Don’t dope – run the code!

By Andrey Krasovskii, IIASA Ecosystems Services and Management Program

During his workout in the IIASA gym, my colleague Pekka Lauri often runs on a treadmill. He adjusts the velocity of running using the control panel, and it indicates the distance and approximate calories burnt. While Pekka may not be thinking about mathematical models during his workout break, running and other athletic performance can be modeled using some of the same techniques that we use for other questions at IIASA.

Outside of my academic work at IIASA, I am highly interested in sports, and athletics in particular. My wife, Katy Kuntsevich, has won Austrian championships in high jump several times, and my brother, Nikolay, was on his university team as a 400 meter runner.

Visiting my hometown Ekaterinburg, Russia, back in 2014, I got into a dispute with my father, who is a university professor in theoretical mechanics. Namely, my argument was that the sprinters’ acceleration at the finish line of the 100 meter race should be negative. Later on, during the Christmas holidays, I decided to mathematically support my statement.

Left: Prof. Krasovskii with a page from his Lectures in Theoretical Mechanics (Chapter 2, Kinematics, in Russian) featuring Valeriy Borzov on finish. This page was a reason for my study. Top right: Portrait of Isaac Newton.

Left: Prof. Krasovskii with a page from his Lectures in Theoretical Mechanics (Chapter 2, Kinematics, in Russian) featuring Valeriy Borzov on finish. This page was a reason for my study. Top right: Portrait of Isaac Newton.

When athletes run a race, their horizontal velocity can be estimated by modern technologies such as high resolution cameras and lasers. Knowing the horizontal instantaneous velocity, one can calculate acceleration. According to Newton’s law, one can introduce the forces applied to the body center mass of the athlete. Outside a gym treadmill it gets a bit more complicated, with air resistance and headwind or tail-wind. Against these aerodynamic forces the runner applies horizontal force, which drags him forward. In reality it is an “aggregate” of impulsive normal forces generated by feet and the stroke frequency.

The dynamics of an athlete have been described by an ordinary differential equation studied in papers on sprint modeling, first published by physicist J. Keller. This equation has been considered in numerous papers, which have shown that the equation fits the real data for short-distance running (100 and 200 meters). The indicated studies are devoted to the calibration of parameters, to the wind impact analysis, and to the choice of force functions such that the solution satisfies the actual motions, e.g. the running records of Usain Bolt.

The problem of running dynamics reminded me of a type of model that we sometimes use at IIASA, called an optimal control model. Optimal control models are used to calculate the best or most efficient way of doing something, for example, driving from one place to another. If I want to drive from Vienna to Laxenburg, I start my car’s engine at my house (point A). I look at the time, and plan to arrive to IIASA (point B) in 30 minutes. In optimal control terms, the car is a control object, and the driver controls it by pushing the gas/brake pedals and steering the wheel. In the driving process the car meets certain constraints (e.g. the engine power, available roads, and speed limits) and disturbances (e.g. traffic jams, lights, and weather conditions). Obviously, there are many ways of controlling the car in order to reach IIASA in 30 minutes. What if among those admissible controls, I wanted to find an optimal control minimizing car’s energy expenditures during the 30-minute trip from A to B? Here energy is an intensity (cost) of control actions, i.e. fuel (petrol/electricity), or corresponding greenhouse gas emissions. Well, in this case one needs to solve the classical minimum energy control problem. The solution to this problem gives an optimal plan that the car driver (or autopilot) needs to implement. Note, that the corresponding time-optimal control problem consists in finding the fastest driving time to IIASA under given fuel reserve. Optimal control theory (OCT) is an efficient tool for solving such dynamic optimization problems.

My research question was: “Can one control his/her running similar to driving a car?” The answer is: “Yes!”

I applied an optimal control model to Usain Bolt’s performance data at the Beijing Olympic Games in 2008, when he ran the 100 meter sprint in 9.69 seconds. According to the model, under the same conditions he could have distributed his energy optimally and run the distance in 9.56 s. It is worth mentioning that this time is close to his current world record, 9.58 s, achieved at the 2009 World Championships in Berlin. In the paper I also provide modeling results for optimal (energy-efficient) running over 100 m: calculation of the minimum energy and trajectories of acceleration, velocity, and distance from start.

In the conclusion, I argue that applying advanced science in the athletic training programs is far better than doping–better in terms of a healthy body, mind and soul.

Here is my hypothesis of what Usain Bolt is doing at his laptop. © Weltklasse Zürich - Marcel Giger

Here is my hypothesis of what Usain Bolt is doing at his laptop. © Weltklasse Zürich – Marcel Giger

Reference:
A. A. Krasovskii, “Application of optimal control to a biomechanics model”, Proceedings of the Steklov Institute of Mathematics, 2015, Vol. 291, pp. 118–126. http://dx.doi.org/10.1134/S0081543815080118

 

I would like to thank Sergey Aseev, Katherine Leitzell, as well as my colleagues in the IIASA Ecosystem Services and Management Program (ESM) for their interest and valuable discussions.

 

Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.

This summer in Moscow: Impressions from Moscow Summer Academy 2015

By M. Nazli Koseoglu, MSA 2015 Participant, School of Geosciences, Environmental Economics, Edinburgh University, Scotland

M. Nazli Koseoglu

M. Nazli Koseoglu

The Summer Academy on Economic Growth and Governance of Natural Resources took place at Lomonosov Moscow State University from 20th July to 1st August 2015.

As an environmental economist working on economic valuation and optimisation of water use, the academy was very interesting for me. Water management is a dynamic process and requires bringing perspectives and expertise from different disciplines together. Application of systems analysis enables us to combine aspects from various domains, come up with models that identify nonlinearities, project regime shifts, and tipping points in the management of water as well as other natural resources. Such projects require interdisciplinary collaboration and communicable results to inform policy. Scientists need to translate their results to a language accessible to the policymakers, in order for society to pick up on and capitalize on the research efforts. The MSA 2015 provided me with necessary training to go deeper into different modelling methodologies, and learn the concepts and principles of science for policy first-hand from IIASA scientists.

The reading list sent before the course gave me the impression that I would probably be the only environmental economist amongst a crowd of mathematical modellers. However, arriving in Moscow, I found that the MSA 2015 participants came from a broad range of backgrounds and countries at different stages of their careers in academia or policy. We all came  to learn and discuss the natural resource constraints to infinite economic growth on finite planet.

During lectures, the theoretical foundations of different mathematical approaches such as dynamical systems theory, optimal control theory and game theory were presented by leading scientists, such as Michael Ghil. Fundamentals of addressing challenges of natural resource management and comparing contemporary models of economic growth were also covered as central themes.

The course acknowledged the issues related with ecosystems services, public goods, inter-generational and international fairness, and public and common pool resource dynamics in the face of economic growth and resource constraints. The training underlined feedbacks between institutional dynamics and resource dynamics in complex social-ecological system and need for interdisciplinary and policy-relevant research, an important take-home message for next generation scientists.

Photos by M. Nazli Koseoglu

Photos by M. Nazli Koseoglu

What makes the MSA so special?
Apart from lectures, we had tutorials, a group project, poster and project presentation sessions, as well as interesting talks on IIASA activities by Margaret Goud-Collins and Elena Rovenskaya, and an inspiring session on the importance of finding the right mentor for a successful career by Prof Nøstbakken. The  MSA 2015 program had a good balance of theory and practice, which encouraged participants to be proactive and engaged.

I particularly liked the poster session. We presented our ongoing projects and received feedback from the lecturers and other participants. It was great to get comments and perspectives that I never thought of, and tips from senior researchers. In the late days of the academy we were assigned to prepare a group project on Artic systems which allowed us to put what we had learned at the lectures into practice and apply important topics outside our exact fields of study; in my case, these topics were petroleum economics and artic futures. I found the multi-disciplinary group work to be a great exercise for the development of my current study.

Attending the MSA 2015 provided useful training, both theoretical and practical, for understanding systems analysis approaches better. The host institution and organizing committee at Lomonosov Moscow State Univesrity provided impeccable hospitality, and the setting, in a landmark building in a landmark city, was a great perk. I received very constructive feedback, and made good connections around the world. I would recommend all early-career researchers in relevant fields to take this great opportunity next summer!

More information about MSA 2015

Participants in the MSA 2015. Photo Credit: M. Nazli Koseoglu

Participants in the MSA 2015. Photo Credit: M. Nazli Koseoglu

Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied

Systems Analysis.

 

10 steps to removing carbon from the global economy

By Nebojsa Nakicenovic, Deputy Director General, International Institute for Applied Systems Analysis (IIASA), Austria (Originally published on the World Economic Forum Agenda Blog.)

Nebojsa Nakicenovic

Nebojsa Nakicenovic

Goal 7 of the Sustainable Development Goals is ambitious: Ensure access to affordable, reliable, sustainable and modern energy for all. This must be accomplished without compromising Goal 13: climate. This is achievable.

In spite of ups-and-downs and outright shocks in the global economy, some quite recent, the economic success stories of the industrialized countries are role models for the countries that are still developing. This puts the entire global community in the dichotomous position of needing to fire up the engine of growth, without producing the greenhouse gases it has been emitting since the beginning of the Industrial Revolution. What is the answer?

Very few questions in the complex area of energy and climate change can have a simplistic answer, but I am going to attempt one here: decarbonization, namely, drastic reduction of carbon dioxide and other greenhouse gas emissions per unit of economic activity.

Back in 1993, I wrote this:

“The possibility of less carbon-intensive and even carbon-free energy as major sources of energy during the next century is consistent with the long-term dynamic transformation and structural change of the energy system.”

My view in 2015 is the same; however, the scientific community 22 years later has a much better understanding of “the decarbonization challenge” and how it can be addressed. I will sketch out a 10-step approach to the removal of carbon from the global economy, but first I’d like to paint in a bit of the background.

Carbon dioxide is the main greenhouse gas and contributor to climate change. The largest source is our use of fossil fuels to drive development. Carbon dioxide emissions have increased exponentially since 1850 at about 2% per year, while decarbonization of the global economy is only around 0.3% per year.

The 2012 Global Energy Assessment, in which IIASA played a leading role, puts the current decarbonization rate at approximately six times too low to offset the increase in global energy use of about 2% per year. To meet the goal of the 2009 climate agreement (the Copenhagen Accord), namely, “the scientific view that the increase in global temperature should be below 2 degrees Celsius” to prevent dangerous anthropogenic interference with the climate system, global net emissions of carbon dioxide and other greenhouse gases will need to approach zero by the second half of this century, implying deep, deep decarbonization rates.

working oil pumps © Kokhanchikov | Dollar Photo Club

“Carbon dioxide is the main greenhouse gas and contributor to climate change. The largest source is our use of fossil fuels to drive development.” © Kokhanchikov | Dollar Photo Club

But we need deep decarbonization while energy needs are increasing to meet the demand of the developing world, including the three billion without access today to sustainable energy. All scenarios in the academic literature that lead to further economic development in the world, universal access to sustainable energy, and the stabilization of climate change to less than 2 degrees Celsius, anticipate deep and urgent decarbonization. Here’s my 10-point plan for doing that.

  1. Change attitudes
    Attitudes to energy use are based on many factors, from cultural norms to overall infrastructure design. We need much greater political will to affect a change in attitudes: it is critical that policy interventions should communicate to citizens the ethical notion of improved well-being and health now and for future generations of a zero-carbon economy. .
  1. Transform governance
    The transformation needed this century is more fundamental than previous transformations, like the replacement of coal by oil, because of the significantly shorter time needed to achieve it. Thus, government policies are essential, and are needed particularly in changing buildings codes, fuel efficiency standards for transportation, mandates for the introduction of renewables, and carbon pricing.
  1. Improve energy efficiency
    More efficient provision of energy services, or doing more with less, and radical improvements in energy efficiency, especially in end use, will reduce the amount of primary energy required and represents a cost-effective, near-term option for reducing carbon dioxide emissions, as well as having multiple benefits in different areas of life.
  1. Ramp up renewable use
    We can show that the share of renewable non-fossil energy from solar, wind, rain, tides, waves, and geothermal sources in global primary energy could increase from the current 17% to between 30% and 75%. In some regions it could exceed 90% by 2050, provided that public attitudes change and efficiency increases.
  1. Reduce global energy intensity
    The energy intensity in the industrial sector in different countries is steadily declining due to improvements in energy efficiency and a change in the structure of the industrial output. Far greater reductions are feasible by combining these improvements with adoption of the best-achievable technology.
  1. Use known technologies
    Carbon dioxide capture and storage (CCS), now being piloted, is a pathway that leads to decarbonization with continued use of fossil energy. It requires: reducing costs, supporting scale-up, assuring carbon storage integrity and environmental capability, and securing approval of storage sites. Nuclear energy could make a significant contribution in some parts of the world, or it could be phased out as, for instance, in Germany.
  1. Improve buildings
    Retrofitting buildings can reduce heating and cooling energy requirements by 50–90%; new buildings can be designed and built using close to zero energy for heating and cooling. Passive energy houses and those that produce energy onsite are another great opportunity to achieve vigorous decarbonization. In conjunction with compatible lifestyles oriented toward rational energy use, efficient buildings are an important decarbonization option.
  1. Cut transport carbon
    A major transformation of transportation is possible over the next 30–40 years and will require improving vehicle designs, infrastructure, fuels and behavior. Electrically powered transportation reduces final energy use by more than a factor of three over gasoline-powered vehicles. A shift toward collective mobility is an essential option. This also implies behavioral changes and new business models like car-sharing, and self-driving cars to replace individual mobility.
  1. Clean industrial processes
    Overall, global industry efficiency is only 30%. Improved energy efficiency in industry results in significant energy productivity gains and, in turn, improved productivity boosts employment and corporate competitiveness. A shift toward low to zero emission energy sources in industry is another important and much-needed change. For example, with an aggressive renewables strategy, near-zero growth in GHG emissions in the industrial sector would be possible. Finally, decarbonization would also involve changes of industrial processes, for example, from high to low temperatures.
  1. Stranded assets and ‘derisking’ renewables.
    The flow of investment needs to be changed away from fossil fuels and toward efficiency, renewables, decarbonization of fossil energy sources, and especially efficient end-use in buildings, transport, and industry. Sustainable energy futures require relatively high up-front investments with the benefit of low long-term costs. They are attractive in the long run, but the up-front investments need derisking and other forms of support, such as feed-in tariffs.

Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.

Network science and marketing: A virus’ tale

By Matthias Wildemeersch,  IIASA Advanced Systems Analysis and Ecosystems Services and Management Programs

FotoQuest Austria is a citizen science campaign initiated by the IIASA Ecosystems Services & Management Program that aims to involve the general public in mapping land use in Austria. Understanding the evolution of urban sprawl is important to estimate the risk of flooding, while the preservation of wetlands has important implications for climate change.

But how can we engage people in environmental monitoring, in particular when they are growing increasingly resistant to traditional forms of advertising? Viral marketing makes use of social networks to spread messages, and takes advantage of the trust that we have in the recommendation coming from a friend rather than from a stranger or a company.

Network science and the formal description of spreading phenomena can shed light on the propagation of messages through communities and can be applied to inform and design viral marketing campaigns.

Viral spreading © kittitee550 | Dollar Photo Club

Viral spreading © kittitee550 | Dollar Photo Club

Network science is a multi-disciplinary field of research that draws on graph theory, statistical mechanics, inference, and other theories to study the behavior of agents in various networks. The spreading phenomena in viral marketing show similarities with well-studied spreading processes over biological, social, physical, and financial networks. For instance, we can think about epidemics,which are well understood and allow for the design of optimal strategies to contain viruses. Another example is opinion dynamics, which received renewed research attention over the last years in the context of social media.  In contrast to diseases or computer viruses, which we aim to contain and stop, the goal of viral marketing is to spread widely, reaching the largest possible fraction of a community.

What makes viral marketing unique?
But some aspects of viral marketing are very different from what we see in other spreading phenomena. First of all, there are many platforms that can be used to spread information at the same time, and the interaction between these platforms is not always transparent. Human psychology is a crucial factor in social networks, as repeated interaction and saturation can decrease the willingness to further spread viral content. Marketing campaigns have a limited budget, and therefore it is meaningful to understand how we can use incentives and how efficient they are. This also means that it is essential to find the group of most influential people that can be used as seeds for the viral campaign.

Network science has addressed to a great extent all these individual questions, mostly under the assumption of full knowledge of the connections between the agents and their influence. Currently, so-called multiplexes are an active research field that studies the behavior of multi-layer networks. This research unveils the relationships between the dynamics of viral marketing, the connection pattern, and strength between the network layers. Although viral spreading may be unachievable in a single layer, for example a social network like Facebook, the critical threshold may be exceeded by joining different platforms. Within a given platform, people alike can be clustered using community detection algorithms. Once the communities are identified, influence maximization algorithms have been established to select these persons that maximize the spread of viral content. Although this discrete optimization problem is computationally difficult—or NP-hard—mathematicians have proposed algorithms that can efficiently predict who to target to give a campaign the best chance of going viral. On top of that, optimal pricing strategies have been developed to reward recommenders.

The FotoQuest Austria app aims to engage citizen scientists in their campaign - network theory may help them go "viral." © IIASA

The FotoQuest Austria app aims to engage citizen scientists in their campaign – network theory may help them go “viral.” © IIASA

Although the literature is extensive, the nature of the results is often theoretical and involves mathematically complex models and algorithms. Considering that only partial information on the network is usually available, it is not straightforward to bring this knowledge back to a practical marketing campaign. So researchers in this field are trying to bridge the gap between theoretical results and practical problems. The generic, powerful methods of network science are sufficiently versatile to capture the specifics of real-world applications. As such, network science can provide guidelines that can bring great value for the design of heuristic methods in marketing strategies.

Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.

What do our models really represent?

By Dan Jessie, IIASA Research Scholar, Advanced Systems Analysis Program

As policymakers turn to the scientific community to inform their decisions on topics such as climate change, public health, and energy policy, scientists and mathematicians face the challenge of providing reliable information regarding trade-offs and outcomes for various courses of action. To generate this information, scientists use a variety of complex models and methods. However, how can we know whether the output of these models is valid?

This question was the focus of a recent conference I attended, arranged by IIASA Council Chair Donald G. Saari and the Institute for Mathematical Behavioral Sciences at the University of California, Irvine. The conference featured a number of talks by leading mathematicians and scientists who research complex systems, including Carl Simon, the founding Director of the University of Michigan’s Center for the Study of Complex Systems, and Simon Levin, Director of the Center for BioComplexity at Princeton University. All talks focused on answering the question, “Validation. What is it?”

To get a feel for how difficult this topic is, consider that during the lunch discussions,  each speaker professed to know less than everybody else! In spite of this self-claimed ignorance, each talk presented challenging new ideas regarding both specifics of how validation can be carried out for a given model, as well as formulations of general guidelines for what is necessary for validation.

How closely does a model need to mirror reality? © Mopic | Dreamstime.com - Binary Background Photo

How closely does a model need to mirror reality? © Mopic | Dreamstime.com – Binary Background Photo

For example, one talk discussed the necessity of understanding the connecting information between the pieces of a system. While it may seem obvious that, to understand a system built from many different components, one needs to understand both the pieces and how the pieces fit together, this talk contained a surprising twist: oftentimes, the methodology we use to model a problem unknowingly ignores this connecting information. By using examples from a variety of fields, such as social choice, nanotechnology, and astrophysics, the speaker showed how many current research problems can be understood in this light. This talk presented a big challenge to the research community to develop the appropriate tools for building valid models of complex systems.

Overall, the atmosphere of the conference was one of debate, and it seemed that no two speakers agreed completely on what validation required, or even meant. Some recurring questions in the arguments were how closely does a model need to mirror reality, and how do we assess predictions given that every model fails in some predictions? What role do funding agencies and peer review play in validation? The arguments generated by the talks weren’t limited to the conference schedule, either, and carried into the dinners and beyond.

I left the conference with a sense of excitement at seeing so many new ideas that challenge the current methods and models. This is still a new and growing topic, but one where advances will have wide-ranging impacts in terms of how we approach and answer scientific questions.

IIASA Council Chair Don Saari: Validation: What is it?

Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.