BY GARETH WILLMER
Game theory mathematics is used to predict outcomes in conflict situations. Now it is being adapted through big data to resolve highly contentious issues between people and the environment.
Game theory is a mathematical concept that aims to predict outcomes and solutions to an issue in which parties with conflicting, overlapping or mixed interests interact.
In ‘theory’, the ‘game’ will bring everyone towards an optimal solution or ‘equilibrium’. It promises a scientific approach to understanding how people make decisions and reach compromises in real-world situations.
Game theory originated in the 1940s in the field of economics. The Oscar-winning movie A Beautiful Mind (2001) is about the life of mathematician John Nash (played by Russell Crowe), who was awarded the 1994 Nobel Prize in Economic Sciences for his work in this area.
Although the concept has been around for many decades, the difference now is the ability to build it into computer-based algorithms, games and apps to apply it more broadly, said Professor Nils Bunnefeld, a social and environmental scientist at the University of Stirling, UK. This is particularly true in the age of big data.
‘Game theory as a theoretical idea has long been around to show solutions to conflict problems,’ he said. ‘We really see the potential to move this to a computer to make the most of the data that can be collected, but also reach many more people.’
Prof Bunnefeld led the EU-backed ConFooBio project, which applied game theory to scenarios where people were in conflict over resources and the environment. His team wanted to develop a model for predicting solutions to conflicts between food security and biodiversity.
‘The starting point was that when we have two or more parties at loggerheads, what should we do, for example, with land or natural resources? Should we produce more food? Or should we protect a certain area for biodiversity?’ he said.
The team focused on seven case studies, ranging from conflicts involving farmers and conservation of geese in Scotland to ones about elephants and crop raiding in Gabon.
ConFooBio conducted more than 300 game workshops with over 900 people in numerous locations including Gabon, Kenya, Madagascar, Tanzania and Scotland.
Prof Bunnefeld realised it became necessary to step back from pure game theory and instead build more complex games to incorporate ecological challenges the world currently faces, like climate change. It also became necessary to adopt a more people-based approach than initially planned, to better target the games.
‘Participants included people directly involved in these conflicts, and in many cases that were very unhappy,’ said Prof Bunnefeld.
‘Through the games, we got high engagement from communities, even from those where conflict is high and people can be reluctant to engage in research. We showed that people are able to solve conflicts when they trust each other and have a say, and when they get adequate payments for conservation efforts.’
The team developed a modelling framework to predict wildlife management outcomes amid conflict. Freely available, it has been downloaded thousands of times from the ConFooBio website.
The researchers also created an accessible game about conservation called Crops vs Creatures, in which players decide between a range of options from shooting creatures to allocating habitat for conservation.
Prof Bunnefeld hopes these types of game become more available on a mainstream basis via app stores – such as one on conflicts in the realm of biodiversity and energy justice in a separate initiative he works on called the Beacon Project. ‘If you tell people you have an exciting game or you have a complex model, which one are they going to engage with? I think the answer is pretty easy,’ he said.
‘In the ConFooBio project, we’ve been able to show that our new models and algorithms can adapt to new situations and respond to environmental and social changes,’ added Prof Bunnefeld. ‘Our models are useful for suggesting ways of managing conflicts between stakeholders with competing objectives.’
Social media dynamics
Another project, Odycceus, harnessed elements of game theory to investigate what social media can tell us about social dynamics and potentially assist in the early detection of emerging social conflicts.
They analysed the language, content and opinions of social media discussions using data tools.
Such tools are required to analyse the vast amount of information in public discourse, explained Eckehard Olbrich, coordinator of the Odycceus project, and a physicist at the Max Planck Institute for Mathematics in the Sciences in Leipzig, Germany.
His work is partially motivated by trying to understand the reasons behind the polarisation of views and the growth of populist movements like far-right organisation Pegida, which was founded in his hometown of Dresden in 2014.
The team created a variety of tools accessible to researchers via an open platform known as Penelope. These included the likes of the Twitter Explorer, which enables researchers to visualise connections between Twitter users and trending topics to help understand how societal debates evolve.
Others included two participatory apps known as the Opinion Observatory and the Opinion Facilitator, which enable people to monitor the dynamics of conflict situations, such as by helping interlink news articles containing related concepts.
Patterns of polarisation
‘These tools have already allowed us to get a better insight into patterns of polarisation and understanding different world views,’ said Olbrich.
He said, for example, that his team managed to develop a model about the effect of social feedback on polarisation that incorporated game-theoretic ideas.
The findings suggested that the formation of polarised groups online was less about the traditional concept of social media bubbles and echo chambers than the way people build their identity by gaining approval from their peers.
He added that connecting the dots between game theory and polarisation could have real-life applications for things like how best to regulate social media.
‘In a game-theoretic formulation, you start with the incentives of the players, and they select their actions to maximise their expected utility,’ he said. ‘This allows predictions to be made of how people would change their behaviour if you, for instance, regulate social media.’
Olbrich added that he hopes such modelling can furnish a better understanding of democracy and debates in the public sphere, as well as indicating to people better ways to participate in public debates. ‘Then we would have better ways to deal with the conflicts we have and that we have to solve,’ he said.
But there are also significant challenges in using game theory for real-world situations, explained Olbrich.
For example, incorporating cultural differences into game theory has proved difficult because such differences may mean two people have hugely varying ways of looking at a problem.
‘The problem with game theory is that it’s looking for solutions to the way a problem can be solved,’ added Prof Bunnefeld.
‘Having looked at conflicts over the last few years, to me it is clear that we can’t solve conflicts, we can only manage them.’ Building in factors like climate change and local context is also complex.
But game theory is a useful way to explore models, games and apps for dealing with conflicts, he said. ‘Game theory is, from its very simple basics to quite complex situations, a good entry point,’ said Prof Bunnefeld.
‘It gives us a framework that you can work through and also captures people’s imagination.’
Research in this article was funded via the EU’s European Research Council and originally published in Horizon, the EU Research and Innovation Magazine.
Competition in 5G Communication Network and the Future of Warfare
The present era is experiencing a shift from 4G (4th Generation) to 5G (5th Generation) networked communication. This shift will radically change all civil and defence communications. In future warfare, it is expected to develop an atmosphere of information or ‘infosphere’ for sharing real-time intelligence characterised by high-speed, low latency and increased bandwidth networks. This potential of 5G is believed to significantly impact the character and future of war. It will enable an agile and fast data communication service that will support the entire battlefield network in integrated and all-domain warfare. This support will allow the speedy transfer of all types of visual and textual data and information from one domain to another, increasing the speed of war. 5G would not only connect all domains of the battleground, but it would also link disconnected networks through network slicing. This will enable remote operations with more private, secure, and restricted access. Due to the super-fast speed of the 5G network, it can afford to carry out multiple isolated functions side-by-side.
5G would also enhance the operational capacity of autonomous military systems such as drones and Unmanned Aerial Vehicles (UAVs). Presently, the capabilities of autonomous military systems are restricted due to their limited onboard processing and data storage capacity; however, with 5G-enabled autonomous military systems, large sets of data, such as terrain maps stored on the cloud, can be downloaded in milliseconds. It is also expected that 5G might initiate the move towards fully autonomous systems due to accelerated networked response and action time. The improved real-time data, collected by the independent system in an autonomous military system through various networked sources and sensors, would be infused with AI and machine learning algorithms to identify, locate, and engage the target without human supervision. Due to such capability of 5G, many countries have shown progress in this arena.
The United States (US) and China have been competing to take the lead in 5G technologies. The major 5G telecoms in the US have deployed their initial nationwide networks. On the other hand, all cities in China and 87% of its rural areas have a 5G network. The Chinese defence forces are now focused on benefitting from 6G communication technologies to adapt to the demands of future warfare.
The US is expected to deploy 5G on its Forward Operating Bases (FOBs) as these are crucial points for collecting intelligence for launching and defending attacks. US troops also have access to 5G-enabled Android Team Awareness Kits that display data on a tablet or smartphone. Similarly, Chinese troops have also been provided with gadgets that will allow tracking of troops, terrain and intelligence on battlegrounds. China has also deployed 5G on the China-India border to monitor Indian military activities.
India has launched an initiative called 5G India (5Gi). Under this initiative, India has given the responsibility to establish end-to-end 5G test beds to research centres such as the Centre of Excellence in Wireless Technology (CEWiT) and the Society for Applied Microwave Electronics Engineering & Research (SAMEER), technical universities such as Indian Institute of Technology Madras, Delhi, Hyderabad, Kanpur and Bombay and Indian Institute of Science, Bangalore. The country is proactive in developing indigenous 5G communication networks. For this, it has provided competitive grants and has created a 5G Alliance Fund that would provide necessary financial assistance for 5G evolution. The Indian Army is also working to develop and deploy 5G networks to improve communication for its frontline forces, which could have implications for Pakistan.
The Ministry of Information Technology and Telecommunication has laid a roadmap for 5G in Pakistan. The Pakistani telecom operators, including PTCL, Telenor, Zong and Jazz, have successfully tested 5G in Pakistan. 5G was expected to be launched in 2023; however, progress were delayed due to political instability in the country. According to a study by the Pakistan Institute of Development Economics (PIDE), the exorbitant tax on phones and lack of availability of 5G enabled phones in Pakistan might hinder the evolution of 5G. Pakistan has also collaborated with China to facilitate the launch of 5G technology. China’s technological support and the efforts of the telecom industry has been the key force behind 5G success in Pakistan. A similar roadmap can be adapted for other emerging technologies such as AI, cyber and space.
5G is a leap forward in complex communication networks. Although it will significantly enhance communication speed, it will neither diminish nor eliminate the importance of 4G and 3G networks. Instead, 5G will support other emerging technologies such as Cloud, Quantum Computing, the Internet of Things, etc. Each decade, the world will upgrade its generation of networks such as 6G and 7G. The deployment of 5G networks is the need of the hour, given the growing demand for connectivity. Therefore, this is a step in the right direction, and Pakistan must also get on board to quickly set up 5G network towers in the country.
The Development of Artificial Intelligence in China: Talent creation and comparison with U.S.
In the process of developing and implementing AI technology, we need to be pragmatic and orderly. AI education intensifies the driving force for developing the related technology and industry, and it is also the fundamental guarantee for nurturing and cultivating high-quality AI talents and for the sustainable development of related technology and industry. China’s AI education initially created a subject teaching system, and curricula and courses at different levels were offered in universities such as computer science, intelligent science and technology, electronic information and automation. The existing problems of AI development in China and the basic construction of AI are inseparable from the education and training of AI experts. Only by nurturing and cultivating a sufficient number of high-quality AI talents can the smooth development of AI in China be ensured, so that it can climb to the top of international AI.
In terms of AI talent training, the State, Commissions, Ministries, and Departments have made and are making the following noteworthy suggestions:
1) increase AI talent training as a national educational priority.
Not long ago, AI-related playful and recreational activities promoted a wave of AI technology to promote economic and social intelligence in China. AI talents are the top priority in the construction activity to do a good job in planning development, mastering key technologies and promotion. Implementing all this requires high-quality talents. With a view to meeting this social demand, we need to comprehensively plan the training of high-quality AI talents and provide a guarantee for China’s AI to enter a new period of opportunities for sustainable development.
We need to further improve the understanding of AI staff training, establish a comprehensive planning system to create experts and raise the level of preparation as a national educational priority.
2) Establish and standardise AI education at all levels.
According to market demand, we need to comprehensively standardise AI education at all levels and open various schools of a certain scale and proportion, including universities, vocational and technical colleges, AI institutes, technical schools. In China, the Ministry of Education comprehensively expands the current intelligent science and technology, as well as the professional environment, which supports its management. The same holds true for other major universities which are taking action to strengthen the academic teaching of AI, also through the establishment of post-graduate education in some related institutes, as well as spreading basic technologies to primary and secondary schools. The same applies to popular science courses, which provide various forms of extracurricular activities, as well as helping to nurture and cultivate the interest of students of all ages and schools. This is because the level of teachers, who standardise and organise the preparation of various teaching materials, has improved.
3) Multimodal and multi-channel training of high-quality AI talents.
Efforts are made in China to explore and search for various types of high-quality AI talents through multimodal and multi-channel ways, carrying out activities aimed at enhancing and perfecting market-oriented products, and having the experience to promote them. The competent government Departments provide relevant policy support, and State and private research institutes primarily carry out AI product innovation, so that AI science and technology staff perform their tasks comprehensively. Besides participating in research and development of AI products, the main task of schools and colleges is to provide high-quality knowledge resources at all levels Companies strive for excellence in the production of AI products, so that skilled technicians and workers can fully perform their roles. An incentive mechanism for AI experts is established to encourage a higher-level elite to stand out. University students, graduates and science and technology practitioners engaged in AI learning and development are encouraged to pursue AI technological innovation and entrepreneurship and provide the business fund support for their innovative ideas and prototype results.
4) Make full use of the Internet to nurture and cultivate AI talents.
Full use is made of the Internet technology to lend effective technical support to provide effective means for nurturing and cultivating AI talents. The high-level AI platform is used, in line with international standards, to create and improve the domestic AI network teaching platform, provide network education services for AI teaching at all levels, and offer auxiliary teaching tools for other courses.
Some scholars or entrepreneurs believe that China’s AI technology level is already comparable to that of the United States of America. We need to scientifically and objectively evaluate the existing results. We also need to fully reaffirm the achievements and fully understand the shortcomings. Overestimating the existing AI achievements in China is neither realistic nor conducive to the healthy development of this industry.
The United States of America is now the country with the highest overall level of AI technology. Analysing the gap in AI between China and the United States of America helps to maintain a clear understanding. Many experts in the field of AI have pointed out that following the US theory in AI has meant that such applications and innovations are making the industry catch up quickly and regain ground. However, there is still a big gap with the United States of America in terms of basic theoretical research.
There are very few people carrying out basic theoretical research on AI in China. For example, the United States of America places brain science and other neurosciences at the top of research, while China’s independent research and development capabilities in this area are relatively weak and there are gaps in discoveries and innovations. Furthermore, many articles on deep learning have been published in China, but little research is truly innovative in theory or has significant application value.
The Americans are already figuring out what the next AI will be, while such a study has not yet begun at full speed in China. This is the biggest challenge facing the country: it is a difficult problem, involving a wide range of aspects, which cannot be solved by one or two teams. This gap is largely due to the national academic evaluation system and the orientation of practical application. There is room for improving the university analysis criterion: it may take 5-15 years to fully catch up with the United States in the field of AI.
US companies invest a lot of money to train a group of pure high-level technical staff who, from the moment they obtain a PhD, will be recruited by companies and employed in research and development of pure AI technology. Not surprisingly, such an elite team, driven by scientific and technological interests and beliefs, is far ahead at world level in AI research. Few companies in China are willing to spend a lot of money to train a purely technical AI research team and there is also a lack of incentive mechanism within companies. The level of AI research in national universities is also far below the world-leading level. (11. continued)
The Development of Artificial Intelligence in China: Development points and projects
Making machines mimic or even surpass human intellectual behaviour and thinking methods has always been a scientific field full of rich imagination and great challenges. The recent great advances in Artificial Intelligence technology represented by driverless cars and the AlphaGo game have led to enthusiasm and a great deal of funding for the AI field. Considering the development bases, existing problems and opportunities of Chinese AI, strategic thinking on the progress of this industry is continuously proposed for discussion and decision-making reference.
The Internet+ action guidance opinions issued by the State Council have clearly stated that AI is one of the key development areas for the creation of new industrial models. Four Departments, in addition to the National Development and Reform Commission and the Ministry of Science and Technology, have jointly issued implementation plans for Internet+.
The development plan has been promoted in three main aspects and nine minor items. Smart homes, smart wearable devices and smart robots will all become key development support projects. The implementation plan clarifies the development priorities and support projects specific to the Artificial Intelligence industry, thus showing that this field has been raised to a national strategic level.
Considering the great attention paid by the State, increased investment in scientific research and an injection of dividends for talents are expected to accelerate industrial transformation, as facial recognition, language recognition, intelligent robots and other application segments will continue to expand and further promote their marketing.
AI has reached the peak of China’s national strategy, and has shown the need to learn from the advanced Western countries’ research practices to discuss, launch and implement the national plan.
In recent years, the United States of America, the European Union and Japan have successively launched numerous programmes and huge investment, covering future information technology, as well as medicine and neuroscience.
Faced with fierce international competition, China is learning from the experience of the above stated and other countries across disciplines and sectors. The agenda includes the implementation of a project that not only involves AI, but is also inseparable from life sciences, particularly neuroscience. This is so that greater resources can be concentrated on solving the most pressing social needs, such as the development of diagnostic and therapeutic methods for the prevention and treatment of brain diseases, in particular neurodevelopmental diseases, mental illnesses, early diagnosis and intervention in neurodegenerative diseases. The main research focus is on the principle of brain functioning and frontier fields relating to the prevention and treatment of major brain diseases.
As already seen, the foundation of AI involves mathematics, physics, economics, neuroscience, psychology, philosophy, computer engineering, cybernetics, linguistics, biology, cognitive science, bionics and other disciplines and their intersections. The subject of AI has a very broad and extremely rich research content, including cognitive modelling; representation, reasoning and knowledge engineering; machine perception; machine thinking and learning; machine behaviour; etc.
Various AI researchers study such content from different angles. For example, from the ones based on brain function simulation; on the application field and application system; on the system structure and supporting environment; on the distributed artificial intelligence system; on machine theorem demonstration; on uncertainty reasoning, etc. Chinese scholars have made some important achievements in machine theorem proving, hierarchical knowledge representation and reasoning, automatic planning, iris and speech recognition, extension, evolutionary optimisation, data mining (the process of extracting and discovering patterns in large datasets involving methods ranging from machine learning intersection to statistics and database systems), etc. In AI basic research, Chinese experts have great international influence. In general terms, however, the results are not sufficient, the scope is not broad and the overall influence needs to be further improved.
AI basic research is the cornerstone of sustainable development of the related technology, and only by laying sound foundations in it can we provide the driving force for the vigorous development and comprehensive upgrading in the field of its applications. AI basic research needs to be comprehensively strengthened. Innovative multidisciplinarity needs to be encouraged, and importance needs to be attached to it on a forward-looking basis.
The demand for software is an inexhaustible source of technological innovation. AI is considered the fourth industrial revolution. Its theme is three intelligences: factory, production and logistics. The main content of the Made in China 2025 plan is to establish a production line, adopt a management and operation model and start with the following five aspects: design, technology, production, service guarantee and management. The key role of AI technology in smart manufacturing can only be seen from the progress of these aspects.
The implementation of AI technology can be extended to all investment classes and subjects. For example, the intelligent development of technology applied to industrial and mining enterprises includes five points:
1) using intelligent machines (including smart robots) to replace work in hazardous, toxic, radioactive and other harmful environments and in heavy, arduous, repetitive, monotonous, high-altitude, dusty and other difficult conditions, to reduce the intensity of physical and mental work and protect workers (the health issue);
2) using AI technology to design factories and mines, production workshops, sections and equipment, as well as quickly optimise the design scheme and achieve the design intelligence of production;
3) implementing AI technology to fully achieve the production process;
4) developing an intelligent consultation and decision-making system: providing scientific advice, decision-making and management of the production process, and moving towards intelligent production and staff management;
5) researching and developing various expert systems for production planning: monitoring and control of the production process; intelligent fault diagnosis of production systems and equipment; and improvement of labour productivity and product quality.
AI developers combine the characteristics of various enterprises and promote Made in China 2025 and Internet + plans as an opportunity. They seize the historic opportunity of the second machine revolution, achieve AI and vigorously develop these fields. Smart technology and industry inject ideas into the “new” normal of the economy. There is a need to improve the research, development and innovation capabilities of AI technology in the industrial field; to develop high-level products and avoid low-result repetition and haphazard competition. We need to deepen the promotion and implementation of these technologies and make the smart industry bigger and stronger.
As a high-tech segment, AI needs to innovate policy mechanisms, management systems, market mechanisms, and performance transformation to provide an excellent environment for its and its industries’ development and to accompany the healthy progress of initiatives.
Policies need to be introduced to encourage the implementation of AI in the promotion and market development of technology and to broaden the support of national policies, so that new funds and applications will be obtained and new technologies from the laboratory to the field be accelerated as soon as possible. (10. continued).
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