This document presents a summary of a talk on synchronization of coupled oscillators modeled as a game. The talk discusses how individual behaviors that seem rational can aggregate to produce undesirable outcomes, using examples like the Millennium Bridge incident. It introduces a finite oscillator model where each oscillator aims to minimize the cost of asynchrony with others against the cost of control, modeling their strategic interaction as a game. The talk outlines results on characterizing the phase transition between incoherent and synchronized states.
Synchronization of coupled oscillators is a gamemehtapgresearch
The document discusses synchronization of coupled oscillators as a game-theoretic problem. It presents a finite oscillator model where each oscillator seeks to minimize the long-term expected cost of being out of sync with other oscillators and the cost of applying control, by choosing a control input. The dynamics of each oscillator depends on its natural frequency, the control input, and noise. The goal is to analyze the phase transition between incoherent and synchronized states as coupling strength varies.
This document summarizes a presentation on mean-field methods in estimation and control. It begins with background on the Kuramoto model of coupled oscillators and examples of oscillators in biology like neurons. It then discusses using mean-field methods to study the functional role of synchrony in neural systems, including for synchronization, neuronal computation, and learning. A specific dynamic game model is presented where each oscillator seeks to minimize its cost by choosing a control input. The goal is to study synchronization as a controlled phase transition in this framework.
EACA represents advertising agencies in Europe and aims to promote high standards in advertising. It focuses on issues like education, social acceptance of advertising's role, and industry developments.
IMCC similarly represents integrated marketing agencies in Europe. It focuses on best practices, lobbying, and hosting the annual IMC European Awards which recognize the best marketing campaigns across Europe. The document provides examples of award-winning campaigns from past IMC European Awards.
HEMA launched a new push-up bra that claimed to add two cup sizes. To stand out against larger competitors, the advertising agency had a man, model Andrej Pejic, wear the bra in outdoor advertisements. This garnered massive media attention globally. Sales of the bra significantly exceeded expectations, with most sizes selling out within days. The unconventional campaign was very successful in bringing publicity and sales to HEMA.
This document summarizes the proceedings of the Regional Humanitarian Partnerships Forum held in Phuket, Thailand on November 14-15, 2013. The forum brought together over 100 participants from 20 countries and 47 organizations to discuss humanitarian partnerships and innovation in Asia-Pacific. Key topics included cash transfer programming, public-private partnerships, communications with affected communities, technology, urban and conflict settings. The forum concluded that national governments are now leading humanitarian response, while international organizations provide technical support. It identified actions to strengthen partnerships and better adapt the humanitarian system to new challenges in the region.
Este documento describe las partes externas e internas del cuerpo humano, incluyendo el cerebro, los ojos, los oídos, la lengua, y los sistemas circulatorio, respiratorio, digestivo, nervioso y excretor.
Chennaistayz provides luxury serviced apartments in Chennai at affordable prices. They aim to make guests feel at home during their stay by offering amenities like air conditioning, room service, laundry, internet access, and 24 hour security and reception. The apartments are conveniently located in Vadapalani, near shopping, entertainment and transportation hubs in the city. Chennaistayz strives to ensure quality service and comfort for all guests.
An invited talk at Talkboctopus: A Virtual Complex Systems & Data Science Seminar Series, Vermont Complex Systems Center, University of Vermont, March 17, 2022, Burlington, VT / online.
Synchronization of coupled oscillators is a gamemehtapgresearch
The document discusses synchronization of coupled oscillators as a game-theoretic problem. It presents a finite oscillator model where each oscillator seeks to minimize the long-term expected cost of being out of sync with other oscillators and the cost of applying control, by choosing a control input. The dynamics of each oscillator depends on its natural frequency, the control input, and noise. The goal is to analyze the phase transition between incoherent and synchronized states as coupling strength varies.
This document summarizes a presentation on mean-field methods in estimation and control. It begins with background on the Kuramoto model of coupled oscillators and examples of oscillators in biology like neurons. It then discusses using mean-field methods to study the functional role of synchrony in neural systems, including for synchronization, neuronal computation, and learning. A specific dynamic game model is presented where each oscillator seeks to minimize its cost by choosing a control input. The goal is to study synchronization as a controlled phase transition in this framework.
EACA represents advertising agencies in Europe and aims to promote high standards in advertising. It focuses on issues like education, social acceptance of advertising's role, and industry developments.
IMCC similarly represents integrated marketing agencies in Europe. It focuses on best practices, lobbying, and hosting the annual IMC European Awards which recognize the best marketing campaigns across Europe. The document provides examples of award-winning campaigns from past IMC European Awards.
HEMA launched a new push-up bra that claimed to add two cup sizes. To stand out against larger competitors, the advertising agency had a man, model Andrej Pejic, wear the bra in outdoor advertisements. This garnered massive media attention globally. Sales of the bra significantly exceeded expectations, with most sizes selling out within days. The unconventional campaign was very successful in bringing publicity and sales to HEMA.
This document summarizes the proceedings of the Regional Humanitarian Partnerships Forum held in Phuket, Thailand on November 14-15, 2013. The forum brought together over 100 participants from 20 countries and 47 organizations to discuss humanitarian partnerships and innovation in Asia-Pacific. Key topics included cash transfer programming, public-private partnerships, communications with affected communities, technology, urban and conflict settings. The forum concluded that national governments are now leading humanitarian response, while international organizations provide technical support. It identified actions to strengthen partnerships and better adapt the humanitarian system to new challenges in the region.
Este documento describe las partes externas e internas del cuerpo humano, incluyendo el cerebro, los ojos, los oídos, la lengua, y los sistemas circulatorio, respiratorio, digestivo, nervioso y excretor.
Chennaistayz provides luxury serviced apartments in Chennai at affordable prices. They aim to make guests feel at home during their stay by offering amenities like air conditioning, room service, laundry, internet access, and 24 hour security and reception. The apartments are conveniently located in Vadapalani, near shopping, entertainment and transportation hubs in the city. Chennaistayz strives to ensure quality service and comfort for all guests.
An invited talk at Talkboctopus: A Virtual Complex Systems & Data Science Seminar Series, Vermont Complex Systems Center, University of Vermont, March 17, 2022, Burlington, VT / online.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
The chapter Lifelines of National Economy in Class 10 Geography focuses on the various modes of transportation and communication that play a vital role in the economic development of a country. These lifelines are crucial for the movement of goods, services, and people, thereby connecting different regions and promoting economic activities.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
2024 State of Marketing Report – by HubspotMarius Sescu
https://www.hubspot.com/state-of-marketing
· Scaling relationships and proving ROI
· Social media is the place for search, sales, and service
· Authentic influencer partnerships fuel brand growth
· The strongest connections happen via call, click, chat, and camera.
· Time saved with AI leads to more creative work
· Seeking: A single source of truth
· TLDR; Get on social, try AI, and align your systems.
· More human marketing, powered by robots
ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
-------------------------------------------------------------------------------
Find out more about ISO training and certification services
Training: ISO/IEC 27001 Information Security Management System - EN | PECB
ISO/IEC 42001 Artificial Intelligence Management System - EN | PECB
General Data Protection Regulation (GDPR) - Training Courses - EN | PECB
Webinars: https://pecb.com/webinars
Article: https://pecb.com/article
-------------------------------------------------------------------------------
For more information about PECB:
Website: https://pecb.com/
LinkedIn: https://www.linkedin.com/company/pecb/
Facebook: https://www.facebook.com/PECBInternational/
Slideshare: http://www.slideshare.net/PECBCERTIFICATION
The chapter Lifelines of National Economy in Class 10 Geography focuses on the various modes of transportation and communication that play a vital role in the economic development of a country. These lifelines are crucial for the movement of goods, services, and people, thereby connecting different regions and promoting economic activities.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
2024 State of Marketing Report – by HubspotMarius Sescu
https://www.hubspot.com/state-of-marketing
· Scaling relationships and proving ROI
· Social media is the place for search, sales, and service
· Authentic influencer partnerships fuel brand growth
· The strongest connections happen via call, click, chat, and camera.
· Time saved with AI leads to more creative work
· Seeking: A single source of truth
· TLDR; Get on social, try AI, and align your systems.
· More human marketing, powered by robots
ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
Product Design Trends in 2024 | Teenage EngineeringsPixeldarts
The realm of product design is a constantly changing environment where technology and style intersect. Every year introduces fresh challenges and exciting trends that mold the future of this captivating art form. In this piece, we delve into the significant trends set to influence the look and functionality of product design in the year 2024.
How Race, Age and Gender Shape Attitudes Towards Mental HealthThinkNow
Mental health has been in the news quite a bit lately. Dozens of U.S. states are currently suing Meta for contributing to the youth mental health crisis by inserting addictive features into their products, while the U.S. Surgeon General is touring the nation to bring awareness to the growing epidemic of loneliness and isolation. The country has endured periods of low national morale, such as in the 1970s when high inflation and the energy crisis worsened public sentiment following the Vietnam War. The current mood, however, feels different. Gallup recently reported that national mental health is at an all-time low, with few bright spots to lift spirits.
To better understand how Americans are feeling and their attitudes towards mental health in general, ThinkNow conducted a nationally representative quantitative survey of 1,500 respondents and found some interesting differences among ethnic, age and gender groups.
Technology
For example, 52% agree that technology and social media have a negative impact on mental health, but when broken out by race, 61% of Whites felt technology had a negative effect, and only 48% of Hispanics thought it did.
While technology has helped us keep in touch with friends and family in faraway places, it appears to have degraded our ability to connect in person. Staying connected online is a double-edged sword since the same news feed that brings us pictures of the grandkids and fluffy kittens also feeds us news about the wars in Israel and Ukraine, the dysfunction in Washington, the latest mass shooting and the climate crisis.
Hispanics may have a built-in defense against the isolation technology breeds, owing to their large, multigenerational households, strong social support systems, and tendency to use social media to stay connected with relatives abroad.
Age and Gender
When asked how individuals rate their mental health, men rate it higher than women by 11 percentage points, and Baby Boomers rank it highest at 83%, saying it’s good or excellent vs. 57% of Gen Z saying the same.
Gen Z spends the most amount of time on social media, so the notion that social media negatively affects mental health appears to be correlated. Unfortunately, Gen Z is also the generation that’s least comfortable discussing mental health concerns with healthcare professionals. Only 40% of them state they’re comfortable discussing their issues with a professional compared to 60% of Millennials and 65% of Boomers.
Race Affects Attitudes
As seen in previous research conducted by ThinkNow, Asian Americans lag other groups when it comes to awareness of mental health issues. Twenty-four percent of Asian Americans believe that having a mental health issue is a sign of weakness compared to the 16% average for all groups. Asians are also considerably less likely to be aware of mental health services in their communities (42% vs. 55%) and most likely to seek out information on social media (51% vs. 35%).
AI Trends in Creative Operations 2024 by Artwork Flow.pdfmarketingartwork
Creative operations teams expect increased AI use in 2024. Currently, over half of tasks are not AI-enabled, but this is expected to decrease in the coming year. ChatGPT is the most popular AI tool currently. Business leaders are more actively exploring AI benefits than individual contributors. Most respondents do not believe AI will impact workforce size in 2024. However, some inhibitions still exist around AI accuracy and lack of understanding. Creatives primarily want to use AI to save time on mundane tasks and boost productivity.
Organizational culture includes values, norms, systems, symbols, language, assumptions, beliefs, and habits that influence employee behaviors and how people interpret those behaviors. It is important because culture can help or hinder a company's success. Some key aspects of Netflix's culture that help it achieve results include hiring smartly so every position has stars, focusing on attitude over just aptitude, and having a strict policy against peacocks, whiners, and jerks.
PEPSICO Presentation to CAGNY Conference Feb 2024Neil Kimberley
PepsiCo provided a safe harbor statement noting that any forward-looking statements are based on currently available information and are subject to risks and uncertainties. It also provided information on non-GAAP measures and directing readers to its website for disclosure and reconciliation. The document then discussed PepsiCo's business overview, including that it is a global beverage and convenient food company with iconic brands, $91 billion in net revenue in 2023, and nearly $14 billion in core operating profit. It operates through a divisional structure with a focus on local consumers.
Content Methodology: A Best Practices Report (Webinar)contently
This document provides an overview of content methodology best practices. It defines content methodology as establishing objectives, KPIs, and a culture of continuous learning and iteration. An effective methodology focuses on connecting with audiences, creating optimal content, and optimizing processes. It also discusses why a methodology is needed due to the competitive landscape, proliferation of channels, and opportunities for improvement. Components of an effective methodology include defining objectives and KPIs, audience analysis, identifying opportunities, and evaluating resources. The document concludes with recommendations around creating a content plan, testing and optimizing content over 90 days.
How to Prepare For a Successful Job Search for 2024Albert Qian
The document provides guidance on preparing a job search for 2024. It discusses the state of the job market, focusing on growth in AI and healthcare but also continued layoffs. It recommends figuring out what you want to do by researching interests and skills, then conducting informational interviews. The job search should involve building a personal brand on LinkedIn, actively applying to jobs, tailoring resumes and interviews, maintaining job hunting as a habit, and continuing self-improvement. Once hired, the document advises setting new goals and keeping skills and networking active in case of future opportunities.
A report by thenetworkone and Kurio.
The contributing experts and agencies are (in an alphabetical order): Sylwia Rytel, Social Media Supervisor, 180heartbeats + JUNG v MATT (PL), Sharlene Jenner, Vice President - Director of Engagement Strategy, Abelson Taylor (USA), Alex Casanovas, Digital Director, Atrevia (ES), Dora Beilin, Senior Social Strategist, Barrett Hoffher (USA), Min Seo, Campaign Director, Brand New Agency (KR), Deshé M. Gully, Associate Strategist, Day One Agency (USA), Francesca Trevisan, Strategist, Different (IT), Trevor Crossman, CX and Digital Transformation Director; Olivia Hussey, Strategic Planner; Simi Srinarula, Social Media Manager, The Hallway (AUS), James Hebbert, Managing Director, Hylink (CN / UK), Mundy Álvarez, Planning Director; Pedro Rojas, Social Media Manager; Pancho González, CCO, Inbrax (CH), Oana Oprea, Head of Digital Planning, Jam Session Agency (RO), Amy Bottrill, Social Account Director, Launch (UK), Gaby Arriaga, Founder, Leonardo1452 (MX), Shantesh S Row, Creative Director, Liwa (UAE), Rajesh Mehta, Chief Strategy Officer; Dhruv Gaur, Digital Planning Lead; Leonie Mergulhao, Account Supervisor - Social Media & PR, Medulla (IN), Aurelija Plioplytė, Head of Digital & Social, Not Perfect (LI), Daiana Khaidargaliyeva, Account Manager, Osaka Labs (UK / USA), Stefanie Söhnchen, Vice President Digital, PIABO Communications (DE), Elisabeth Winiartati, Managing Consultant, Head of Global Integrated Communications; Lydia Aprina, Account Manager, Integrated Marketing and Communications; Nita Prabowo, Account Manager, Integrated Marketing and Communications; Okhi, Web Developer, PNTR Group (ID), Kei Obusan, Insights Director; Daffi Ranandi, Insights Manager, Radarr (SG), Gautam Reghunath, Co-founder & CEO, Talented (IN), Donagh Humphreys, Head of Social and Digital Innovation, THINKHOUSE (IRE), Sarah Yim, Strategy Director, Zulu Alpha Kilo (CA).
Trends In Paid Search: Navigating The Digital Landscape In 2024Search Engine Journal
The search marketing landscape is evolving rapidly with new technologies, and professionals, like you, rely on innovative paid search strategies to meet changing demands.
It’s important that you’re ready to implement new strategies in 2024.
Check this out and learn the top trends in paid search advertising that are expected to gain traction, so you can drive higher ROI more efficiently in 2024.
You’ll learn:
- The latest trends in AI and automation, and what this means for an evolving paid search ecosystem.
- New developments in privacy and data regulation.
- Emerging ad formats that are expected to make an impact next year.
Watch Sreekant Lanka from iQuanti and Irina Klein from OneMain Financial as they dive into the future of paid search and explore the trends, strategies, and technologies that will shape the search marketing landscape.
If you’re looking to assess your paid search strategy and design an industry-aligned plan for 2024, then this webinar is for you.
5 Public speaking tips from TED - Visualized summarySpeakerHub
From their humble beginnings in 1984, TED has grown into the world’s most powerful amplifier for speakers and thought-leaders to share their ideas. They have over 2,400 filmed talks (not including the 30,000+ TEDx videos) freely available online, and have hosted over 17,500 events around the world.
With over one billion views in a year, it’s no wonder that so many speakers are looking to TED for ideas on how to share their message more effectively.
The article “5 Public-Speaking Tips TED Gives Its Speakers”, by Carmine Gallo for Forbes, gives speakers five practical ways to connect with their audience, and effectively share their ideas on stage.
Whether you are gearing up to get on a TED stage yourself, or just want to master the skills that so many of their speakers possess, these tips and quotes from Chris Anderson, the TED Talks Curator, will encourage you to make the most impactful impression on your audience.
See the full article and more summaries like this on SpeakerHub here: https://speakerhub.com/blog/5-presentation-tips-ted-gives-its-speakers
See the original article on Forbes here:
http://www.forbes.com/forbes/welcome/?toURL=http://www.forbes.com/sites/carminegallo/2016/05/06/5-public-speaking-tips-ted-gives-its-speakers/&refURL=&referrer=#5c07a8221d9b
ChatGPT and the Future of Work - Clark Boyd Clark Boyd
Everyone is in agreement that ChatGPT (and other generative AI tools) will shape the future of work. Yet there is little consensus on exactly how, when, and to what extent this technology will change our world.
Businesses that extract maximum value from ChatGPT will use it as a collaborative tool for everything from brainstorming to technical maintenance.
For individuals, now is the time to pinpoint the skills the future professional will need to thrive in the AI age.
Check out this presentation to understand what ChatGPT is, how it will shape the future of work, and how you can prepare to take advantage.
The document provides career advice for getting into the tech field, including:
- Doing projects and internships in college to build a portfolio.
- Learning about different roles and technologies through industry research.
- Contributing to open source projects to build experience and network.
- Developing a personal brand through a website and social media presence.
- Networking through events, communities, and finding a mentor.
- Practicing interviews through mock interviews and whiteboarding coding questions.
Google's Just Not That Into You: Understanding Core Updates & Search IntentLily Ray
1. Core updates from Google periodically change how its algorithms assess and rank websites and pages. This can impact rankings through shifts in user intent, site quality issues being caught up to, world events influencing queries, and overhauls to search like the E-A-T framework.
2. There are many possible user intents beyond just transactional, navigational and informational. Identifying intent shifts is important during core updates. Sites may need to optimize for new intents through different content types and sections.
3. Responding effectively to core updates requires analyzing "before and after" data to understand changes, identifying new intents or page types, and ensuring content matches appropriate intents across video, images, knowledge graphs and more.
A brief introduction to DataScience with explaining of the concepts, algorithms, machine learning, supervised and unsupervised learning, clustering, statistics, data preprocessing, real-world applications etc.
It's part of a Data Science Corner Campaign where I will be discussing the fundamentals of DataScience, AIML, Statistics etc.
Time Management & Productivity - Best PracticesVit Horky
Here's my presentation on by proven best practices how to manage your work time effectively and how to improve your productivity. It includes practical tips and how to use tools such as Slack, Google Apps, Hubspot, Google Calendar, Gmail and others.
The six step guide to practical project managementMindGenius
The six step guide to practical project management
If you think managing projects is too difficult, think again.
We’ve stripped back project management processes to the
basics – to make it quicker and easier, without sacrificing
the vital ingredients for success.
“If you’re looking for some real-world guidance, then The Six Step Guide to Practical Project Management will help.”
Dr Andrew Makar, Tactical Project Management
Beginners Guide to TikTok for Search - Rachel Pearson - We are Tilt __ Bright...
Maryland 2010
1. CSLCOORDINATED SCIENCE LABORATORY
Synchronization of coupled oscillators is a game
Prashant G. Mehta1
1Coordinated Science Laboratory
Department of Mechanical Science and Engineering
University of Illinois at Urbana-Champaign
University of Maryland, March 4, 2010
Acknowledgment: AFOSR, NSF
2. Huibing Yin Sean P. Meyn Uday V. Shanbhag
H. Yin, P. G. Mehta, S. P. Meyn and U. V. Shanbhag, “Synchronization of coupled oscillators is a game,” ACC 2010
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 2 / 69
3. Millennium bridge
Video of London Millennium bridge from youtube
[11] S. H. Strogatz et al., Nature, 2005
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 3 / 69
4. Classical Kuramoto model
dθi(t) =
�
ωi +
κ
N
N
∑
j=1
sin(θj(t)−θi(t))
�
dt +σ dξi(t), i = 1,...,N
ωi taken from distribution g(ω) over [1−γ,1+γ]
γ — measures the heterogeneity of the population
κ — measures the strength of coupling
[6] Y. Kuramoto (1975)
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 4 / 69
5. Classical Kuramoto model
dθi(t) =
�
ωi +
κ
N
N
∑
j=1
sin(θj(t)−θi(t))
�
dt +σ dξi(t), i = 1,...,N
ωi taken from distribution g(ω) over [1−γ,1+γ]
γ — measures the heterogeneity of the population
κ — measures the strength of coupling 1- 1+1
[6] Y. Kuramoto (1975)
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 4 / 69
6. Classical Kuramoto model
dθi(t) =
�
ωi +
κ
N
N
∑
j=1
sin(θj(t)−θi(t))
�
dt +σ dξi(t), i = 1,...,N
ωi taken from distribution g(ω) over [1−γ,1+γ]
γ — measures the heterogeneity of the population
κ — measures the strength of coupling
[6] Y. Kuramoto (1975)
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 4 / 69
7. Classical Kuramoto model
dθi(t) =
�
ωi +
κ
N
N
∑
j=1
sin(θj(t)−θi(t))
�
dt +σ dξi(t), i = 1,...,N
ωi taken from distribution g(ω) over [1−γ,1+γ]
γ — measures the heterogeneity of the population
κ — measures the strength of coupling
0 0.1 0.2
0.1
0.15
0.2
0.25
0.3 Locking
Incoherence
κ
κ < κc(γ)
γ
Synchrony
Incoherence
[6] Y. Kuramoto (1975)
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 4 / 69
8. Movies of incoherence and synchrony solution
Incoherence Synchrony
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 5 / 69
9. Problem statement
Dynamics of ith
oscillator
dθi = (ωi +ui(t))dt +σ dξi, i = 1,...,N, t ≥ 0
ui(t) — control 1- 1+1
ith
oscillator seeks to minimize
ηi(ui;u−i) = lim
T→∞
1
T
� T
0
E[ c(θi;θ−i)
� �� �
cost of anarchy
+ 1
2Ru2
i
� �� �
cost of control
]ds
θ−i = (θj)j�=i
R — control penalty
c(·) — cost function
c(θi;θ−i) =
1
N ∑
j�=i
c•
(θi,θj), c•
≥ 0
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 6 / 69
10. Problem statement
Dynamics of ith
oscillator
dθi = (ωi +ui(t))dt +σ dξi, i = 1,...,N, t ≥ 0
ui(t) — control 1- 1+1
ith
oscillator seeks to minimize
ηi(ui;u−i) = lim
T→∞
1
T
� T
0
E[ c(θi;θ−i)
� �� �
cost of anarchy
+ 1
2Ru2
i
� �� �
cost of control
]ds
θ−i = (θj)j�=i
R — control penalty
c(·) — cost function
c(θi;θ−i) =
1
N ∑
j�=i
c•
(θi,θj), c•
≥ 0
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 6 / 69
11. Problem statement
Dynamics of ith
oscillator
dθi = (ωi +ui(t))dt +σ dξi, i = 1,...,N, t ≥ 0
ui(t) — control 1- 1+1
ith
oscillator seeks to minimize
ηi(ui;u−i) = lim
T→∞
1
T
� T
0
E[ c(θi;θ−i)
� �� �
cost of anarchy
+ 1
2Ru2
i
� �� �
cost of control
]ds
θ−i = (θj)j�=i
R — control penalty
c(·) — cost function
c(θi;θ−i) =
1
N ∑
j�=i
c•
(θi,θj), c•
≥ 0
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 6 / 69
12. 1 Motivation
Why a game?
Why Oscillators?
2 Problems and results
Problem statement
Main results
3 Derivation of model
Overview
Derivation steps
PDE model
4 Analysis of phase transition
Incoherence solution
Bifurcation analysis
Numerics
5 Learning
Q-function approximation
Steepest descent algorithm
13. Motivation Why a game?
Quiz
In the video you just watched, why were the
individuals walking strangely?
A. To show respect to the Queen.
B. Anarchists in the crowd were trying to destabilize the bridge.
C. They were stepping to the beat of the soundtrack "Walk Like an
Egyptian."
D. The individuals were trying to maintain their balance.
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 8 / 69
14. Motivation Why a game?
Quiz
In the video you just watched, why were the
individuals walking strangely?
A. To show respect to the Queen.
B. Anarchists in the crowd were trying to destabilize the bridge.
C. They were stepping to the beat of the soundtrack "Walk Like an
Egyptian."
D. The individuals were trying to maintain their balance.
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 8 / 69
15. Motivation Why a game?
Quiz
In the video you just watched, why were the
individuals walking strangely?
A. To show respect to the Queen.
B. Anarchists in the crowd were trying to destabilize the bridge.
C. They were stepping to the beat of the soundtrack "Walk Like an
Egyptian."
D. The individuals were trying to maintain their balance.
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 8 / 69
16. Motivation Why a game?
Quiz
In the video you just watched, why were the
individuals walking strangely?
A. To show respect to the Queen.
B. Anarchists in the crowd were trying to destabilize the bridge.
C. They were stepping to the beat of the soundtrack "Walk Like an
Egyptian."
D. The individuals were trying to maintain their balance.
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 8 / 69
17. Motivation Why a game?
Quiz
In the video you just watched, why were the
individuals walking strangely?
A. To show respect to the Queen.
B. Anarchists in the crowd were trying to destabilize the bridge.
C. They were stepping to the beat of the soundtrack "Walk Like an
Egyptian."
D. The individuals were trying to maintain their balance.
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 8 / 69
18. Motivation Why a game?
“Rational irrationality”
“—behavior that, on the individual level, is perfectly reasonable but
that, when aggregated in the marketplace, produces calamity.”
Examples
Millennium bridge
Financial market
John Cassidy, “Rational Irrationality: The real reason that capitalism is so crash-prone,” The New Yorker, 2009
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 9 / 69
19. Motivation Why a game?
“Rational irrationality”
“—behavior that, on the individual level, is perfectly reasonable but
that, when aggregated in the marketplace, produces calamity.”
Examples
Millennium bridge
Financial market
John Cassidy, “Rational Irrationality: The real reason that capitalism is so crash-prone,” The New Yorker, 2009
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 9 / 69
20. 1 Motivation
Why a game?
Why Oscillators?
2 Problems and results
Problem statement
Main results
3 Derivation of model
Overview
Derivation steps
PDE model
4 Analysis of phase transition
Incoherence solution
Bifurcation analysis
Numerics
5 Learning
Q-function approximation
Steepest descent algorithm
21. Motivation Why Oscillators?
Hodgkin-Huxley type Neuron model
C
dV
dt
= −gT ·m2
∞(V)·h·(V −ET)
−gh ·r ·(V −Eh)−......
dh
dt
=
h∞(V)−h
τh(V)
dr
dt
=
r∞(V)−r
τr(V)
2000 2200 2400 2600 2800 3000 3200 3400 3600 3800 4000
−150
−100
−50
0
50
100
Voltage
time
Neural spike train
[4] J. Guckenheimer, J. Math. Biol., 1975; [2] J. Moehlis et al., Neural Computation, 2004
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 11 / 69
22. Motivation Why Oscillators?
Hodgkin-Huxley type Neuron model
C
dV
dt
= −gT ·m2
∞(V)·h·(V −ET)
−gh ·r ·(V −Eh)−......
dh
dt
=
h∞(V)−h
τh(V)
dr
dt
=
r∞(V)−r
τr(V)
2000 2200 2400 2600 2800 3000 3200 3400 3600 3800 4000
−150
−100
−50
0
50
100
Voltage
time
Neural spike train
[4] J. Guckenheimer, J. Math. Biol., 1975; [2] J. Moehlis et al., Neural Computation, 2004
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 11 / 69
23. Motivation Why Oscillators?
Hodgkin-Huxley type Neuron model
C
dV
dt
= −gT ·m2
∞(V)·h·(V −ET)
−gh ·r ·(V −Eh)−......
dh
dt
=
h∞(V)−h
τh(V)
dr
dt
=
r∞(V)−r
τr(V)
2000 2200 2400 2600 2800 3000 3200 3400 3600 3800 4000
−150
−100
−50
0
50
100
Voltage
time
Neural spike train
−100
−50
0
50
100
0
0.2
0.4
0.6
0.8
1
0
0.1
0.2
0.3
0.4
Vh
r
Limit cyle
r
h v
[4] J. Guckenheimer, J. Math. Biol., 1975; [2] J. Moehlis et al., Neural Computation, 2004
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 11 / 69
24. Motivation Why Oscillators?
Hodgkin-Huxley type Neuron model
C
dV
dt
= −gT ·m2
∞(V)·h·(V −ET)
−gh ·r ·(V −Eh)−......
dh
dt
=
h∞(V)−h
τh(V)
dr
dt
=
r∞(V)−r
τr(V)
2000 2200 2400 2600 2800 3000 3200 3400 3600 3800 4000
−150
−100
−50
0
50
100
Voltage
time
Neural spike train
−100
−50
0
50
100
0
0.2
0.4
0.6
0.8
1
0
0.1
0.2
0.3
0.4
Vh
r
Limit cyle
r
h v
Normal form reduction
−−−−−−−−−−−−−→
˙θi = ωi +ui ·Φ(θi)
[4] J. Guckenheimer, J. Math. Biol., 1975; [2] J. Moehlis et al., Neural Computation, 2004
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 11 / 69
25. 1 Motivation
Why a game?
Why Oscillators?
2 Problems and results
Problem statement
Main results
3 Derivation of model
Overview
Derivation steps
PDE model
4 Analysis of phase transition
Incoherence solution
Bifurcation analysis
Numerics
5 Learning
Q-function approximation
Steepest descent algorithm
26. Problems and results Problem statement
Finite oscillator model
Dynamics of ith
oscillator
dθi = (ωi +ui(t))dt +σ dξi, i = 1,...,N, t ≥ 0
ui(t) — control 1- 1+1
ith
oscillator seeks to minimize
ηi(ui;u−i) = lim
T→∞
1
T
� T
0
E[ c(θi;θ−i)
� �� �
cost of anarchy
+ 1
2Ru2
i
� �� �
cost of control
]ds
θ−i = (θj)j�=i
R — control penalty
c(·) — cost function
c(θi;θ−i) =
1
N ∑
j�=i
c•
(θi,θj), c•
≥ 0
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 13 / 69
27. 1 Motivation
Why a game?
Why Oscillators?
2 Problems and results
Problem statement
Main results
3 Derivation of model
Overview
Derivation steps
PDE model
4 Analysis of phase transition
Incoherence solution
Bifurcation analysis
Numerics
5 Learning
Q-function approximation
Steepest descent algorithm
28. Problems and results Main results
1. Synchronization is a solution of game
Locking
0 0.1 0.2
0.15
0.2
0.25
R−1/ 2
γ
Incoherence
R > Rc(γ)
Synchrony
Incoherence
dθi = (ωi +ui)dt +σ dξi
ηi(ui;u−i) = lim
T→∞
1
T
� T
0
E[c(θi;θ−i)+ 1
2 Ru2
i ]ds
1- 1+1
Yin et al., ACC 2010 Strogatz et al., J. Stat. Phy., 1992
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 15 / 69
29. Problems and results Main results
1. Synchronization is a solution of game
Locking
0 0.1 0.2
0.15
0.2
0.25
R−1/ 2
γ
Incoherence
R > Rc(γ)
Synchrony
Incoherence
dθi = (ωi +ui)dt +σ dξi
ηi(ui;u−i) = lim
T→∞
1
T
� T
0
E[c(θi;θ−i)+ 1
2 Ru2
i ]ds
0 0.1 0.2
0.1
0.15
0.2
0.25
0.3 Locking
Incoherence
κ
κ < κc(γ)
γ
Synchrony
Incoherence
dθi =
�
ωi +
κ
N
N
∑
j=1
sin(θj −θi)
�
dt +σ dξi
Yin et al., ACC 2010 Strogatz et al., J. Stat. Phy., 1992
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 15 / 69
30. Problems and results Main results
2. Kuramoto control is approximately optimal
−0.2
0
0.2
0.4
0.6
ω = 1
Kuramoto
Population
Density
Control laws
0 π 2π θ
ui = −
A∗
i
R
1
N ∑
j�=i
sin(θ −θj(t))
0 50 100 150 200 250 300
2
2.5
3
3.5
4
4.5
5
5.5
6
t
k = 0.01; R = 1000
A
i
A*
Learning algorithm:
dAi
dt
= −ε ...
Yin et.al. CDC 2010
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 16 / 69
31. 1 Motivation
Why a game?
Why Oscillators?
2 Problems and results
Problem statement
Main results
3 Derivation of model
Overview
Derivation steps
PDE model
4 Analysis of phase transition
Incoherence solution
Bifurcation analysis
Numerics
5 Learning
Q-function approximation
Steepest descent algorithm
32. Derivation of model Overview
Overview of model derivation
dθi = (ωi +ui(t))dt +σ dξi
ηi(ui;u−i) = lim
T→∞
1
T
� T
0
E[¯c(θi,t)+ 1
2 Ru2
i ]ds
Influence
Influence
Mass
1 Mean-field approximation
Assumption:
c(θi;θ−i(t)) =
1
N ∑
j�=i
c•
(θi,θj)
N→∞
−−−−−−→ ¯c(θ,t)
2 Optimal control of single oscillator
Decentralized control structure
[5] M. Huang, P. Caines, and R. Malhame, IEEE TAC, 2007 [HCM]
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 18 / 69
33. 1 Motivation
Why a game?
Why Oscillators?
2 Problems and results
Problem statement
Main results
3 Derivation of model
Overview
Derivation steps
PDE model
4 Analysis of phase transition
Incoherence solution
Bifurcation analysis
Numerics
5 Learning
Q-function approximation
Steepest descent algorithm
34. Derivation of model Derivation steps
Single oscillator with given cost
Dynamics of the oscillator
dθi = (ωi +ui(t))dt +σ dξi, t ≥ 0
The cost function is assumed known
ηi(ui; ¯c) = lim
T→∞
1
T
� T
0
E[ c(θi;θ−i) + 1
2Ru2
i (s)]ds
⇑
¯c(θi(s),s)
HJB equation:
∂thi +ωi∂θ hi =
1
2R
(∂θ hi)2
− ¯c(θ,t)+η∗
i −
σ2
2
∂2
θθ hi
Optimal control law: u∗
i (t) = ϕi(θ,t) = −
1
R
∂θ hi(θ,t)
[1] D. P. Bertsekas (1995); [9] S. P. Meyn, IEEE TAC, 1997
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 20 / 69
35. Derivation of model Derivation steps
Single oscillator with optimal control
Dynamics of the oscillator
dθi(t) =
�
ωi −
1
R
∂θ hi(θi,t)
�
dt +σ dξi(t)
Fokker-Planck equation for pdf p(θ,t,ωi)
FPK: ∂tp+ωi∂θ p =
1
R
∂θ [p(∂θ h)]+
σ2
2
∂2
θθ p
[7] A. Lasota and M. C. Mackey, “Chaos, Fractals and Noise,” Springer 1994
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 21 / 69
36. Derivation of model Derivation steps
Mean-field Approximation
HJB equation for population
∂th+ω∂θ h =
1
2R
(∂θ h)2
− ¯c(θ,t)+η(ω)−
σ2
2
∂2
θθ h h(θ,t,ω)
Population density
∂tp+ω∂θ p =
1
R
∂θ [p(∂θ h)]+
σ2
2
∂2
θθ p p(θ,t,ω)
Enforce cost consistency
¯c(θ,t) =
�
Ω
� 2π
0
c•
(θ,ϑ)p(ϑ,t,ω)g(ω)dϑ dω
≈
1
N ∑
j�=i
c•
(θ,ϑ)
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 22 / 69
37. 1 Motivation
Why a game?
Why Oscillators?
2 Problems and results
Problem statement
Main results
3 Derivation of model
Overview
Derivation steps
PDE model
4 Analysis of phase transition
Incoherence solution
Bifurcation analysis
Numerics
5 Learning
Q-function approximation
Steepest descent algorithm
38. Derivation of model PDE model
Summary
HJB: ∂th+ω∂θ h =
1
2R
(∂θ h)2
− ¯c(θ,t) +η∗
−
σ2
2
∂2
θθ h ⇒ h(θ,t,ω)
FPK: ∂tp+ω∂θ p =
1
R
∂θ [p( ∂θ h )]+
σ2
2
∂2
θθ p ⇒ p(θ,t,ω)
Mean-field approx.: ¯c(ϑ,t) =
�
Ω
� 2π
0
c•
(ϑ,θ) p(θ,t,ω) g(ω)dθ dω
1 Bellman’s optimality principle (H,J,B)
2 Propagation of chaos (F,P,K, Mckean, Vlasov,. . . )
3 Mean-field approximation (Boltzmann, Kac,. . . )
4 Connection to Nash game (Weintraub, HCM, Altman,. . . )
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 24 / 69
39. Derivation of model PDE model
Summary
HJB: ∂th+ω∂θ h =
1
2R
(∂θ h)2
− ¯c(θ,t) +η∗
−
σ2
2
∂2
θθ h ⇒ h(θ,t,ω)
FPK: ∂tp+ω∂θ p =
1
R
∂θ [p( ∂θ h )]+
σ2
2
∂2
θθ p ⇒ p(θ,t,ω)
Mean-field approx.: ¯c(ϑ,t) =
�
Ω
� 2π
0
c•
(ϑ,θ) p(θ,t,ω) g(ω)dθ dω
1 Bellman’s optimality principle (H,J,B)
2 Propagation of chaos (F,P,K, Mckean, Vlasov,. . . )
3 Mean-field approximation (Boltzmann, Kac,. . . )
4 Connection to Nash game (Weintraub, HCM, Altman,. . . )
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 24 / 69
40. Derivation of model PDE model
Summary
HJB: ∂th+ω∂θ h =
1
2R
(∂θ h)2
− ¯c(θ,t) +η∗
−
σ2
2
∂2
θθ h ⇒ h(θ,t,ω)
FPK: ∂tp+ω∂θ p =
1
R
∂θ [p( ∂θ h )]+
σ2
2
∂2
θθ p ⇒ p(θ,t,ω)
Mean-field approx.: ¯c(ϑ,t) =
�
Ω
� 2π
0
c•
(ϑ,θ) p(θ,t,ω) g(ω)dθ dω
1 Bellman’s optimality principle (H,J,B)
2 Propagation of chaos (F,P,K, Mckean, Vlasov,. . . )
3 Mean-field approximation (Boltzmann, Kac,. . . )
4 Connection to Nash game (Weintraub, HCM, Altman,. . . )
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 24 / 69
41. Derivation of model PDE model
Summary
HJB: ∂th+ω∂θ h =
1
2R
(∂θ h)2
− ¯c(θ,t) +η∗
−
σ2
2
∂2
θθ h ⇒ h(θ,t,ω)
FPK: ∂tp+ω∂θ p =
1
R
∂θ [p( ∂θ h )]+
σ2
2
∂2
θθ p ⇒ p(θ,t,ω)
Mean-field approx.: ¯c(ϑ,t) =
�
Ω
� 2π
0
c•
(ϑ,θ) p(θ,t,ω) g(ω)dθ dω
1 Bellman’s optimality principle (H,J,B)
2 Propagation of chaos (F,P,K, Mckean, Vlasov,. . . )
3 Mean-field approximation (Boltzmann, Kac,. . . )
4 Connection to Nash game (Weintraub, HCM, Altman,. . . )
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 24 / 69
42. Derivation of model PDE model
Summary
HJB: ∂th+ω∂θ h =
1
2R
(∂θ h)2
− ¯c(θ,t) +η∗
−
σ2
2
∂2
θθ h ⇒ h(θ,t,ω)
FPK: ∂tp+ω∂θ p =
1
R
∂θ [p( ∂θ h )]+
σ2
2
∂2
θθ p ⇒ p(θ,t,ω)
Mean-field approx.: ¯c(ϑ,t) =
�
Ω
� 2π
0
c•
(ϑ,θ) p(θ,t,ω) g(ω)dθ dω
1 Bellman’s optimality principle (H,J,B)
2 Propagation of chaos (F,P,K, Mckean, Vlasov,. . . )
3 Mean-field approximation (Boltzmann, Kac,. . . )
4 Connection to Nash game (Weintraub, HCM, Altman,. . . )
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 24 / 69
43. Derivation of model PDE model
1. Solution of PDE gives ε-Nash equilibrium
Optimal control law
uo
i = −
1
R
∂θ h(θ(t),t,ω)
�
�
ω=ωi
ε-Nash property (as N → ∞)
ηi(uo
i ;uo
−i) ≤ ηi(ui;uo
−i)+O(
1
√
N
), i = 1,...,N.
So, we look for solutions of PDEs.
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 25 / 69
44. Derivation of model PDE model
1. Solution of PDE gives ε-Nash equilibrium
Optimal control law
uo
i = −
1
R
∂θ h(θ(t),t,ω)
�
�
ω=ωi
ε-Nash property (as N → ∞)
ηi(uo
i ;uo
−i) ≤ ηi(ui;uo
−i)+O(
1
√
N
), i = 1,...,N.
So, we look for solutions of PDEs.
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 25 / 69
45. Derivation of model PDE model
1. Solution of PDE gives ε-Nash equilibrium
Optimal control law
uo
i = −
1
R
∂θ h(θ(t),t,ω)
�
�
ω=ωi
ε-Nash property (as N → ∞)
ηi(uo
i ;uo
−i) ≤ ηi(ui;uo
−i)+O(
1
√
N
), i = 1,...,N.
So, we look for solutions of PDEs.
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 25 / 69
46. Derivation of model PDE model
2. Incoherence solution (PDE)
Incoherence solution
h(θ,t,ω) = h0(θ) := 0 p(θ,t,ω) = p0(θ) :=
1
2π
incoherence
h(θ,t,ω) = 0 ⇒ ∂th+ω∂θ h =
1
2R
(∂θ h)2
− ¯c(θ,t)+η∗
−
σ2
2
∂2
θθ h
∂tp+ω∂θ p =
1
R
∂θ [p(∂θ h)]+
σ2
2
∂2
θθ p
¯c(θ,t) =
�
Ω
� 2π
0
c•
(θ,ϑ)p(ϑ,t,ω)g(ω)dϑ dω
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 26 / 69
47. Derivation of model PDE model
2. Incoherence solution (PDE)
Incoherence solution
h(θ,t,ω) = h0(θ) := 0 p(θ,t,ω) = p0(θ) :=
1
2π
incoherence
h(θ,t,ω) = 0 ⇒ ∂th+ω∂θ h =
1
2R
(∂θ h)2
− ¯c(θ,t)+η∗
−
σ2
2
∂2
θθ h
p(θ,t,ω) = 1
2π ⇒ ∂tp+ω∂θ p =
1
R
∂θ [p(∂θ h)]+
σ2
2
∂2
θθ p
¯c(θ,t) =
�
Ω
� 2π
0
c•
(θ,ϑ)p(ϑ,t,ω)g(ω)dϑ dω
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 26 / 69
48. Derivation of model PDE model
2. Incoherence solution (PDE)
Assume c•
(ϑ,θ) = c•
(ϑ −θ) = 1
2 sin2
�
ϑ −θ
2
�
Incoherence solution
h(θ,t,ω) = h0(θ) := 0 p(θ,t,ω) = p0(θ) :=
1
2π
Optimal control u = −
1
R
∂θ h = 0
Average cost
¯c(θ,t) =
�
Ω
� 2π
0
1
2 sin2
�
θ −ϑ
2
�
1
2π
g(ω)dϑ dω
η∗
(ω) = ¯c(θ,t) =
1
4
=: η0 for all ω ∈ Ω
incoherence soln.
No cost of control
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 27 / 69
49. Derivation of model PDE model
2. Incoherence solution (Finite population)
Closed-loop dynamics dθi = (ωi + ui
����
=0
)dt +σ dξi(t)
Average cost
ηi = lim
T→∞
1
T
� T
0
E[c(θi;θ−i)+ 1
2 Ru2
i
� �� �
=0
]dt
= lim
T→∞
1
N ∑
j�=i
1
T
� T
0
E[1
2 sin2
�
θi(t)−θj(t)
2
�
]dt
=
1
N ∑
j�=i
� 2π
0
E[1
2 sin2
�
θi(t)−ϑ
2
�
]
1
2π
dϑ =
N −1
N
η0
incoherence
ε-Nash property
ηi(uo
i ;uo
−i) ≤ ηi(ui;uo
−i)+O(
1
√
N
), i = 1,...,N.
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 28 / 69
50. Derivation of model PDE model
2. Incoherence solution (Finite population)
Closed-loop dynamics dθi = (ωi + ui
����
=0
)dt +σ dξi(t)
Average cost
ηi = lim
T→∞
1
T
� T
0
E[c(θi;θ−i)+ 1
2 Ru2
i
� �� �
=0
]dt
= lim
T→∞
1
N ∑
j�=i
1
T
� T
0
E[1
2 sin2
�
θi(t)−θj(t)
2
�
]dt
=
1
N ∑
j�=i
� 2π
0
E[1
2 sin2
�
θi(t)−ϑ
2
�
]
1
2π
dϑ =
N −1
N
η0
incoherence
ε-Nash property
ηi(uo
i ;uo
−i) ≤ ηi(ui;uo
−i)+O(
1
√
N
), i = 1,...,N.
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 28 / 69
51. Derivation of model PDE model
3. Synchronization is a solution of game
Locking
0 0.1 0.2
0.15
0.2
0.25
R−1/ 2
γ
Incoherence
R > Rc(γ)
Synchrony
Incoherence
R−1/ 2
η(ω)
0. 1 0.15 0. 2 0.25 0. 3 0.35
0. 1
0.15
0. 2
0.25
ω= 0.95
ω= 1
ω= 1.05
R > Rc
η(ω) = η0
R < R
c
η(ω) < η0
c
dθi = (ωi +ui)dt +σ dξi
ηi(ui;u−i) = lim
T→∞
1
T
� T
0
E[c(θi;θ−i)+ 1
2 Ru2
i ]ds η(ω) = min
ui
ηi(ui;uo
−i)
0 1 2 3 4 5 6
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
t = 38.24
Synchrony solution of
Yin et al., “Synchronization of oscillators is a game,” ACC2010
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 29 / 69
52. Derivation of model PDE model
3. Synchronization is a solution of game
Locking
0 0.1 0.2
0.15
0.2
0.25
R−1/ 2
γ
Incoherence
R > Rc(γ)
Synchrony
Incoherence
R−1/ 2
η(ω)
0. 1 0.15 0. 2 0.25 0. 3 0.35
0. 1
0.15
0. 2
0.25
ω= 0.95
ω= 1
ω= 1.05
R > Rc
η(ω) = η0
R < R
c
η(ω) < η0
c
incoherence soln.
dθi = (ωi +ui)dt +σ dξi
ηi(ui;u−i) = lim
T→∞
1
T
� T
0
E[c(θi;θ−i)+ 1
2 Ru2
i ]ds η(ω) = min
ui
ηi(ui;uo
−i)
0 1 2 3 4 5 6
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
t = 38.24
Synchrony solution of
Yin et al., “Synchronization of oscillators is a game,” ACC2010
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 29 / 69
53. Derivation of model PDE model
3. Synchronization is a solution of game
Locking
0 0.1 0.2
0.15
0.2
0.25
R−1/ 2
γ
Incoherence
R > Rc(γ)
Synchrony
Incoherence
R−1/ 2
η(ω)
0. 1 0.15 0. 2 0.25 0. 3 0.35
0. 1
0.15
0. 2
0.25
ω= 0.95
ω= 1
ω= 1.05
R > Rc
η(ω) = η0
R < R
c
η(ω) < η0
c
synchrony soln.
dθi = (ωi +ui)dt +σ dξi
ηi(ui;u−i) = lim
T→∞
1
T
� T
0
E[c(θi;θ−i)+ 1
2 Ru2
i ]ds η(ω) = min
ui
ηi(ui;uo
−i)
0 1 2 3 4 5 6
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
t = 38.24
Synchrony solution of
Yin et al., “Synchronization of oscillators is a game,” ACC2010
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 29 / 69
54. 1 Motivation
Why a game?
Why Oscillators?
2 Problems and results
Problem statement
Main results
3 Derivation of model
Overview
Derivation steps
PDE model
4 Analysis of phase transition
Incoherence solution
Bifurcation analysis
Numerics
5 Learning
Q-function approximation
Steepest descent algorithm
55. Analysis of phase transition Incoherence solution
Overview of the steps
HJB: ∂th+ω∂θ h =
1
2R
(∂θ h)2
− ¯c(θ,t) +η∗
−
σ2
2
∂2
θθ h ⇒ h(θ,t,ω)
FPK: ∂tp+ω∂θ p =
1
R
∂θ [p( ∂θ h )]+
σ2
2
∂2
θθ p ⇒ p(θ,t,ω)
¯c(ϑ,t) =
�
Ω
� 2π
0
c•
(ϑ,θ) p(θ,t,ω) g(ω)dθ dω
Assume c•
(ϑ,θ) = c•
(ϑ −θ) = 1
2 sin2
�
ϑ −θ
2
�
Incoherence solution
h(θ,t,ω) = h0(θ) := 0 p(θ,t,ω) = p0(θ) :=
1
2π
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 31 / 69
56. 1 Motivation
Why a game?
Why Oscillators?
2 Problems and results
Problem statement
Main results
3 Derivation of model
Overview
Derivation steps
PDE model
4 Analysis of phase transition
Incoherence solution
Bifurcation analysis
Numerics
5 Learning
Q-function approximation
Steepest descent algorithm
57. Analysis of phase transition Bifurcation analysis
Linearization and spectra
Linearized PDE (about incoherence solution)
∂
∂t
˜z(θ,t,ω) =
�
−ω∂θ
˜h− ¯c− σ2
2 ∂2
θθ
˜h
−ω∂θ ˜p+ 1
2πR ∂2
θθ
˜h+ σ2
2 ∂2
θθ ˜p
�
=: LR˜z(θ,t,ω)
Spectrum of the linear operator
1 Continuous spectrum {S(k)
}+∞
k=−∞
S(k)
:=�
λ ∈ C
�
�λ = ±
σ2
2
k2
−kωi for all ω ∈ Ω
�
2 Discrete spectrum
Characteristic eqn:
1
8R
�
Ω
g(ω)
(λ − σ2
2 +ωi)(λ + σ2
2 +ωi)
dω +1 = 0.
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 33 / 69
58. Analysis of phase transition Bifurcation analysis
Linearization and spectra
Linearized PDE (about incoherence solution)
∂
∂t
˜z(θ,t,ω) =
�
−ω∂θ
˜h− ¯c− σ2
2 ∂2
θθ
˜h
−ω∂θ ˜p+ 1
2πR ∂2
θθ
˜h+ σ2
2 ∂2
θθ ˜p
�
=: LR˜z(θ,t,ω)
Spectrum of the linear operator
1 Continuous spectrum {S(k)
}+∞
k=−∞
S(k)
:=�
λ ∈ C
�
�λ = ±
σ2
2
k2
−kωi for all ω ∈ Ω
�
2 Discrete spectrum
Characteristic eqn:
1
8R
�
Ω
g(ω)
(λ − σ2
2 +ωi)(λ + σ2
2 +ωi)
dω +1 = 0.
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 33 / 69
59. Analysis of phase transition Bifurcation analysis
Linearization and spectra
Linearized PDE (about incoherence solution)
∂
∂t
˜z(θ,t,ω) =
�
−ω∂θ
˜h− ¯c− σ2
2 ∂2
θθ
˜h
−ω∂θ ˜p+ 1
2πR ∂2
θθ
˜h+ σ2
2 ∂2
θθ ˜p
�
=: LR˜z(θ,t,ω)
Spectrum of the linear operator
1 Continuous spectrum {S(k)
}+∞
k=−∞
S(k)
:=�
λ ∈ C
�
�λ = ±
σ2
2
k2
−kωi for all ω ∈ Ω
�
−0.2 −0.1 0 0.1 0.2 0.3
−3
−2
−1
0
1
2
3
real
imag
γ = 0.1
R decreases
k=2 k=2
k=1 k=1
2 Discrete spectrum
Characteristic eqn:
1
8R
�
Ω
g(ω)
(λ − σ2
2 +ωi)(λ + σ2
2 +ωi)
dω +1 = 0.
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 33 / 69
60. Analysis of phase transition Bifurcation analysis
Linearization and spectra
Linearized PDE (about incoherence solution)
∂
∂t
˜z(θ,t,ω) =
�
−ω∂θ
˜h− ¯c− σ2
2 ∂2
θθ
˜h
−ω∂θ ˜p+ 1
2πR ∂2
θθ
˜h+ σ2
2 ∂2
θθ ˜p
�
=: LR˜z(θ,t,ω)
Spectrum of the linear operator
1 Continuous spectrum {S(k)
}+∞
k=−∞
S(k)
:=�
λ ∈ C
�
�λ = ±
σ2
2
k2
−kωi for all ω ∈ Ω
�
−0.2 −0.1 0 0.1 0.2 0.3
−3
−2
−1
0
1
2
3
real
imag
γ = 0.1
R decreases
k=2 k=2
k=1 k=1
2 Discrete spectrum
Characteristic eqn:
1
8R
�
Ω
g(ω)
(λ − σ2
2 +ωi)(λ + σ2
2 +ωi)
dω +1 = 0.
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 33 / 69
61. Analysis of phase transition Bifurcation analysis
Bifurcation diagram (Hamiltonian Hopf)
Characteristic eqn:
1
8R
�
Ω
g(ω)
(λ − σ2
2 +ωi)(λ + σ2
2 +ωi)
dω +1 = 0.
Stability proof
[3] Dellnitz et al., Int. Series Num. Math., 1992
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 34 / 69
62. Analysis of phase transition Bifurcation analysis
Bifurcation diagram (Hamiltonian Hopf)
Characteristic eqn:
1
8R
�
Ω
g(ω)
(λ − σ2
2 +ωi)(λ + σ2
2 +ωi)
dω +1 = 0.
Stability proof
−0.2 −0.1 0 0.1 0.2
-0.6
-0.8
-1
-1.2
-1.4
real
imag
(a)
Cont.spectrum;ind.ofR
Disc.spectrum;fn.ofR
Bifurcation point
[3] Dellnitz et al., Int. Series Num. Math., 1992
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 34 / 69
63. Analysis of phase transition Bifurcation analysis
Bifurcation diagram (Hamiltonian Hopf)
Characteristic eqn:
1
8R
�
Ω
g(ω)
(λ − σ2
2 +ωi)(λ + σ2
2 +ωi)
dω +1 = 0.
Stability proof
−0.2 −0.1 0 0.1 0.2
-0.6
-0.8
-1
-1.2
-1.4
real
imag
(a)
Cont.spectrum;ind.ofR
Disc.spectrum;fn.ofR
Bifurcation point
0 0.05 0.1 0.15 0.2
15
20
25
30
35
40
45
50
Incoherence
R > R
R
c(γ
γ
) (c)
Synchrony
0.05
[3] Dellnitz et al., Int. Series Num. Math., 1992
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 34 / 69
64. 1 Motivation
Why a game?
Why Oscillators?
2 Problems and results
Problem statement
Main results
3 Derivation of model
Overview
Derivation steps
PDE model
4 Analysis of phase transition
Incoherence solution
Bifurcation analysis
Numerics
5 Learning
Q-function approximation
Steepest descent algorithm
65. Analysis of phase transition Numerics
Numerical solution of PDEs
Incoherence; R = 60
incoherence
incoherence
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 36 / 69
66. Analysis of phase transition Numerics
Numerical solution of PDEs
Incoherence; R = 60
incoherence
incoherence
Synchrony; R = 10
synchrony
synchrony
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 36 / 69
67. Analysis of phase transition Numerics
Bifurcation diagram
Locking
0 0.1 0.2
0.15
0.2
0.25
R−1/ 2
γ
Incoherence
R > Rc(γ)
Synchrony
Incoherence R−1/2
η(ω)
0. 1 0.15 0. 2 0.25 0. 3 0.35
0. 1
0.15
0. 2
0.25
ω = 0.95
ω = 1
ω = 1.05
R > Rc
η(ω) = η0
R < Rc
η(ω) < η0
dθi = (ωi +ui)dt +σ dξi
ηi(ui;u−i) = lim
T→∞
1
T
� T
0
E[c(θi;θ−i)+ 1
2Ru2
i ]ds
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 37 / 69
68. Analysis of phase transition Numerics
Bifurcation diagram
Locking
0 0.1 0.2
0.15
0.2
0.25
R−1/ 2
γ
Incoherence
R > Rc(γ)
Synchrony
Incoherence R−1/2
η(ω)
0. 1 0.15 0. 2 0.25 0. 3 0.35
0. 1
0.15
0. 2
0.25
ω = 0.95
ω = 1
ω = 1.05
R > Rc
η(ω) = η0
R < Rc
η(ω) < η0
incoherence soln.
dθi = (ωi +ui)dt +σ dξi
ηi(ui;u−i) = lim
T→∞
1
T
� T
0
E[c(θi;θ−i)+ 1
2Ru2
i ]ds
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 37 / 69
69. Analysis of phase transition Numerics
Bifurcation diagram
Locking
0 0.1 0.2
0.15
0.2
0.25
R−1/ 2
γ
Incoherence
R > Rc(γ)
Synchrony
Incoherence R−1/2
η(ω)
0. 1 0.15 0. 2 0.25 0. 3 0.35
0. 1
0.15
0. 2
0.25
ω = 0.95
ω = 1
ω = 1.05
R > Rc
η(ω) = η0
R < Rc
η(ω) < η0
synchrony soln.
dθi = (ωi +ui)dt +σ dξi
ηi(ui;u−i) = lim
T→∞
1
T
� T
0
E[c(θi;θ−i)+ 1
2Ru2
i ]ds
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 37 / 69
70. 1 Motivation
Why a game?
Why Oscillators?
2 Problems and results
Problem statement
Main results
3 Derivation of model
Overview
Derivation steps
PDE model
4 Analysis of phase transition
Incoherence solution
Bifurcation analysis
Numerics
5 Learning
Q-function approximation
Steepest descent algorithm
71. Learning Q-function approximation
Comparison to Kuramoto law
Control law u = ϕ(θ,t,ω)
−0.2
0
0.2
0.4
0.6
ω = 0.95
ω = 1
ω = 1.05
Population
Density
Control laws
0 π 2π θ
Equivalent control law in Kuramoto oscillator
u
(Kur)
i =
κ
N
N
∑
j=1
sin(θj(t)−θi)
N→∞
≈ κ0 sin(ϑ0 +t −θi)
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 39 / 69
72. Learning Q-function approximation
Comparison to Kuramoto law
Control law u = ϕ(θ,t,ω)
−0.2
0
0.2
0.4
0.6
ω = 0.95
ω = 1
ω = 1.05
Kuramoto
Population
Density
Control laws
0 π 2π θ
Equivalent control law in Kuramoto oscillator
u
(Kur)
i =
κ
N
N
∑
j=1
sin(θj(t)−θi)
N→∞
≈ κ0 sin(ϑ0 +t −θi)
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 39 / 69
73. Learning Q-function approximation
Optimality equation min
ui
{c(θ;θ−i(t))+ 1
2 Ru2
i +Dui hi(θ,t)
� �� �
=: Hi(θ,ui;θ−i(t))
} = η∗
i
Optimal control law Kuramoto law
u∗
i = −
1
R
∂θ hi(θ,t) u
(Kur)
i = −
κ
N ∑
j�=i
sin(θi −θj(t))
Parameterization:
H
(Ai,φi)
i (θ,ui;θ−i(t)) = c(θ;θ−i(t))+ 1
2 Ru2
i +(ωi −1+ui)AiS(φi)
+
σ2
2
AiC(φi)
where
S(φ)
(θ,θ−i) =
1
N ∑
j�=i
sin(θ −θj −φ), C(φ)
(θ,θ−i) =
1
N ∑
j�=i
cos(θ −θj −φ)
Approx. optimal control:
u
(Ai,φi)
i = argmin
ui
{H
(Ai,φi)
i (θ,ui;θ−i(t))} = −
Ai
R
S(φi)
(θ,θ−i)
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 40 / 69
74. Learning Q-function approximation
Optimality equation min
ui
{c(θ;θ−i(t))+ 1
2 Ru2
i +Dui hi(θ,t)
� �� �
=: Hi(θ,ui;θ−i(t))
} = η∗
i
Optimal control law Kuramoto law
u∗
i = −
1
R
∂θ hi(θ,t) u
(Kur)
i = −
κ
N ∑
j�=i
sin(θi −θj(t))
Parameterization:
H
(Ai,φi)
i (θ,ui;θ−i(t)) = c(θ;θ−i(t))+ 1
2 Ru2
i +(ωi −1+ui)AiS(φi)
+
σ2
2
AiC(φi)
where
S(φ)
(θ,θ−i) =
1
N ∑
j�=i
sin(θ −θj −φ), C(φ)
(θ,θ−i) =
1
N ∑
j�=i
cos(θ −θj −φ)
Approx. optimal control:
u
(Ai,φi)
i = argmin
ui
{H
(Ai,φi)
i (θ,ui;θ−i(t))} = −
Ai
R
S(φi)
(θ,θ−i)
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 40 / 69
75. Learning Q-function approximation
Optimality equation min
ui
{c(θ;θ−i(t))+ 1
2 Ru2
i +Dui hi(θ,t)
� �� �
=: Hi(θ,ui;θ−i(t))
} = η∗
i
Optimal control law Kuramoto law
u∗
i = −
1
R
∂θ hi(θ,t) u
(Kur)
i = −
κ
N ∑
j�=i
sin(θi −θj(t))
Parameterization:
H
(Ai,φi)
i (θ,ui;θ−i(t)) = c(θ;θ−i(t))+ 1
2 Ru2
i +(ωi −1+ui)AiS(φi)
+
σ2
2
AiC(φi)
where
S(φ)
(θ,θ−i) =
1
N ∑
j�=i
sin(θ −θj −φ), C(φ)
(θ,θ−i) =
1
N ∑
j�=i
cos(θ −θj −φ)
Approx. optimal control:
u
(Ai,φi)
i = argmin
ui
{H
(Ai,φi)
i (θ,ui;θ−i(t))} = −
Ai
R
S(φi)
(θ,θ−i)
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 40 / 69
76. Learning Q-function approximation
Optimality equation min
ui
{c(θ;θ−i(t))+ 1
2 Ru2
i +Dui hi(θ,t)
� �� �
=: Hi(θ,ui;θ−i(t))
} = η∗
i
Optimal control law Kuramoto law
u∗
i = −
1
R
∂θ hi(θ,t) u
(Kur)
i = −
κ
N ∑
j�=i
sin(θi −θj(t))
Parameterization:
H
(Ai,φi)
i (θ,ui;θ−i(t)) = c(θ;θ−i(t))+ 1
2 Ru2
i +(ωi −1+ui)AiS(φi)
+
σ2
2
AiC(φi)
where
S(φ)
(θ,θ−i) =
1
N ∑
j�=i
sin(θ −θj −φ), C(φ)
(θ,θ−i) =
1
N ∑
j�=i
cos(θ −θj −φ)
Approx. optimal control:
u
(Ai,φi)
i = argmin
ui
{H
(Ai,φi)
i (θ,ui;θ−i(t))} = −
Ai
R
S(φi)
(θ,θ−i)
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 40 / 69
77. 1 Motivation
Why a game?
Why Oscillators?
2 Problems and results
Problem statement
Main results
3 Derivation of model
Overview
Derivation steps
PDE model
4 Analysis of phase transition
Incoherence solution
Bifurcation analysis
Numerics
5 Learning
Q-function approximation
Steepest descent algorithm
78. Learning Steepest descent algorithm
Bellman error:
Pointwise: L (Ai,φi)
(θ,t) = min
ui
{H
(Ai,φi)
i }−η
(A∗
i ,φ∗
i )
i
Simple gradient descent algorithm
˜e(Ai,φi) =
2
∑
k=1
|�L (Ai,φi)
, ˜ϕk(θ)�|2
dAi
dt
= −ε
d˜e(Ai,φi)
dAi
,
dφi
dt
= −ε
d˜e(Ai,φi)
dφi
(∗)
Theorem (Convergence)
Assume population is in synchrony. The ith
oscillator updates
according to (∗). Then
Ai(t) → A∗
=
1
2σ2
The pointwise Bellman error L (Ai,0)
(θ,t) = ε(R)cos2(θ −t)
where ε(R) =
1
16Rσ4
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 42 / 69
79. Learning Steepest descent algorithm
Phase transition
Suppose all oscillators use approx. optimal control law:
ui = −
A∗
R
1
N ∑
j�=i
sin(θi −θj(t))
then the phase transition boundary is
Rc(γ) =
� 1
2σ4 if γ = 0
1
4σ2γ
tan−1
�
2γ
σ2
�
if γ > 0
0 50 100 150 200 250 300
2
2.5
3
3.5
4
4.5
5
5.5
6
t
k = 0.01; R = 1000
A
i
A
*
0 0.05 0.1 0.15 0.2
15
20
25
30
35
40
45
50
γ
R
PDE
Learning
Incoherence
Synchrony
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 43 / 69
81. Bibliography
Dimitri P. Bertsekas.
Dynamic Programming and Optimal Control, volume 1.
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Eric Brown, Jeff Moehlis, and Philip Holmes.
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oscillator populations.
Neural Computation, 16(4):673–715, 2004.
M. Dellnitz, J.E. Marsden, I. Melbourne, and J. Scheurle.
Generic bifurcations of pendula.
Int. Series Num. Math., 104:111–122, 1992.
J. Guckenheimer.
Isochrons and phaseless sets.
J. Math. Biol., 1:259–273, 1975.
Minyi Huang, Peter E. Caines, and Roland P. Malhame.
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 69 / 69
82. Bibliography
Large-population cost-coupled LQG problems with nonuniform
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equilibria.
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Y. Kuramoto.
International Symposium on Mathematical Problems in Theoretical
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Andrzej Lasota and Michael C. Mackey.
Chaos, Fractals and Noise.
Springer, 1994.
P. Mehta and S. Meyn.
Q-learning and Pontryagin’s Minimum Principle.
To appear, 48th IEEE Conference on Decision and Control,
December 16-18 2009.
Sean P. Meyn.
P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 69 / 69
83. Bibliography
The policy iteration algorithm for average reward markov decision
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P. G. Mehta (UIUC) Univ. of Maryland Mar. 4, 2010 69 / 69