Podcasts about netlogo

This lecture (slides embedded below) provides some historical background and motivation for System Dynamics Modeling (SDM) and Agent-Based Modeling (ABM), two other simulation modeling approaches that contrast with Discrete Event System (DES) simulation.In particular, in this lecture, we briefly introduce System Dynamics Modeling (SDM) and Agent-Based/Individual-Based Modeling (ABM/IBM) as the two ends of the simulation modeling spectrum (from low resolution to high resolution). The introduction of ABM describes applications in life sciences, social sciences, and engineering (Multi-Agent Systems, MAS)/operations research. NetLogo is introduced (as part of preparation for Lab 4), and it is used to present examples of running ABM's as well as the code behind them. This lecture is also coupled with notes discussing the Lab 3 (Monte Carlo simulation) results and general experience. These comments focus on interval estimation (which is right 95% of the time, as opposed to point estimation that is right 0% of the time) and the role of non-trivial distributions of random variables (as opposed to just their means).

This lecture covers content related to implementing simulations with spreadsheets and the motivations for the use of special-purpose Discrete Event System Simulation tools. In particular, we discuss different approaches to implementing Discrete Event System (DES) simulations (DESS) with simple spreadsheets (e.g., Microsoft Excel, Google Sheets, Apple Numbers, etc.). We cover inventory management problems (such as the newsvendor model) as well as Monte Carlo sampling and stochastic activity networks (SAN's). Although we show that spreadsheets can be very powerful for this kind of work, we highlight that this approach is cumbersome for systems with increasing complexity. So this motivates why we would use more sophisticated tools specifically built for simulation (but perhaps not so great for data analysis by themselves), like Arena, FlexSim, Simio, and NetLogo.This lecture was recorded by Theodore Pavlic as part of IEE 475 (Simulating Stochastic Systems) at Arizona State University.

In this lecture, we briefly introduce System Dynamics Modeling (SDM) and Agent-Based/Individual-Based Modeling (ABM/IBM) as the two ends of the simulation modeling spectrum (from low resolution to high resolution). The introduction of ABM describes applications in life sciences, social sciences, and engineering (Multi-Agent Systems, MAS)/operations research. NetLogo is introduced, and it is used to present examples of running ABM's as well as the code behind them. At the end of the ABM/NetLogo introduction, comments about the previous lab on Monte Carlo simulation are given. These comments focus on interval estimation (which is right 95% of the time, as opposed to point estimation that is right 0% of the time) and the role of non-trivial distributions of random variables (as opposed to just their means).

In this lecture, we discuss different approaches to implementing Discrete Event System (DES) simulations (DESS) with simple spreadsheets (e.g., Microsoft Excel, Google Sheets, Apple Numbers, etc.). We cover inventory management problems (such as the newsvendor model) as well as Monte Carlo sampling and stochastic activity networks (SAN's). Although we show that spreadsheets can be very powerful for this kind of work, we highlight that this approach is cumbersome for systems with increasing complexity. So this motivates why we would use more sophisticated tools specifically built for simulation (but perhaps not so great for data analysis by themselves), like Arena, FlexSim, Simio, and NetLogo.

In this lecture, we review results from the Monte Carlo simulation lab (Lab 3) and setup motivation for the agent-based modeling/NetLogo lab (Lab 4). For the MC-lab review, we cover the estimation of pi by drawing random coordinates in the unit cube. We also discuss the possibly counter-intuitive results from estimating the length of a 3-path stochastic activity network. To prepare for Lab 4, we review the three different types of simulation methodologies (ABM/IBM, DES, and SDM) and then give a brief introduction to NetLogo. A more detailed/tutorial introduction to NetLogo will take place during Lab 4.

In this lecture, we first discussion results from Lab 3 on Monte Carlo simulations. Then we transition to motivate other forms of simulation outside of Discrete Event System simulation, such as System Dynamics Modeling and Agent-Based Modeling. This allows for introducing NetLogo, which is the subject of the upcoming Lab 4.

Russell Thomas discusses complex systems. You can find Russell's writings and works at Exploring Possibility Space and where you can also find the post "Think You Understand Black Swans? Think Again." If you want to learn more about complex systems make sure to check out Complexity Explorer, that features coursework and resources for the study of complex systems, and NetLogo, a free and open source platform for agent-based modeling and dynamical systems. The interview today was conducted by Sina Kashefipour, and the show is produced by Chelsea Daymon and Sina Kashefipour. If you have enjoyed listening to The Loopcast please consider making a donation to the show through our Patreon. We greatly appreciate it.

Is it legal for source code containing undefined behavior to crash the compiler?https://stackoverflow.com/questions/57652799/is-it-legal-for-source-code-containing-undefined-behavior-to-crash-the-compilerTrue, you’re the boss, and the compiler works for you. But that doesn’t mean it always behaves just as you instructed. And that’s not necessarily a bad thing.https://stackoverflow.com/questions/56802645/understanding-the-as-if-rule-the-program-was-executed-as-writtenWhat is Logo, you ask?https://en.wikipedia.org/wiki/Logo_(programming_language)And what about Netlogo? https://ccl.northwestern.edu/netlogo/docs/programming.htmlWilliam Chipps’ golden years - so close, and yet so farhttp://wacretiring.com/

Is it legal for source code containing undefined behavior to crash the compiler?https://stackoverflow.com/questions/57652799/is-it-legal-for-source-code-containing-undefined-behavior-to-crash-the-compilerTrue, you're the boss, and the compiler works for you. But that doesn't mean it always behaves just as you instructed. And that's not necessarily a bad thing.https://stackoverflow.com/questions/56802645/understanding-the-as-if-rule-the-program-was-executed-as-writtenWhat is Logo, you ask?https://en.wikipedia.org/wiki/Logo_(programming_language)And what about Netlogo? https://ccl.northwestern.edu/netlogo/docs/programming.htmlWilliam Chipps' golden years - so close, and yet so farhttp://wacretiring.com/

Turning the universal mouse button on its head: this week, Paul and Rich discuss the importance of getting into new skills and unlearning old habits. We look at Rich’s new interest in Blender, how it’s led to him making a beautiful hotdog, and the time it takes to learn how to use a 6 button mouse (spoiler: it doesn’t take long!). We talk about how the phone is the new computer and what that means for the future of the desktop. We also invite you all to attend our live podcast taping on April 11th at Postlight! Links: blender https://www.blender.org/ blender guru https://www.blenderguru.com/ the architecture of open source applications http://aosabook.org/en/index.html net logo https://en.wikipedia.org/wiki/NetLogo jupyter https://jupyter.org/ raspberry pi https://www.raspberrypi.org/ little bits  https://shop.littlebits.com logitech MX Anywhere 2 https://support.logitech.com/en_us/product/mx-anywhere2

Programın ilk 20 dakikası teknik sorunlar dolayısıyla kaydedilememiştir. Bu kaydedilememiş bölümde öncelikle katılımcılar kendilerini tanıtmışlar, sonrasında Açık Bilim, Yalansavar ve Bilim iletişimi konusunda konuşulmuştur. Konu: Bilimin değişimi, hesaplamalı bilimler ve düşünme, veri analizi, NetLogo ve diğer çok ajanlı modellemeler  Tartışılan Hedef Kazanımlar:  Fen Bilimleri: Fizik 10.2.1.2. Akışkanlarda akış sürati ile akışkan basıncı arasında ilişki kurar. Biyoloji 12.3.2.1. Köklerde su ve mineral emilimini açıklar. Teknoloji: Bilgisayar Bilimi 1.3.3.6. Tanımladığı dizi tipindeki veriye ait temel fonksiyonların yer aldığı programları geliştirir. 1.3.3.7. Farklı veri yapılarını (listeler, sözlükler vb.) kullanarak programlar geliştirir. Mühendislik:  TT. 7. C. 2. 7. Bir tasarım için gerekli yapısal özellikleri açıklar. TT. 7. C. 2. 8. Yapısal özellikleri dikkate alarak bir tasarım yapar. Matematik: TD.12.2.1.1. Gerçek hayat durumlarıyla ilgili istatistik problemleri çözer.  Öğretmenler: Dr Kaan Ozturk (Boğaziçi Üniversitesi), Senem Süral (Uğur Okulları), Asiye Karademir (Uğur Okulları) ve Başak Helvacı (University of Calgary).

Kevin Zollman (CMU) gives a lecture (first session) at the Summer School on Mathematical Philosophy for Female Students (26 July - 1 Agusut, 2015) titled "Introduction to Networks". Abstract: Social networks have become a central feature of the scientific study of social behavior and have been imported into philosophical discussions – like ethics, epistemology, and the philosophy of science – where social behavior is important. In ethics, scholars have asked what effect social networks might have on the evolution and maintenance of different ethical norms like fairness, cooperation, and altruism. As epistemologists have begun to take the social nature of knowledge more seriously, they too have begun to ask about how networks might influence the way knowledge is generated and transmitted. Finally, in philosophy of science scholars have asked how incorporating networks might change scientific theory, and how networks of scientists might come to learn about the world. This course will introduce students to the basics of social networks, some of the uses of social networks in philosophy, and how to understand and analyze networks for original research. Because some of the analysis of social networks requires the use of computer simulation, this course will also teach students how to use the computational tool NetLogo for analyzing networks. No prior knowledge of programing is expected.