هل الكون لعبة؟

قارنت أجيال من العلماء الكون بلعبة عملاقة ومعقدة، مما يثير تساؤلات حول من يلعبها – وماذا يعني الفوز فيها.

إذا كان الكون لعبة، فمن الذي يلعبها؟

فيما يلي مقتطف من نشرتنا الإخبارية “ضائع في الزمكان”. في كل شهر، نسلم لوحة المفاتيح إلى فيزيائي أو عالم رياضيات ليخبرك عن أفكار مثيرة من زاويته في الكون. يمكنك الاشتراك في “ضائع في الزمكان” مجانًا من هنا.

هل الكون لعبة؟ بالتأكيد اعتقد الفيزيائي الشهير ريتشارد فاينمان ذلك: “العالم أشبه بلعبة شطرنج عظيمة تلعبها الآلهة، ونحن مراقبون للعبة.” وبينما نراقب، مهمتنا كعلماء هي محاولة فهم قواعد اللعبة.

كما نظر عالم الرياضيات في القرن السابع عشر جوتفريد فيلهلم لايبنتز إلى الكون كلعبة، وحتى أنه مول تأسيس أكاديمية في برلين مخصصة لدراسة الألعاب: ”أنا أؤيد بشدة دراسة ألعاب المنطق ليس لذاتها ولكن لأنها تساعدنا على إتقان فن التفكير.“

يحب جنسنا البشري لعب الألعاب، ليس فقط كأطفال بل حتى في مرحلة البلوغ. يُعتقد أنها كانت جزءًا مهمًا من التطور التكاملي – لدرجة أن المنظر الثقافي يوهان هويزينغا اقترح أن نُسمى Homo ludens، أي الإنسان اللاعب، بدلاً من Homo sapiens. اقترح البعض أنه بمجرد أن أدركنا أن الكون محكوم بقواعد، بدأنا في تطوير الألعاب كوسيلة للتجربة مع نتائج هذه القواعد.

خذ، على سبيل المثال، واحدة من أولى ألعاب اللوحة التي ابتكرناها. تعود اللعبة الملكية لأور إلى حوالي 2500 قبل الميلاد وتم العثور عليها في مدينة أور السومرية، جزء من بلاد ما بين النهرين. تُستخدم النرد رباعي الأوجه لتسابق خمس قطع تنتمي لكل لاعب عبر تسلسل مشترك من 12 مربعًا. أحد تفسيرات اللعبة هو أن المربعات الـ 12 تمثل الأبراج الـ 12 التي تشكل خلفية ثابتة للسماء الليلية والقطع الخمس تتوافق مع الكواكب الخمسة المرئية التي لاحظها القدماء وهي تتحرك عبر السماء الليلية.

لكن هل يمكن اعتبار الكون نفسه لعبة؟ كان تحديد ما يشكل لعبة بالفعل موضوع نقاش حاد. اعتقد المنطقي لودفيغ فيتغنشتاين أن الكلمات لا يمكن تحديدها بتعريف قاموسي وأنها تكتسب معناها فقط من خلال الطريقة التي تُستخدم بها، في عملية أطلق عليها ”لعبة اللغة“. مثال على كلمة اعتقد أنها تكتسب معناها من خلال الاستخدام وليس التعريف هي كلمة ”لعبة“. في كل مرة تحاول فيها تعريف كلمة ”لعبة“، ينتهي بك الأمر بتضمين بعض الأشياء التي ليست ألعابًا واستبعاد أخرى كنت تقصد تضمينها.

كان الفلاسفة الآخرون أقل استسلامًا وحاولوا تحديد الصفات التي تعرّف اللعبة. يتفق الجميع، بما في ذلك فيتغنشتاين، على أن أحد الجوانب المشتركة لجميع الألعاب هو أنها محددة بقواعد. هذه القواعد تتحكم فيما يمكنك أو لا يمكنك فعله في اللعبة. لهذا السبب، بمجرد أن فهمنا أن الكون محكوم بقواعد تحدد تطوره، ترسخت فكرة الكون كلعبة.

في كتابه الإنسان واللعب والألعاب، اقترح المنظر روجر كايوا خمس سمات رئيسية أخرى تحدد اللعبة: عدم اليقين، عدم الإنتاجية، الانفصال، الخيال والحرية. فكيف يتطابق الكون مع هذه الخصائص الأخرى؟

دور عدم اليقين مثير للاهتمام. نحن ندخل لعبة لأن هناك فرصة لأي من الجانبين للفوز – إذا كنا نعرف مسبقًا كيف ستنتهي اللعبة، فإنها تفقد كل قوتها. لهذا السبب فإن ضمان استمرار عدم اليقين لأطول فترة ممكنة هو عنصر أساسي في تصميم الألعاب.

صرح العالم الموسوعي بيير-سيمون لابلاس بشكل مشهور أن تحديد إسحاق نيوتن لقوانين الحركة قد أزال كل عدم يقين من لعبة الكون: ”يمكننا اعتبار الحالة الحالية للكون نتيجة لماضيه وسبب مستقبله. ذكاء يعرف في لحظة معينة جميع القوى التي تحرك الطبيعة، وجميع مواقع جميع العناصر التي تتكون منها الطبيعة، إذا كان هذا الذكاء واسعًا بما يكفي لإخضاع هذه البيانات للتحليل، فإنه سيحتضن في صيغة واحدة حركات أعظم أجسام الكون وتلك الخاصة بأصغر ذرة؛ بالنسبة لمثل هذا الذكاء لن يكون هناك شيء غير مؤكد والمستقبل مثل الماضي يمكن أن يكون حاضرًا أمام عينيه.“

تعاني الألعاب المحلولة من نفس المصير. لعبة Connect 4 هي لعبة محلولة بمعنى أننا نعرف الآن خوارزمية ستضمن دائمًا فوز اللاعب الأول. مع اللعب المثالي، لا يوجد عدم يقين. لهذا السبب تعاني أحيانًا ألعاب الاستراتيجية البحتة – إذا كان أحد اللاعبين أفضل بكثير من خصمه، فهناك القليل من عدم اليقين في النتيجة. دونالد ترامب ضد غاري كاسباروف في لعبة شطرنج لن تكون لعبة مثيرة للاهتمام.

ومع ذلك، فإن اكتشافات القرن العشرين أعادت إدخال فكرة عدم اليقين إلى قواعد الكون. تؤكد الفيزياء الكمية أن نتيجة التجربة ليست محددة مسبقًا بحالتها الحالية. قد تتجه القطع في اللعبة في اتجاهات متعددة مختلفة وفقًا لانهيار دالة الموجة. على عكس ما اعتقده ألبرت أينشتاين، يبدو أن الله يلعب لعبة بالنرد.

حتى لو كانت اللعبة حتمية، فإن رياضيات نظرية الفوضى تشير أيضًا إلى أن اللاعبين والمراقبين لن يتمكنوا من معرفة الحالة الحالية للعبة بالتفصيل الكامل وأن الاختلافات الصغيرة في الحالة الحالية يمكن أن تؤدي إلى نتائج مختلفة جدًا.

أن تكون اللعبة غير منتجة هي صفة مثيرة للاهتمام. إذا لعبنا لعبة من أجل المال أو لتعليمنا شيئًا ما، اعتقد كايوا أن اللعبة أصبحت عملاً: اللعبة هي ”مناسبة للإهدار المحض: إهدار الوقت والطاقة والبراعة والمهارة“. لسوء الحظ، ما لم تؤمن بقوة أعلى، تشير كل الأدلة إلى عدم وجود غرض نهائي للكون. الكون ليس موجودًا لسبب ما. إنه موجود فقط.

الصفات الثلاث الأخرى التي يحددها كايوا ربما تنطبق بشكل أقل على الكون ولكنها تصف اللعبة كشيء متميز عن الكون، رغم أنها تسير بالتوازي معه. اللعبة منفصلة – تعمل خارج الزمان والمكان العاديين. للعبة مساحتها الخاصة المحددة التي تُلعب فيها ضمن حد زمني محدد. لها بدايتها الخاصة ونهايتها الخاصة. اللعبة هي استراحة من كوننا. إنها هروب إلى كون موازٍ.

حقيقة أن اللعبة يجب أن يكون لها نهاية مثيرة للاهتمام أيضًا. هناك مفهوم اللعبة اللانهائية التي قدمها الفيلسوف جيمس ب. كارس في كتابه الألعاب المحدودة واللانهائية. أنت لا تهدف للفوز في لعبة لانهائية. الفوز ينهي اللعبة وبالتالي يجعلها محدودة. بدلاً من ذلك، مهمة لاعب اللعبة اللانهائية هي استمرار اللعبة – التأكد من أنها لا تنتهي أبدًا. يختتم كارس كتابه بالعبارة الغامضة إلى حد ما، ”لا توجد سوى لعبة لانهائية واحدة.“ يدرك المرء أنه يشير إلى حقيقة أننا جميعًا لاعبون في اللعبة اللانهائية التي تجري من حولنا، اللعبة اللانهائية التي هي الكون. على الرغم من أن الفيزياء الحالية تفترض حركة نهائية: الموت الحراري للكون يعني أن هذا الكون قد يكون له نهاية لا يمكننا فعل أي شيء لتجنبها.

تشير صفة الخيال عند كايوا إلى فكرة أن الألعاب هي خيال. تتكون اللعبة من خلق واقع ثانٍ يسير بالتوازي مع الحياة الحقيقية. إنه كون خيالي يستحضره اللاعبون طواعية بشكل مستقل عن الواقع الصارم للكون المادي الذي نحن جزء منه.

أخيرًا، يعتقد كايوا أن اللعبة تتطلب الحرية. أي شخص يُجبر على لعب لعبة يعمل بدلاً من أن يلعب. لذلك، ترتبط اللعبة بجانب مهم آخر من الوعي البشري: إرادتنا الحرة.

هذا يثير سؤالاً: إذا كان الكون لعبة، فمن الذي يلعبها وماذا سيعني الفوز؟ هل نحن مجرد بيادق في هذه اللعبة بدلاً من لاعبين؟ افترض البعض أن كوننا هو في الواقع محاكاة ضخمة. قام شخص ما ببرمجة القواعد، وإدخال بعض البيانات الأولية وترك المحاكاة تعمل. لهذا السبب تبدو لعبة الحياة لجون كونواي أقرب إلى نوع اللعبة التي قد يكون الكون عليها. في لعبة كونواي، تولد البكسلات على شبكة لانهائية، وتعيش وتموت وفقًا لبيئتها وقواعد اللعبة. كان نجاح كونواي في إنشاء مجموعة من القواعد التي أدت إلى مثل هذا التعقيد المثير للاهتمام.

إذا كان الكون لعبة، فيبدو أننا محظوظون جدًا لنجد أنفسنا جزءًا من لعبة لديها التوازن المثالي بين البساطة والتعقيد، الصدفة والاستراتيجية، الدراما والمخاطرة لجعلها مثيرة للاهتمام. حتى عندما نكتشف قواعد اللعبة، فإنها تعد بأن تكون مباراة رائعة حتى اللحظة التي تصل فيها إلى نهايتها.

لمزيد من هذه الرؤى، قم بتسجيل الدخول إلى www.international-maths-challenge.com.

*الفضل في المقال يعود إلى ماركوس دو سوتوي*


Losing Count: The Mathematical Magic of Counting Curves

How can you figure out which points lie on a certain curve? And how many possible curves do you count by a given number of points? These are the kinds of questions Pim Spelier of the Mathematical Institute studied during his Ph.D. research. Spelier received his doctorate with distinction on June 12.

What does counting curves mean on an average day? “A lot of sitting and gazing,” Spelier replies. “When I’m asked what exactly do, can’t always answer that easily. Usually give the example about the particle traveling through time.”

All possible curves

Imagine a particle moving through space and you follow the path the particle makes through time. That path is a curve, a geometric object. How many possible paths can the particle follow, if we assume certain properties? For example, a straight line can only pass through two points in one way. But how many paths are possible for the particle if we look at more difficult curves? And how do you study that?

By looking at all possible curves at the same time. For example, all possible directions from a given point form with each other a circle, and that is called a modulspace. And that circle is itself a geometric object.

The mathematical magic can happen because this set of all curves itself has geometrical properties, Spelier says, to which you can apply geometrical tricks. Next, you can make that far more complicated with even more complex curves and spaces. So not counting in three but, for example, in eleven dimensions.

Spelier tries to find patterns that always apply to the curves he studies. His approach? Breaking up complicated spaces into small, easy spaces. You can also break curves into partial curves. That way, the spaces in which you’re counting are easier. But the curves sometimes get complicated properties, because you have to be able to glue them back together.

Spelier says, “The goal is to find enough principles to determine the number of curves exactly.”

In addition to curves, Spelier also counted points on curves. He studied the question: how many solutions does a given mathematical equation have?

These are equations that are a bit more complicated than the a2 + b2 = c2 of the Pythagorean theorem. That equation is about the lengths of the sides of a right triangle. If you replace the squares with higher powers, it is more difficult to investigate solutions. Spelier studied solutions in whole numbers, for example, 32 + 42 = 52.

Meanwhile, there is a method to find those solutions. Professor of Mathematics Bas Edixhoven, who died in 2022, and his Ph.D. student Guido Lido developed an alternative approach to the same problem. But to what extent the two methods match and differ was still unclear. During his Ph.D. research, Spelier developed an algorithm to investigate this.

The first person with an answer

Developing that algorithm is necessary to implement the method. If you want to do it by hand, you get pages and pages of equations. Edixhoven’s method uses algebraic geometry. Through clever geometric tricks, you can calculate exactly the whole number points of a given curve. Spelier proved that the Edixhoven-Lido method is better than the old one.

David Holmes, professor of Pure Mathematics and supervisor of Spelier, praises the proof provided. “When you’re the first person to answer a question that everyone in our community wants an answer to, that’s very impressive. Pim proves that these two methods for finding rational points are similar, an issue that really kept mathematicians busy.”

Doing math together

The best part of his Ph.D.? The meetings with his supervisor. After the first year, it was more collaboration than supervision, both for Spelier and Holmes. Spelier says, “Doing math together is still more fun than doing it alone.”

Spelier starts in September as a postdoc in Utrecht and is apparently not yet done with counting. After counting points and curves, he will soon start counting surfaces.

For more insights like this, visit our website at www.international-maths-challenge.com.

Credit of the article given to Leiden University.


How Mathematics Can Help You Divide Anything Up Fairly

Whether you are sharing a cake or a coastline, maths can help make sure everyone is happy with their cut, says Katie Steckles.

One big challenge in life is dividing things fairly. From sharing a tasty snack to allocating resources between nations, having a strategy to divvy things up equitably will make everyone a little happier.

But it gets complicated when the thing you are dividing isn’t an indistinguishable substance: maybe the cake you are sharing has a cherry on top, and the piece with the cherry (or the area of coastline with good fish stocks) is more desirable. Luckily, maths – specifically game theory, which deals with strategy and decision-making when people interact – has some ideas.

When splitting between two parties, you might know a simple rule, proven to be mathematically optimal: I cut, you choose. One person divides the cake (or whatever it is) and the other gets to pick which piece they prefer.

Since the person cutting the cake doesn’t choose which piece they get, they are incentivised to cut the cake fairly. Then when the other person chooses, everyone is satisfied – the cutter would be equally happy with either piece, and the chooser gets their favourite of the two options.

This results in what is called an envy-free allocation – neither participant can claim they would rather have the other person’s share. This also takes care of the problem of non-homogeneous objects: if some parts of the cake are more desirable, the cutter can position their cut so the two pieces are equal in value to them.

What if there are more people? It is more complicated, but still possible, to produce an envy-free allocation with several so-called fair-sharing algorithms.

Let’s say Ali, Blake and Chris are sharing a cake three ways. Ali cuts the cake into three pieces, equal in value to her. Then Blake judges if there are at least two pieces he would be happy with. If Blake says yes, Chris chooses a piece (happily, since he gets free choice); Blake chooses next, pleased to get one of the two pieces he liked, followed by Ali, who would be satisfied with any of the pieces. If Blake doesn’t think Ali’s split was equitable, Chris looks to see if there are two pieces he would take. If yes, Blake picks first, then Chris, then Ali.

If both Blake and Chris reject Ali’s initial chop, then there must be at least one piece they both thought was no good. This piece goes to Ali – who is still happy, because she thought the pieces were all fine – and the remaining two pieces get smooshed back together (that is a mathematical term) to create one piece of cake for Blake and Chris to perform “I cut, you choose” on.

While this seems long-winded, it ensures mathematically optimal sharing – and while it does get even more complicated, it can be extended to larger groups. So whether you are sharing a treat or a divorce settlement, maths can help prevent arguments.

For more such insights, log into www.international-maths-challenge.com.

*Credit for article given to Katie Steckles*


Stress Testing Pension Funds—Researchers Present Technique Based on Hidden Markov Regime Switching Model

“We wanted to investigate how second pillar pension funds react to financial crises and how to protect them from the crises,” says Kaunas University of Technology (KTU) professor Dr. Audrius Kabašinskas, who, together with his team, discovered a way to achieve this goal. The discovery in question is the development of stress tests for pension funds. Lithuanian researchers were the first in the world to come up with such an adaptation of the stress tests.

Stress tests are usually carried out on banks or other financial institutions to allow market regulators to determine and assess their ability to withstand adverse economic conditions.

According to the professor at KTU Faculty of Mathematics and Natural Sciences, this innovative pension fund stress testing approach will benefit both regulators and pension fund managers.

“Making sure your pension fund is resilient to harsh financial market conditions will help you sleep better, save more, and have increased trust in your funds and the pension system itself,” Kabašinskas adds.

Results based on two major crises

First, the study needed to collect data from previous periods. “Two major events that shocked the whole world—COVID-19 and the first year of Russian invasion of Ukraine—just happened to occur during the project. This allowed us to gather a lot of relevant information and data on changes in the performance of pension funds,” says Kabašinskas.

The Hidden Markov Model (HMM), which, according to a professor at KTU Department of Mathematical Modelling, is quite simple in its principle of operation, helped to forecast future market conditions in this study.

The paper is published in the journal Annals of Operations Research.

“The observation of air temperature could be an analogy for it. All year round, without looking at the calendar, we observe the temperature outside and, based on the temperature level, we decide what time of the year it is. Of course, 15 degrees can occur in winter and sometimes it snows in May but these are random events. The state of the next day depends only on today,” he explains vividly.

According to the KTU researcher, this describes the idea of the Hidden Markov Model: by observing the changes in value, one can judge the state of global markets and try to forecast the future.

“In our study, we observed two well-known investment funds from 2019 to 2022. Collected information helped us identify that global markets at any given moment are in one of four states: no shock regime, a state of shock in stock markets, a state of shock in bond markets, and a state of global financial shock—a global crisis,” says Kabašinskas.

Using certain methods, the research team led by a professor Miloš Kopa representing KTU and Charles University in Prague found that these periods were aligned with the global events in question. Once the transition probabilities between the states were identified, it was possible to link the data of pension funds to these periods and simulate the future evolution of the pension funds’ value.

That’s where the innovation of stress testing came in. The purpose of this test is to determine whether a particular pension fund can deliver positive growth in the future when faced with a shock in the financial markets.

“In our study, we applied several scenarios, extending financial crises and modeling the evolution of fund values over the next 5 years,” says a KTU researcher.

This methodology can be applied not only to pension funds but also to other investments.

Example of Lithuanian pension funds

The research and the new stress tests were carried out on Lithuanian pension funds.

Kabašinskas says that the study revealed several interesting things. Firstly, on average, Lithuanian second pillar pension funds can withstand crises that are twice as long.

“However, the results show that some Lithuanian funds struggle to cope with inflation, while others, the most conservative funds for citizens who are likely to retire within next few years or who have already retired, are very slow in recovering after negative shocks,” adds the KTU expert.

This can be explained by regulatory aspects and the related investment strategy, as stock markets recover several times faster than bond markets, and the above-mentioned funds invest more than 90% in bonds and other less risky instruments.

A complementary study has also been carried out to show how pension funds should change their investment strategy to avoid the drastic negative consequences of various financial crises and shocks.

“Funds that invest heavily in stocks and other risky instruments should increase the number of risk-free instruments slightly, up to 10%, before or after the financial crisis hits. Meanwhile, funds investing mainly in bonds should increase the number of stocks in their holdings. In both cases, the end of the crisis should be followed by a slow return to the typical strategy,” advises a mathematician.

Although the survey did not aim to increase people’s confidence in pension funds, the results showed that Lithuania’s second pillar pension funds are resilient to crisis and are worth trust. Historically they have delivered long-term growth, some have even outperformed inflation and price increases.

“Although short-term changes can be drastic, long-term growth is clearly visible,” says KTU professor Dr. Kabašinskas. “Lithuania, by the way, has a better system than many European countries,” he adds.

For more insights like this, visit our website at www.international-maths-challenge.com.


Maths makes finding bat roosts much easier, our research shows

Finding bats is hard. They are small, fast and they primarily fly at night.  But our new research could improve the way conservationists find bat roosts. We’ve developed a new algorithm that significantly reduces the area that needs to be searched, which could save time and cut labour cost.

Of course, you may wonder why we would want to find bats in the first place. But these flying mammals are natural pest controllers and pollinators, and they help disperse seeds. So they are extremely useful in contributing to the health of our environment.

Despite their importance though, bat habitats are threatened by human activities such as increased lighting, noise and land use. To ensure that we can study and enhance the health of our bat population, we need to locate their roosts. But finding bat roosts is a bit like finding a needle in a haystack.

Our previous work measured and modelled the motion of greater horseshoe bats in flight. Having such a model means we can predict where bats will be, depending on their roost position. But the position of the roost is something we often don’t know.

Our new research combines our previous mathematical model of bat motion with data gathered from acoustic recorders known as “bat detectors”. These bat detectors are placed around the environment and left there for several nights.

Seeing with sound

Bats use echolocation, which allows them to “see with sound” when they’re flying. If these ultrasonic calls are made within ten to 15 metres of a bat detector, the device is triggered to make a recording, providing an accurate record of where and when a bat was present.

The sound recordings also provide clues about the identity of the species. Greater horseshoe bats make a very distinctive “warbling” call at almost exactly 82kHz in frequency, so we can easily tell whether the species is present or not.

Assuming that a bat detector’s batteries last for a few nights, its memory card is not full, and the units are not stolen or vandalised, then we can use the bat call data to generate a map that shows the proportion of bat calls at each detector location.

Our model can also be used to predict the proportion of bat calls based on a given roost location. So, we split the environment up into a grid and simulate bats flying from each grid square. The grid square, or squares, whose simulations best reproduce the bat detector data will then be the most likely locations of the roost.

This simple algorithm can then be applied to whole terrains, meaning that we can create a map of likely roost locations. Cutting out the regions that are least likely to contain the roost can mean we shrink the search space to less than 1% of the initially surveyed area. Simplifying the process of finding bat roosts allows more of an ecologist’s time to be spent on conservation projects, rather than laborious searching.

In 2022, we developed an app that uses publicly available data to predict bat flight lines. At the moment the app can help ecologists, developers or local authority planners, know how the environment is used by bats. However, it needs a roost location to be specified first, and this information is not always known. Our new research removes this barrier, making the app easier to use.

Our work offers a way of identifying likely roost locations. These estimates can then be verified either by directly observing particular features, or by capturing bats at a nearby location and following them back home, using radiotracking.

Over the past two decades, bat detectors have gone from simple hand-held machines to high-performance devices that can collect data for days at a time. Yet they are usually deployed only to identify bat species. We have shown they can be used to identify the areas most likely to contain bat roosts, uncovering critical information about these most secretive of animals.

We hope that this will provide further tools for ecologists to optimise the initial microphone detector locations, thereby providing a holistic way of detecting bat roosts.

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Credit of the article given to Thomas Woolley and Fiona Mathews, The Conversation

 


Merging AI and Human Efforts to Tackle Complex Mathematical Problems

By rapidly analysing large amounts of data and making accurate predictions, artificial intelligence (AI) tools could help to answer many long-standing research questions. For instance, they could help to identify new materials to fabricate electronics or the patterns in brain activity associated with specific human behaviours.

One area in which AI has so far been rarely applied is number theory, a branch of mathematics focusing on the study of integers and arithmetic functions. Most research questions in this field are solved by human mathematicians, often years or decades after their initial introduction.

Researchers at the Israel Institute of Technology (Technion) recently set out to explore the possibility of tackling long-standing problems in number theory using state-of-the-art computational models.

In a recent paper, published in the Proceedings of the National Academy of Sciences, they demonstrated that such a computational approach can support the work of mathematicians, helping them to make new exciting discoveries.

“Computer algorithms are increasingly dominant in scientific research, a practice now broadly called ‘AI for Science,'” Rotem Elimelech and Ido Kaminer, authors of the paper, told Phys.org.

“However, in fields like number theory, advances are often attributed to creativity or human intuition. In these fields, questions can remain unresolved for hundreds of years, and while finding an answer can be as simple as discovering the correct formula, there is no clear path for doing so.”

Elimelech, Kaminer and their colleagues have been exploring the possibility that computer algorithms could automate or augment mathematical intuition. This inspired them to establish the Ramanujan Machine research group, a new collaborative effort aimed at developing algorithms to accelerate mathematical research.

Their research group for this study also included Ofir David, Carlos de la Cruz Mengual, Rotem Kalisch, Wolfram Berndt, Michael Shalyt, Mark Silberstein, and Yaron Hadad.

“On a philosophical level, our work explores the interplay between algorithms and mathematicians,” Elimelech and Kaminer explained. “Our new paper indeed shows that algorithms can provide the necessary data to inspire creative insights, leading to discoveries of new formulas and new connections between mathematical constants.”

The first objective of the recent study by Elimelech, Kaminer and their colleagues was to make new discoveries about mathematical constants. While working toward this goal, they also set out to test and promote alternative approaches for conducting research in pure mathematics.

“The ‘conservative matrix field’ is a structure analogous to the conservative vector field that every math or physics student learns about in first year of undergrad,” Elimelech and Kaminer explained. “In a conservative vector field, such as the electric field created by a charged particle, we can calculate the change in potential using line integrals.

“Similarly, in conservative matrix fields, we define a potential over a discrete space and calculate it through matrix multiplications rather than using line integrals. Traveling between two points is equivalent to calculating the change in the potential and it involves a series of matrix multiplications.”

In contrast with the conservative vector field, the so-called conservative matrix field is a new discovery. An important advantage of this structure is that it can generalize the formulas of each mathematical constant, generating infinitely many new formulas of the same kind.

“The way by which the conservative matrix field creates a formula is by traveling between two points (or actually, traveling from one point all the way to infinity inside its discrete space),” Elimelech and Kaminer said. “Finding non-trivial matrix fields that are also conservative is challenging.”

As part of their study, Elimelech, Kaminer and their colleagues used large-scale distributed computing, which entails the use of multiple interconnected nodes working together to solve complex problems. This approach allowed them to discover new rational sequences that converge to fundamental constants (i.e., formulas for these constants).

“Each sequence represents a path hidden in the conservative matrix field,” Elimelech and Kaminer explained. “From the variety of such paths, we reverse-engineered the conservative matrix field. Our algorithms were distributed using BOINC, an infrastructure for volunteer computing. We are grateful to the contribution by hundreds of users worldwide who donated computation time over the past two and a half years, making this discovery possible.”

The recent work by the research team at the Technion demonstrates that mathematicians can benefit more broadly from the use of computational tools and algorithms to provide them with a “virtual lab.” Such labs provide an opportunity to try ideas experimentally in a computer, resembling the real experiments available in physics and in other fields of science. Specifically, algorithms can carry out mathematical experiments providing formulas that can be used to formulate new mathematical hypotheses.

“Such hypotheses, or conjectures, are what drives mathematical research forward,” Elimelech and Kaminer said. “The more examples supporting a hypothesis, the stronger it becomes, increasing the likelihood to be correct. Algorithms can also discover anomalies, pointing to phenomena that are the building-blocks for new hypotheses. Such discoveries would not be possible without large-scale mathematical experiments that use distributed computing.”

Another interesting aspect of this recent study is that it demonstrates the advantages of building communities to tackle problems. In fact, the researchers published their code online from their project’s early days and relied on contributions by a large network of volunteers.

“Our study shows that scientific research can be conducted without exclusive access to supercomputers, taking a substantial step toward the democratization of scientific research,” Elimelech and Kaminer said. “We regularly post unproven hypotheses generated by our algorithms, challenging other math enthusiasts to try proving these hypotheses, which when validated are posted on our project website. This happened on several occasions so far. One of the community contributors, Wolfgang Berndt, got so involved that he is now part of our core team and a co-author on the paper.”

The collaborative and open nature of this study allowed Elimelech, Kaminer and the rest of the team to establish new collaborations with other mathematicians worldwide. In addition, their work attracted the interest of some children and young people, showing them how algorithms and mathematics can be combined in fascinating ways.

In their next studies, the researchers plan to further develop the theory of conservative matrix fields. These matrix fields are a highly powerful tool for generating irrationality proofs for fundamental constants, which Elimelech, Kaminer and the team plan to continue experimenting with.

“Our current aim is to address questions regarding the irrationality of famous constants whose irrationality is unknown, sometimes remaining an open question for over a hundred years, like in the case of the Catalan constant,” Elimelech and Kaminer said.

“Another example is the Riemann zeta function, central in number theory, with its zeros at the heart of the Riemann hypothesis, which is perhaps the most important unsolved problem in pure mathematics. There are many open questions about the values of this function, including the irrationality of its values. Specifically, whether ζ(5) is irrational is an open question that attracts the efforts of great mathematicians.”

The ultimate goal of this team of researchers is to successfully use their experimental mathematics approach to prove the irrationality of one of these constants. In the future, they also hope to systematically apply their approach to a broader range of problems in mathematics and physics. Their physics-inspired hands-on research style arises from the interdisciplinary nature of the team, which combines people specialized in CS, EE, math, and physics.

“Our Ramanujan Machine group can help other researchers create search algorithms for their important problems and then use distributed computing to search over large spaces that cannot be attempted otherwise,” Elimelech and Kaminer added. “Each such algorithm, if successful, will help point to new phenomena and eventually new hypotheses in mathematics, helping to choose promising research directions. We are now considering pushing forward this strategy by setting up a virtual user facility for experimental mathematics,” inspired by the long history and impact of user facilities for experimental physics.

For more insights like this, visit our website at www.international-maths-challenge.com.

Credit of the article given to Ingrid Fadelli , Phys.org


Study Finds Cooperation Can Still Evolve Even With Limited Payoff Memory

Direct reciprocity facilitates cooperation in repeated social interactions. Traditional models suggest that individuals learn to adopt conditionally cooperative strategies if they have multiple encounters with their partner. However, most existing models make rather strong assumptions about how individuals decide to keep or change their strategies. They assume individuals make these decisions based on a strategy’s average performance. This in turn suggests that individuals would remember their exact payoffs against everyone else.

In a recent study, researchers from the Max Planck Institute for Evolutionary Biology, the School of Data Science and Society, and the Department of Mathematics at the University of North Carolina at Chapel Hill examine the effects of realistic memory constraints. They find that cooperation can evolve even with minimal memory capacities. The research is published in the journal Proceedings of the Royal Society B: Biological Sciences.

Direct reciprocity is based on repeated interactions between two individuals. This concept, often described as “you scratch my back, I’ll scratch yours,” has proven to be a pivotal mechanism in maintaining cooperation within groups or societies.

While models of direct reciprocity have deepened our understanding of cooperation, they frequently make strong assumptions about individuals’ memory and decision-making processes. For example, when strategies are updated through social learning, it is commonly assumed that individuals compare their average payoffs.

This would require them to compute (or remember) their payoffs against everyone else in the population. To understand how more realistic constraints influence direct reciprocity, the current study considers the evolution of conditional behaviours when individuals learn based on more recent experiences.

Two extreme scenarios

This study first compares the classical modeling approach with another extreme approach. In the classical approach, individuals update their strategies based on their expected payoffs, considering every single interaction with each member of the population (perfect memory). Conversely, the opposite extreme is considering only the very last interaction (limited memory).

Comparing these two scenarios shows that individuals with limited payoff memory tend to adopt less generous strategies. They are less forgiving when someone defects against them. Yet, moderate levels of cooperation can still evolve.

Intermediate cases

The study also considers intermediate cases, where individuals consider their last two or three or four recent experiences. The results show that cooperation rates quickly approach the levels observed under perfect payoff memory.

Overall, this study contributes to a wider literature that explores which kinds of cognitive capacities are required for reciprocal altruism to be feasible. While more memory is always favourable, reciprocal cooperation can already be sustained if individuals have a record of two or three past outcomes.

This work’s results have been derived entirely within a theoretical model. The authors feel that such studies are crucial for making model-informed deductions about reciprocity in natural systems.

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Credit of the article given to Michael Hesse, Max Planck Society


Students’ Awareness of Their Cognitive Processes Facilitates The Learning of Math, Finds Study

The purpose of education is to ensure that students acquire the skills necessary for succeeding in a world that is constantly changing. Self-assessment, or teaching students how to examine and evaluate their own learning and cognitive processes, has proven to be an effective method, and this competence is partly based on metacognitive knowledge.

A new study conducted at the University of Eastern Finland shows that metacognitive knowledge, i.e., awareness of one’s cognitive processes, is also a key factor in the learning of mathematics. The work is published in the journal Cogent Education.

The study explored thinking skills and possible grade-level differences in children attending comprehensive school in Finland. The researchers investigated 6th, 7th and 9th graders’ metacognitive knowledge in the context of mathematics.

“The study showed that ninth graders excelled at explaining their use of learning strategies, while 7th graders demonstrated proficiency in understanding when and why certain strategies should be used. No other differences between grade levels were observed, which highlights the need for continuous support throughout the learning path,” says Susanna Toikka of the University of Eastern Finland, the first author of the article.

The findings emphasize the need to incorporate elements that support metacognitive knowledge into mathematics learning materials, as well as into teachers’ pedagogical practices.

Self-assessment and understanding of one’s own learning help to face new challenges

Metacognitive knowledge helps students not only to learn mathematics, but also more broadly in self-assessment and lifelong learning. Students who can assess their own learning and understanding are better equipped to face new challenges and adapt to changing environments. Such skills are crucial for lifelong learning, as they enable continuous development and learning throughout life.

“Metacognitive knowledge is a key factor in learning mathematics and problem-solving, but its significance also extends to self-assessment and lifelong learning,” says Toikka.

In schools, metacognitive knowledge can be effectively developed as part of education. Based on earlier studies, Toikka and colleagues have developed a combination of frameworks for metacognitive knowledge, which helps to identify students’ needs for development regarding metacognitive knowledge by offering an alternative perspective to that of traditional developmental psychology.

“This also supports teachers in promoting students’ metacognitive knowledge. Teachers can use the combination of frameworks to design and implement targeted interventions that support students’ skills in lifelong learning.”

According to Toikka, the combination of frameworks enhances understanding of metacognitive knowledge and helps to identify areas where individual support is needed: “This type of understanding is crucial for the development of metacognitive knowledge among diverse learners.”

For more insights like this, visit our website at www.international-maths-challenge.com.

Credit of the article given to University of Eastern Finland


Why expanding access to algebra is a matter of civil rights

Bob Moses, who helped register Black residents to vote in Mississippi during the Civil Rights Movement, believed civil rights went beyond the ballot box. To Moses, who was a teacher as well as an activist, math literacy is a civil right: a requirement to earning a living wage in modern society. In 1982, he founded the Algebra Project to ensure that “students at the bottom get the math literacy they need.”

As a researcher who studies ways to improve the math experiences of students, WEbelieve a new approach that expands access to algebra may help more students get the math literacy Moses, who died in 2021, viewed as so important. It’s a goal districts have long been struggling to meet.

Efforts to improve student achievement in algebra have been taking place for decades. Unfortunately, the math pipeline in the United States is fraught with persistent opportunity gaps. According to the Nation’s Report Card – a congressionally mandated project administered by the Department of Education – in 2022 only 29% of U.S. fourth graders and 20% of U.S. eighth graders were proficient in math. Low-income students, students of color and multilingual learners, who tend to have lower scoreson math assessments, often do not have the same access as others to qualified teachers, high-quality curriculum and well-resourced classrooms.

A new approach

The Dallas Independent School District – or Dallas ISD – is gaining national attention for increasing opportunities to learn by raising expectations for all students. Following in the footsteps of more than 60 districts in the state of Washington, in 2019 the Dallas ISD implemented an innovative approach of having students be automatically enrolled rather than opt in to honours math in middle school.

Under an opt-in policy, students need a parent or teacher recommendation to take honours math in middle school and Algebra 1 in eighth grade. That policy led both to low enrollment and very little diversity in honours math. Some parents, especially those who are Black or Latino, were not aware how to enroll their students in advanced classes due to a lack of communication in many districts.

In addition, implicit bias, which exists in all demographic groups, may influence teachers’ perceptions of the behaviour and academic potential of students, and therefore their subsequent recommendations. Public school teachers in the U.S. are far less racially and ethnically diverse than the students they serve.

Dallas ISD’s policy overhaul aimed to foster inclusivity and bridge educational gaps among students. Through this initiative, every middle school student, regardless of background, was enrolled in honours math, the pathway that leads to taking Algebra 1 in eighth grade, unless they opted out.

Flipping the switch from opt-in to opt-out led to a dramatic increase in the number of Black and Latino learners, who constitute the majority of Dallas students. And the district’s overall math scores remained steady. About 60% of Dallas ISD eighth graders are now taking Algebra 1, triple the prior level. Moreover, more than 90% are passing the state exam.

Civil rights activist Bob Moses believed math literacy was critical for students to be able to make a living. Robert Elfstrom/Villon Films via Getty Images

Efforts spread

Other cities are taking notice of the effects of Dallas ISD’s shifting policy. The San Francisco Unified School District, for example, announced plans in February 2024 to implement Algebra 1 in eighth grade in all schools by the 2026-27 school year.

In fall 2024, the district will pilot three programs to offer Algebra 1 in eighth grade. The pilots range from an opt-out program for all eighth graders – with extra support for students who are not proficient – to a program that automatically enrolls proficient students in Algebra 1, offered as an extra math class during the school day. Students who are not proficient can choose to opt in.

Nationwide, however, districts that enroll all students in Algebra 1 and allow them to opt out are still in the minority. And some stopped offering eighth grade Algebra 1 entirely, leaving students with only pre-algebra classes. Cambridge, Massachusetts – the city in which Bob Moses founded the Algebra Project – is among them.

Equity concerns linger

Between 2017 and 2019, district leaders in the Cambridge Public Schools phased out the practice of placing middle school students into “accelerated” or “grade-level” math classes. Few middle schools in the district now offer Algebra 1 in eighth grade.

The policy shift, designed to improve overall educational outcomes, was driven by concerns over significant racial disparities in advanced math enrollment in high school. Completion of Algebra 1 in eighth grade allows students to climb the math ladder to more difficult classes, like calculus, in high school. In Cambridge, the students who took eighth grade Algebra 1 were primarily white and Asian; Black and Latino students enrolled, for the most part, in grade-level math.

Some families and educators contend that the district’s decision made access to advanced math classes even more inequitable. Now, advanced math in high school is more likely to be restricted to students whose parents can afford to help them prepare with private lessons, after-school programs or private schooling, they said.

While the district has tried to improve access to advanced math in high school by offering a free online summer program for incoming ninth graders, achievement gaps have remained persistently wide.

Perhaps striking a balance between top-down policy and bottom-up support will help schools across the U.S. realize the vision Moses dreamed of in 1982 when he founded the Algebra Project: “That in the 21st century every child has a civil right to secure math literacy – the ability to read, write and reason with the symbol systems of mathematics.”

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Credit of the article given to Liza Bondurant, The Conversation


Mathematicians Find Odd Shapes That Roll Like A Wheel In Any Dimension

Not content with shapes in two or three dimensions, mathematicians like to explore objects in any number of spatial dimensions. Now they have discovered shapes of constant width in any dimension, which roll like a wheel despite not being round.

A 3D shape of constant width as seen from three different angles. The middle view resembles a 2D Reuleaux triangle

Mathematicians have reinvented the wheel with the discovery of shapes that can roll smoothly when sandwiched between two surfaces, even in four, five or any higher number of spatial dimensions. The finding answers a question that researchers have been puzzling over for decades.

Such objects are known as shapes of constant width, and the most familiar in two and three dimensions are the circle and the sphere. These aren’t the only such shapes, however. One example is the Reuleaux triangle, which is a triangle with curved edges, while people in the UK are used to handling equilateral curve heptagons, otherwise known as the shape of the 20 and 50 pence coins. In this case, being of constant width allows them to roll inside coin-operated machines and be recognised regardless of their orientation.

Crucially, all of these shapes have a smaller area or volume than a circle or sphere of the equivalent width – but, until now, it wasn’t known if the same could be true in higher dimensions. The question was first posed in 1988 by mathematician Oded Schramm, who asked whether constant-width objects smaller than a higher-dimensional sphere might exist.

While shapes with more than three dimensions are impossible to visualise, mathematicians can define them by extending 2D and 3D shapes in logical ways. For example, just as a circle or a sphere is the set of points that sits at a constant distance from a central point, the same is true in higher dimensions. “Sometimes the most fascinating phenomena are discovered when you look at higher and higher dimensions,” says Gil Kalai at the Hebrew University of Jerusalem in Israel.

Now, Andrii Arman at the University of Manitoba in Canada and his colleagues have answered Schramm’s question and found a set of constant-width shapes, in any dimension, that are indeed smaller than an equivalent dimensional sphere.

Arman and his colleagues had been working on the problem for several years in weekly meetings, trying to come up with a way to construct these shapes before they struck upon a solution. “You could say we exhausted this problem until it gave up,” he says.

The first part of the proof involves considering a sphere with n dimensions and then dividing it into 2n equal parts – so four parts for a circle, eight for a 3D sphere, 16 for a 4D sphere and so on. The researchers then mathematically stretch and squeeze these segments to alter their shape without changing their width. “The recipe is very simple, but we understood that only after all of our elaboration,” says team member Andriy Bondarenko at the Norwegian University of Science and Technology.

The team proved that it is always possible to do this distortion in such a way that you end up with a shape that has a volume at most 0.9n times that of the equivalent dimensional sphere. This means that as you move to higher and higher dimensions, the shape of constant width gets proportionally smaller and smaller compared with the sphere.

Visualising this is difficult, but one trick is to imagine the lower-dimensional silhouette of a higher-dimensional object. When viewed at certain angles, the 3D shape appears as a 2D Reuleaux triangle (see the middle image above). In the same way, the 3D shape can be seen as a “shadow” of the 4D one, and so on.  “The shapes in higher dimensions will be in a certain sense similar, but will grow in complexity as [the] dimension grows,” says Arman.

Having identified these shapes, mathematicians now hope to study them further. “Even with the new result, which takes away some of the mystery about them, they are very mysterious sets in high dimensions,” says Kalai.

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*Credit for article given to Alex Wilkins*