DATA4900 Complexity science and AI Report Sample

DATA4900 Complexity Science and AI Report

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 centres, public parks, shops and recreation centres, 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

• a section discussing urban planning and design in the context of complexity science

• a section on the role of artificial intelligence in business

• a section on how artificial intelligence is being used in urban planning and design

• a section recommending how artificial intelligence can be used in conjunction with complexity science in the future for urban planning and design

• a summary

• At least ten references in Harvard format

Solution

1. Introduction

Complexity science has a relationship to Artificial Intelligence competency development. This is really an amazing fact because complexity science is associated with the traversing of disciplinary boundaries that occur both between and within the multiple types of systems. However, the emergence of complexity science has followed the overlapping and interdependence of different fields. Those are biology, sociology, physics, economics and computer science. As opined by Ray (2019), Complexity science generally strives to acquire deeper knowledge about the "system" and related phenomenon that is unpredictable and characterised by changes. On the other hand, the "system", is a set of interdependent as well as connected agents or things such as an organisation, a person, a species and a molecule (Tambe, Cappelli and Yakubovich, 2019). Hence both the systems complexity and theory of sciences has relationships between the respective elements rather than the individual with a particular element within the system.

Another important contribution of complexity science is that it has the efficiency of producing emergence. According to Crane et al. (2021), emergence always has a particular type of importance in the aspects of innovation at which one requires to appreciate and be patient constant persistence and inquiry. Hence it can be stated that there are requirements of understanding the talent depth within complexity science to make sure that the programs of AI are optimised for appropriate success for the organisation. The respective report is going to enlighten the relationship and connection between Complexity Science and Artificial Intelligence. At first, the report introduced the study topic, then it considered planning and designing of urban areas in the context of complexity science, and then it followed with the role of AI in business and usage of AI in Urban designing and planning. The MBA Assignment Expert report is going to end with appropriate recommendations and conclusions.

2. Discussing Urban planning and design on the basis of Complexity Science

As per the view of Ding et al. (2019), urban planning is one of the pioneer academics in the area of complexity science. On the other hand, cities are more clearly understood as an organism rather than machines. So, cities are complex types of systems as per their nature from the sense of firms, individuals, and institutions and also interact accurately across the multiple dimensions and support to make decisions. However, no such authority has the power to control the actions of others, while the urban dwellers always self-organise themselves within the groups and communities to increase engagement within the collective activities. Complexity Science always offers a new type of perspective to achieve knowledge about various urban issues and also of the dynamics within the city.

However, there are still requirements for development in applications and theories of complexity science to establish appropriate research guides to observe the development of the cities at three different levels. At the level of Marco, a city is always mentioned as a holistic type of system (Montiel, et al., 2020). The work of Geoffrey West and Luis Bettencourt is a particular illustration of the emergence of cities from the perspectives of systems. It also shows that the quantitatively a whole and complete part which is always greater than the sum of its actual parts.

The mentioned approach has the capability of seeing a city as an organism that grows in size, produces waste material and also consumes energy (Heinrich Mora et al., 2021). On the other hand, in the respective work cities always manifest super linear means the gradient>1, which is a macroscopic type of scaling pattern in the socioeconomic properties that resulted from the social interactions of various types of agents.

Figure 1: Creative industries and wages are scaled super-linearly as the growth in the size of the cities takes place
(Sources: Heinrich Mora et al., 2021)

However, space that is occupied through the infrastructure of Urban areas such as the roads, streets and so on, scale sub-linearly which is gradient <1 as there is the growth of cities has been observed and demonstrates more optimal and efficient use of resources.

Figure 2: Scaling of socioeconomic and road infrastructure on the basis of population growth
(Source: Heinrich Mora et al., 2021)

The respective macro observations further can be used to appropriately gauge the cities for actual performance in relation to the expected performance as per the size, areas for appropriate improvement and also highlighting the inefficiencies. The model of planners of the digital, social and physical networks always supports understanding the resilience and robustness of these (Gębczyńska and Brajer-Marczak, 2020). The respective responses also help to give answers to various important questions such as, how it is effective for protecting the entire failure due to disruption of small sections and also the answer of how to exchange all the ideas to foster the environment of enterprise and innovation.

Based on the view of De Meester et al. (2019), mapping all the networks are also effective for mapping the networks that reveal various patterns that inform the designing of the cities and also allocate resources. A wide variety of analytical approaches are used to analyse and describe complex systems that are generally developed to make appropriate relationships between various actors as well as the choice modelling to appropriately model the process of decisions.

3. Role of Artificial Intelligence in Business

The sudden onset of the rise of technological insights has led businesses to generate considerable focus on the management of AI. The rise of this sector has been able to gain a proper deluge of data which further manages computational capacities in general. Using this enables businesses to maintain smart pricing strategies and maintain customised recommendations that are vital (Yigitcanlar et al., 2020). This would lead to the further enhancement of the support launched by the customers and maintain cyber security controls. These considerations and developments in businesses have led companies to shed light on the usage of AI in maintaining their business goals in general.

4. Usage of Artificial Intelligence in urban planning and Design

The development of AI has been in recent times considered for the proper expansion of urban planning and designs that are associated with the business. In recent times, cities have been able to focus on developing and manoeuvring on specialised technologies that are able to address the issues related to ecology, morphology and other aspects in general (Allam and Dhunny, 2019). The emergence of Smart Cities in recent times has led to the development of IoT and Big Data that lead to the management of ecological prospects in general. The usage of AI has been able to establish proper dominance over the aspects of working that are associated with the contribution of urban fabric and dimensions of sustainability in this context.

Figure 3: Usage of big data in businesses
(Source: Allam and Dhunny, 2019)

The data available in recent times has led to the optimal usage of data as well as enabling generating of forced information related to the sectors. Smart cities in recent times have adopted the notion of encouraging efficiency and maintaining the fabric based on a performance matrix. The development of increased communication and maintaining connectivity has been important as it would lead to the gathering and managing of digitised modes of information (Ullah et al., 2020). Along with these certain dimensions that are associated with the handling of the subjects are related to the usage of volume and analytical methods. The dimensions associated with exhaustively, rationality, scalability and resolutions have been vital as that would lead to the understanding of the major beneficiaries that are associated. This interpretation and processing of the data are associated with the enrichment of the urban fabric which is important.

The gaining of data from various neighbourhood sources has led the planners and makers of policy to shift from considerable open and closed-based systems that are necessary. This has led to the derivation of the idea of "open fragmented peri-urban fabric" that would lead to the cohesion and understanding of areas of density, compactness and cohesion in the areas. Other than this, the installations that are pertained to the development of the prospects are vital as that would lead to the proper installation of the sensors, and other telecommunications systems in general (Eli-Chukwu, 2019). The above-mentioned concepts are generally tied and manage the maintenance of AI and machine learning that are necessary for the collection of real-time data and focusing on the issues of adaptability, evolving and maintaining socio-economic dimensions in this context. These dimensions have further enabled the growth of sustainability features that are necessary to comprehend and bring liveability components.

Figure 4: Smart city framework
(Source: Yigitcanlar et al., 2020)

The usage of urban planning has led AI to capture the hidden structures that are related to the urban cells and in the course provides a deeper understanding of common features that are related to the context. This has further led to the encouragement of the usage of cognitive computing integration and of course the management of governance and processes of planning in general. The development of dynamics has led to the development of “New Public Management" for the proper development of the digitised manner of working in general (Chui, Lytras and Visvizi, 2018). The decisions that have been derived from urban governance have also led to the derivation of citizen-oriented approaches that are necessary to develop sustainability in general. These too have acknowledged the growth of Blockchain technologies that are important and have led to a better determination of the parties in general (Bragazzi et al., 2020). The emergence of self-executing computational programs and transferring of data are important to develop digital ledgers in general.

5. Recommendations

As the usage of urban planning has been denoted as an important area of concern in the 21st century, businesses need to adapt to varied situations which would lead them to manage the issues (Shi et al., 2020). The major areas that would lead to the development of AI are based on the
Improvement of road infrastructure and traffic systems based on AI

The developments of the narrow roads have acted as a major area of inconvenience and therefore need proper upgradation. The entire notion can be controlled by the usage of AI-based mobility and traffic systems that are necessary to comprehend and establish better connectivity in general (Florida and Cowls, 2022). The systems that are balanced by them have led to the development of the proper solutions that are necessary to comprehend and maintain the proper behaviour of the traffic (Rigby, 2019). The usage of the ARCADIS model has been vital as that has led to the development of an automatic manner of recognition of images from the camera that is vital to the usage of software-developed modules in general.

Population Forecasting

The management of proper understanding of the forecasts is important to acknowledge the population derivations that are important to comprehend. The major infrastructure issues that are associated with this context are based on the land resources and other aspects that are necessary (Contreras and Vehi, 2018). Along with this, the derivation of the mathematical models to has been analysed as an important one as it would lead to the understanding of the mechanisms based on machine learning (Schwalbe and Wahl, 2020). The algorithms that are managed have led to the derivation of diverse variants of data sets that lead to the increase in the overall population and reduction of errors of humans in general.

6. Conclusion

The report has concluded that the availability of data from the “Internet of Things” means the system IoT within the cities and also of the other devices provides new opportunities for the complexity of science to create accurate solutions for critical urban issues. Such as proper flow of data about transactions and goods that also benefited the economic sector and also informed the policies for growth of the respective sector. Amenities of the cities can also be planned with more perfection through the usage of data that is effective to capture more visitor ship. The solutions of AI also have the ability to enhance the planning and designing of urban areas. Most of cases the usage of Complexity Science and AI are used in the Engineering and the financial sector. So it can be concluded that both Artificial Intelligence and Complexity Science have the capability of planning and designing urban areas and also allocating appropriate resources to support overall growth.

References

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Bragazzi, N.L., Dai, H., Damiani, G., Behzadifar, M., Martini, M. and Wu, J., (2020). How big data and artificial intelligence can help better manage the COVID-19 pandemic. International journal of environmental research and public health, 17(9), p.3176.

Chui, K.T., Lytras, M.D. and Visvizi, A., (2018). Energy sustainability in smart cities: Artificial intelligence, smart monitoring, and optimization of energy consumption. Energies, 11(11), p.2869.

Contreras, I. and Vehi, J., (2018). Artificial intelligence for diabetes management and decision support: a literature review. Journal of medical Internet research, 20(5), p.e10775.

Crane, A., LeBaron, G., Phung, K., Behbahani, L. and Allain, J., (2021). Confronting the business models of modern slavery. Journal of Management Inquiry, p.1056492621994904.

De Meester, L., Brans, K.I., Govaert, L., Souffreau, C., Mukherjee, S., Vanvelk, H., Korzeniowski, K., Kilsdonk, L., Decaestecker, E., Stoks, R. and Urban, M.C., (2019). Analysing eco‐evolutionary dynamics—The challenging complexity of the real world. Functional Ecology, 33(1), pp.43-59.

Ding, R., Ujang, N., Hamid, H.B., Manan, M.S.A., Li, R., Albadareen, S.S.M., Nochian, A. and Wu, J., (2019). Application of complex networks theory in urban traffic network research. Networks and Spatial Economics, 19(4), pp.1281-1317.

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