45 points for G and 75 for VG.
Computer labs
The computer lab sessions serve two purposes: 1) To get help if you are stuck in a problem set (and have not managed to get unstuck by consulting some of your classmates); and 2) To have your homework graded. Previous terms, we have experienced queues during lab hours. Therefore, we emphasise the “home” in homework; being prepared before you enter the lab is crucial for these sessions to run smoothly.
Groups
You will be randomly divided into groups of three (possibly two). The reason for this is to allow for efficient execution of the computer labs; we only have two people available this year assisting in the lab sessions. But: Everyone is meant to program their own code, or at least contribute significantly to each problem set. That is, while you can split up the work in each problem set, you are not allowed to split the problem sets between you. Honor code: If you have not contributed to solving the problems, you should say so during assessment.
The topics of the homework are given below. Instructions for the specific homework's will follow throughout the term.
Lab schedule
All computer labs take place in F-T7204.
Each lab is intended to focus on the specified homework, but there will be an extra lab in the end where you can have assessed any of the homeworks.
The purpose of the project work is to provide you with training in
The project accounts for 50% of your total course grade.
General informationHere are a few project titles from Simulation of Complex Systems 2013:
There are lots of interesting papers to be found under publications (and working papers) at the Santa Fe Institute (SFI). Many ideas that have been brought up and discussed at the Complex Systems seminar can be developed into projects in this course. Also, projects that some of you have done in earlier courses, like Stochastic optimization algorithms, can be continued and developed into new projects here. I also recommend that you search the web; there are so many good papers and ideas to be found out there.
Many of the links below point to papers describing the investigation of a certain model. You may use such a model a starting point for the project, which usually means that you have to do your own implementation. Then you may critically test the limits of the model. What are critical assumptions and parameter values? To what extent are the conclusions drawn by the authors general, etc?
Social systems
Societal structure - hierarchies, norms, institutions. The online Journal of Artificial Societies and Social Simulation provides some ideas and papers.
Voting models
The results of a democratic voting process may heavily depend on the voting system. Explore this by modelling a population of people voting. Read "The complexity of voting" by David A. Meyer.
Modelling segregation in urban areas
See, for example, relevant chapters in Schelling's book on micromotives and macrobehaviour (at Kristian Lindgren's office).
Evolution of strategies in game-theoretic models
There are several possible ideas that could be developed into projects here. Kristian Lindgren and Anders Eriksson have a set of papers, most of them dealing with the Prisoner's Dilemma game, that could serve as a starting point or for inspiration. There are several examples of games in Herbert Gintis' book "Game Theory Evolving" that can be developed into interesting multi-agent models. There are several models (some with source code) presented at Axelrod's "Complexity of Cooperation" web site.
a) The ultimatum game. Make a critical analysis or a modification of the model presented in Science: M. A. Nowak, K. M. Page, and K. Sigmund: "Fairness versus reason in the Ultimatum Game", Science 289, 1773-1775 (2000).
b) Evolution of Cooperation through Indirect Reciprocity, see Leimar, O. and Hammerstein, P. (2001), Proc.Royal.Soc.Lond.B, 745–753. (online text version). This is a reply and critisism of a paper by Nowak and Sigmund i Nature.
Panic and other types of basic group behaviour
How to model the group behaviour in panic situations. See link related to Nature paper Simulating dynamical features of escape panic by Helbing et. al.
Networks and infrastructure systems
Traffic simulation
Make a realistic model of a small part of Göteborg's traffic system. Model a highway with some exits, include tunnel, etc... Very nice Java simulations are available on Helbing's page (two-lane version). A quantitative analysis of how traffic flow is affected by road characteristics, could be a suitable goal for one project.
City growth simulation
How will Göteborg develop in the future? Or, from the map of 1809, how could Göteborg have developed to today? See Project Gigalopolis for an example on how such models can be constructed. We also have a number of papers in the Complex Systems Group, see our project description. One project may involve exploring how different local rules can give rise to different types of growth.
Small world networks
See book and papers by Duncan J. Watts (Small Worlds: The dynamics of networks between order and randomness, Princeton Univ. Press, 1999). A very good review by Mark Newman is available here. There are a lot of new papers published on these ideas.
Economic systems
Artificial markets, software agents, etc.
Lots of information to be found under Leigh Tesfatsion's web page on Agent-Based Computational Economics. She also offers a number of links to software useful for multi-agent modelling, and she offers a long list of suitable student projects (covering a lot of complex systems), see at the bottom of the syllabus page for her course).
a) A simple stock market with a stock (or two).
b) Cournot duopoly, or a simple oligopoly model; see, for example, Fudenberg & Tirole p.14.
c) A Sugarscape inspired model, see Epstein's and Axtell's book.
If you are interested in working on software agents, check the online tutorial by Patty Maes from Software Agents Group at MIT Media Laboratory.
Natural systems, ecosystems
Pattern formation in chemical, biological, and ecological systems.
a) Make a PDE model of a simple chemical system that exhibits pattern formation (2X -> 3X model, see Science paper).
b) Host-parasitoid interactions may lead to complicated space-time patterns; Hassell has done a lot in this area, see, e.g., Hassell, M.P., H.N. Comins, and R.M. May. 1991. Spatial structure and chaos in insect populationd dynamics. Nature 353: 255-258 and Pacala, S., M.P. Hassell, and R.M. May. 1990. Host-parasitoid associations in patchy environments. Nature 344: 150-153.
c) Spatial dynamics in a host-pathogen system, see, for example, paper by G.M. Hood with model description.
Modelling population dynamics - comparing agent-based models with ODEs
Make an agent-based model of a few variations of simple one and two species population dynamics and compare the behaviour with simpler models in terms of ordinary differential equations. When does an ODE work as a good approximation to the more complicated agent-based model?
Ecosystems in fragmented environments
Build an individual based model of a species that lives in a fragmented environment.
Decentralized dynamics of animal flocks
Construct a model in which various types of flocking behaviour may evolve.
Global issues, resources, and the environment
See, for example, the link to Beyond the Limits (incl Mac program and hints on how to get the model for PC)
How many will there be 2050? Try to make a projection of the world population growth to year 2050. There are several approaches possible, see for example paper in Nature by Lutz et al. (For fun, see web page on "world population since creation" with figure that shows a 9 billion population before the flood...)
Modelling resource use in multi-agent systems; tragedy of the commons
Fishery models: Make a model of fishermen harvesting a population that follows a simple dynamics model. Model the fishermen as adaptive agents. Think on how to model their behaviour. How do they adapt their strategies to each other and to the resource?
Artifical worlds and virtual reality
"Physical" simulations in artificial worlds. Here one may use the software package, breve, that Jon Klein has developed in his Master's thesis (runs under MacOSX).
a) Make models of and simulate flock behaviour in 2D or 3D worlds. Follow links from breve; see also Craig Reynolds web page on Boids.
b) Simm's evolving creatures, see, e.g., "Evolving 3D Morphology and behaviour by competition", Artificial Life 1, 353-372 (1995).
Complex system games
Use complex systems ideas in order to design simple but tricky games (either computer-human or human-human). The first versions of the game Blobity of Catchy Software was a project of this course in 2000.
[08.29.2016] Update for 2016-2017 class
[01.05.2017] Schedule completed through LP3 and LP4
This course runs through the entire first year of the CAS program. The course consists of two components:
1) Quarter 1 - An introduction to complexity science: concepts and history.
2) Quarter 2-4 - Student presentations
The first part is intended to provide concepts and opportunities for some reflection on complexity that goes beneath the level of learning about specific tools and theories.
The second part of the course gives the Master's students practice in oral presentation of scientific topics, practice in searching for scientific information, and opportunities to discuss ongoing projects with faculty and other students.
Download group subdivision of the class as PDF
See timeedit for room information, and note that rooms are subject to rapid change.
(Note that this preliminary and subject to change. If timeedit and the schedule are contradictory, trust timeedit.)
Introduction Classes LP 1
| Date | Time | Topic | Resources |
|---|---|---|---|
| Wednesday August 31 | 10:00-11:45 | Intro to course; introduction to LP 1 lectures. |
|
| Wednesday September 7 | 10:00-11:45 | Complex and Complicated systems |
|
| Wednesday September 14 | 13:15-15:00 | From early primates to higher primates: from complexity to trans-complexity | |
| Wednesday September 21 | 13:15-15:00 | From Great Apes to Early Homo: sub-wicked and new complicatedness | |
| Wednesday September 28 | 13:15-15:00 | From the Palaeolithic to the Anthropocene: from sub-wicked to trans-complicated and wicked. | |
| Wednesday October 5 | 13:15-15:00 | The history of complexity |
|
| Wednesday October 12 | 13:15-15:00 | Giving oral presentation (hand-out) |
Presentation Schedule LP 2 - LP 4
Please be on time since it is highly distracting for both the audience and the presenter to have people showing up late.
Please download the present feedback form here!
The course only has a pass and fail grade. It is not a difficult course, in the sense that if you do your best you should pass. Not doing your best includes:
Either of these will lead to a fail grade. The rules are not overly burdensome or many, but they are there and they are strict.
Claes works at the department for Physical Resource Theory (Energy and Environment) in the Electro (EDIT) building, floor 3V. If you have questions or comments that are not covered here, please feel free to send me an email, call or to stop by the office (best to make an appointment).
Student representatives
Please contact the student representatives if you wish to convey any feedback to the teachers regarding the course, if you do not wish to communicate with the teachers directly.The course representatives are selected randomly and automatically by a large clunky robot at the central administration.
| S. Sigurdson, Robin | This email address is being protected from spambots. You need JavaScript enabled to view it. |
| Srinivasan, Abhishek | This email address is being protected from spambots. You need JavaScript enabled to view it. |
| Wallengren, Elona | This email address is being protected from spambots. You need JavaScript enabled to view it. |
| Suarez Madrigal, Andres | This email address is being protected from spambots. You need JavaScript enabled to view it. |
Due to popular demand, I've assembled a list of examples of possible topics. If you can, please don't be lazy and just pick a topic from the list, but use it as inspiration and pick something new and innovative!
See also the list of presentations from the previous year below (15/16 and 14/15). While, obviously, you are not allowed to just pick one, the students are new every year, and many of the topics can be well worth considering.
Other examples:
It has taken life on earth about 3.8 billion years to produce humans -- an intelligent species able to consciously solve complex tasks. Many researchers believe that this intelligence can be explained solely by the immense computational abilities of the human brain. With computational power of supercomputers having grown at rates of about 100% every two years throughout the last few decades, recent developments beg the question: will machines ever outsmart humans -- and what happens then?
The topic becomes a quite philosophical one, and gives rise to a multi-facetted discussion of what our future might look like. Could machines develop such things as a conscience, sentient or free will? If so, how could we test it, and which moral standards should apply when dealing with machines? Which threats may arise in the short and long term? How will the labor market cope with machines that become better workers than any human? Can we control AI agents that may ultimately become smarter than us? Does strong AI pose an existential threat to humanity? If so, how can we responsibly research AI in the future?
Creativity is an important part of human intelligence. Novel acts can improve a situation or completely backfire like a joke falling flat. What qualifies as a creative act? How is it different from random behavior?
In our presentation we look at what constitutes a creative act and how it can be achieved by artificial intelligence.
We then look at some AIs that use creative elements. Can the decisions made by such AIs be called creative?
We invite you to discussion of how to evaluate creativity and the implications of automating this process.
Effective and well functioning financial markets is a cornerstone in every capitalist society. Since the early 1980s increased market integration and product innovation has made things ever more complex. In this presentation we take a look at complexity in financial markets and how to model it.
The presentation begins with a quick historical review of how simple bartering in pre-agricultural society grew into the world’s first stock market with opening of the Amsterdam Stock Exchange in 1602. What characterizes a financial market as a complex adaptive system? Three areas of complexity can be identified and a modern case study of a specific financial instrument is presented. Different approaches to model each level of complexity is suggested and demonstrated in an easy to follow non-technical way. We also provide many real life example of successful and less successful attempts to apply them. The presentation is ended with a open ended discussion with questions about today’s challenges and dilemmas with financial markets.
Research in artificial neural networks has improved a lot the past few years. Big companies like Google utilize deep learning algorithms for their services.
In the course of this presentation we would like to show some examples of applications where deep learning has been used to solve and improve existing system performance. We are going to cover three examples where deep learning algorithms have reached or even surpassed the human ability to solve certain tasks, like playing complex board games, language translating and optimizing dynamical systems with many parameters such as data-center cooling.
This will give a brief insight in different fields where deep learning networks are used and might inspire the research of utilization in other applications.
Twenty five years ago, self organized criticality was a concept put forward by Per Bak and other scientists for describing complexity in systems. The basic model revolves around the avalanches occurring in the growing sand-pile, the distribution of which follow a power law. Since then, the model has been used in applications in other complex systems, such as biological evolution, forest fires, traffic networks etc. The interesting thing about these systems is that they can be said to be at the edge of chaos.
During our presentation, we will start by defining some important aspects of systems that demonstrate self organized criticality, such as the fact that they are dissipative and out of equilibrium. Then we will show how this applies to earthquakes, and finally, in neural systems in the brain. Can the concept be used to model earthquakes and give us useful information? Is self organized criticality a fundamental property of biological neural systems?
Since the first larger complex civilization was erected by mankind some thousands of years ago we have seen them come and go through history. We have all heard tales of how they fell. In this seminar we will discuss two different perspectives on what makes civilizations fall. Joseph Tainter argues that civilizations fall due rising complexity and cost of solving problems. Jared Diamonds view on the other hand is that they fall due to an inability to cooperate with nature. Historical examples of societal collapses are analysed from the two perspectives.
This analysis naturally raises the question of whether our current civilization is doomed to fall or if it stands a chance of surviving for a long time? Will our inability to live in harmony with our nature make long term survival on the earth impossible, or is the ever rising complexity of our society in itself an impossibility in the long run? We will analyze these types of questions from the viewpoint of both Joseph Tainter and Jared Diamond.
In the last few decades there have been great developments within the field of Evolutionary Computation, where the algorithms used are based on the processes governing natural selection.
Not only can these algorithms be used to solve engineering problems. Research shows that they can also be used in areas which were long thought to be outside the domain of mathematics and science: subjectivity and personal preference. This is achieved by using human evaluators to evaluate the different solutions generated by the algorithm. The field dealing with these types of algorithms is known as Interactive Evolutionary Computation.
We will explore the benefits and drawbacks of this method. We will also look at how these methods have been applied to optimize systems according to one's own aesthetic tastes as well as help construct personalized products.
Healthcare today is in heavy need of improvement. A larger global population together with longer life spans has lead to a personnel deficit within medicine, increasing treatment costs and decreasing individualized care.
Due to the large amounts of research and clinical data in medicine it is an area which is perfect for applications of AI-powered systems. An example of this is IBM’s Watson, which is able to diagnose cancer patients and provide treatment plans.
We will talk about recent progress of AI in healthcare by exploring different applications, as well as look at some challenges and future prospects of the subject, hoping to give a larger insight into the wide array of application areas of AIs and machine learning.
As we all know, the field of applied data analysis and machine learning is on the rise. While typical applications so far has been in online retail and other interned-based services, greater availability to data from all sorts of sources paves the way for many new and exciting uses.
In this presentation, we discover some of the huge potential of machine learning as a tool in medicine. Specifically, we focus on three chronic conditions, namely Parkinson's Disease, Diabetes and Hashimoto's Disease, and see how emerging data analysis-tools can improve the life of sufferers in more than one way.
The Fermi Paradox is an apparent contradiction between the expectation of the existence of many highly advanced civilizations in the Universe and the lack of observation/contact with any of them. Many theories have been put forward as attempts to explain the discrepancy. In this presentation we will explain estimates for finding intelligent civilizations in the Galaxy and the main possible explanations to the paradox.
Are we alone in the Universe?
For over a millennia mankind have tried to predict the weather. There is evidence that weather forecasting dates as far back as the babylonians which predicted weather from cloud patterns as well as astrology. In today's weather forecasting some of the world's most powerful supercomputers are used to solve partial differential equations that describes the time evolution of the atmosphere. Even with the increasing power of these supercomputers, the accuracy of the forecasts are limited to approximately 6 days. The main problem with producing accurate forecasts are due to the chaotic behaviour of the atmosphere.
In our presentation we introduce the most basic partial differential equations used to describe the atmosphere as well as the basic conceptual numerical approach used to solve the equations. We proceed by discussing how to reduce the inherent uncertainty remaining in numerical predictions by the use of statistical methods to gauge confidence in the forecasts. Lastly, we discuss the challenges and possible future developments in the field.
Botany is an often forgotten field of research, often thought of as something quite isolated from other science fields. In this presentation we will talk about why this isn’t true and how plants and other vegetation has a complex underlying structure that proves difficult to simulate.
We present the basic theory of Lindenmayer systems (L-systems) that was developed in the late 60’s by means to describe how the development in cells of plants works. An L-system is a type of formal grammar, or string-rewriting system, that goes well in hand with many features of plants such as branching structures, repeating patterns and self-similarity.
Because of this, L-systems and what is called turtle graphics can be combined to generate 3D models of plants for applications in for example computer games and animated movies. Lastly we will discuss other uses for L-systems and how botany in this sense can be the inspiration for work in other scientific fields.
We begin with a brief history of artificial intelligence in video games to see how it has evolved over the years.
Then we take a deeper dive into the fascinating world of RTS games to see how artificial intelligence is utilized in this genre. What problems are there for the AI to solve, and how does the best human players measure against their computer controlled counterpart.
If the AI can learn complex game concepts, can it later utilize this knowledge for something else? We present some thoughts and ongoing projects that investigate the possibilities of training AI in games to prepare it for real world tasks.
For many of us machine Learning has become a very familiar term. Possibly the biggest buzzword of recent years in the compute world. To some it is the start of A.I. others just see it as the hot new tool. It's a broad term and encompasses quite a few methods, and no matter what you think machine learning is changing the IT landscape
We want to stop looking forward for a second and instead look at the origins of what we call machine learning. Travel back to 1950 when the computer was in its infancy and machine learning was just an idea. Then we jump forward in time, stopping at some of the major events that had an impact on the development of machine learning. Eventually coming back to the present and look at some of the implementations that are being used today.
There are often lessons to be learned from history, to know what is in store for the future of Machine learning there may be something to be learned from its past.
Social complexity is defined by the number and types of interactions within a society. Understanding this concept can give us a better understanding of topics ranging from trends to causes of instability in society.
In this presentation, we take a look at the concept of social complexity and how it leads to other benefits. Starting from parrots and simple human societies, we discuss the role of social complexity in advancing a species and its connection to advanced cognitive skills. We then look at how social complexity has helped shape our society today and how recent technological advancements have affected the stability of this theory.
Watch slides
Natural Language Understanding is one of the most important topics of research in the attempts to create a general AI. Early attempts to implement Natural Language Understanding in computers dates back more than 50 years and the research has come far, and many of us have some program using limited Natural Language Understanding in our phones.
In this presentation we will give a short overview of how it works, both in the human brain and in computers, as well as give a brief introduction to in what ways a computer can generate text with some actual meaning.
Watch slides
Autonomous vehicles are on the rise, with manufacturers like Tesla, Volvo and even Google working on autonomous cars and developments being made in an increasing ammount of fields.
In order to understand were we're coming from, this seminar will start with a history lesson and a short look on autonomous vehicles outside of cars. We'll then present some of the technology behind todays autonomous cars, and have a look at some current examples. Finally we'll take a look into what the future might bring for autonomous vehicles, and what dilemmas and consequences this future might bring with it.
Watch slides
One of most important building blocks for biological evolution is selection for reproduction for many species. The actual selection of mating partners is a complex system which depends on many variables including mating systems. There are many types of reproduction and mating systems, but this seminar will focus on sexual selection and the most common mating systems for species who undergo male-female reproduction. Can we understand why the selection strategies and mating systems used by humans and animals have emerged? Throughout the presentation we will explain important concepts of mating systems as well as provide relevant examples of where different mating systems and mating strategies occur in the world. We will then combine the different parts of the presentation to show how the selection of mates is a complex system.
Watch slides
Since the conception of AI and machine learning the field has been subject of critisism. Today it's still unclear what the main goal is: to create an intelligence on par with human intelligence or to create powerful tools that acts like a brain to perform difficult tasks and improve well-being in society.
We will discuss past criticism of humanlike intelligence and bring it into the light of current developments to see what is still applicable and what is deprecated.
We will also do a case study of current issues in AI and machine learning by considering two recent papers in which differences in perception between human vision and computer vision are highlighted.
Watch slides
Evolution is the mechanism that has given rise to the diverse ecosystem that surrounds us, and over time, this process has resulted in a multitude of intelligent species. In this seminar we will discuss how the pressure to adapt has driven this development, and some of the shapes intelligence can take in different animals, and their limitations.
Next, we will see that intelligence is not restricted to being an individual property, but can also emerge as a collective property in a complex system comprised of many relatively simple individuals. This is exemplified in social insects, such as ants, and we will see a few examples of this in coordination, task partitioning and self-organizing building schemes. We subsequently discuss some models from literature, and how these models have inspired implementations of artificial swarm intelligence.
We finish the presentation with an outlook on how natural intelligence and the human created artificial intelligence evolve at different timescales, and what that might mean in the future.
Today there is an abundance of music easily available thanks to services like Spotify, etc. One problem that arises for the user is to browse through this material to find the music that suits him/her best. In this presentation, we will describe proposed methods to solve this using the acoustic information in audio files. Music is built up by very simple parts but give rise to advanced patterns such as emotions in listeners. Can these patterns be predicted by analysing the basic features of a given song?
During the presentation we will:
Watch slides
November 29, 2016 "Resilience of Social-Ecological Systems"
Steven Lade, Stockholm Resilience Centre and Stockholm University
10:00-11:45, Room VasaC
Abstract:
How do we solve the grand social and environmental challenges that face humanity today? `Resilience' is emerging as an approach that researchers and organisations (as large as the United Nations) use to understand and deal with these problems. Resilience thinking is based on a fundamentally complex adaptive system view of the world, together with an understanding that social and ecological systems are fundamentally intertwined.
In this lecture I provide a brief introduction to the resilience of social-ecological systems, and the cross-fertilisation between resilience and complex adaptive systems. First, local stability concepts of nonlinear dynamics are closely linked to the original, resistance to shock conception of resilience. Concepts such as the 'planetary boundaries' recognise that there exist basins of attraction beyond which sustainable human development may be difficult. Second, it is increasingly being recognised that appropriately modeling human behaviour is crucial to dealing with contemporary environmental challenges. Human behaviour needs to be appropriately modelled in settings where social and ecological relationships are contested and uncertain is highly challenging. Third, the understanding of resilience has expanded to include the ability of a system to adapt and transform in response to threats and challenges, which demands new modeling approaches such as adaptive networks. We will work through together some case studies from local to global scales.
Simulation of traffic has been done for over 50 years, and has long been a crucial part of any new roading project. Recent increases in the amount of data we are able to collect and process have renewed interest in the field, improved the accuracy of our models, and facilitated modelling traffic as a complex system of individual agents.
We will talk about the importance to society of understanding traffic, and how it relates to complexity. Then we will outline some of the techniques used to simulate traffic flows. We will also give some examples of emergent behaviour in traffic networks, and finish up with an example of how complexity theory can solve some of the problems we have with traffic.
April 27, 2016 "Artificial Creativity: The difference between man and machine"
PG U: Martin Henoch and Nils Carlsson
Creativity is something so fundamental for humans we seldom reflect on what it actually means. Even the actual definition of creativity is not set in stone, and has been the subject of a philosophical debate that's been going on since the Renaissance, and maybe earlier. And as AI and machines continue to develop, the ability or non-ability to be creative is possibly the last divider between humans and "true" AI. Ada Lovelace famously declared that no "analytical machine" would ever be able to create anything new, and "Lady Lovelace's objection" was included in Alan Turings paper Computing Machinery and intelligence.
In this seminar we'll discuss what creativity means, along with common objections to the idea of that an AI could be truly creative. And since pilosophical objections never stopped anyone from trying, the discussion nowadays isn't only purely theoretical. There have been several projects trying to achieve a creative AI, in fields varying from visual arts, poetry, music and even mathematics, which we will showcase and try to give a picture of their "creative" process.
PG T: Eduardo Sesma and Mahmod Nazari
It is no overstatement to voice that the human brain is a spectacular organ and a complex system yet to bring to light. It governs our thinking, problem solving and our behaviors. Continuously, it helps modulating critical aspects of our physiology, such as heart rate and breathing. This most complex structure in the known universe is the ultimate aim of neuroscientific enquiry to be understood further.
Not long ago, the studies of the brain relied either on looking at aggregation changes resulting from head injuries or on plotting oxygen and glucose consumption in the brain. Needless to say that, the results of such approaches had been too little that the new techniques such as network sciences have emerged and become more fashionable. But how to take advantage the most from the network science in the aim of the discovery of such a complex system?
April 13, 2016 "Meteorology and Neural Networks"
PG S: Donal Scanlan, Victor Tengnäs and Richard Lan
From the dawn of civilization, humans have attempted to both control and predict the weather. While early people believed this could be achieved by rituals and mysticism, the limits of our capabilities were recognized by Lorentz and the birth of chaos theory in the 60’s. Since then, we have relied almost solely on computers to overcome the implications of a chaotic climate. Up to this point, numerical weather prediction (NWP) has been the staple of forecasting. With its evolution however, we have still been unable to fully encapsulate climatic complexities and the past few decades have not been without costly, and deadly, forecasting errors.
Currently, there has been significant scientific interest in employing neural networks, and hybrid NN and NWP models, as a possible way of providing more accurate forecasts. Furthermore, the importance of accurate forecasts is growing with the extremity and variability of weather events associated with climate change. In our presentation, we hope to trace the evolution of these methods and give a brief forecast of forecasting.
March 23, 2016 "Group Dynamics"
PG Q: David Hansson, Konstantina Elmpasidou and Radenko Ristic
Understanding how groups develop and evolve has been the subject of studies by organizational behaviorists and researchers since early in the 20th century.
From early research, many models have been developed to explain what we can expect in typical group development. The application of CAS theory to Group Dynamics is relatively new. In the last decades, CAS theory has been suggested as a constructive way to view groups by researchers.
In our presentation we will cover the fundamentals of Group Dynamics and give some examples of social applications that have to do with the Group Dynamics theory and sub-theories.
March 10, 2016 "Tragedy of the Commons"
PG R: Dennis Sångberg, Ludvig Vikström and Victor Nilsson
History shows us many examples of when independent agents acting rationally in their own interest while exploiting a shared resource has been able to deplete that shared resource in contrary to the common good.
In 1968 the ecologist Garret Hardin wrote an article on this subject naming the system collapse transition as the ‘tragedy of the commons’ and this is in our days the the most widespread name of systems exhibiting a phase transition that depletes a common resource.
There are many examples of systems where such events has occurred throughout history including ecological systems involving air, water or forests. But also in completely different topics such as information technology where email spam or subjective wikipedia article editing can be seen as a ’tragedy of the commons’. In our presentation we will cover some of these topics in depth and talk about the subjects history in science.
March 3, 2016 "Complexity Economics"
PG P: Daniel Viknander, Karl Bäckström and Ulf Hjolman
For centuries, the global economy has generally been considered a static system in equilibrium. In recent years, the idea that it is more accurately modelled as a constantly evolving complex system, has gained more traction. These controversial ideas have become known as Complexity economics.
This new field generally consists of models based on individual profit-seeking agents, which can develop personal strategies, through e.g. evolutionary algorithms, and on occasion even make mistakes.
February 25, 2016 "Biologically Inspired Computing: Artificial Immune Systems"
PG N: Andreas Magnusson, Eirik Seljelid and Benjamin Waubert
For many years we have gained knowledge and inspiration from natural phenomena and mechanisms, and learned to adapt these into different areas to improve and optimize the outcome. One commonly known biological system of this sort is our immune system. It is a robust and very complex system which protects us from foreign pathogens. It does so through self-organization, without any central control, in a highly distributed fashion and is able to quickly adapt to never before encountered pathogens.
In addition it exhibits a distributed form of memory which enables it to identify previously encountered pathogens and react appropriately. All of these features have attracted the attention of engineers and computer scientist, who’re looking to mimic the underlying mechanisms and create new approaches to complex problems.
In artificial immune systems (AIS) the immune system is used as a metaphor due to its properties as self-organized, adaptive, highly distributed and most importantly: it has no central point of control. The idea of studying using artificial immune systems is not to model the immune system itself, but rather to make use of mechanisms that can be used to develop computational tools.
PG L: Joel Thorstensson, Julia Reibring and Simon Nilsson
Throughout recorded history, consciousness has been one of the largest unanswered mysteries. Countless ancient philosophers and mystics have struggled with questions like “What is existence? “ and “How did we get here?”. In the 17th century philosopher René Descartes expressed the famous words “Cogito ergo sum” (I think therefore I am) and in the same century John Locke laid the foundations to the modern concept of consciousness. Most people today have an an intuitive understanding of what consciousness is. However, even if we conceptually understand what consciousness is, we still haven’t been able to pin down its essential properties. In recent times interest in consciousness has grown in the natural sciences. One framework intended to understand and explain consciousness which has gained traction is Integrated Information Theory (IIT). IIT is an attempt to characterize consciousness mathematically both in quantity and quality. By studying the complexity of mechanisms in systems formed out of phenomenological axioms, IIT is able to derive a measurement of consciousness. IIT is far from a fully developed theory though, and one of the major limits is that it takes a huge amount of modeling and computational power to calculate the level of consciousness for larger systems as our brain.
PG K: August von Hacht, Joakim Johansson and Simon Andersson
In the 17th century science went trough a revolution when a mathematical approach was introduced in multiple scientific areas. New ideas were formed of how rules based on mathematical equations could describe the natural world. Until today this has been a well-recognized truth in science, but will it last forever? A person who is critical to this idea is Stephen Wolfram. In his book A New Kind of Science he reveals the flaws of this approach and proposes a new kind of science based on more general rules that can be used by simple computer programs forming complex systems.
In our presentation we are going to bring up some of the ideas presented in A New Kind of Science. We are going to explain Wolframs criticism to traditional mathematical approach and compare it to his new ideas. Cellular Automata is a significant part of his ideas and we will describe this in general and Wolfram ideas in particular. We will also give example of areas where the new kind of science could be applied including fundamental physics and biology. The book and ideas of Wolfram has also been criticized among other scientist and we will try to describe and discuss this in the presentation.
PG J: Barbara Schnitzer, Joakim Andersson and Viktor Wänerlöv
'Men argue, nature acts', Voltaire.
This sums up what we are facing today. Political leaders discuss a lot about the climate and what to do while the nature keeps on changing. In order to react we have to understand the mechanisms involved and develop and analyse models to come up with measures to prevent the negative consequences of these changes.
In this seminar we will give an introduction to climate change and the complexities involved with modelling it, as well as a short discussion of ethical problems that arise.
PG I: Benjamin Lindberg, Jacob Nolkrantz and Rickard Johansson
Machine learning was defined in 1959 by Arthur Mitchel as a "Field of study that gives computers the ability to learn without being explicitly programmed". The field has exploded in the last years, mostly thanks to more computing power and more data that can be analyzed. Machine learning has now caught up with its' parent subject artificial intelligence and papers in the two subjects are now published at about the same rate. The interest in getting valuable information from large amount of data is bigger. An example of this is the big internet companies that want to categorize most of the internet users, to be able to personalize commercials. So now machine learning contains studies such as big data, also medical diagnosis, self-driving cars and hopefully, in the future, a path to general AI. This seminar hopes to mainly give a good introduction to Machine Learning and also dive into some specific examples..
December 16, 2015 "Natural Language Processing"
PG G: Rebecka Jacobsson, Britta Thörnblom and Isac Boström
In the age of smart phones and deep neural networks, the field of natural language processing is developing faster than ever. Its relevance is continuously increasing as we rely more and more on machines to help us perform everyday tasks such as asking our phone to provide us with driving instructions or speaking with automated customer service to resolve issues with our tax returns. But how do we get machines to actually understand what we mean when we ask for the nearest pharmacy or tomorrow's weather? This seminar will discuss how an algorithm based on representing words as points in a high-dimensional vector space can lead to everything from improved translation services to automatic generation of clickbait headlines.
PG F: Calle Ekdahl, Rafal Piwowarczyk and Jonas Meinel
In the last decades fractals have gone from being something that was previously only used in abstract mathematics to having important real-world applications. Starting with Leibniz in 17th century we will trace the history of fractals up to today, through some of the canonical forms and familiar names. We will also explain where the name ”fractal” comes from. Then we will show how fractals appear in nature in the form of trees, broccoli and lightning as well as in other phenomenas. Finally we will show that fractals can be used to generate computer graphics for trees and mountains, among other applications of fractals in modern technology.
December 2, 2015 "Deep Learning"
PG E: Carl Asplund, Victor Bäckman and Henrik Adolfsson
A reincarnation and re-branding of Artificial Neural Networks in to the field of Machine Learning. Why is it that the interest in these learning algorithms has risen again? What could big companies, like Google, achieve by using them?
Whatever the future holds for this field, its popularity has exploded and companies are trying their best to get on top of the wave. New applications are popping up at a fast pace, and these deep learning algorithms that were once considered science fiction are now becoming a part of our daily life.
In the next seminar, we will talk about how Google has implemented a large scale deep learning network and how deep learning is used in their applications.
November 25, 2015 "Liguistics and Complex Systems"
PG D: Andreas Johansson, Gustavo Stolf Jeuken and Philip Rasko Nguyen
Linguistics:
A topic as dull to us physicists, mathematicians and engineers as complex variable analysis is to a fiveyearold.
A subject as pointless as sitting on chair and watching sourdough rise. Blindfolded.
Or, is it?
We’ll introduce you to the Complex Adaptive Systems approach to linguistics and also present some other approaches that have been used to study languages throughout history. What similarities do languages share with other complex systems? How does language change how we perceive reality? How can the evolution of languages be understood from a CAS perspective?
These are some of the questions we’re going explore during our presentation “ Complex Systems and Linguistics ” this Wednesday! We hope to see you there!
Blindfolds are not provided.
November 18, 2015 "Network Theory"
PG C: Andreas Falkovén, Evangelia Soultani and Oskar Lilja
How are social norms created? How can global outbreaks be triggered by very few adopters and why do people sometimes overestimate risky behaviours of their friends? Many real systems such as social systems, can be represented as networks where the elements of the system are nodes and interactions between elements are represented by links. Networks have become a crucial component of many complex systems and theoretical and computational network analysis allows us to gain valuable insights into numerous applications and make intelligent decisions.
The presentation aims to introduce you to the most important aspects of the theory of networks, show you how to model a real world system and discuss interesting effects and applications.
Ready to hear about networks? We would be happy to see you in our presentation "Network theory" this Wednesday!
November 11, 2015 "Search and Rescue Robotics"
PG B: Amanda Nilsson, Erik Roos and Kristian Onsjö.
Abstract:
With the world growing smaller each day, the reports of natural disasters and terrorist attacks from around the world never seem to end. In each of these occasions there are people going through the rubble to try to find survivors and save them. With the development of robots the goal of search and rescue robotics is to develop robots that can assist in these kinds of situations. Being quite a new science, there are many problems that need to be dealt with.
The presentation means to discuss the problems faced today and how search and rescue robotics may work in the future. Challenges addressed in the presentation will be the task of navigating through complex environments, human-robot interaction at the operator side and efficient exploration.
If you’re interested in knowing how robots may one day save victims in disasters all around the world, come to our presentation “Search and Rescue Robotics” this Wednesday!
November 4, 2015 "Chaos Theory and some of its applications"
PG: A: Albin Lorentszon, Timmy Forsberg
Abstract:
Mathematics and physics have for a long time analyzed systems with linear equations. Some models have worked perfectly for their purpose and some haven’t. For example, in cases like the weather and sea currents scientists found that their models couldn’t predict what would happen as time went on. In the middle of the 20th century a man named Edward Lorenz studied equations for vorticity and discovered a small change in the initial conditions made a huge change in the outcome of his model, and so chaos theory was born.
The presentation ”CHAOS THEORY AND SOME OF ITS APPLICATIONS” will introduce Chaos Theory and present two different applications of the field. By introducing chaotic maps like the Logistic map and the Lorenz map some key elements in the theory will be explained.
The two applications displays a wide range of what chaos theory can be used for. The first one is cryptography, where the aim is to find algorithms to encrypt messages that only can be read by the receiver. The second application is Neural Networks, relevant to the Complex Adaptive Systems master program.
Would you like to learn about cryptography, or perhaps what four-dimensional chaos sounds like, welcome to the lecture next Wednesday.
Shamit Bagchi
"Consciousness as Integrated Information"
Ivo Batkovic
"Unsupervised Evolutionary Art"
Milica Bijelovic
"Chaos - Making a new science"
Jens Carlsson
"Micro-simulation of disease spread"
Siamak Esmi Erkani
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Oskar Fridell
"Matching Social and Ecological Systems in Complex Ocean Fisheries"
Johan Frisch
"Anticipating Critical Transitions"
Pontus Granström
"The Fractal Geometry of Nature"
Amrit Krishnan
"Rethinking Economics Using Complexity Theory"
Marcus Hägerstrand
"Predicting the Stock Market Using Twitter"
Mats Uddgård
Selvin Cephus Jayakumar
"Self-Programming Matter and Artificial Life"
Hjalmar Karlsson
"Cellular Automata Approaches to Biological Modeling"
Jared Karr
Mattias Kjelltoft
Fredrik Hoxell
Paul Lange
"Climate and Weather Simulation - Using Ensemble Methods to Predict Chaos"
Jonathan Larsson
"Models for simulating pedestrian behaviour and escape panic"
Olof Gustavsson
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Laura Masaracchia
"Human Dynamics, From Small Group Interactions to Anthropology"
Björn Persson Mattson
"Honeybee Democracy - Consensus mechanisms in social insect societies"
Björnborg Nguyen
"Modeling vehicular traffic as a complex system"
Elin Romare
"Immunizing well-connected networks - why random immunization doesn't work"
Vitalii Iarko
"How I could be a millionaire - or - a bit about BitCoin"
Marcus Schmidt Birgersson
"Prediction and classification of cancer using artificial neural networks"
Niclas Ståhl
Alireza Tashivir
"Artificial Immune System inspired by Human Immune Systems"
Sebastian Hörl
"Modeling the dynamics of belief systems"
Nils Wireklint
"Fooling Neural Networks and adversarial examples"
Kristín Ólafsdóttir
"Rumor Spreading Model with Trust Mechanism in Complex Social Networks"
Peter Svensson
Toby St. Clere Smithe
"Methods for a complex system: The neuroscience of visual object recognition"
Tomas Jacobsson
""
Kirill Blazhko
""
Kalle Hansson
""
Gustav Olsson
""
The aim of the course is to give an introduction to fundamental concepts of game theory and to explore the concept of rationality and a series of applications and extensions of game theory. We focus on the effects of individual rationality and also the aggregate behaviour between agents in a large population. What are general principles for rational action? How well does this describe human behavior in practice?
The final content of the course can and will be influenced by the students attending it (i.e. other topics may be added to this list). Topics that were covered in last year’s version of the course include:
Basic game-theoretic concepts, theory and principles of rational decision-making, backward induction and the rationality paradox, analysis of repeated interaction, tragedy of the commons, evolutionary game theory, public good games, agent-based models in economics, behavioural economics and the environment, bargaining theory and dynamic games.
The course was developed by Kristian Lindgren and Erik Sterner following a request (from Erik and a few of his classmates) to Kristian, asking him if he could give a course in game theory. It was first given in 2010 and 2011. After a break and work on the course format the formal criteria for becomming an electable masters course was reached during 2013 and the course will be given starting autumn 2014 (Quarter 2).
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