DATA4900 Innovation and Creativity in Business Analytics Report 3 Sample
Your Task
• This assessment is to be done individually.
• Students are to write a 2,000-word report using on Complexity Science principles and AI in business and submit it as a Microsoft word file via the Turnitin portal at the end of week 9.
• You will receive marks for content, appropriate structure and referencing.
Assessment Description
In this assessment, you will be writing an individual report that encourages you to be creative with business analytics, whilst also developing a response to brief containing a “wicked problem”, to be addressed using Complexity Science principles and Artificial Intelligence.
Urban Planning and Design
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Assessment Instructions
This assessment is set in the year 2023. Imagine that you are an expert in complexity science and manage staff using artificial intelligence. Land in your area has become scarce, however the populations is ever growing. There’s a housing shortage and also evolving demand for many types of services, such as childcare centers, public parks, shops and recreation centers, and car parking. You have been asked to write a report on
1. how this issue can be viewed in terms of complexity science
2. how artificial intelligence can be used in urban planning, re-developing and design of facilities and infrastructure.
Your report should have
• an introduction (250 words)
• a section discussing urban planning and design in the context of complexity science (600 words)
• a section on the role of artificial intelligence in business (100)
• a section on how artificial intelligence is being used in urban planning and design (600 words)
• a section recommending how artificial intelligence can be used in conjunction with complexity science in the future for urban planning and design (250 words)
• a summary (200 words)
• At least ten references in Harvard format
Solution
Introduction
Complexity science and artificial intelligence (AI) are two of the most powerful tools available to help solve the problems of the future. Complexity science is the study of complex systems, such as those found in nature and in society, that are composed of many interacting elements. AI is a branch of computer science that focuses on creating machines that can think, learn, and act independently. University Assignment Help, Both of these fields are helping us to better understand and solve problems of the future, such as climate change, global poverty, and the proliferation of nuclear weapons. Complexity science is helping us to better understand the interconnectedness of different systems, such as the environment, the economy, and society, and how they interact (Portugali, 2012). By using complexity science, we can better understand how changes in one system can affect the others, and how we can use interventions to improve them. This can help us to make better decisions about how to address global problems such as climate change, poverty, and inequality. AI is helping us to create intelligent machines that can think and act independently. AI is being used in a variety of fields, such as healthcare, finance, and transportation. In healthcare, for example, AI is being used to help doctors diagnose and treat diseases more quickly and accurately. In finance, AI is being used to help predict market trends and make better investment decisions. In transportation, AI is being used to develop self-driving cars and other autonomous vehicles (Stevens, et.al. 2019). AI is also being used to create smarter cities, with sensors that can detect traffic patterns, crime rates, and air quality. By combining complexity science and AI, we can create intelligent systems that can solve some of the most complex problems of the future. For example, AI can be used to analyze large data sets to better understand the causes of climate change and global poverty, while complexity science can be used to create algorithms that can predict how different systems will interact. This can help us to make better decisions about how to address these problems and create solutions that work for everyone.
How is Complexity Science Useful in Urban Planning?
Urban planning and design in the context of complexity science is a complex issue that requires consideration of a wide range of factors. Complexity science looks at the ways in which the structure and behavior of complex systems can be understood, managed and designed (Portugali, 2012). This approach to urban planning and design has become increasingly important as cities become more complex and difficult to manage. Urban planning and design is a field that involves the development of urban environments in a way that meets the needs of the people who live and work in the city. It involves the consideration of a wide range of factors, including the physical environment, social and economic factors, and the cultural and historical context of the city. In the context of complexity science, urban planners consider the interactions between these different elements and how they influence the design of the city (Patel, and Shah, 2021). One of the key concepts of complexity science is the idea of emergence. This involves understanding how a city emerges from the combined interactions of its components. Emergence leads to the development of new patterns and structures that are not necessarily predictable but can have a significant impact on the functioning of the city. The study of emergence can provide valuable insight into urban planning and design. Complexity science also looks at the ways in which the behavior of a city is affected by feedback loops. These are systems in which the output of one element affects the input of another, creating a loop of feedback that can result in the emergence of new behavior. This can be seen in the way that the development of new infrastructure, such as transport systems, can lead to increased economic activity and increased population, which in turn can create new demands on the infrastructure. Finally, complexity science also looks at the ways in which cities can be designed to be resilient to change. Resilience is the ability of a city to adapt to changing conditions and maintain its functioning. Resilience can be achieved through the development of strategies such as diversification and decentralization of services, which can help to reduce the vulnerability of the city to shocks such as natural disasters or economic downturns (Srikanth, et.al. 2022). Urban planning and design in the context of complexity science requires the consideration of a wide range of factors and an understanding of the ways in which these elements interact. It is a complex field that is constantly evolving and requires the development of new strategies and approaches to ensure the best possible outcomes for the people who live and work in the city.
In recent years, technology has revolutionized the way cities are modeled and managed. New models of cities as complex systems are emerging, allowing cities to become smarter, more efficient, more sustainable, and more livable. These new models of cities give cities unprecedented insight into the way their citizens live and the way their systems interact. To understand why cities are becoming more complex systems, it is important to first understand what a complex system is. A complex system is one that is composed of many interacting elements and can be unpredictable. In cities, these elements can include people, infrastructure, economic systems, and the environment. All of these elements interact in complex ways and can have unpredictable outcomes. New models of cities as complex systems are emerging in response to this complexity (Quan, et.al. 2019). One such model is the use of agent-based modeling to simulate the behavior of a city. Agent-based models use computer simulations to simulate the behavior of a city’s population, infrastructure, and environment. These models can be used to identify and predict how changes in one element might affect the behavior of other elements. For example, an agent-based model could be used to show how a change in population density might affect the amount of traffic in a city. Another new model of cities as complex systems is the use of big data to better understand the behavior of cities. Big data is a collection of large-scale data that can be used to analyze the behavior of cities. By analyzing large amounts of data, it is possible to identify patterns in the way cities behave and to make predictions about how cities will respond to changes. For example, big data can be used to identify trends in urban development or to predict how a new transportation system might affect the environment. Finally, cities are increasingly being modeled as dynamic ecosystems. This model of cities takes into account the interconnectivity of all elements of the city, including the environment, infrastructure, and economy (As, I. et.al. 2022). Cities are seen as a dynamic system, with all elements interacting to form a complex whole. This model can be used to identify how changes in one element of the city can have a ripple effect on other elements. These new models of cities as complex systems are revolutionizing the way cities are managed. By understanding the complexity of cities and their systems, cities can become smarter, more efficient, and more livable. By using agent-based models, big data, and dynamic ecosystems, cities can better identify trends, predict the future, and create more sustainable solutions.
Role of Artificial Intelligence in Business.
Artificial intelligence (AI) is playing an increasingly important role in business. AI can help automate processes and make them more efficient, reduce costs, and improve customer service. AI can also help businesses gain insights from data to make better decisions, predict customer behaviour, and create personalized experiences (Stevens, et.al 2019). AI can also be used to develop new products and services, automate marketing tasks, and improve customer service. AI can also be used for predictive analytics, which can help businesses anticipate customer needs and develop strategies to better meet those needs.
Use of Artificial Intelligence in Urban Planning
Artificial intelligence (AI) is revolutionizing the way cities are designed and managed. AI is being used to create smarter, more efficient cities that are better able to respond to the needs of their citizens. From autonomous vehicles to predictive analytics, AI is helping cities become more connected, sustainable, and resilient. AI is helping urban planners make better decisions when it comes to designing and managing cities. AI algorithms can be used to analyze data from a variety of sources, such as traffic patterns, population density, and energy usage. This data can be used to identify potential problems and opportunities, as well as to create simulations that can help urban planners make informed decisions (Quan, et.al. 2019). AI is also being used to improve the efficiency of transportation systems.
Autonomous vehicles, such as self-driving cars and buses, are being used to reduce traffic congestion and improve safety. AI-enabled traffic management systems can be used to optimize the flow of traffic, reducing travel times and improving air quality. AI can also be used to enhance public safety. AI-based surveillance systems can be used to monitor public areas and detect suspicious activity. AI-enabled facial recognition systems can be used to identify individuals and alert authorities to potential threats. Finally, AI is being used to help cities become more sustainable. AI-enabled systems can be used to monitor energy usage and identify opportunities for efficiency. AI can also be used to analyze data from sensors to identify potential environmental risks and develop strategies for mitigating them. AI is transforming the way cities are designed and managed. By leveraging the power of AI, cities can become smarter, more efficient, and more sustainable.
AI can be used to improve urban planning by automating complex planning tasks, such as creating 3D models, analyzing data, and predicting future trends (Nason and Reid, 2019). AI can also help to identify and prioritize areas for development, optimize land use, and identify environmental risks. AI can also be used to identify and assess potential development opportunities and to create more efficient and sustainable transportation systems. AI can help to reduce traffic congestion and improve public safety by providing real-time traffic information and analyzing data from surveillance cameras. Additionally, AI can be used to improve public health by predicting air pollution levels and identifying areas where air quality is poor. Finally, AI can be used to improve urban governance by providing insights into public opinion and helping to create more efficient and transparent decision-making processes.
How AI and Complexity Can Be Used Together for Urban Planning in Future?
In the future, artificial intelligence (AI) and complexity science can be used together to create more efficient and effective urban planning and design. AI can be used to analyze data from urban environments, such as population density, building types, and traffic patterns, and to identify patterns and trends (Quan, et.al. 2019). This information can then be used to create predictive models of how different urban design elements, such as land use, transportation, and environmental factors, interact with each other. AI can also be used to create simulations of how different urban design scenarios may affect the environment, economy, and social dynamics of a city. Meanwhile, complexity science can be used to understand the emergent behavior of cities as a complex system, and to identify the underlying principles and dynamics that shape urban environments (Zhongwen, 2021). This understanding can then be used to inform the development of adaptive and resilient urban planning and design strategies. For example, complexity science can be used to identify the most effective ways to manage urban growth and development, to create sustainable transportation networks, and to design public spaces that promote social interaction and well-being. By combining AI and complexity science, urban planners and designers can create more effective and efficient urban planning and design strategies. This will help to create cities that are more resilient, adaptive, and sustainable, and that meet the needs of their citizens.
Conclusion
The idea that artificial intelligence (AI) is the next great disruptive technology has been widely discussed in recent years. However, the truth is that AI is not the only disruptive force in the world. Complexity science is a powerful tool that can be used to solve complex problems and create meaningful change (Nason and Reid, 2019). Complexity science is a field of study that focuses on the behaviour of complex systems. It studies the interactions between different elements within a system and how they affect the whole. It is a powerful tool for understanding and predicting the behaviour of complex systems and can be used to create solutions to complex problems (Allam, and Dhunny, 2019). Complexity science can be used to create solutions to problems that are too complex for AI to solve. For example, it can be used to develop models to predict the behaviour of complex systems, such as the stock market. It can also be used to create solutions to problems such as climate change, which require a holistic approach to problem-solving. Complexity science is an important tool for creating meaningful change and solving complex problems. It is an essential part of the modern world and should not be overlooked in favour of AI. AI may be a powerful tool, but complexity science is the real disruptive force.
References
Allam, Z. and Dhunny, Z.A., 2019. On big data, artificial intelligence and smart cities. Cities, 89, pp.80-91.
As, I., Basu, P. and Talwar, P. eds., 2022. Artificial Intelligence in Urban Planning and Design: Technologies, Implementation, and Impacts. Elsevier.
Nason, R., and Reid, D., 2019. AI Ain’t Disruptive: Complexity Science Is! https://www.cmc-canada.ca/blog/Our%20Blog/ai-aint-disruptive-complexity-science-is
Patel, A. and Shah, P., 2021. Rethinking slums, cities, and urban planning: lessons from the COVID-19 pandemic. Cities & Health, 5(sup1), pp.S145-S147.
Portugali, J., 2012. Complexity theories of cities: Implications to urban planning. In Complexity theories of cities have come of age (pp. 221-244). Springer, Berlin, Heidelberg.
Quan, S.J., Park, J., Economou, A. and Lee, S., 2019. Artificial intelligence-aided design: Smart design for sustainable city development. Environment and Planning B: Urban Analytics and City Science, 46(8), pp.1581-1599.
Quan, S.J., Park, J., Economou, A. and Lee, S., 2019. Artificial intelligence-aided design: Smart design for sustainable city development. Environment and Planning B: Urban Analytics and City Science, 46(8), pp.1581-1599.
Srikanth, A.D.S., Chien, B.C.W., Bouffanais, R. and Schroepfer, T., 2022. Complexity science for urban solutions. In Artificial Intelligence in Urban Planning and Design (pp. 39-58). Elsevier.
Stevens, N., Youssef, M. and Salmon, P.M., 2019, December. New ways to model cities as complex systems. 9th State of Australian Cities National Conference, 30 November-5 December 2019, Perth, Australia.
Zhongwen, H., 2021. AI in Urban Planning: 3 Ways it will Strengthen how we Plan for the Future. published by urban redevelopment authority, Singapore government. https://www.ura.gov.sg/Corporate/Resources/Ideas-and-Trends/AI-in-Urban-Planning
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